Compare commits
3 Commits
aleix/mode
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hush/TurnT
| Author | SHA1 | Date | |
|---|---|---|---|
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8bbfa829d3 | ||
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c2eb663bdc | ||
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bf055843e6 |
@@ -1,27 +0,0 @@
|
||||
{
|
||||
"name": "pipecat-dev-skills",
|
||||
"owner": {
|
||||
"name": "Pipecat"
|
||||
},
|
||||
"metadata": {
|
||||
"description": "Development workflow skills for contributing to the Pipecat project",
|
||||
"version": "1.0.0"
|
||||
},
|
||||
"plugins": [
|
||||
{
|
||||
"name": "pipecat-dev",
|
||||
"description": "Development workflow skills for contributing to the Pipecat project",
|
||||
"version": "1.0.0",
|
||||
"source": "./",
|
||||
"skills": [
|
||||
"./.claude/skills/changelog",
|
||||
"./.claude/skills/cleanup",
|
||||
"./.claude/skills/code-review",
|
||||
"./.claude/skills/docstring",
|
||||
"./.claude/skills/pr-description",
|
||||
"./.claude/skills/pr-submit",
|
||||
"./.claude/skills/update-docs"
|
||||
]
|
||||
}
|
||||
]
|
||||
}
|
||||
@@ -1,5 +0,0 @@
|
||||
{
|
||||
"attribution": {
|
||||
"commit": ""
|
||||
}
|
||||
}
|
||||
@@ -1,61 +0,0 @@
|
||||
---
|
||||
name: changelog
|
||||
description: Create changelog files for important commits in a PR
|
||||
---
|
||||
|
||||
Create changelog files for the important commits in this PR. The PR number is provided as an argument.
|
||||
|
||||
## Instructions
|
||||
|
||||
1. Skip changelog for: documentation-only, internal refactoring, test-only, CI changes.
|
||||
|
||||
2. First, check what commits are on the current branch compared to main:
|
||||
```
|
||||
git log main..HEAD --oneline
|
||||
```
|
||||
|
||||
3. For each significant change, create a changelog file in the `changelog/` folder using the format:
|
||||
Allowed types: `added`, `changed`, `deprecated`, `removed`, `fixed`, `security`, `performance`, `other`
|
||||
- `{PR_NUMBER}.added.md` - for new features
|
||||
- `{PR_NUMBER}.added.2.md`, `{PR_NUMBER}.added.3.md` - for additional entries of the same type
|
||||
- `{PR_NUMBER}.changed.md` - for changes to existing functionality
|
||||
- `{PR_NUMBER}.fixed.md` - for bug fixes
|
||||
- `{PR_NUMBER}.deprecated.md` - for deprecations
|
||||
- `{PR_NUMBER}.removed.md` - for removed features
|
||||
- `{PR_NUMBER}.security.md` - for security fixes
|
||||
- `{PR_NUMBER}.performance.md` - for performance improvements
|
||||
- `{PR_NUMBER}.other.md` - for other changes
|
||||
|
||||
4. Each changelog file should at least contain a main single line starting with `- ` followed by a clear description of the change. No line wrapping.
|
||||
|
||||
5. If the change is complicated, changelog files can have indented lines after the main line with additional details or code samples.
|
||||
|
||||
6. Use ⚠️ emoji prefix for breaking changes.
|
||||
|
||||
7. **Write changes in user-facing terms first.** Lead with what users of the framework will notice: new APIs, changed behavior, new parameters, fixed bugs they might have hit, etc. Implementation details (internal refactoring, how something is wired up under the hood) can be included as secondary context after the user-facing description, but should never be the *only* content of a changelog entry when there is a user-visible effect.
|
||||
|
||||
**Good** (user-facing first, implementation detail as context):
|
||||
```
|
||||
- Turn completion instructions now persist correctly across full context updates when using `system_instruction`. Previously they were injected as a context system message, which caused warning spam and didn't survive context updates.
|
||||
```
|
||||
|
||||
**Bad** (implementation detail only, no user-facing framing):
|
||||
```
|
||||
- Fixed turn completion instructions being injected as a context system message instead of using `system_instruction`.
|
||||
```
|
||||
|
||||
Ask yourself: "If I'm a developer building on Pipecat, what would I notice changed?" Start there.
|
||||
|
||||
## Example
|
||||
|
||||
For PR #3519 with a new feature and a bug fix:
|
||||
|
||||
`changelog/3519.added.md`:
|
||||
```
|
||||
- Added `SomeNewFeature` for doing something useful.
|
||||
```
|
||||
|
||||
`changelog/3519.fixed.md`:
|
||||
```
|
||||
- Fixed an issue where something was not working correctly in some user-visible scenario. The root cause was an internal implementation detail.
|
||||
```
|
||||
@@ -1,307 +0,0 @@
|
||||
# Code Cleanup Skill
|
||||
|
||||
The **Code Cleanup Skill** reviews, refactors, and documents code changes in your current branch, ensuring alignment with **Pipecat's architecture, coding standards, and example patterns**.
|
||||
It focuses on **readability, correctness, performance, and consistency**, while avoiding breaking changes.
|
||||
|
||||
---
|
||||
|
||||
## Skill Overview
|
||||
|
||||
This skill analyzes all changes introduced in your branch and performs the following actions:
|
||||
|
||||
1. **Analyze Branch Changes**
|
||||
- Review uncommitted changes and outgoing commits
|
||||
2. **Refactor for Readability**
|
||||
- Improve clarity, naming, structure, and modern Python usage
|
||||
3. **Enhance Performance**
|
||||
- Identify safe, conservative optimization opportunities
|
||||
4. **Add Documentation**
|
||||
- Apply Pipecat-style, Google-format docstrings
|
||||
5. **Ensure Pattern Consistency**
|
||||
- Match existing Pipecat services, pipelines, and examples
|
||||
6. **Validate Examples**
|
||||
- Ensure examples follow foundational patterns (e.g. `07-interruptible.py`)
|
||||
|
||||
---
|
||||
|
||||
## Usage
|
||||
|
||||
Invoke the skill using any of the following commands:
|
||||
|
||||
- "Clean up my branch code"
|
||||
- "Refactor the changes in my branch"
|
||||
- "Review and improve my branch code"
|
||||
- `/cleanup`
|
||||
|
||||
---
|
||||
|
||||
## What This Skill Does
|
||||
|
||||
### 1. Analyze Branch Changes
|
||||
|
||||
The skill retrieves all uncommitted changes and outgoing commits to understand:
|
||||
|
||||
- New files added
|
||||
- Modified files
|
||||
- Code additions and deletions
|
||||
- Overall scope and intent of changes
|
||||
|
||||
---
|
||||
|
||||
### 2. Code Refactoring
|
||||
|
||||
#### Readability Improvements
|
||||
|
||||
- Replace tuples with named classes or dataclasses
|
||||
- Improve variable, method, and class naming
|
||||
- Extract complex logic into well-named helper methods
|
||||
- Add missing type hints
|
||||
- Simplify nested or complex conditionals
|
||||
- Replace deprecated methods and features
|
||||
- Normalize formatting to match Pipecat style
|
||||
|
||||
#### Performance Enhancements
|
||||
|
||||
- Identify inefficient loops or repeated work
|
||||
- Suggest appropriate data structures
|
||||
- Optimize async workflows and I/O
|
||||
- Remove redundant operations
|
||||
|
||||
> Performance changes are conservative and non-breaking.
|
||||
|
||||
---
|
||||
|
||||
### 3. Documentation
|
||||
|
||||
Documentation follows **Google-style docstrings**, consistent with Pipecat conventions.
|
||||
|
||||
#### Class Documentation
|
||||
|
||||
```python
|
||||
class ExampleService:
|
||||
"""Brief one-line description.
|
||||
|
||||
Detailed explanation of the class purpose, responsibilities,
|
||||
and important behaviors.
|
||||
|
||||
Supported features:
|
||||
|
||||
- Feature 1
|
||||
- Feature 2
|
||||
- Feature 3
|
||||
"""
|
||||
```
|
||||
|
||||
#### Method Documentation
|
||||
|
||||
```python
|
||||
def process_data(self, data: str, options: Optional[dict] = None) -> bool:
|
||||
"""Process incoming data with optional configuration.
|
||||
|
||||
Args:
|
||||
data: The input data to process.
|
||||
options: Optional configuration dictionary.
|
||||
|
||||
Returns:
|
||||
True if processing succeeded, False otherwise.
|
||||
|
||||
Raises:
|
||||
ValueError: If data is empty or invalid.
|
||||
"""
|
||||
```
|
||||
|
||||
#### Pydantic Model Parameters
|
||||
|
||||
```python
|
||||
class InputParams(BaseModel):
|
||||
"""Configuration parameters for the service.
|
||||
|
||||
Parameters:
|
||||
timeout: Request timeout in seconds.
|
||||
retry_count: Number of retry attempts.
|
||||
enable_logging: Whether to enable debug logging.
|
||||
"""
|
||||
|
||||
timeout: Optional[float] = None
|
||||
retry_count: int = 3
|
||||
enable_logging: bool = False
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### 4. Pattern Consistency Checks
|
||||
|
||||
#### Service Classes
|
||||
|
||||
- Correct inheritance (`TTSService`, `STTService`, `LLMService`)
|
||||
- Consistent constructor signatures
|
||||
- Frame emission patterns
|
||||
- Metrics support:
|
||||
- `can_generate_metrics()`
|
||||
- TTFB metrics
|
||||
- Usage metrics
|
||||
- Alignment with similar existing services
|
||||
|
||||
#### Examples
|
||||
|
||||
Validated against `examples/07-interruptible.py`:
|
||||
|
||||
- Proper `create_transport()` usage
|
||||
- Correct pipeline structure
|
||||
- Task setup and observers
|
||||
- Event handler registration
|
||||
- Runner and bot entrypoint consistency
|
||||
|
||||
---
|
||||
|
||||
### 5. Specific Implementation Patterns
|
||||
|
||||
#### Service Implementation
|
||||
|
||||
```python
|
||||
class ExampleTTSService(TTSService):
|
||||
|
||||
def __init__(self, *, api_key: Optional[str] = None, **kwargs):
|
||||
super().__init__(**kwargs)
|
||||
self._api_key = api_key or os.getenv("SERVICE_API_KEY")
|
||||
|
||||
def can_generate_metrics(self) -> bool:
|
||||
return True
|
||||
|
||||
async def run_tts(self, text: str) -> AsyncGenerator[Frame, None]:
|
||||
try:
|
||||
await self.start_ttfb_metrics()
|
||||
yield TTSStartedFrame()
|
||||
# ... processing ...
|
||||
yield TTSAudioRawFrame(...)
|
||||
finally:
|
||||
await self.stop_ttfb_metrics()
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
#### Example Structure Pattern
|
||||
|
||||
```python
|
||||
transport_params = {
|
||||
"daily": lambda: DailyParams(...),
|
||||
"twilio": lambda: FastAPIWebsocketParams(...),
|
||||
"webrtc": lambda: TransportParams(...),
|
||||
}
|
||||
|
||||
async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
stt = DeepgramSTTService(...)
|
||||
tts = SomeTTSService(...)
|
||||
llm = OpenAILLMService(...)
|
||||
|
||||
context = LLMContext(messages)
|
||||
user_aggregator, assistant_aggregator = LLMContextAggregatorPair(...)
|
||||
|
||||
pipeline = Pipeline([...])
|
||||
task = PipelineTask(pipeline, params=..., observers=[...])
|
||||
|
||||
@transport.event_handler("on_client_connected")
|
||||
async def on_client_connected(transport, client):
|
||||
await task.queue_frames([LLMRunFrame()])
|
||||
|
||||
runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
|
||||
await runner.run(task)
|
||||
|
||||
async def bot(runner_args: RunnerArguments):
|
||||
"""Main bot entry point compatible with Pipecat Cloud."""
|
||||
transport = await create_transport(runner_args, transport_params)
|
||||
await run_bot(transport, runner_args)
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Execution Flow
|
||||
|
||||
1. Fetch uncommitted and outgoing changes
|
||||
2. Categorize files (services, examples, tests, utilities)
|
||||
3. Analyze each file:
|
||||
- Readability
|
||||
- Performance
|
||||
- Documentation
|
||||
- Pattern consistency
|
||||
4. Generate actionable recommendations
|
||||
5. Apply Pipecat standards
|
||||
|
||||
---
|
||||
|
||||
## Examples
|
||||
|
||||
### Before: Tuple Usage
|
||||
|
||||
```python
|
||||
def get_audio_info(self) -> Tuple[int, int]:
|
||||
return (48000, 1)
|
||||
```
|
||||
|
||||
### After: Named Class
|
||||
|
||||
```python
|
||||
class AudioInfo:
|
||||
"""Audio configuration information.
|
||||
|
||||
Parameters:
|
||||
sample_rate: Sample rate in Hz.
|
||||
num_channels: Number of audio channels.
|
||||
"""
|
||||
|
||||
sample_rate: int
|
||||
num_channels: int
|
||||
|
||||
def get_audio_info(self) -> AudioInfo:
|
||||
return AudioInfo(sample_rate=48000, num_channels=1)
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### Before: Missing Documentation
|
||||
|
||||
```python
|
||||
class NewTTSService(TTSService):
|
||||
def __init__(self, api_key: str, voice: str):
|
||||
self._api_key = api_key
|
||||
self._voice = voice
|
||||
```
|
||||
|
||||
### After: Fully Documented
|
||||
|
||||
```python
|
||||
class NewTTSService(TTSService):
|
||||
"""Text-to-speech service using NewProvider API.
|
||||
|
||||
Streams PCM audio and emits TTSAudioRawFrame frames compatible
|
||||
with Pipecat transports.
|
||||
|
||||
Supported features:
|
||||
- Text-to-speech synthesis
|
||||
- Streaming PCM audio
|
||||
- Voice customization
|
||||
- TTFB metrics
|
||||
"""
|
||||
|
||||
def __init__(self, *, api_key: str, voice: str, **kwargs):
|
||||
"""Initialize the NewTTSService.
|
||||
|
||||
Args:
|
||||
api_key: API key for authentication.
|
||||
voice: Voice identifier to use.
|
||||
**kwargs: Additional arguments passed to the parent service.
|
||||
"""
|
||||
super().__init__(**kwargs)
|
||||
self._api_key = api_key
|
||||
self.set_voice(voice)
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Notes
|
||||
|
||||
- Non-breaking improvements only
|
||||
- Backward compatibility preserved
|
||||
- Conservative performance changes
|
||||
- Google-style docstrings
|
||||
- Pattern checks follow recent Pipecat code
|
||||
@@ -1,107 +0,0 @@
|
||||
---
|
||||
name: code-review
|
||||
description: Automated code review for pull requests using multiple specialized agents
|
||||
disable-model-invocation: true
|
||||
allowed-tools: Bash(gh issue view:*), Bash(gh search:*), Bash(gh issue list:*), Bash(gh pr comment:*), Bash(gh pr diff:*), Bash(gh pr view:*), Bash(gh pr list:*)
|
||||
---
|
||||
|
||||
Provide a code review for the given pull request.
|
||||
|
||||
**Agent assumptions (applies to all agents and subagents):**
|
||||
|
||||
- All tools are functional and will work without error. Do not test tools or make exploratory calls. Make sure this is clear to every subagent that is launched.
|
||||
- Only call a tool if it is required to complete the task. Every tool call should have a clear purpose.
|
||||
|
||||
To do this, follow these steps precisely:
|
||||
|
||||
1. Launch a haiku agent to check if any of the following are true:
|
||||
- The pull request is closed
|
||||
- The pull request is a draft
|
||||
- The pull request does not need code review (e.g. automated PR, trivial change that is obviously correct)
|
||||
- Claude has already commented on this PR (check `gh pr view <PR> --comments` for comments left by claude)
|
||||
|
||||
If any condition is true, stop and do not proceed.
|
||||
|
||||
Note: Still review Claude generated PR's.
|
||||
|
||||
2. Launch a haiku agent to return a list of file paths (not their contents) for all relevant CLAUDE.md files including:
|
||||
- The root CLAUDE.md file, if it exists
|
||||
- Any CLAUDE.md files in directories containing files modified by the pull request
|
||||
|
||||
3. Launch a sonnet agent to view the pull request and return a summary of the changes
|
||||
|
||||
4. Launch 4 agents in parallel to independently review the changes. Each agent should return the list of issues, where each issue includes a description and the reason it was flagged (e.g. "CLAUDE.md adherence", "bug"). The agents should do the following:
|
||||
|
||||
Agents 1 + 2: CLAUDE.md compliance sonnet agents
|
||||
Audit changes for CLAUDE.md compliance in parallel. Note: When evaluating CLAUDE.md compliance for a file, you should only consider CLAUDE.md files that share a file path with the file or parents.
|
||||
|
||||
Agent 3: Opus bug agent (parallel subagent with agent 4)
|
||||
Scan for obvious bugs. Focus only on the diff itself without reading extra context. Flag only significant bugs; ignore nitpicks and likely false positives. Do not flag issues that you cannot validate without looking at context outside of the git diff.
|
||||
|
||||
Agent 4: Opus bug agent (parallel subagent with agent 3)
|
||||
Look for problems that exist in the introduced code. This could be security issues, incorrect logic, etc. Only look for issues that fall within the changed code.
|
||||
|
||||
**CRITICAL: We only want HIGH SIGNAL issues.** Flag issues where:
|
||||
- The code will fail to compile or parse (syntax errors, type errors, missing imports, unresolved references)
|
||||
- The code will definitely produce wrong results regardless of inputs (clear logic errors)
|
||||
- Clear, unambiguous CLAUDE.md violations where you can quote the exact rule being broken
|
||||
|
||||
Do NOT flag:
|
||||
- Code style or quality concerns
|
||||
- Potential issues that depend on specific inputs or state
|
||||
- Subjective suggestions or improvements
|
||||
|
||||
If you are not certain an issue is real, do not flag it. False positives erode trust and waste reviewer time.
|
||||
|
||||
In addition to the above, each subagent should be told the PR title and description. This will help provide context regarding the author's intent.
|
||||
|
||||
5. For each issue found in the previous step by agents 3 and 4, launch parallel subagents to validate the issue. These subagents should get the PR title and description along with a description of the issue. The agent's job is to review the issue to validate that the stated issue is truly an issue with high confidence. For example, if an issue such as "variable is not defined" was flagged, the subagent's job would be to validate that is actually true in the code. Another example would be CLAUDE.md issues. The agent should validate that the CLAUDE.md rule that was violated is scoped for this file and is actually violated. Use Opus subagents for bugs and logic issues, and sonnet agents for CLAUDE.md violations.
|
||||
|
||||
6. Filter out any issues that were not validated in step 5. This step will give us our list of high signal issues for our review.
|
||||
|
||||
7. If issues were found, skip to step 8 to post comments.
|
||||
|
||||
If NO issues were found, post a summary comment using `gh pr comment` (if `--comment` argument is provided):
|
||||
"No issues found. Checked for bugs and CLAUDE.md compliance."
|
||||
|
||||
8. Create a list of all comments that you plan on leaving. This is only for you to make sure you are comfortable with the comments. Do not post this list anywhere.
|
||||
|
||||
9. Post inline comments for each issue using `gh pr review` with inline comments. For each comment:
|
||||
- Provide a brief description of the issue
|
||||
- For small, self-contained fixes, include a committable suggestion block
|
||||
- For larger fixes (6+ lines, structural changes, or changes spanning multiple locations), describe the issue and suggested fix without a suggestion block
|
||||
- Never post a committable suggestion UNLESS committing the suggestion fixes the issue entirely. If follow up steps are required, do not leave a committable suggestion.
|
||||
|
||||
**IMPORTANT: Only post ONE comment per unique issue. Do not post duplicate comments.**
|
||||
|
||||
Use this list when evaluating issues in Steps 4 and 5 (these are false positives, do NOT flag):
|
||||
|
||||
- Pre-existing issues
|
||||
- Something that appears to be a bug but is actually correct
|
||||
- Pedantic nitpicks that a senior engineer would not flag
|
||||
- Issues that a linter will catch (do not run the linter to verify)
|
||||
- General code quality concerns (e.g., lack of test coverage, general security issues) unless explicitly required in CLAUDE.md
|
||||
- Issues mentioned in CLAUDE.md but explicitly silenced in the code (e.g., via a lint ignore comment)
|
||||
|
||||
Notes:
|
||||
|
||||
- Use gh CLI to interact with GitHub (e.g., fetch pull requests, create comments). Do not use web fetch.
|
||||
- Create a todo list before starting.
|
||||
- You must cite and link each issue in inline comments (e.g., if referring to a CLAUDE.md, include a link to it).
|
||||
- If no issues are found, post a comment with the following format:
|
||||
|
||||
---
|
||||
|
||||
## Code review
|
||||
|
||||
No issues found. Checked for bugs and CLAUDE.md compliance.
|
||||
|
||||
---
|
||||
|
||||
- When linking to code in inline comments, follow the following format precisely, otherwise the Markdown preview won't render correctly: `https://github.com/OWNER/REPO/blob/FULL_SHA/path/to/file.py#L10-L15`
|
||||
- Requires full git sha
|
||||
- You must provide the full sha. Commands like `https://github.com/owner/repo/blob/$(git rev-parse HEAD)/foo/bar` will not work, since your comment will be directly rendered in Markdown.
|
||||
- Repo name must match the repo you're code reviewing
|
||||
- # sign after the file name
|
||||
- Line range format is L[start]-L[end]
|
||||
- Provide at least 1 line of context before and after, centered on the line you are commenting about (eg. if you are commenting about lines 5-6, you should link to `L4-7`)
|
||||
@@ -1,256 +0,0 @@
|
||||
---
|
||||
name: docstring
|
||||
description: Document a Python module and its classes using Google style
|
||||
---
|
||||
|
||||
Document a Python module or class using Google-style docstrings following project conventions. The argument can be a class name or a module path.
|
||||
|
||||
## Instructions
|
||||
|
||||
1. Determine what to document based on the argument:
|
||||
|
||||
**If a module path is provided** (e.g. `src/pipecat/audio/vad/vad_analyzer.py`):
|
||||
- Use that file directly
|
||||
|
||||
**If a class name is provided** (e.g. `VADAnalyzer`):
|
||||
- Search for `class ClassName` in `src/pipecat/`
|
||||
- If multiple files contain that class name, list all matches with their file paths, ask the user which one they want to document, and wait for confirmation
|
||||
|
||||
2. Once the file is identified, read the module to understand its structure:
|
||||
- Identify all classes, functions, and important type aliases
|
||||
- Understand the purpose of each component
|
||||
|
||||
4. Apply documentation in this order:
|
||||
- Module docstring (at top, after imports)
|
||||
- Class docstrings
|
||||
- `__init__` methods (always document constructor parameters)
|
||||
- Public methods (not starting with `_`)
|
||||
- Dataclass/config classes with field descriptions
|
||||
|
||||
5. Skip documentation for:
|
||||
- Private methods (starting with `_`)
|
||||
- Simple dunder methods (`__str__`, `__repr__`, `__post_init__`)
|
||||
- Very simple pass-through properties
|
||||
- **Already documented code** - If a class, method, or function already has a complete docstring that follows the project style, do not modify it. A docstring is complete if it has:
|
||||
- A one-line summary
|
||||
- Args section (if it has parameters)
|
||||
- Returns section (if it returns something meaningful)
|
||||
- Only add or improve documentation where it is missing or incomplete
|
||||
|
||||
## Module Docstring Format
|
||||
|
||||
```python
|
||||
"""[One-line description of module purpose].
|
||||
|
||||
[Optional: Longer explanation of functionality, key classes, or use cases.]
|
||||
"""
|
||||
```
|
||||
|
||||
Example:
|
||||
```python
|
||||
"""Neuphonic text-to-speech service implementations.
|
||||
|
||||
This module provides WebSocket and HTTP-based integrations with Neuphonic's
|
||||
text-to-speech API for real-time audio synthesis.
|
||||
"""
|
||||
```
|
||||
|
||||
## Class Docstring Format
|
||||
|
||||
```python
|
||||
class ClassName:
|
||||
"""One-line summary describing what the class does.
|
||||
|
||||
[Longer description explaining purpose, behavior, and key features.
|
||||
Use action-oriented language.]
|
||||
|
||||
[Optional: Event handlers, usage notes, or important caveats.]
|
||||
"""
|
||||
```
|
||||
|
||||
Example:
|
||||
```python
|
||||
class FrameProcessor(BaseObject):
|
||||
"""Base class for all frame processors in the pipeline.
|
||||
|
||||
Frame processors are the building blocks of Pipecat pipelines, they can be
|
||||
linked to form complex processing pipelines. They receive frames, process
|
||||
them, and pass them to the next or previous processor in the chain.
|
||||
|
||||
Event handlers available:
|
||||
|
||||
- on_before_process_frame: Called before a frame is processed
|
||||
- on_after_process_frame: Called after a frame is processed
|
||||
|
||||
Example::
|
||||
|
||||
@processor.event_handler("on_before_process_frame")
|
||||
async def on_before_process_frame(processor, frame):
|
||||
...
|
||||
|
||||
@processor.event_handler("on_after_process_frame")
|
||||
async def on_after_process_frame(processor, frame):
|
||||
...
|
||||
"""
|
||||
```
|
||||
|
||||
Note: When listing event handlers, do NOT use backticks. Include an `Example::` section (with double colon for Sphinx) showing the decorator pattern and function signature for each event.
|
||||
|
||||
## Constructor (`__init__`) Format
|
||||
|
||||
```python
|
||||
def __init__(self, *, param1: Type, param2: Type = default, **kwargs):
|
||||
"""Initialize the [ClassName].
|
||||
|
||||
Args:
|
||||
param1: Description of param1 and its purpose.
|
||||
param2: Description of param2. Defaults to [default].
|
||||
**kwargs: Additional arguments passed to parent class.
|
||||
"""
|
||||
```
|
||||
|
||||
Example:
|
||||
```python
|
||||
def __init__(
|
||||
self,
|
||||
*,
|
||||
api_key: str,
|
||||
voice_id: Optional[str] = None,
|
||||
sample_rate: Optional[int] = 22050,
|
||||
**kwargs,
|
||||
):
|
||||
"""Initialize the Neuphonic TTS service.
|
||||
|
||||
Args:
|
||||
api_key: Neuphonic API key for authentication.
|
||||
voice_id: ID of the voice to use for synthesis.
|
||||
sample_rate: Audio sample rate in Hz. Defaults to 22050.
|
||||
**kwargs: Additional arguments passed to parent InterruptibleTTSService.
|
||||
"""
|
||||
```
|
||||
|
||||
## Method Docstring Format
|
||||
|
||||
```python
|
||||
async def method_name(self, param1: Type) -> ReturnType:
|
||||
"""One-line summary of what method does.
|
||||
|
||||
[Longer description if behavior isn't obvious.]
|
||||
|
||||
Args:
|
||||
param1: Description of param1.
|
||||
|
||||
Returns:
|
||||
Description of return value.
|
||||
|
||||
Raises:
|
||||
ExceptionType: When this exception is raised.
|
||||
"""
|
||||
```
|
||||
|
||||
Example:
|
||||
```python
|
||||
async def put(self, item: Tuple[Frame, FrameDirection, FrameCallback]):
|
||||
"""Put an item into the priority queue.
|
||||
|
||||
System frames (`SystemFrame`) have higher priority than any other
|
||||
frames. If a non-frame item is provided it will have the highest priority.
|
||||
|
||||
Args:
|
||||
item: The item to enqueue.
|
||||
"""
|
||||
```
|
||||
|
||||
## Dataclass/Config Format
|
||||
|
||||
```python
|
||||
@dataclass
|
||||
class ConfigName:
|
||||
"""One-line description of configuration.
|
||||
|
||||
[Explanation of when/how to use this config.]
|
||||
|
||||
Parameters:
|
||||
field1: Description of field1.
|
||||
field2: Description of field2. Defaults to [default].
|
||||
"""
|
||||
|
||||
field1: Type
|
||||
field2: Type = default_value
|
||||
```
|
||||
|
||||
Example:
|
||||
```python
|
||||
@dataclass
|
||||
class FrameProcessorSetup:
|
||||
"""Configuration parameters for frame processor initialization.
|
||||
|
||||
Parameters:
|
||||
clock: The clock instance for timing operations.
|
||||
task_manager: The task manager for handling async operations.
|
||||
observer: Optional observer for monitoring frame processing events.
|
||||
"""
|
||||
|
||||
clock: BaseClock
|
||||
task_manager: BaseTaskManager
|
||||
observer: Optional[BaseObserver] = None
|
||||
```
|
||||
|
||||
## Enum Documentation Format
|
||||
|
||||
```python
|
||||
class EnumName(Enum):
|
||||
"""One-line description of the enum purpose.
|
||||
|
||||
[Longer description of how the enum is used.]
|
||||
|
||||
Parameters:
|
||||
VALUE1: Description of VALUE1.
|
||||
VALUE2: Description of VALUE2.
|
||||
"""
|
||||
|
||||
VALUE1 = 1
|
||||
VALUE2 = 2
|
||||
```
|
||||
|
||||
## Writing Style Guidelines
|
||||
|
||||
- **Concise and professional** - No casual language or filler words
|
||||
- **Action-oriented** - Start with verbs: "Processes...", "Manages...", "Converts..."
|
||||
- **Purpose before implementation** - Explain WHY before HOW
|
||||
- **Clear parameter descriptions** - Include type hints, defaults, and purpose
|
||||
- **No redundant type info** - Type hints are in the signature, don't repeat in description
|
||||
- **Use backticks for code references** - Wrap class names, method names, event names, parameter names, and code snippets in backticks
|
||||
|
||||
Good: "Neuphonic API key for authentication."
|
||||
Bad: "str: The API key (string) that is used for authenticating with Neuphonic."
|
||||
|
||||
Good: "Triggers `on_speech_started` when the `VADAnalyzer` detects speech."
|
||||
Bad: "Triggers on_speech_started when the VADAnalyzer detects speech."
|
||||
|
||||
## Deprecation Notice Format
|
||||
|
||||
When documenting deprecated code:
|
||||
|
||||
```python
|
||||
"""[Description].
|
||||
|
||||
.. deprecated:: X.X.X
|
||||
`ClassName` is deprecated and will be removed in a future version.
|
||||
Use `NewClassName` instead.
|
||||
"""
|
||||
```
|
||||
|
||||
## Checklist
|
||||
|
||||
Before finishing, verify:
|
||||
|
||||
- [ ] Module has a docstring at the top (after copyright header and imports)
|
||||
- [ ] All public classes have docstrings
|
||||
- [ ] All `__init__` methods document their parameters
|
||||
- [ ] All public methods have docstrings with Args/Returns/Raises as needed
|
||||
- [ ] Dataclasses use "Parameters:" section for field descriptions
|
||||
- [ ] Enums document each value in "Parameters:" section
|
||||
- [ ] Writing is concise and action-oriented
|
||||
- [ ] No documentation added to private methods (starting with `_`)
|
||||
- [ ] Existing complete docstrings were left unchanged
|
||||
@@ -1,128 +0,0 @@
|
||||
---
|
||||
name: pr-description
|
||||
description: Update a GitHub PR description with a summary of changes
|
||||
---
|
||||
|
||||
Update a GitHub pull request description based on the changes in the PR.
|
||||
|
||||
## Arguments
|
||||
|
||||
```
|
||||
/pr-description <PR_NUMBER> [--fixes <ISSUE_NUMBERS>]
|
||||
```
|
||||
|
||||
- `PR_NUMBER` (required): The pull request number to update
|
||||
- `--fixes` (optional): Comma-separated issue numbers that this PR fixes (e.g., `--fixes 123,456`)
|
||||
|
||||
Examples:
|
||||
- `/pr-description 3534`
|
||||
- `/pr-description 3534 --fixes 123`
|
||||
- `/pr-description 3534 --fixes 123,456,789`
|
||||
|
||||
## Instructions
|
||||
|
||||
1. First, gather information about the PR:
|
||||
- Use GitHub plugin to get PR details (title, current description, base branch)
|
||||
- Use local git to get commits: `git log main..HEAD --oneline`
|
||||
- Use local git to get the diff: `git diff main..HEAD`
|
||||
- Parse any `--fixes` argument for issue numbers
|
||||
|
||||
2. Check the existing PR description:
|
||||
- If it already has a complete, accurate description that reflects the changes, do nothing
|
||||
- If it's missing sections, incomplete, or outdated compared to the actual changes, proceed to update
|
||||
- If it only has the template placeholder text, generate a full description
|
||||
|
||||
3. Analyze the changes:
|
||||
- Understand the purpose of each commit
|
||||
- Identify any breaking changes (API changes, removed features, behavior changes)
|
||||
- Look for new features, bug fixes, refactoring, or documentation changes
|
||||
- Collect issue numbers from:
|
||||
- The `--fixes` argument (if provided)
|
||||
- Commit messages (patterns like "Fixes #123", "Closes #456", "Resolves #789")
|
||||
|
||||
4. Generate or update the PR description with these sections:
|
||||
|
||||
## PR Description Format
|
||||
|
||||
### Summary (always include)
|
||||
|
||||
Brief bullet points describing what changed and why. Focus on the *purpose* and *impact*, not implementation details.
|
||||
|
||||
```markdown
|
||||
## Summary
|
||||
|
||||
- Added X to enable Y
|
||||
- Fixed bug where Z would happen
|
||||
- Refactored W for better maintainability
|
||||
```
|
||||
|
||||
### Breaking Changes (include only if applicable)
|
||||
|
||||
Document any changes that affect existing users or APIs.
|
||||
|
||||
```markdown
|
||||
## Breaking Changes
|
||||
|
||||
- `ClassName.method()` now requires a `param` argument
|
||||
- Removed deprecated `old_function()` - use `new_function()` instead
|
||||
```
|
||||
|
||||
### Testing (include when non-obvious)
|
||||
|
||||
How to verify the changes work. Skip for trivial changes.
|
||||
|
||||
```markdown
|
||||
## Testing
|
||||
|
||||
- Run `uv run pytest tests/test_feature.py` to verify the fix
|
||||
- Example usage: `uv run examples/new_feature.py`
|
||||
```
|
||||
|
||||
### Fixes (include if issues are provided or found in commits)
|
||||
|
||||
List issues this PR fixes. GitHub will automatically close these issues when the PR is merged.
|
||||
|
||||
```markdown
|
||||
## Fixes
|
||||
|
||||
- Fixes #123
|
||||
- Fixes #456
|
||||
```
|
||||
|
||||
Note: Use "Fixes #X" format (not "Closes" or "Resolves") for consistency. Each issue should be on its own line with "Fixes" to ensure GitHub auto-closes them.
|
||||
|
||||
## Guidelines
|
||||
|
||||
- **Be concise** - Reviewers should understand the PR in 30 seconds
|
||||
- **Focus on why** - The diff shows *what* changed, explain *why*
|
||||
- **Skip empty sections** - Only include sections that have content
|
||||
- **Use bullet points** - Easier to scan than paragraphs
|
||||
- **Don't duplicate the diff** - Avoid listing every file or line changed
|
||||
|
||||
## Example Output
|
||||
|
||||
```markdown
|
||||
## Summary
|
||||
|
||||
- Added `/docstring` skill for documenting Python modules with Google-style docstrings
|
||||
- Skill finds classes by name and handles conflicts when multiple matches exist
|
||||
- Skips already-documented code to avoid unnecessary changes
|
||||
|
||||
## Testing
|
||||
|
||||
/docstring ClassName
|
||||
|
||||
## Fixes
|
||||
|
||||
- Fixes #123
|
||||
```
|
||||
|
||||
## Checklist
|
||||
|
||||
Before updating the PR:
|
||||
|
||||
- [ ] Verified existing description needs updating (not already complete)
|
||||
- [ ] Summary accurately reflects the changes
|
||||
- [ ] Breaking changes are clearly documented (if any)
|
||||
- [ ] No unnecessary sections included
|
||||
- [ ] Description is concise and scannable
|
||||
@@ -1,28 +0,0 @@
|
||||
---
|
||||
name: pr-submit
|
||||
description: Create and submit a GitHub PR from the current branch
|
||||
---
|
||||
|
||||
Submit the current changes as a GitHub pull request.
|
||||
|
||||
## Instructions
|
||||
|
||||
1. Check the current state of the repository:
|
||||
- Run `git status` to see staged, unstaged, and untracked changes
|
||||
- Run `git diff` to see current changes
|
||||
- Run `git log --oneline -10` to see recent commits
|
||||
|
||||
2. If there are uncommitted changes relevant to the PR:
|
||||
- Ask the user if they want a specific prefix for the branch name (e.g., `alice/`, `fix/`, `feat/`)
|
||||
- Create a new branch based on the current branch
|
||||
- Commit the changes using multiple commits if the changes are unrelated
|
||||
|
||||
3. Push the branch and create the PR:
|
||||
- Push with `-u` flag to set upstream tracking
|
||||
- Create the PR using `gh pr create`
|
||||
|
||||
4. After the PR is created:
|
||||
- Run `/changelog <pr_number>` to generate changelog files, then commit and push them
|
||||
- Run `/pr-description <pr_number>` to update the PR description
|
||||
|
||||
5. Return the PR URL to the user.
|
||||
@@ -1,306 +0,0 @@
|
||||
---
|
||||
name: update-docs
|
||||
description: Update documentation pages to match source code changes on the current branch
|
||||
---
|
||||
|
||||
Update documentation pages to reflect source code changes on the current branch. Analyzes the diff against main, maps changed source files to their corresponding doc pages, and makes targeted edits.
|
||||
|
||||
## Arguments
|
||||
|
||||
```
|
||||
/update-docs [DOCS_PATH]
|
||||
```
|
||||
|
||||
- `DOCS_PATH` (optional): Path to the docs repository root. If not provided, ask the user.
|
||||
|
||||
Examples:
|
||||
- `/update-docs /Users/me/src/docs`
|
||||
- `/update-docs`
|
||||
|
||||
## Instructions
|
||||
|
||||
### Step 1: Resolve docs path
|
||||
|
||||
If `DOCS_PATH` was provided as an argument, use it. Otherwise, ask the user for the path to their docs repository.
|
||||
|
||||
Verify the path exists and contains `server/services/` subdirectory.
|
||||
|
||||
### Step 2: Create docs branch
|
||||
|
||||
Get the current pipecat branch name:
|
||||
```bash
|
||||
git rev-parse --abbrev-ref HEAD
|
||||
```
|
||||
|
||||
In the docs repo, create a new branch off main with a matching name:
|
||||
```bash
|
||||
cd DOCS_PATH && git checkout main && git pull && git checkout -b {branch-name}-docs
|
||||
```
|
||||
|
||||
For example, if the pipecat branch is `feat/new-service`, the docs branch becomes `feat/new-service-docs`.
|
||||
|
||||
All doc edits in subsequent steps are made on this branch.
|
||||
|
||||
### Step 3: Detect changed source files
|
||||
|
||||
Run:
|
||||
```bash
|
||||
git diff main..HEAD --name-only
|
||||
```
|
||||
|
||||
Filter to files that could affect documentation:
|
||||
- `src/pipecat/services/**/*.py` (service implementations)
|
||||
- `src/pipecat/transports/**/*.py` (transport implementations)
|
||||
- `src/pipecat/serializers/**/*.py` (serializer implementations)
|
||||
- `src/pipecat/processors/**/*.py` (processor implementations)
|
||||
- `src/pipecat/audio/**/*.py` (audio utilities)
|
||||
- `src/pipecat/turns/**/*.py` (turn management)
|
||||
- `src/pipecat/observers/**/*.py` (observers)
|
||||
- `src/pipecat/pipeline/**/*.py` (pipeline core)
|
||||
|
||||
Ignore `__init__.py`, `__pycache__`, test files, and files that only contain type re-exports.
|
||||
|
||||
### Step 4: Map source files to doc pages
|
||||
|
||||
For each changed source file, find the corresponding doc page. Read the mapping file at `.claude/skills/update-docs/SOURCE_DOC_MAPPING.md` and apply its tiered lookup: tier 1 (known exceptions) → tier 2 (pattern matching) → tier 3 (search fallback). **First match wins.**
|
||||
|
||||
### Step 5: Analyze each source-doc pair
|
||||
|
||||
For each mapped pair:
|
||||
|
||||
1. **Read the full source file** to understand current state
|
||||
2. **Read the diff** for that file: `git diff main..HEAD -- <source_file>`
|
||||
3. **Read the current doc page** in full
|
||||
|
||||
Identify what changed by comparing source to docs:
|
||||
|
||||
- **Constructor parameters**: Compare `__init__` signature to the Configuration section's `<ParamField>` entries
|
||||
- **InputParams fields**: Compare `InputParams(BaseModel)` class fields to the InputParams table
|
||||
- **Event handlers**: Compare `_register_event_handler` calls and event handler definitions to Event Handlers section
|
||||
- **Class names / imports**: Check if Usage examples reference correct names
|
||||
- **Behavioral changes**: Check if Notes section needs updating
|
||||
|
||||
### Step 6: Make targeted edits
|
||||
|
||||
For each doc page that needs updates, edit **only the sections that need changes**. Preserve all other content exactly as-is.
|
||||
|
||||
#### Rules
|
||||
|
||||
- **Never remove content** unless the corresponding source code was removed
|
||||
- **Never rewrite sections** that are already accurate
|
||||
- **Match existing formatting** — if the page uses `<ParamField>` tags, use them; if it uses tables, use tables
|
||||
- **Keep descriptions concise** — match the tone and length of surrounding content
|
||||
- **Preserve CardGroup, links, and examples** unless they reference removed functionality
|
||||
- **Don't touch frontmatter** unless the class was renamed
|
||||
|
||||
#### Section-specific guidance
|
||||
|
||||
**Configuration** (constructor params):
|
||||
- Use `<ParamField path="name" type="type" default="value">` format if the page already uses it
|
||||
- Add new params in logical order (required first, then optional)
|
||||
- Remove params that no longer exist in source
|
||||
- Update types/defaults that changed
|
||||
|
||||
**InputParams** (runtime settings):
|
||||
- Use markdown table format: `| Parameter | Type | Default | Description |`
|
||||
- Match the field names and types from the `InputParams(BaseModel)` class
|
||||
- Include the default values from the source
|
||||
|
||||
**Usage** (code examples):
|
||||
- Update import paths, class names, and parameter names
|
||||
- Only modify examples if they would break or be misleading with the new API
|
||||
- Don't rewrite working examples just to add new optional params
|
||||
|
||||
**Notes**:
|
||||
- Add notes for new behavioral gotchas or breaking changes
|
||||
- Remove notes about limitations that were fixed
|
||||
- Keep existing notes that are still accurate
|
||||
|
||||
**Event Handlers**:
|
||||
- Update the event table and example code
|
||||
- Add new events, remove deleted ones
|
||||
- Update handler signatures if they changed
|
||||
|
||||
**Overview / Key Features / Prerequisites**:
|
||||
- Only update if the PR fundamentally changes what the service does (new capability, removed capability, renamed class)
|
||||
- Most PRs will NOT need changes to these sections
|
||||
|
||||
### Step 7: Update guides
|
||||
|
||||
Guides at `DOCS_PATH/guides/` reference specific class names, parameters, imports, and code patterns. After completing reference doc edits, check if any guides need updates too.
|
||||
|
||||
For each changed source file, collect the class names, renamed parameters, and changed imports from the diff. Search the guides directory:
|
||||
```bash
|
||||
grep -rl "ClassName\|old_param_name" DOCS_PATH/guides/
|
||||
```
|
||||
|
||||
For each guide that references changed code:
|
||||
1. Read the full guide
|
||||
2. Update class names, parameter names, import paths, and code examples that are now incorrect
|
||||
3. **Don't rewrite prose** — only fix the specific references that changed
|
||||
4. Leave guides alone if they reference the service generally but don't use any changed APIs
|
||||
|
||||
Guide directories:
|
||||
- `guides/learn/` — conceptual tutorials (pipeline, LLM, STT, TTS, etc.)
|
||||
- `guides/fundamentals/` — practical how-tos (metrics, recording, transcripts, etc.)
|
||||
- `guides/features/` — feature-specific guides (Gemini Live, OpenAI audio, WhatsApp, etc.)
|
||||
- `guides/telephony/` — telephony integration guides (Twilio, Plivo, Telnyx, etc.)
|
||||
|
||||
### Step 8: Identify doc gaps
|
||||
|
||||
After processing all mapped pairs, check for two kinds of gaps:
|
||||
|
||||
**Missing pages**: Source files that had no doc page mapping (neither tier 1, 2, nor 3) and are not marked as "(skip)". For each, tell the user:
|
||||
- The source file path
|
||||
- The main class(es) it defines
|
||||
- Whether a new doc page should be created
|
||||
|
||||
**Missing sections**: Mapped doc pages that are missing standard sections compared to the source. For example, a transport page with no Configuration section, or a service page with no InputParams table when the source defines `InputParams(BaseModel)`. Flag these and offer to add the missing sections.
|
||||
|
||||
If the user wants a new page, do all three of the following:
|
||||
|
||||
#### 8a: Create the doc page
|
||||
|
||||
Create the new `.mdx` file using this template structure:
|
||||
```
|
||||
---
|
||||
title: "Service Name"
|
||||
description: "Brief description"
|
||||
---
|
||||
|
||||
## Overview
|
||||
|
||||
[Description from class docstring or source analysis]
|
||||
|
||||
<CardGroup cols={2}>
|
||||
[Cards for API reference and examples if available]
|
||||
</CardGroup>
|
||||
|
||||
## Installation
|
||||
|
||||
```bash
|
||||
pip install "pipecat-ai[package-name]"
|
||||
```
|
||||
|
||||
## Prerequisites
|
||||
|
||||
[Environment variables and account setup]
|
||||
|
||||
## Configuration
|
||||
|
||||
[ParamField entries for constructor params]
|
||||
|
||||
## InputParams
|
||||
|
||||
[Table of InputParams fields, if the service has them]
|
||||
|
||||
## Usage
|
||||
|
||||
### Basic Setup
|
||||
|
||||
```python
|
||||
[Minimal working example]
|
||||
```
|
||||
|
||||
## Notes
|
||||
|
||||
[Important caveats]
|
||||
|
||||
## Event Handlers
|
||||
|
||||
[Event table and example code]
|
||||
```
|
||||
|
||||
#### 8b: Add to docs.json
|
||||
|
||||
Add the new page path to `DOCS_PATH/docs.json` in the correct navigation group. The path format is `server/services/{category}/{provider}` (without the `.mdx` extension).
|
||||
|
||||
Find the matching group in the navigation structure:
|
||||
- **STT** → `"group": "Speech-to-Text"` under Services
|
||||
- **TTS** → `"group": "Text-to-Speech"` under Services
|
||||
- **LLM** → `"group": "LLM"` under Services
|
||||
- **S2S** → `"group": "Speech-to-Speech"` under Services
|
||||
- **Transport** → `"group": "Transport"` under Services
|
||||
- **Serializer** → `"group": "Serializers"` under Services
|
||||
- **Image generation** → `"group": "Image Generation"` under Services
|
||||
- **Video** → `"group": "Video"` under Services
|
||||
- **Memory** → `"group": "Memory"` under Services
|
||||
- **Vision** → `"group": "Vision"` under Services
|
||||
- **Analytics** → `"group": "Analytics & Monitoring"` under Services
|
||||
|
||||
Insert the new entry **alphabetically** within the group's `pages` array. For example, adding a new STT service "foo":
|
||||
```json
|
||||
{
|
||||
"group": "Speech-to-Text",
|
||||
"pages": [
|
||||
"server/services/stt/assemblyai",
|
||||
"server/services/stt/aws",
|
||||
...
|
||||
"server/services/stt/foo",
|
||||
...
|
||||
]
|
||||
}
|
||||
```
|
||||
|
||||
#### 8c: Add to supported-services.mdx
|
||||
|
||||
Add a new row to the correct category table in `DOCS_PATH/server/services/supported-services.mdx`.
|
||||
|
||||
Use this format:
|
||||
```
|
||||
| [DisplayName](/server/services/{category}/{provider}) | `pip install "pipecat-ai[package]"` |
|
||||
```
|
||||
|
||||
To determine the correct values:
|
||||
- **DisplayName**: Use the service's human-readable name (e.g., "ElevenLabs", "AWS Polly", "Google Gemini")
|
||||
- **package**: Look at the service's `pyproject.toml` extras or the import pattern in the source code. For example, if the service is in `src/pipecat/services/foo/`, the package is typically `foo`.
|
||||
- If no pip dependencies are required, use `No dependencies required` instead.
|
||||
|
||||
Insert the new row **alphabetically** within the table. Match the column alignment of the existing rows.
|
||||
|
||||
### Step 9: Output summary
|
||||
|
||||
After all edits are complete, print a summary:
|
||||
|
||||
```
|
||||
## Documentation Updates
|
||||
|
||||
### Updated reference pages
|
||||
- `server/services/stt/deepgram.mdx` — Updated Configuration (added `new_param`), InputParams (updated `language` default)
|
||||
- `server/services/tts/elevenlabs.mdx` — Updated Event Handlers (added `on_connected`)
|
||||
|
||||
### Updated guides
|
||||
- `guides/learn/speech-to-text.mdx` — Updated code example (renamed `old_param` → `new_param`)
|
||||
|
||||
### New service pages
|
||||
- `server/services/tts/newprovider.mdx` — Created page, added to docs.json (Text-to-Speech), added to supported-services.mdx
|
||||
|
||||
### Unmapped source files
|
||||
- `src/pipecat/services/newprovider/tts.py` — NewProviderTTSService (no doc page exists)
|
||||
|
||||
### Skipped files
|
||||
- `src/pipecat/services/ai_service.py` — internal base class
|
||||
```
|
||||
|
||||
## Guidelines
|
||||
|
||||
- **Be conservative** — only change what the diff warrants. Don't "improve" docs beyond what changed in source.
|
||||
- **Read before editing** — always read the full doc page before making changes so you understand the existing structure.
|
||||
- **Preserve voice** — match the writing style of the existing doc page, don't impose a different tone.
|
||||
- **One PR at a time** — this skill operates on the current branch's diff against main. Don't look at other branches.
|
||||
- **Parallel analysis** — when multiple source files map to different doc pages, analyze and edit them in parallel for efficiency.
|
||||
- **Shared source files** — files like `services/google/google.py` are shared bases. Check which services import from them and update all affected doc pages.
|
||||
|
||||
## Checklist
|
||||
|
||||
Before finishing, verify:
|
||||
|
||||
- [ ] All changed source files were checked against the mapping table
|
||||
- [ ] Each doc page edit matches the actual source code change (not guessed)
|
||||
- [ ] No content was removed unless the corresponding source was removed
|
||||
- [ ] New parameters have accurate types and defaults from source
|
||||
- [ ] Formatting matches the existing page style
|
||||
- [ ] Guides referencing changed APIs were checked and updated
|
||||
- [ ] New service pages were added to `docs.json` in the correct group, alphabetically
|
||||
- [ ] New service pages were added to `supported-services.mdx` in the correct table, alphabetically
|
||||
- [ ] Unmapped files were reported to the user
|
||||
@@ -1,79 +0,0 @@
|
||||
# Source-to-Doc Mapping
|
||||
|
||||
Maps pipecat source files to their documentation pages. Source paths are relative to `src/pipecat/`. Doc paths are relative to `DOCS_PATH`.
|
||||
|
||||
## Name mismatches
|
||||
|
||||
These source paths don't follow the standard `services/{provider}/{type}.py` → `server/services/{type}/{provider}.mdx` pattern.
|
||||
|
||||
| Source path | Doc page |
|
||||
|---|---|
|
||||
| `services/google/llm.py` | `server/services/llm/gemini.mdx` |
|
||||
| `services/google/llm_vertex.py` | `server/services/llm/google-vertex.mdx` |
|
||||
| `services/google/google.py` | (shared base — check which services use it) |
|
||||
| `services/google/gemini_live/**` | `server/services/s2s/gemini-live.mdx` |
|
||||
| `services/google/gemini_live/llm_vertex.py` | `server/services/s2s/gemini-live-vertex.mdx` |
|
||||
| `services/aws_nova_sonic/**` | `server/services/s2s/aws.mdx` |
|
||||
| `services/ultravox/**` | `server/services/s2s/ultravox.mdx` |
|
||||
| `services/grok/realtime/**` | `server/services/s2s/grok.mdx` |
|
||||
| `services/openai/realtime/**` | `server/services/s2s/openai.mdx` |
|
||||
| `processors/frameworks/rtvi.py` | `server/frameworks/rtvi/rtvi-processor.mdx` and `server/frameworks/rtvi/rtvi-observer.mdx` |
|
||||
| `processors/transcript_processor.py` | `server/utilities/transcript-processor.mdx` |
|
||||
| `processors/user_idle_processor.py` | `server/utilities/user-idle-processor.mdx` |
|
||||
| `processors/idle_frame_processor.py` | `server/pipeline/pipeline-idle-detection.mdx` |
|
||||
| `pipeline/task.py` | `server/pipeline/pipeline-task.mdx` |
|
||||
| `pipeline/runner.py` | `server/utilities/runner/guide.mdx` |
|
||||
| `transports/base_transport.py` | `server/services/transport/transport-params.mdx` |
|
||||
|
||||
## Skip list
|
||||
|
||||
These files should never trigger doc updates.
|
||||
|
||||
| Pattern | Reason |
|
||||
|---|---|
|
||||
| `services/ai_service.py` | Internal base class |
|
||||
| `services/stt_service.py` | Internal base class |
|
||||
| `services/tts_service.py` | Internal base class |
|
||||
| `services/llm_service.py` | Internal base class |
|
||||
| `services/websocket_service.py` | Internal base class |
|
||||
| `services/openai_realtime_beta/**` | Deprecated |
|
||||
| `services/openai_realtime/**` | Deprecated |
|
||||
| `services/gemini_multimodal_live/**` | Deprecated |
|
||||
| `services/aws/agent_core.py` | Internal |
|
||||
| `services/aws/sagemaker/**` | No doc page |
|
||||
| `transports/base_input.py` | Internal base class |
|
||||
| `transports/base_output.py` | Internal base class |
|
||||
| `transports/websocket/client.py` | No doc page |
|
||||
| `serializers/base_serializer.py` | Internal base class |
|
||||
| `serializers/protobuf.py` | Internal |
|
||||
| `processors/audio/**` | Internal |
|
||||
| `pipeline/pipeline.py` | Core architecture, not a service doc |
|
||||
|
||||
## Pattern matching
|
||||
|
||||
For files not in the tables above, apply these patterns. Convert underscores to hyphens in provider names for doc filenames.
|
||||
|
||||
| Source pattern | Doc pattern |
|
||||
|---|---|
|
||||
| `services/{provider}/stt*.py` | `server/services/stt/{provider}.mdx` |
|
||||
| `services/{provider}/tts*.py` | `server/services/tts/{provider}.mdx` |
|
||||
| `services/{provider}/llm*.py` | `server/services/llm/{provider}.mdx` |
|
||||
| `services/{provider}/image*.py` | `server/services/image-generation/{provider}.mdx` |
|
||||
| `services/{provider}/video*.py` | `server/services/video/{provider}.mdx` |
|
||||
| `services/{provider}/realtime/**` | `server/services/s2s/{provider}.mdx` |
|
||||
| `transports/{name}/**` | `server/services/transport/{name}.mdx` |
|
||||
| `serializers/{name}.py` | `server/services/serializers/{name}.mdx` |
|
||||
| `observers/**` | `server/utilities/observers/` (match by class name) |
|
||||
| `audio/vad/**` | `server/utilities/audio/` (match by class name) |
|
||||
| `audio/filters/**` | `server/utilities/audio/` (match by class name) |
|
||||
| `audio/mixers/**` | `server/utilities/audio/` (match by class name) |
|
||||
| `processors/filters/**` | `server/utilities/filters/` (match by class name) |
|
||||
|
||||
If the doc file doesn't exist at the resolved path, the file is **unmapped**.
|
||||
|
||||
## Search fallback
|
||||
|
||||
For files that don't match any table or pattern above:
|
||||
1. Extract the main class name(s) from the source file
|
||||
2. Search the docs directory for that class name: `grep -r "ClassName" DOCS_PATH/server/`
|
||||
3. If found in a doc page, use that as the mapping
|
||||
30
.dockerignore
Normal file
30
.dockerignore
Normal file
@@ -0,0 +1,30 @@
|
||||
# flyctl launch added from .gitignore
|
||||
**/.vscode
|
||||
**/env
|
||||
**/__pycache__
|
||||
**/*~
|
||||
**/venv
|
||||
#*#
|
||||
|
||||
# Distribution / packaging
|
||||
**/.Python
|
||||
**/build
|
||||
**/develop-eggs
|
||||
**/dist
|
||||
**/downloads
|
||||
**/eggs
|
||||
**/.eggs
|
||||
**/lib
|
||||
**/lib64
|
||||
**/parts
|
||||
**/sdist
|
||||
**/var
|
||||
**/wheels
|
||||
**/share/python-wheels
|
||||
**/*.egg-info
|
||||
**/.installed.cfg
|
||||
**/*.egg
|
||||
**/MANIFEST
|
||||
**/.DS_Store
|
||||
**/.env
|
||||
fly.toml
|
||||
14
.github/workflows/build.yaml
vendored
14
.github/workflows/build.yaml
vendored
@@ -21,20 +21,20 @@ jobs:
|
||||
runs-on: ubuntu-latest
|
||||
steps:
|
||||
- uses: actions/checkout@v4
|
||||
|
||||
|
||||
- name: Install uv
|
||||
uses: astral-sh/setup-uv@v3
|
||||
with:
|
||||
version: "latest"
|
||||
|
||||
|
||||
- name: Set up Python
|
||||
run: uv python install 3.12
|
||||
|
||||
run: uv python install 3.10
|
||||
|
||||
- name: Install development dependencies
|
||||
run: uv sync --group dev
|
||||
|
||||
|
||||
- name: Build project
|
||||
run: uv build
|
||||
|
||||
|
||||
- name: Install project in editable mode
|
||||
run: uv pip install --editable .
|
||||
run: uv pip install --editable .
|
||||
13
.github/workflows/coverage.yaml
vendored
13
.github/workflows/coverage.yaml
vendored
@@ -29,22 +29,11 @@ jobs:
|
||||
|
||||
- name: Install system packages
|
||||
run: |
|
||||
sudo apt-get update
|
||||
sudo apt-get install -y portaudio19-dev
|
||||
|
||||
- name: Install dependencies
|
||||
run: |
|
||||
uv sync --group dev \
|
||||
--extra anthropic \
|
||||
--extra aws \
|
||||
--extra deepgram \
|
||||
--extra google \
|
||||
--extra langchain \
|
||||
--extra livekit \
|
||||
--extra piper \
|
||||
--extra sagemaker \
|
||||
--extra tracing \
|
||||
--extra websocket
|
||||
uv sync --group dev --extra anthropic --extra aws --extra google --extra langchain
|
||||
|
||||
- name: Run tests with coverage
|
||||
run: |
|
||||
|
||||
14
.github/workflows/format.yaml
vendored
14
.github/workflows/format.yaml
vendored
@@ -22,22 +22,22 @@ jobs:
|
||||
steps:
|
||||
- name: Checkout repo
|
||||
uses: actions/checkout@v4
|
||||
|
||||
|
||||
- name: Install uv
|
||||
uses: astral-sh/setup-uv@v3
|
||||
with:
|
||||
version: "latest"
|
||||
|
||||
|
||||
- name: Set up Python
|
||||
run: uv python install 3.12
|
||||
|
||||
run: uv python install 3.10
|
||||
|
||||
- name: Install development dependencies
|
||||
run: uv sync --group dev
|
||||
|
||||
|
||||
- name: Ruff formatter
|
||||
id: ruff-format
|
||||
run: uv run ruff format --diff
|
||||
|
||||
|
||||
- name: Ruff linter (all rules)
|
||||
id: ruff-check
|
||||
run: uv run ruff check
|
||||
run: uv run ruff check
|
||||
174
.github/workflows/generate-changelog.yml
vendored
174
.github/workflows/generate-changelog.yml
vendored
@@ -1,174 +0,0 @@
|
||||
name: Generate Changelog for Release
|
||||
|
||||
on:
|
||||
workflow_dispatch:
|
||||
inputs:
|
||||
version:
|
||||
description: "Release version (e.g., 0.0.97)"
|
||||
required: true
|
||||
type: string
|
||||
date:
|
||||
description: "Release date (YYYY-MM-DD format, defaults to today)"
|
||||
required: false
|
||||
type: string
|
||||
default: ""
|
||||
|
||||
permissions:
|
||||
contents: write
|
||||
pull-requests: write
|
||||
|
||||
jobs:
|
||||
generate-changelog:
|
||||
runs-on: ubuntu-latest
|
||||
steps:
|
||||
- name: Checkout repository
|
||||
uses: actions/checkout@v4
|
||||
with:
|
||||
fetch-depth: 0
|
||||
|
||||
- name: Set up Python
|
||||
uses: actions/setup-python@v5
|
||||
with:
|
||||
python-version: "3.12"
|
||||
|
||||
- name: Install uv
|
||||
uses: astral-sh/setup-uv@v4
|
||||
with:
|
||||
enable-cache: true
|
||||
|
||||
- name: Install dependencies
|
||||
run: |
|
||||
uv sync --group dev
|
||||
|
||||
- name: Set release date
|
||||
id: set_date
|
||||
run: |
|
||||
if [ -z "${{ inputs.date }}" ]; then
|
||||
RELEASE_DATE=$(date +%Y-%m-%d)
|
||||
echo "Using today's date: $RELEASE_DATE"
|
||||
else
|
||||
RELEASE_DATE="${{ inputs.date }}"
|
||||
echo "Using provided date: $RELEASE_DATE"
|
||||
fi
|
||||
echo "release_date=$RELEASE_DATE" >> $GITHUB_OUTPUT
|
||||
|
||||
- name: Validate inputs
|
||||
run: |
|
||||
# Validate version format (basic check)
|
||||
if ! [[ "${{ inputs.version }}" =~ ^[0-9]+\.[0-9]+\.[0-9]+.*$ ]]; then
|
||||
echo "Error: Version must be in format X.Y.Z (e.g., 0.0.97)"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
# Validate date format if provided
|
||||
if [ -n "${{ inputs.date }}" ]; then
|
||||
if ! date -d "${{ inputs.date }}" >/dev/null 2>&1; then
|
||||
# Try macOS date format
|
||||
if ! date -j -f "%Y-%m-%d" "${{ inputs.date }}" >/dev/null 2>&1; then
|
||||
echo "Error: Date must be in YYYY-MM-DD format (e.g., 2025-12-04)"
|
||||
exit 1
|
||||
fi
|
||||
fi
|
||||
fi
|
||||
|
||||
- name: Check for changelog fragments
|
||||
id: check_fragments
|
||||
run: |
|
||||
FRAGMENT_COUNT=$(find changelog -name "*.md" ! -name "_template.md.j2" | wc -l | tr -d ' ')
|
||||
echo "fragment_count=$FRAGMENT_COUNT" >> $GITHUB_OUTPUT
|
||||
|
||||
if [ "$FRAGMENT_COUNT" -eq "0" ]; then
|
||||
echo "❌ Error: No changelog fragments found in changelog/"
|
||||
echo ""
|
||||
echo "Cannot create a release without changelog entries."
|
||||
echo "Add changelog fragments to the changelog/ directory (e.g., 1234.added.md) and try again."
|
||||
exit 1
|
||||
fi
|
||||
|
||||
# Validate fragment types
|
||||
VALID_TYPES="added changed deprecated removed fixed performance security other"
|
||||
INVALID_FRAGMENTS=""
|
||||
|
||||
for file in changelog/*.md; do
|
||||
# Skip template
|
||||
if [[ "$file" == "changelog/_template.md.j2" ]]; then
|
||||
continue
|
||||
fi
|
||||
|
||||
# Extract type from filename (e.g., 1234.added.md -> added)
|
||||
filename=$(basename "$file")
|
||||
# Handle both 1234.added.md and 1234.added.2.md patterns
|
||||
type=$(echo "$filename" | sed -E 's/^[0-9]+\.([a-z]+)(\.[0-9]+)?\.md$/\1/')
|
||||
|
||||
# Check if type is valid
|
||||
if ! echo "$VALID_TYPES" | grep -wq "$type"; then
|
||||
INVALID_FRAGMENTS="$INVALID_FRAGMENTS\n - $filename (type: '$type')"
|
||||
fi
|
||||
done
|
||||
|
||||
if [ -n "$INVALID_FRAGMENTS" ]; then
|
||||
echo "❌ Error: Invalid changelog fragment types found:"
|
||||
echo -e "$INVALID_FRAGMENTS"
|
||||
echo ""
|
||||
echo "Valid types are: $VALID_TYPES"
|
||||
echo "Example: 1234.added.md, 5678.fixed.md"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
echo "✓ Found $FRAGMENT_COUNT changelog fragment(s)"
|
||||
echo "has_fragments=true" >> $GITHUB_OUTPUT
|
||||
|
||||
- name: Preview changelog
|
||||
run: |
|
||||
echo "## Preview of changelog for version ${{ inputs.version }}"
|
||||
echo ""
|
||||
uv run towncrier build --draft --version "${{ inputs.version }}" --date "${{ steps.set_date.outputs.release_date }}"
|
||||
|
||||
- name: Build changelog
|
||||
run: |
|
||||
uv run towncrier build --version "${{ inputs.version }}" --date "${{ steps.set_date.outputs.release_date }}" --yes
|
||||
|
||||
- name: Create Pull Request
|
||||
uses: peter-evans/create-pull-request@v7
|
||||
with:
|
||||
token: ${{ secrets.GITHUB_TOKEN }}
|
||||
commit-message: "Update changelog for version ${{ inputs.version }}"
|
||||
title: "Release ${{ inputs.version }} - Changelog Update"
|
||||
body: |
|
||||
## Changelog Update for Release ${{ inputs.version }}
|
||||
|
||||
This PR updates the CHANGELOG.md with all changes for version **${{ inputs.version }}**.
|
||||
|
||||
### Summary
|
||||
- **Version:** ${{ inputs.version }}
|
||||
- **Date:** ${{ steps.set_date.outputs.release_date }}
|
||||
- **Fragments processed:** ${{ steps.check_fragments.outputs.fragment_count }}
|
||||
|
||||
### What this PR does
|
||||
- ✅ Adds new release section to CHANGELOG.md
|
||||
- ✅ Removes processed changelog fragments
|
||||
- ✅ Ready to merge for release
|
||||
|
||||
### Next Steps
|
||||
1. Review the changelog entries below
|
||||
2. Make any necessary edits to CHANGELOG.md if needed
|
||||
3. Merge this PR
|
||||
4. Continue with your release process
|
||||
|
||||
---
|
||||
|
||||
<details>
|
||||
<summary>📋 Preview of changes</summary>
|
||||
|
||||
The changelog has been updated with entries from the following fragments:
|
||||
|
||||
```bash
|
||||
${{ steps.check_fragments.outputs.fragment_count }} fragments processed
|
||||
```
|
||||
|
||||
</details>
|
||||
branch: changelog-${{ inputs.version }}
|
||||
delete-branch: true
|
||||
labels: |
|
||||
changelog
|
||||
release
|
||||
17
.github/workflows/python-compatibility.yaml
vendored
17
.github/workflows/python-compatibility.yaml
vendored
@@ -14,7 +14,7 @@ jobs:
|
||||
strategy:
|
||||
fail-fast: false
|
||||
matrix:
|
||||
python-version: ['3.11.15', '3.12.13', '3.13.12', '3.14.3']
|
||||
python-version: ['3.10.18', '3.11.13', '3.12.11', '3.13.5']
|
||||
|
||||
name: Python ${{ matrix.python-version }}
|
||||
steps:
|
||||
@@ -40,9 +40,20 @@ jobs:
|
||||
uv python install ${{ matrix.python-version }}
|
||||
uv python pin ${{ matrix.python-version }}
|
||||
|
||||
- name: Test uv sync with all extras
|
||||
- name: Test uv sync with all extras (Python < 3.13)
|
||||
if: "!startsWith(matrix.python-version, '3.13.')"
|
||||
run: |
|
||||
uv sync --group dev --all-extras
|
||||
uv sync --group dev --all-extras --no-extra krisp
|
||||
|
||||
- name: Test uv sync without PyTorch extras (Python 3.13+)
|
||||
if: startsWith(matrix.python-version, '3.13.')
|
||||
run: |
|
||||
uv sync --group dev --all-extras \
|
||||
--no-extra krisp \
|
||||
--no-extra ultravox \
|
||||
--no-extra local-smart-turn \
|
||||
--no-extra moondream \
|
||||
--no-extra mlx-whisper
|
||||
|
||||
- name: Verify installation
|
||||
run: |
|
||||
|
||||
51
.github/workflows/sync-quickstart.yaml
vendored
Normal file
51
.github/workflows/sync-quickstart.yaml
vendored
Normal file
@@ -0,0 +1,51 @@
|
||||
name: Sync Quickstart to pipecat-quickstart repo
|
||||
|
||||
on:
|
||||
push:
|
||||
branches: [main]
|
||||
paths:
|
||||
- 'examples/quickstart/**'
|
||||
workflow_dispatch: # Manual trigger
|
||||
|
||||
jobs:
|
||||
sync-quickstart:
|
||||
runs-on: ubuntu-latest
|
||||
steps:
|
||||
- name: Checkout main repo
|
||||
uses: actions/checkout@v4
|
||||
with:
|
||||
fetch-depth: 0
|
||||
|
||||
- name: Checkout quickstart repo
|
||||
uses: actions/checkout@v4
|
||||
with:
|
||||
repository: pipecat-ai/pipecat-quickstart
|
||||
token: ${{ secrets.QUICKSTART_SYNC_TOKEN }}
|
||||
path: quickstart-repo
|
||||
|
||||
- name: Sync files (excluding uv.lock and README.md)
|
||||
run: |
|
||||
# Copy all files except uv.lock and README.md
|
||||
find examples/quickstart -type f \
|
||||
-not -name "README.md" \
|
||||
-not -name "uv.lock" \
|
||||
-exec cp {} quickstart-repo/ \;
|
||||
|
||||
- name: Commit and push changes
|
||||
run: |
|
||||
cd quickstart-repo
|
||||
git config user.name "GitHub Action"
|
||||
git config user.email "action@github.com"
|
||||
git add .
|
||||
|
||||
# Only commit if there are changes
|
||||
if ! git diff --staged --quiet; then
|
||||
git commit -m "Sync from pipecat main repo
|
||||
|
||||
Updated files from examples/quickstart/
|
||||
Commit: ${{ github.sha }}
|
||||
"
|
||||
git push
|
||||
else
|
||||
echo "No changes to sync"
|
||||
fi
|
||||
13
.github/workflows/tests.yaml
vendored
13
.github/workflows/tests.yaml
vendored
@@ -33,22 +33,11 @@ jobs:
|
||||
|
||||
- name: Install system packages
|
||||
run: |
|
||||
sudo apt-get update
|
||||
sudo apt-get install -y portaudio19-dev
|
||||
|
||||
- name: Install dependencies
|
||||
run: |
|
||||
uv sync --group dev \
|
||||
--extra anthropic \
|
||||
--extra aws \
|
||||
--extra deepgram \
|
||||
--extra google \
|
||||
--extra langchain \
|
||||
--extra livekit \
|
||||
--extra piper \
|
||||
--extra sagemaker \
|
||||
--extra tracing \
|
||||
--extra websocket
|
||||
uv sync --group dev --extra anthropic --extra aws --extra google --extra langchain
|
||||
|
||||
- name: Test with pytest
|
||||
run: |
|
||||
|
||||
147
.github/workflows/update-docs.yml
vendored
147
.github/workflows/update-docs.yml
vendored
@@ -1,147 +0,0 @@
|
||||
name: Update Documentation on PR Merge
|
||||
|
||||
on:
|
||||
pull_request_target:
|
||||
types: [closed]
|
||||
branches: [main]
|
||||
paths:
|
||||
- "src/pipecat/services/**"
|
||||
- "src/pipecat/transports/**"
|
||||
- "src/pipecat/serializers/**"
|
||||
- "src/pipecat/processors/**"
|
||||
- "src/pipecat/audio/**"
|
||||
- "src/pipecat/turns/**"
|
||||
- "src/pipecat/observers/**"
|
||||
- "src/pipecat/pipeline/**"
|
||||
workflow_dispatch:
|
||||
inputs:
|
||||
pr_number:
|
||||
description: "PR number to generate docs for"
|
||||
required: true
|
||||
type: string
|
||||
|
||||
jobs:
|
||||
update-docs:
|
||||
if: >-
|
||||
github.event_name == 'workflow_dispatch' ||
|
||||
github.event.pull_request.merged == true
|
||||
runs-on: ubuntu-latest
|
||||
timeout-minutes: 15
|
||||
permissions:
|
||||
contents: read
|
||||
pull-requests: read
|
||||
id-token: write
|
||||
steps:
|
||||
- name: Checkout pipecat
|
||||
uses: actions/checkout@v4
|
||||
with:
|
||||
fetch-depth: 0
|
||||
|
||||
- name: Checkout docs
|
||||
uses: actions/checkout@v4
|
||||
with:
|
||||
repository: pipecat-ai/docs
|
||||
token: ${{ secrets.DOCS_SYNC_TOKEN }}
|
||||
path: _docs
|
||||
|
||||
- name: Resolve PR number
|
||||
id: pr
|
||||
run: |
|
||||
if [ "${{ github.event_name }}" = "workflow_dispatch" ]; then
|
||||
echo "number=${{ inputs.pr_number }}" >> "$GITHUB_OUTPUT"
|
||||
else
|
||||
echo "number=${{ github.event.pull_request.number }}" >> "$GITHUB_OUTPUT"
|
||||
fi
|
||||
|
||||
- name: Update documentation
|
||||
uses: anthropics/claude-code-action@v1
|
||||
env:
|
||||
DOCS_SYNC_TOKEN: ${{ secrets.DOCS_SYNC_TOKEN }}
|
||||
with:
|
||||
anthropic_api_key: ${{ secrets.ANTHROPIC_API_KEY }}
|
||||
github_token: ${{ secrets.GITHUB_TOKEN }}
|
||||
prompt: |
|
||||
You are updating documentation for the pipecat-ai/docs repository based on
|
||||
changes merged in PR #${{ steps.pr.outputs.number }} of pipecat-ai/pipecat.
|
||||
|
||||
## Setup
|
||||
|
||||
1. Read the skill instructions at `.claude/skills/update-docs/SKILL.md`
|
||||
2. Read the source-to-doc mapping at `.claude/skills/update-docs/SOURCE_DOC_MAPPING.md`
|
||||
3. The docs repository is checked out at `./_docs/`
|
||||
|
||||
## Get the diff
|
||||
|
||||
Run `gh pr diff ${{ steps.pr.outputs.number }}` to see what changed in the PR.
|
||||
Also run `gh pr diff ${{ steps.pr.outputs.number }} --name-only` to get the list of changed files.
|
||||
Filter to source files matching the directories listed in SKILL.md Step 3.
|
||||
|
||||
If no relevant source files were changed, exit with "No documentation changes needed."
|
||||
|
||||
## Follow the skill instructions
|
||||
|
||||
Apply the SKILL.md workflow (Steps 3-9) with these adaptations for automation:
|
||||
|
||||
### Docs path
|
||||
Use `./_docs/` — it's already checked out. Do not ask for a path.
|
||||
|
||||
### Branch management
|
||||
- Branch name: `docs/pr-${{ steps.pr.outputs.number }}`
|
||||
- Work inside `./_docs/` for all doc edits and git operations
|
||||
- Check if the branch already exists on the remote:
|
||||
```bash
|
||||
cd _docs && git fetch origin docs/pr-${{ steps.pr.outputs.number }} 2>/dev/null
|
||||
```
|
||||
- If it exists: check it out (supports workflow re-runs)
|
||||
- If not: create it from main
|
||||
|
||||
### Git config
|
||||
Before committing in `_docs`, set:
|
||||
```bash
|
||||
git config user.name "github-actions[bot]"
|
||||
git config user.email "github-actions[bot]@users.noreply.github.com"
|
||||
```
|
||||
|
||||
### No interactive questions
|
||||
Do not ask questions. If you encounter gaps (unmapped files, missing sections,
|
||||
ambiguous changes), note them in the PR body under "## Gaps identified".
|
||||
|
||||
### Creating the docs PR
|
||||
After committing all changes in `_docs`, push and create a PR:
|
||||
```bash
|
||||
cd _docs
|
||||
git push -u origin docs/pr-${{ steps.pr.outputs.number }}
|
||||
GH_TOKEN=$DOCS_SYNC_TOKEN gh pr create \
|
||||
--repo pipecat-ai/docs \
|
||||
--label auto-docs \
|
||||
--title "docs: update for pipecat PR #${{ steps.pr.outputs.number }}" \
|
||||
--body "$(cat <<'BODY'
|
||||
Automated documentation update for [pipecat PR #${{ steps.pr.outputs.number }}](https://github.com/pipecat-ai/pipecat/pull/${{ steps.pr.outputs.number }}).
|
||||
|
||||
## Changes
|
||||
<summarize each doc page updated and what changed>
|
||||
|
||||
## Gaps identified
|
||||
<any unmapped files, missing doc pages, or missing sections — or "None">
|
||||
BODY
|
||||
)"
|
||||
```
|
||||
|
||||
### Re-run handling
|
||||
If `gh pr create` fails because a PR from that branch already exists,
|
||||
push the updated commits and use `gh pr edit` to update the body instead.
|
||||
|
||||
### No-op
|
||||
If after analyzing the diff you determine no documentation changes are needed
|
||||
(e.g., only skip-listed files changed, or changes don't affect public API docs),
|
||||
exit cleanly without creating a branch or PR. Output "No documentation changes needed."
|
||||
|
||||
## Important rules
|
||||
- Only modify files inside `./_docs/` — never modify pipecat source code
|
||||
- Follow the conservative editing rules from SKILL.md Step 6
|
||||
- Read each doc page fully before editing (SKILL.md Guidelines)
|
||||
- Use `GH_TOKEN=$DOCS_SYNC_TOKEN` for all `gh` commands targeting pipecat-ai/docs
|
||||
claude_args: |
|
||||
--model claude-sonnet-4-5-20250929
|
||||
--max-turns 30
|
||||
--allowedTools "Read,Write,Edit,Glob,Grep,Bash"
|
||||
16
.gitignore
vendored
16
.gitignore
vendored
@@ -4,14 +4,7 @@ __pycache__/
|
||||
*~
|
||||
venv
|
||||
.venv
|
||||
.idea
|
||||
.gradle
|
||||
.next
|
||||
next-env.d.ts
|
||||
local.properties
|
||||
*.log
|
||||
*.lock
|
||||
smart_turn_audio_log
|
||||
/.idea
|
||||
#*#
|
||||
|
||||
# Distribution / Packaging
|
||||
@@ -34,7 +27,7 @@ share/python-wheels/
|
||||
*.egg
|
||||
MANIFEST
|
||||
.DS_Store
|
||||
.env*
|
||||
.env
|
||||
fly.toml
|
||||
|
||||
# Examples
|
||||
@@ -58,7 +51,4 @@ docs/api/_build/
|
||||
docs/api/api
|
||||
|
||||
# uv
|
||||
.python-version
|
||||
|
||||
# Pipecat
|
||||
whisker_setup.py
|
||||
.python-version
|
||||
@@ -1,13 +1,8 @@
|
||||
repos:
|
||||
- repo: local
|
||||
- repo: https://github.com/astral-sh/ruff-pre-commit
|
||||
rev: v0.12.1
|
||||
hooks:
|
||||
- id: ruff
|
||||
name: ruff
|
||||
entry: uv run ruff check --fix
|
||||
language: system
|
||||
types: [python]
|
||||
language_version: python3
|
||||
args: [--fix]
|
||||
- id: ruff-format
|
||||
name: ruff-format
|
||||
entry: uv run ruff format
|
||||
language: system
|
||||
types: [python]
|
||||
|
||||
@@ -11,7 +11,7 @@ build:
|
||||
jobs:
|
||||
post_install:
|
||||
- pip install uv
|
||||
- UV_PROJECT_ENVIRONMENT=$READTHEDOCS_VIRTUALENV_PATH uv sync --group docs --all-extras --no-extra gstreamer --no-extra local_smart_turn --no-extra moondream --no-extra mlx-whisper
|
||||
- UV_PROJECT_ENVIRONMENT=$READTHEDOCS_VIRTUALENV_PATH uv sync --group docs --all-extras --no-extra krisp --no-extra gstreamer --no-extra ultravox --no-extra local_smart_turn --no-extra moondream --no-extra riva --no-extra mlx-whisper
|
||||
|
||||
sphinx:
|
||||
configuration: docs/api/conf.py
|
||||
|
||||
3800
CHANGELOG.md
3800
CHANGELOG.md
File diff suppressed because it is too large
Load Diff
62
CHANGELOG.md.template
Normal file
62
CHANGELOG.md.template
Normal file
@@ -0,0 +1,62 @@
|
||||
# Changelog
|
||||
|
||||
All notable changes to the **<project name>** SDK will be documented in this file.
|
||||
|
||||
The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
|
||||
and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
|
||||
|
||||
Please make sure to add your changes to the appropriate categories:
|
||||
|
||||
## [Unreleased]
|
||||
|
||||
### Added
|
||||
|
||||
<!-- for new functionality -->
|
||||
|
||||
- n/a
|
||||
|
||||
### Changed
|
||||
|
||||
<!-- for changed functionality -->
|
||||
|
||||
- n/a
|
||||
|
||||
### Deprecated
|
||||
|
||||
<!-- for soon-to-be removed functionality -->
|
||||
|
||||
- n/a
|
||||
|
||||
### Removed
|
||||
|
||||
<!-- for removed functionality -->
|
||||
|
||||
- n/a
|
||||
|
||||
### Fixed
|
||||
|
||||
<!-- for fixed bugs -->
|
||||
|
||||
- n/a
|
||||
|
||||
### Performance
|
||||
|
||||
<!-- for performance-relevant changes -->
|
||||
|
||||
- n/a
|
||||
|
||||
### Security
|
||||
|
||||
<!-- for security-relevant changes -->
|
||||
|
||||
- n/a
|
||||
|
||||
### Other
|
||||
|
||||
<!-- for everything else -->
|
||||
|
||||
- n/a
|
||||
|
||||
## [0.1.0] - YYYY-MM-DD
|
||||
|
||||
Initial release.
|
||||
157
CLAUDE.md
157
CLAUDE.md
@@ -1,157 +0,0 @@
|
||||
# CLAUDE.md
|
||||
|
||||
This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
|
||||
|
||||
## Project Overview
|
||||
|
||||
Pipecat is an open-source Python framework for building real-time voice and multimodal conversational AI agents. It orchestrates audio/video, AI services, transports, and conversation pipelines using a frame-based architecture.
|
||||
|
||||
## Common Commands
|
||||
|
||||
```bash
|
||||
# Setup development environment
|
||||
uv sync --group dev --all-extras --no-extra gstreamer
|
||||
|
||||
# Install pre-commit hooks
|
||||
uv run pre-commit install
|
||||
|
||||
# Run all tests
|
||||
uv run pytest
|
||||
|
||||
# Run a single test file
|
||||
uv run pytest tests/test_name.py
|
||||
|
||||
# Run a specific test
|
||||
uv run pytest tests/test_name.py::test_function_name
|
||||
|
||||
# Preview changelog
|
||||
uv run towncrier build --draft --version Unreleased
|
||||
|
||||
# Lint and format check
|
||||
uv run ruff check
|
||||
uv run ruff format --check
|
||||
|
||||
# Update dependencies (after editing pyproject.toml)
|
||||
uv lock && uv sync
|
||||
```
|
||||
|
||||
## Architecture
|
||||
|
||||
### Frame-Based Pipeline Processing
|
||||
|
||||
All data flows as **Frame** objects through a pipeline of **FrameProcessors**:
|
||||
|
||||
```
|
||||
[Processor1] → [Processor2] → ... → [ProcessorN]
|
||||
```
|
||||
|
||||
**Key components:**
|
||||
|
||||
- **Frames** (`src/pipecat/frames/frames.py`): Data units (audio, text, video) and control signals. Flow DOWNSTREAM (input→output) or UPSTREAM (acknowledgments/errors).
|
||||
|
||||
- **FrameProcessor** (`src/pipecat/processors/frame_processor.py`): Base processing unit. Each processor receives frames, processes them, and pushes results downstream.
|
||||
|
||||
- **Pipeline** (`src/pipecat/pipeline/pipeline.py`): Chains processors together.
|
||||
|
||||
- **ParallelPipeline** (`src/pipecat/pipeline/parallel_pipeline.py`): Runs multiple pipelines in parallel.
|
||||
|
||||
- **Transports** (`src/pipecat/transports/`): Transports are frame processors used for external I/O layer (Daily WebRTC, LiveKit WebRTC, WebSocket, Local). Abstract interface via `BaseTransport`, `BaseInputTransport` and `BaseOutputTransport`.
|
||||
|
||||
- **Pipeline Task (`src/pipecat/pipeline/task.py`)**: Runs and manages a pipeline. Pipeline tasks send the first frame, `StartFrame`, to the pipeline in order for processors to know they can start processing and pushing frames. Pipeline tasks internally create a pipeline with two additional processors, a source processor before the user-defined pipeline and a sink processor at the end. Those are used for multiple things: error handling, pipeline task level events, heartbeat monitoring, etc.
|
||||
|
||||
- **Pipeline Runner (`src/pipecat/pipeline/runner.py`)**: High-level entry point for executing pipeline tasks. Handles signal management (SIGINT/SIGTERM) for graceful shutdown and optional garbage collection. Run a single pipeline task with `await runner.run(task)` or multiple concurrently with `await asyncio.gather(runner.run(task1), runner.run(task2))`.
|
||||
|
||||
- **Services** (`src/pipecat/services/`): 60+ AI provider integrations (STT, TTS, LLM, etc.). Extend base classes: `AIService`, `LLMService`, `STTService`, `TTSService`, `VisionService`.
|
||||
|
||||
- **Serializers** (`src/pipecat/serializers/`): Convert frames to/from wire formats for WebSocket transports. `FrameSerializer` base class defines `serialize()` and `deserialize()`. Telephony serializers (Twilio, Plivo, Vonage, Telnyx, Exotel, Genesys) handle provider-specific protocols and audio encoding (e.g., μ-law).
|
||||
|
||||
- **RTVI** (`src/pipecat/processors/frameworks/rtvi.py`): Real-Time Voice Interface protocol bridging clients and the pipeline. `RTVIProcessor` handles incoming client messages (text input, audio, function call results). `RTVIObserver` converts pipeline frames to outgoing messages: user/bot speaking events, transcriptions, LLM/TTS lifecycle, function calls, metrics, and audio levels.
|
||||
|
||||
- **Observers** (`src/pipecat/observers/`): Monitor frame flow without modifying the pipeline. Passed to `PipelineTask` via the `observers` parameter. Implement `on_process_frame()` and `on_push_frame()` callbacks.
|
||||
|
||||
### Important Patterns
|
||||
|
||||
- **Context Aggregation**: `LLMContext` accumulates messages for LLM calls; `UserResponse` aggregates user input
|
||||
|
||||
- **Turn Management**: Turn management is done through `LLMUserAggregator` and
|
||||
`LLMAssistantAggregator`, created with `LLMContextAggregatorPair`
|
||||
|
||||
- **User turn strategies**: Detection of when the user starts and stops speaking is done via user turn start/stop strategies. They push `UserStartedSpeakingFrame` and `UserStoppedSpeakingFrame` respectively.
|
||||
|
||||
- **Interruptions**: Interruptions are usually triggered by a user turn start strategy (e.g. `VADUserTurnStartStrategy`) but they can be triggered by other processors as well, in which case the user turn start strategies don't need to. An `InterruptionFrame` carries an optional `asyncio.Event` that is set when the frame reaches the pipeline sink. If a processor stops an `InterruptionFrame` from propagating downstream (i.e., doesn't push it), it **must** call `frame.complete()` to avoid stalling `push_interruption_task_frame_and_wait()` callers.
|
||||
|
||||
- **Uninterruptible Frames**: These are frames that will not be removed from internal queues even if there's an interruption. For example, `EndFrame` and `StopFrame`.
|
||||
|
||||
- **Events**: Most classes in Pipecat have `BaseObject` as the very base class. `BaseObject` has support for events. Events can run in the background in an async task (default) or synchronously (`sync=True`) if we want immediate action. Synchronous event handlers need to execute fast.
|
||||
|
||||
- **Async Task Management**: Always use `self.create_task(coroutine, name)` instead of raw `asyncio.create_task()`. The `TaskManager` automatically tracks tasks and cleans them up on processor shutdown. Use `await self.cancel_task(task, timeout)` for cancellation.
|
||||
|
||||
- **Error Handling**: Use `await self.push_error(msg, exception, fatal)` to push errors upstream. Services should use `fatal=False` (the default) so application code can handle errors and take action (e.g. switch to another service).
|
||||
|
||||
### Key Directories
|
||||
|
||||
| Directory | Purpose |
|
||||
| -------------------------- | -------------------------------------------------- |
|
||||
| `src/pipecat/frames/` | Frame definitions (100+ types) |
|
||||
| `src/pipecat/processors/` | FrameProcessor base + aggregators, filters, audio |
|
||||
| `src/pipecat/pipeline/` | Pipeline orchestration |
|
||||
| `src/pipecat/services/` | AI service integrations (60+ providers) |
|
||||
| `src/pipecat/transports/` | Transport layer (Daily, LiveKit, WebSocket, Local) |
|
||||
| `src/pipecat/serializers/` | Frame serialization for WebSocket protocols |
|
||||
| `src/pipecat/observers/` | Pipeline observers for monitoring frame flow |
|
||||
| `src/pipecat/audio/` | VAD, filters, mixers, turn detection, DTMF |
|
||||
| `src/pipecat/turns/` | User turn management |
|
||||
|
||||
## Code Style
|
||||
|
||||
- **Docstrings**: Google-style. Classes describe purpose; `__init__` has `Args:` section; dataclasses use `Parameters:` section.
|
||||
- **Linting**: Ruff (line length 100). Pre-commit hooks enforce formatting.
|
||||
- **Type hints**: Required for complex async code.
|
||||
- **Dataclass vs Pydantic**: Use `@dataclass` for frames and internal pipeline data (high-frequency, no validation needed). Use Pydantic `BaseModel` for configuration, parameters, metrics, and external API data (benefits from validation and serialization). Specifically:
|
||||
- `@dataclass`: Frame types, context aggregator pairs, internal data containers
|
||||
- `BaseModel`: Service `InputParams`, transport/VAD/turn params, metrics data, API request/response models, serializer params
|
||||
|
||||
### Docstring Example
|
||||
|
||||
```python
|
||||
class MyService(LLMService):
|
||||
"""Description of what the service does.
|
||||
|
||||
More detailed description.
|
||||
|
||||
Event handlers available:
|
||||
|
||||
- on_connected: Called when we are connected
|
||||
|
||||
Example::
|
||||
|
||||
@service.event_handler("on_connected")
|
||||
async def on_connected(service, frame):
|
||||
...
|
||||
"""
|
||||
|
||||
def __init__(self, param1: str, **kwargs):
|
||||
"""Initialize the service.
|
||||
|
||||
Args:
|
||||
param1: Description of param1.
|
||||
**kwargs: Additional arguments passed to parent.
|
||||
"""
|
||||
super().__init__(**kwargs)
|
||||
```
|
||||
|
||||
## Service Implementation
|
||||
|
||||
When adding a new service:
|
||||
|
||||
1. Extend the appropriate base class (`STTService`, `TTSService`, `LLMService`, etc.)
|
||||
2. Implement required abstract methods
|
||||
3. Handle necessary frames
|
||||
4. By default, all frames should be pushed in the direction they came
|
||||
5. Push `ErrorFrame` on failures
|
||||
6. Add metrics tracking via `MetricsData` if relevant
|
||||
7. Follow the pattern of existing services in `src/pipecat/services/`
|
||||
|
||||
## Testing
|
||||
|
||||
Test utilities live in `src/pipecat/tests/utils.py`. Use `run_test()` to send frames through a pipeline and assert expected output frames in each direction. Use `SleepFrame(sleep=N)` to add delays between frames.
|
||||
@@ -23,8 +23,9 @@ Create your integration following the patterns and examples shown in the "Integr
|
||||
Your repository must contain these components:
|
||||
|
||||
- **Source code** - Complete implementation following Pipecat patterns
|
||||
- **Foundational example** - Single file example showing basic usage (see [Pipecat examples](https://github.com/pipecat-ai/pipecat/tree/main/examples))
|
||||
- **Foundational example** - Single file example showing basic usage (see [Pipecat examples](https://github.com/pipecat-ai/pipecat/tree/main/examples/foundational))
|
||||
- **README.md** - Must include:
|
||||
|
||||
- Introduction and explanation of your integration
|
||||
- Installation instructions
|
||||
- Usage instructions with Pipecat Pipeline
|
||||
@@ -65,25 +66,12 @@ Once your PR is submitted, post in the `#community-integrations` Discord channel
|
||||
|
||||
#### Websocket-based Services
|
||||
|
||||
**Base class:** `WebsocketSTTService`
|
||||
|
||||
**Use for:** Services where you manage the websocket connection directly. Combines `STTService` with `WebsocketService` for automatic reconnection and keepalive support.
|
||||
|
||||
**Examples:**
|
||||
|
||||
- [CartesiaSTTService](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/services/cartesia/stt.py)
|
||||
- [ElevenLabsRealtimeSTTService](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/services/elevenlabs/stt.py)
|
||||
|
||||
#### SDK-based Streaming Services
|
||||
|
||||
**Base class:** `STTService`
|
||||
|
||||
**Use for:** Streaming services where the provider's Python SDK manages the connection internally.
|
||||
|
||||
**Examples:**
|
||||
|
||||
- [DeepgramSTTService](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/services/deepgram/stt.py)
|
||||
- [GoogleSTTService](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/services/google/stt.py)
|
||||
- [SpeechmaticsSTTService](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/services/speechmatics/stt.py)
|
||||
|
||||
#### File-based Services
|
||||
|
||||
@@ -91,7 +79,7 @@ Once your PR is submitted, post in the `#community-integrations` Discord channel
|
||||
|
||||
**Examples:**
|
||||
|
||||
- [NvidiaSTTService](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/services/nvidia/stt.py)
|
||||
- [RivaSTTService](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/services/riva/stt.py)
|
||||
- [FalSTTService](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/services/fal/stt.py)
|
||||
|
||||
#### Key requirements:
|
||||
@@ -121,59 +109,56 @@ Once your PR is submitted, post in the `#community-integrations` Discord channel
|
||||
|
||||
#### Key requirements:
|
||||
|
||||
- **`_process_context(self, context: LLMContext)`** — The main method that processes an LLM context and generates a response. Each LLM service overrides `process_frame` to extract context from `LLMContextFrame` and calls `_process_context`.
|
||||
|
||||
- **`adapter_class`** — Class attribute pointing to a `BaseLLMAdapter` subclass. Defaults to `OpenAILLMAdapter`. Non-OpenAI services must implement their own adapter (see `src/pipecat/adapters/base_llm_adapter.py`) with methods:
|
||||
- `get_llm_invocation_params(context)` — Extract provider-specific params from universal context
|
||||
- `to_provider_tools_format(tools_schema)` — Convert standard tools to provider format
|
||||
- `get_messages_for_logging(context)` — Format messages for logging
|
||||
- Reference adapters: `src/pipecat/adapters/services/` (anthropic, gemini, bedrock, etc.)
|
||||
|
||||
- **Frame sequence:** Output must follow this frame sequence pattern:
|
||||
- `LLMFullResponseStartFrame` — Signals the start of an LLM response
|
||||
- `LLMTextFrame` — Contains LLM content, typically streamed as tokens
|
||||
- `LLMFullResponseEndFrame` — Signals the end of an LLM response
|
||||
|
||||
- **Thought frames (reasoning models):** If the model supports extended thinking / chain-of-thought, emit thought frames alongside the response:
|
||||
- `LLMThoughtStartFrame` — Signals the start of a thought
|
||||
- `LLMThoughtTextFrame` — Contains thought content, streamed as tokens
|
||||
- `LLMThoughtEndFrame` — Signals the end of a thought
|
||||
- `LLMFullResponseStartFrame` - Signals the start of an LLM response
|
||||
- `LLMTextFrame` - Contains LLM content, typically streamed as tokens
|
||||
- `LLMFullResponseEndFrame` - Signals the end of an LLM response
|
||||
|
||||
- **Context aggregation** is handled by the framework via `LLMContext` + `LLMContextAggregatorPair`. The LLM service just processes context it receives — no need to implement aggregators.
|
||||
- **Context aggregation:** Implement context aggregation to collect user and assistant content:
|
||||
- Aggregators come in pairs with a `user()` instance and `assistant()` instance
|
||||
- Context must adhere to the `LLMContext` universal format
|
||||
- Aggregators should handle adding messages, function calls, and images to the context
|
||||
|
||||
### TTS (Text-to-Speech) Services
|
||||
|
||||
#### WebsocketTTSService
|
||||
#### AudioContextWordTTSService
|
||||
|
||||
**Use for:** Websocket-based streaming services (with or without word timestamps)
|
||||
**Use for:** Websocket-based services supporting word/timestamp alignment
|
||||
|
||||
**Examples:**
|
||||
**Example:**
|
||||
|
||||
- [CartesiaTTSService](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/services/cartesia/tts.py)
|
||||
- [ElevenLabsTTSService](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/services/elevenlabs/tts.py)
|
||||
|
||||
#### InterruptibleTTSService
|
||||
|
||||
**Use for:** Websocket-based services without word timestamps that reconnect on interruption (e.g. don't support a context ID or interruption message)
|
||||
**Use for:** Websocket-based services without word/timestamp alignment, requiring disconnection on interruption
|
||||
|
||||
**Example:**
|
||||
|
||||
- [SarvamTTSService](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/services/sarvam/tts.py)
|
||||
|
||||
#### WordTTSService
|
||||
|
||||
**Use for:** HTTP-based services supporting word/timestamp alignment
|
||||
|
||||
**Example:**
|
||||
|
||||
- [ElevenLabsHttpTTSService](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/services/elevenlabs/tts.py)
|
||||
|
||||
#### TTSService
|
||||
|
||||
**Use for:** HTTP-based services (word timestamps are supported in the base class)
|
||||
**Use for:** HTTP-based services without word/timestamp alignment
|
||||
|
||||
**Examples:**
|
||||
**Example:**
|
||||
|
||||
- [GoogleHttpTTSService](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/services/google/tts.py)
|
||||
- [OpenAITTSService](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/services/openai/tts.py)
|
||||
|
||||
#### Key requirements:
|
||||
|
||||
- For websocket services, use asyncio WebSocket implementation
|
||||
- For websocket services, use asyncio WebSocket implementation (required for v13+ support)
|
||||
- Handle idle service timeouts with keepalives
|
||||
- TTS services push both audio (`TTSAudioRawFrame`) and text (`TTSTextFrame`) frames
|
||||
- TTSServices push both audio (`TTSRawAudioFrame`) and text (`TTSTextFrame`) frames
|
||||
|
||||
### Telephony Serializers
|
||||
|
||||
@@ -217,25 +202,14 @@ Vision services process images and provide analysis such as descriptions, object
|
||||
|
||||
#### Key requirements:
|
||||
|
||||
- Must implement `run_vision` method that takes a `UserImageRawFrame` and returns an `AsyncGenerator[Frame, None]`
|
||||
- The method processes the image frame and yields frames with analysis results
|
||||
- Must yield the frame sequence: `VisionFullResponseStartFrame`, `VisionTextFrame`, `VisionFullResponseEndFrame`
|
||||
- Must implement `run_vision` method that takes an `LLMContext` and returns an `AsyncGenerator[Frame, None]`
|
||||
- The method processes the latest image in the context and yields frames with analysis results
|
||||
- Typically yields `TextFrame` objects containing descriptions or answers
|
||||
|
||||
## Implementation Guidelines
|
||||
|
||||
### Naming Conventions
|
||||
|
||||
#### Package and Repository Naming
|
||||
|
||||
Use the `pipecat-{vendor}` naming convention for your PyPI package and repository:
|
||||
|
||||
- `pipecat-{vendor}` — for single-service integrations (e.g., `pipecat-deepdub`)
|
||||
- `pipecat-{vendor}-{type}` — when a vendor offers multiple service types (e.g., `pipecat-upliftai-stt`, `pipecat-upliftai-tts`)
|
||||
|
||||
This convention makes community packages easily discoverable via PyPI search and clearly identifies them as part of the Pipecat ecosystem.
|
||||
|
||||
#### Class Naming
|
||||
|
||||
- **STT:** `VendorSTTService`
|
||||
- **LLM:** `VendorLLMService`
|
||||
- **TTS:**
|
||||
@@ -259,137 +233,24 @@ def can_generate_metrics(self) -> bool:
|
||||
return True
|
||||
```
|
||||
|
||||
### Service Settings
|
||||
### Dynamic Settings Updates
|
||||
|
||||
Every AI service (STT, LLM, TTS, image generation, etc.) exposes a **Settings dataclass** that serves two roles:
|
||||
|
||||
1. **Store mode** — the service's `self._settings` holds the current value of every runtime-updatable field.
|
||||
2. **Delta mode** — an update frame (e.g. `TTSUpdateSettingsFrame`) specifies only the fields that should change; unspecified fields remain `NOT_GIVEN`.
|
||||
|
||||
#### Defining your Settings class
|
||||
|
||||
Extend `STTSettings`, `TTSSettings`, `LLMSettings`, or `ImageGenSettings` (or, if your service directly subclasses `AIService`, `ServiceSettings`). The base classes already provide common fields (e.g. `model`, `voice`, `language`). You only need to add **service-specific knobs that should be runtime-updatable**:
|
||||
STT, LLM, and TTS services support `ServiceUpdateSettingsFrame` for dynamic configuration changes. The base STTService has an `_update_settings()` method that handles settings, and the private `_settings` `Dict` is used to store settings and provide access to the subclass.
|
||||
|
||||
```python
|
||||
from dataclasses import dataclass, field
|
||||
async def set_language(self, language: Language):
|
||||
"""Set the recognition language and reconnect.
|
||||
|
||||
from pipecat.services.settings import TTSSettings, NOT_GIVEN
|
||||
|
||||
@dataclass
|
||||
class MyTTSSettings(TTSSettings):
|
||||
"""Settings for MyTTS service.
|
||||
|
||||
Parameters:
|
||||
speaking_rate: Speed multiplier (0.5–2.0).
|
||||
Args:
|
||||
language: The language to use for speech recognition.
|
||||
"""
|
||||
|
||||
speaking_rate: float | None = field(default_factory=lambda: NOT_GIVEN)
|
||||
```
|
||||
|
||||
**What goes in Settings vs. `__init__` params:**
|
||||
|
||||
| Belongs in Settings | Stays as `__init__` params |
|
||||
| -------------------------------------------------------- | ----------------------------------------- |
|
||||
| Model name, voice, language | API keys, auth tokens |
|
||||
| Service-specific tuning knobs (rate, pitch, temperature) | Base URLs, endpoint overrides |
|
||||
| Anything users may want to change mid-session | Audio encoding, sample format |
|
||||
| | Connection parameters (timeouts, retries) |
|
||||
|
||||
The rule of thumb: if a caller might send an update frame to change it at runtime, it belongs in Settings. Everything else is init-only config stored as `self._xxx`.
|
||||
|
||||
#### Wiring settings into `__init__`
|
||||
|
||||
Accept an **optional** `settings` parameter. Build a `default_settings` object with all fields set to real values, then merge any caller overrides with `apply_update`.
|
||||
|
||||
Add a `Settings` **class attribute** that points to your settings dataclass. This lets callers access the settings class through the service itself (e.g. `MyTTSService.Settings(...)`) without a separate import:
|
||||
|
||||
```python
|
||||
from typing import Optional
|
||||
|
||||
class MyTTSService(TTSService):
|
||||
Settings = MyTTSSettings
|
||||
_settings: Settings
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
*,
|
||||
api_key: str,
|
||||
settings: Optional[Settings] = None,
|
||||
**kwargs,
|
||||
):
|
||||
# 1. Defaults — every field has a real value (store mode).
|
||||
default_settings = self.Settings(
|
||||
model="my-model-v1",
|
||||
voice="default-voice",
|
||||
language="en",
|
||||
speaking_rate=1.0,
|
||||
)
|
||||
|
||||
# 2. Merge caller overrides (only given fields win).
|
||||
if settings is not None:
|
||||
default_settings.apply_update(settings)
|
||||
|
||||
# 3. Pass the fully-populated settings to the base class.
|
||||
super().__init__(settings=default_settings, **kwargs)
|
||||
|
||||
# 4. Init-only config stored separately.
|
||||
self._api_key = api_key
|
||||
```
|
||||
|
||||
This pattern lets callers override only what they care about:
|
||||
|
||||
```python
|
||||
# Uses all defaults
|
||||
svc = MyTTSService(api_key="sk-xxx")
|
||||
|
||||
# Overrides just the voice — access Settings through the service class
|
||||
svc = MyTTSService(
|
||||
api_key="sk-xxx",
|
||||
settings=MyTTSService.Settings(voice="custom-voice"),
|
||||
)
|
||||
```
|
||||
|
||||
#### Reacting to runtime changes
|
||||
|
||||
AI services support runtime configuration changes via `*UpdateSettingsFrame`s (e.g. `STTUpdateSettingsFrame`, `TTSUpdateSettingsFrame`, `LLMUpdateSettingsFrame`).
|
||||
|
||||
To react to runtime setting changes, override `_update_settings`. The base implementation applies the delta to `self._settings` and returns a `dict` mapping each changed field name to its **pre-update** value. Your override should call `super()` first, then act on the changed fields. A common implementation might look like:
|
||||
|
||||
```python
|
||||
async def _update_settings(self, update: TTSSettings) -> dict[str, Any]:
|
||||
"""Apply a settings update, reconfiguring the connection if needed."""
|
||||
changed = await super()._update_settings(update)
|
||||
|
||||
if not changed:
|
||||
return changed
|
||||
|
||||
logger.info(f"Switching STT language to: [{language}]")
|
||||
self._settings["language"] = language
|
||||
await self._disconnect()
|
||||
await self._connect()
|
||||
|
||||
return changed
|
||||
```
|
||||
|
||||
The dict keys work like a set for membership tests (`"language" in changed`) and truthiness (`if changed`). Use `changed.keys() - {"language"}` for set difference, or `changed["language"]` to inspect the previous value of a field.
|
||||
|
||||
Note that, in this example, the service requires a reconnect to apply the new language. Consider, for each setting, whether your service requires reconnection or can apply changes in-place.
|
||||
|
||||
If your service can't yet apply certain settings at runtime, call `self._warn_unhandled_updated_settings(changed)` with any unhandled field names so users get a clear log message:
|
||||
|
||||
```python
|
||||
async def _update_settings(self, update: TTSSettings) -> dict[str, Any]:
|
||||
changed = await super()._update_settings(update)
|
||||
|
||||
if not changed:
|
||||
return changed
|
||||
|
||||
if "language" in changed:
|
||||
await self._update_language()
|
||||
else:
|
||||
# TODO: this should be temporary - handle changes to other settings soon!
|
||||
self._warn_unhandled_updated_settings(changed.keys() - {"language"})
|
||||
|
||||
return changed
|
||||
```
|
||||
Note that, in this example, Deepgram requires the websocket connection be disconnected and reconnected to reinitialize the service with the new value. Consider if your service requires reconnection.
|
||||
|
||||
### Sample Rate Handling
|
||||
|
||||
@@ -399,7 +260,7 @@ Sample rates are set via PipelineParams and passed to each frame processor at in
|
||||
async def start(self, frame: StartFrame):
|
||||
"""Start the service."""
|
||||
await super().start(frame)
|
||||
self._settings.output_sample_rate = self.sample_rate
|
||||
self._settings["output_format"]["sample_rate"] = self.sample_rate
|
||||
await self._connect()
|
||||
```
|
||||
|
||||
@@ -409,7 +270,7 @@ Note that `self.sample_rate` is a `@property` set in the TTSService base class,
|
||||
|
||||
Use Pipecat's tracing decorators:
|
||||
|
||||
- **STT:** `@traced_stt` - decorate `_handle_transcription(self, transcript, is_final, language)` (the standard method name convention)
|
||||
- **STT:** `@traced_stt` - decorate a function that handles `transcript`, `is_final`, `language` as args
|
||||
- **LLM:** `@traced_llm` - decorate the `_process_context()` method
|
||||
- **TTS:** `@traced_tts` - decorate the `run_tts()` method
|
||||
|
||||
@@ -417,9 +278,8 @@ Use Pipecat's tracing decorators:
|
||||
|
||||
### Packaging and Distribution
|
||||
|
||||
- Name your package `pipecat-{vendor}` (see [Naming Conventions](#naming-conventions))
|
||||
- Use [uv](https://docs.astral.sh/uv/) for packaging (encouraged)
|
||||
- Publish to PyPI for easier installation
|
||||
- Consider releasing to PyPI for easier installation
|
||||
- Follow semantic versioning principles
|
||||
- Maintain a changelog
|
||||
|
||||
@@ -432,15 +292,17 @@ For REST-based communication, use aiohttp. Pipecat includes this as a required d
|
||||
- Wrap API calls in appropriate try/catch blocks
|
||||
- Handle rate limits and network failures gracefully
|
||||
- Provide meaningful error messages
|
||||
- When errors occur, raise exceptions AND push errors to notify the pipeline:
|
||||
- When errors occur, raise exceptions AND push `ErrorFrame`s to notify the pipeline:
|
||||
|
||||
```python
|
||||
from pipecat.frames.frames import ErrorFrame
|
||||
|
||||
try:
|
||||
# Your API call
|
||||
result = await self._make_api_call()
|
||||
except Exception as e:
|
||||
# Push error upstream to notify the pipeline
|
||||
await self.push_error(f"{self} error: {e}", exception=e)
|
||||
# Push error frame to pipeline
|
||||
await self.push_error(ErrorFrame(error=f"{self} error: {e}"))
|
||||
# Raise or handle as appropriate
|
||||
raise
|
||||
```
|
||||
|
||||
104
CONTRIBUTING.md
104
CONTRIBUTING.md
@@ -17,120 +17,24 @@ We welcome contributions of all kinds! Your help is appreciated. Follow these st
|
||||
git checkout -b your-branch-name
|
||||
```
|
||||
4. **Make your changes**: Edit or add files as necessary.
|
||||
5. **Add a changelog entry**: Create a changelog fragment file (see [Changelog Entries](#changelog-entries) below).
|
||||
6. **Test your changes**: Ensure that your changes look correct and follow the style set in the codebase.
|
||||
7. **Commit your changes**: Once you're satisfied with your changes, commit them with a meaningful message.
|
||||
5. **Test your changes**: Ensure that your changes look correct and follow the style set in the codebase.
|
||||
6. **Commit your changes**: Once you're satisfied with your changes, commit them with a meaningful message.
|
||||
|
||||
```bash
|
||||
git commit -m "Description of your changes"
|
||||
```
|
||||
|
||||
8. **Push your changes**: Push your branch to your forked repository.
|
||||
7. **Push your changes**: Push your branch to your forked repository.
|
||||
|
||||
```bash
|
||||
git push origin your-branch-name
|
||||
```
|
||||
|
||||
9. **Submit a Pull Request (PR)**: Open a PR from your forked repository to the main branch of this repo.
|
||||
8. **Submit a Pull Request (PR)**: Open a PR from your forked repository to the main branch of this repo.
|
||||
> Important: Describe the changes you've made clearly!
|
||||
|
||||
Our maintainers will review your PR, and once everything is good, your contributions will be merged!
|
||||
|
||||
## Changelog Entries
|
||||
|
||||
Every pull request that makes a user-facing change should include a changelog entry. We use a changelog fragment system to avoid merge conflicts.
|
||||
|
||||
### Creating a Changelog Fragment
|
||||
|
||||
1. Create a new file in the `changelog/` directory with this naming pattern:
|
||||
|
||||
```
|
||||
<PR_number>.<type>.md
|
||||
```
|
||||
|
||||
2. Choose the appropriate type:
|
||||
- `added.md` - New features
|
||||
- `changed.md` - Changes in existing functionality
|
||||
- `deprecated.md` - Soon-to-be removed features
|
||||
- `removed.md` - Removed features
|
||||
- `fixed.md` - Bug fixes
|
||||
- `performance.md` - Performance improvements
|
||||
- `security.md` - Security fixes
|
||||
- `other.md` - Other changes (documentation, dependencies, etc.)
|
||||
|
||||
3. Write your changelog entry as a Markdown bullet point. Include the `-` at the start:
|
||||
|
||||
**Example files:**
|
||||
|
||||
`changelog/1234.added.md`:
|
||||
|
||||
```markdown
|
||||
- Added support for Anthropic Claude 3.5 Sonnet with improved streaming performance.
|
||||
```
|
||||
|
||||
`changelog/5678.fixed.md`:
|
||||
|
||||
```markdown
|
||||
- Fixed an issue where audio frames were dropped during high-load scenarios.
|
||||
```
|
||||
|
||||
**For entries with nested bullets:**
|
||||
|
||||
`changelog/1234.changed.md`:
|
||||
|
||||
```markdown
|
||||
- Updated service configuration:
|
||||
- Changed default timeout to 30 seconds
|
||||
- Added retry logic for failed connections
|
||||
```
|
||||
|
||||
### Multiple Changes in One PR
|
||||
|
||||
**Different types of changes:** Create separate fragment files for each type:
|
||||
|
||||
```
|
||||
changelog/1234.added.md
|
||||
changelog/1234.fixed.md
|
||||
```
|
||||
|
||||
**Multiple changes of the same type:** Create numbered fragment files:
|
||||
|
||||
```
|
||||
changelog/1234.changed.md
|
||||
changelog/1234.changed.2.md
|
||||
```
|
||||
|
||||
**Related changes:** Use nested bullets in a single fragment:
|
||||
|
||||
```markdown
|
||||
- Updated service configuration:
|
||||
- Changed default timeout to 30 seconds
|
||||
- Added retry logic for failed connections
|
||||
```
|
||||
|
||||
**Rule of thumb:** One logical change per fragment file. If changes are unrelated, use separate files.
|
||||
|
||||
### Preview Your Changes
|
||||
|
||||
To see what your changelog entry will look like:
|
||||
|
||||
```bash
|
||||
towncrier build --draft --version Unreleased
|
||||
```
|
||||
|
||||
This won't modify any files, just show you a preview.
|
||||
|
||||
### When to Skip Changelog Entries
|
||||
|
||||
You can skip adding a changelog entry for:
|
||||
|
||||
- Documentation-only changes
|
||||
- Internal refactoring with no user-facing impact
|
||||
- Test-only changes
|
||||
- CI/build configuration changes
|
||||
|
||||
If you're unsure whether your change needs a changelog entry, ask in your PR!
|
||||
|
||||
## Dependency Management
|
||||
|
||||
This project uses [uv](https://docs.astral.sh/uv/) for dependency management. The `uv.lock` file is committed to ensure reproducible builds.
|
||||
|
||||
2
LICENSE
2
LICENSE
@@ -1,6 +1,6 @@
|
||||
BSD 2-Clause License
|
||||
|
||||
Copyright (c) 2024–2026, Daily
|
||||
Copyright (c) 2024–2025, Daily
|
||||
|
||||
Redistribution and use in source and binary forms, with or without
|
||||
modification, are permitted provided that the following conditions are met:
|
||||
|
||||
65
README.md
65
README.md
@@ -3,12 +3,13 @@
|
||||
</div></h1>
|
||||
|
||||
[](https://pypi.org/project/pipecat-ai)  [](https://codecov.io/gh/pipecat-ai/pipecat) [](https://docs.pipecat.ai) [](https://discord.gg/pipecat) [](https://deepwiki.com/pipecat-ai/pipecat)
|
||||
[](https://getmanta.ai/pipecat)
|
||||
|
||||
# 🎙️ Pipecat: Real-Time Voice & Multimodal AI Agents
|
||||
|
||||
**Pipecat** is an open-source Python framework for building real-time voice and multimodal conversational agents. Orchestrate audio and video, AI services, different transports, and conversation pipelines effortlessly—so you can focus on what makes your agent unique.
|
||||
|
||||
> Want to dive right in? Run `pipecat init quickstart` or follow the [quickstart guide](https://docs.pipecat.ai/getting-started/quickstart).
|
||||
> Want to dive right in? Try the [quickstart](https://docs.pipecat.ai/getting-started/quickstart).
|
||||
|
||||
## 🚀 What You Can Build
|
||||
|
||||
@@ -55,20 +56,6 @@ Looking for help debugging your pipeline and processors? Check out [Whisker](htt
|
||||
|
||||
Love terminal applications? Check out [Tail](https://github.com/pipecat-ai/tail), a terminal dashboard for Pipecat.
|
||||
|
||||
### 🤖 Claude Code Skills
|
||||
|
||||
Use [Pipecat Skills](https://github.com/pipecat-ai/skills) with [Claude Code](https://claude.ai/code) to scaffold projects, deploy to Pipecat Cloud, and more. Install the marketplace with:
|
||||
|
||||
```
|
||||
claude plugin marketplace add pipecat-ai/skills
|
||||
```
|
||||
|
||||
and install any of the available plugins.
|
||||
|
||||
### 🧩 Community Integrations
|
||||
|
||||
Build and share your own Pipecat service integrations! Browse existing [community integrations](https://docs.pipecat.ai/server/services/community-integrations) or check out our [guide](COMMUNITY_INTEGRATIONS.md) to create your own.
|
||||
|
||||
### 📺️ Pipecat TV Channel
|
||||
|
||||
Catch new features, interviews, and how-tos on our [Pipecat TV](https://www.youtube.com/playlist?list=PLzU2zoMTQIHjqC3v4q2XVSR3hGSzwKFwH) channel.
|
||||
@@ -79,26 +66,25 @@ Catch new features, interviews, and how-tos on our [Pipecat TV](https://www.yout
|
||||
<a href="https://github.com/pipecat-ai/pipecat-examples/tree/main/simple-chatbot"><img src="https://raw.githubusercontent.com/pipecat-ai/pipecat-examples/main/simple-chatbot/image.png" width="400" /></a>
|
||||
<a href="https://github.com/pipecat-ai/pipecat-examples/tree/main/storytelling-chatbot"><img src="https://raw.githubusercontent.com/pipecat-ai/pipecat-examples/main/storytelling-chatbot/image.png" width="400" /></a>
|
||||
<br/>
|
||||
<a href="https://github.com/pipecat-ai/pipecat-examples/tree/main/daily-multi-translation"><img src="https://raw.githubusercontent.com/pipecat-ai/pipecat-examples/main/daily-multi-translation/image.png" width="400" /></a>
|
||||
<a href="https://github.com/pipecat-ai/pipecat/blob/main/examples/vision/vision-moondream.py"><img src="https://github.com/pipecat-ai/pipecat/blob/main/examples/assets/moondream.png" width="400" /></a>
|
||||
<a href="https://github.com/pipecat-ai/pipecat-examples/tree/main/translation-chatbot"><img src="https://raw.githubusercontent.com/pipecat-ai/pipecat-examples/main/translation-chatbot/image.png" width="400" /></a>
|
||||
<a href="https://github.com/pipecat-ai/pipecat/blob/main/examples/foundational/12-describe-video.py"><img src="https://github.com/pipecat-ai/pipecat/blob/main/examples/foundational/assets/moondream.png" width="400" /></a>
|
||||
</p>
|
||||
|
||||
## 🧩 Available services
|
||||
|
||||
| Category | Services |
|
||||
| ------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
|
||||
| Speech-to-Text | [AssemblyAI](https://docs.pipecat.ai/server/services/stt/assemblyai), [AWS](https://docs.pipecat.ai/server/services/stt/aws), [Azure](https://docs.pipecat.ai/server/services/stt/azure), [Cartesia](https://docs.pipecat.ai/server/services/stt/cartesia), [Deepgram](https://docs.pipecat.ai/server/services/stt/deepgram), [ElevenLabs](https://docs.pipecat.ai/server/services/stt/elevenlabs), [Fal Wizper](https://docs.pipecat.ai/server/services/stt/fal), [Gladia](https://docs.pipecat.ai/server/services/stt/gladia), [Google](https://docs.pipecat.ai/server/services/stt/google), [Gradium](https://docs.pipecat.ai/server/services/stt/gradium), [Groq (Whisper)](https://docs.pipecat.ai/server/services/stt/groq), [Mistral](https://docs.pipecat.ai/server/services/stt/mistral), [NVIDIA Riva](https://docs.pipecat.ai/server/services/stt/riva), [OpenAI (Whisper)](https://docs.pipecat.ai/server/services/stt/openai), [Sarvam](https://docs.pipecat.ai/server/services/stt/sarvam), [Soniox](https://docs.pipecat.ai/server/services/stt/soniox), [Speechmatics](https://docs.pipecat.ai/server/services/stt/speechmatics), [Whisper](https://docs.pipecat.ai/server/services/stt/whisper) |
|
||||
| LLMs | [Anthropic](https://docs.pipecat.ai/server/services/llm/anthropic), [AWS](https://docs.pipecat.ai/server/services/llm/aws), [Azure](https://docs.pipecat.ai/server/services/llm/azure), [Cerebras](https://docs.pipecat.ai/server/services/llm/cerebras), [DeepSeek](https://docs.pipecat.ai/server/services/llm/deepseek), [Fireworks AI](https://docs.pipecat.ai/server/services/llm/fireworks), [Gemini](https://docs.pipecat.ai/server/services/llm/gemini), [Grok](https://docs.pipecat.ai/server/services/llm/grok), [Groq](https://docs.pipecat.ai/server/services/llm/groq), [Mistral](https://docs.pipecat.ai/server/services/llm/mistral), [Nebius](https://docs.pipecat.ai/server/services/llm/nebius), [Novita](https://docs.pipecat.ai/server/services/llm/novita), [NVIDIA NIM](https://docs.pipecat.ai/server/services/llm/nvidia), [Ollama](https://docs.pipecat.ai/server/services/llm/ollama), [OpenAI](https://docs.pipecat.ai/server/services/llm/openai), [OpenRouter](https://docs.pipecat.ai/server/services/llm/openrouter), [Perplexity](https://docs.pipecat.ai/server/services/llm/perplexity), [Qwen](https://docs.pipecat.ai/server/services/llm/qwen), [SambaNova](https://docs.pipecat.ai/server/services/llm/sambanova), [Sarvam](https://docs.pipecat.ai/server/services/llm/sarvam), [Together AI](https://docs.pipecat.ai/server/services/llm/together) |
|
||||
| Text-to-Speech | [Async](https://docs.pipecat.ai/server/services/tts/asyncai), [AWS](https://docs.pipecat.ai/server/services/tts/aws), [Azure](https://docs.pipecat.ai/server/services/tts/azure), [Camb AI](https://docs.pipecat.ai/server/services/tts/camb), [Cartesia](https://docs.pipecat.ai/server/services/tts/cartesia), [Deepgram](https://docs.pipecat.ai/server/services/tts/deepgram), [ElevenLabs](https://docs.pipecat.ai/server/services/tts/elevenlabs), [Fish](https://docs.pipecat.ai/server/services/tts/fish), [Google](https://docs.pipecat.ai/server/services/tts/google), [Gradium](https://docs.pipecat.ai/server/services/tts/gradium), [Groq](https://docs.pipecat.ai/server/services/tts/groq), [Hume](https://docs.pipecat.ai/server/services/tts/hume), [Inworld](https://docs.pipecat.ai/server/services/tts/inworld), [Kokoro](https://docs.pipecat.ai/server/services/tts/kokoro), [LMNT](https://docs.pipecat.ai/server/services/tts/lmnt), [MiniMax](https://docs.pipecat.ai/server/services/tts/minimax), [Mistral](https://docs.pipecat.ai/server/services/tts/mistral), [Neuphonic](https://docs.pipecat.ai/server/services/tts/neuphonic), [NVIDIA Riva](https://docs.pipecat.ai/server/services/tts/riva), [OpenAI](https://docs.pipecat.ai/server/services/tts/openai), [Piper](https://docs.pipecat.ai/server/services/tts/piper), [Resemble](https://docs.pipecat.ai/server/services/tts/resemble), [Rime](https://docs.pipecat.ai/server/services/tts/rime), [Sarvam](https://docs.pipecat.ai/server/services/tts/sarvam), [Smallest](https://docs.pipecat.ai/server/services/tts/smallest), [Speechmatics](https://docs.pipecat.ai/server/services/tts/speechmatics), [xAI](https://docs.pipecat.ai/server/services/tts/xai), [XTTS](https://docs.pipecat.ai/server/services/tts/xtts) |
|
||||
| Speech-to-Speech | [AWS Nova Sonic](https://docs.pipecat.ai/server/services/s2s/aws), [Gemini Multimodal Live](https://docs.pipecat.ai/server/services/s2s/gemini), [Grok Voice Agent](https://docs.pipecat.ai/server/services/s2s/grok), [OpenAI Realtime](https://docs.pipecat.ai/server/services/s2s/openai), [Ultravox](https://docs.pipecat.ai/server/services/s2s/ultravox), |
|
||||
| Transport | [Daily (WebRTC)](https://docs.pipecat.ai/server/services/transport/daily), [FastAPI Websocket](https://docs.pipecat.ai/server/services/transport/fastapi-websocket), [LiveKit (WebRTC)](https://docs.pipecat.ai/server/services/transport/livekit), [SmallWebRTCTransport](https://docs.pipecat.ai/server/services/transport/small-webrtc), [WebSocket Server](https://docs.pipecat.ai/server/services/transport/websocket-server), [WhatsApp](https://docs.pipecat.ai/server/services/transport/whatsapp), Local |
|
||||
| Serializers | [Exotel](https://docs.pipecat.ai/server/services/serializers/exotel), [Genesys](https://docs.pipecat.ai/server/services/serializers/genesys), [Plivo](https://docs.pipecat.ai/server/services/serializers/plivo), [Twilio](https://docs.pipecat.ai/server/services/serializers/twilio), [Telnyx](https://docs.pipecat.ai/server/services/serializers/telnyx), [Vonage](https://docs.pipecat.ai/server/services/serializers/vonage) |
|
||||
| Video | [HeyGen](https://docs.pipecat.ai/server/services/video/heygen), [LemonSlice](https://docs.pipecat.ai/server/services/transport/lemonslice), [Tavus](https://docs.pipecat.ai/server/services/video/tavus), [Simli](https://docs.pipecat.ai/server/services/video/simli) |
|
||||
| Memory | [mem0](https://docs.pipecat.ai/server/services/memory/mem0) |
|
||||
| Vision & Image | [fal](https://docs.pipecat.ai/server/services/image-generation/fal), [Google Imagen](https://docs.pipecat.ai/server/services/image-generation/google-imagen), [Moondream](https://docs.pipecat.ai/server/services/vision/moondream) |
|
||||
| Audio Processing | [Silero VAD](https://docs.pipecat.ai/server/utilities/audio/silero-vad-analyzer), [Krisp Viva](https://docs.pipecat.ai/guides/features/krisp-viva), [Koala](https://docs.pipecat.ai/server/utilities/audio/koala-filter), [ai-coustics](https://docs.pipecat.ai/server/utilities/audio/aic-filter), [RNNoise](https://docs.pipecat.ai/server/utilities/audio/rnnoise-filter) |
|
||||
| Analytics & Metrics | [OpenTelemetry](https://docs.pipecat.ai/server/utilities/opentelemetry), [Sentry](https://docs.pipecat.ai/server/services/analytics/sentry) |
|
||||
| Community | [Browse community integrations →](https://docs.pipecat.ai/server/services/community-integrations) |
|
||||
| Category | Services |
|
||||
| ------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
|
||||
| Speech-to-Text | [AssemblyAI](https://docs.pipecat.ai/server/services/stt/assemblyai), [AWS](https://docs.pipecat.ai/server/services/stt/aws), [Azure](https://docs.pipecat.ai/server/services/stt/azure), [Cartesia](https://docs.pipecat.ai/server/services/stt/cartesia), [Deepgram](https://docs.pipecat.ai/server/services/stt/deepgram), [ElevenLabs](https://docs.pipecat.ai/server/services/stt/elevenlabs), [Fal Wizper](https://docs.pipecat.ai/server/services/stt/fal), [Gladia](https://docs.pipecat.ai/server/services/stt/gladia), [Google](https://docs.pipecat.ai/server/services/stt/google), [Groq (Whisper)](https://docs.pipecat.ai/server/services/stt/groq), [NVIDIA Riva](https://docs.pipecat.ai/server/services/stt/riva), [OpenAI (Whisper)](https://docs.pipecat.ai/server/services/stt/openai), [SambaNova (Whisper)](https://docs.pipecat.ai/server/services/stt/sambanova), [Sarvam](https://docs.pipecat.ai/server/services/stt/sarvam), [Soniox](https://docs.pipecat.ai/server/services/stt/soniox), [Speechmatics](https://docs.pipecat.ai/server/services/stt/speechmatics), [Ultravox](https://docs.pipecat.ai/server/services/stt/ultravox), [Whisper](https://docs.pipecat.ai/server/services/stt/whisper) |
|
||||
| LLMs | [Anthropic](https://docs.pipecat.ai/server/services/llm/anthropic), [AWS](https://docs.pipecat.ai/server/services/llm/aws), [Azure](https://docs.pipecat.ai/server/services/llm/azure), [Cerebras](https://docs.pipecat.ai/server/services/llm/cerebras), [DeepSeek](https://docs.pipecat.ai/server/services/llm/deepseek), [Fireworks AI](https://docs.pipecat.ai/server/services/llm/fireworks), [Gemini](https://docs.pipecat.ai/server/services/llm/gemini), [Grok](https://docs.pipecat.ai/server/services/llm/grok), [Groq](https://docs.pipecat.ai/server/services/llm/groq), [Mistral](https://docs.pipecat.ai/server/services/llm/mistral), [NVIDIA NIM](https://docs.pipecat.ai/server/services/llm/nim), [Ollama](https://docs.pipecat.ai/server/services/llm/ollama), [OpenAI](https://docs.pipecat.ai/server/services/llm/openai), [OpenRouter](https://docs.pipecat.ai/server/services/llm/openrouter), [Perplexity](https://docs.pipecat.ai/server/services/llm/perplexity), [Qwen](https://docs.pipecat.ai/server/services/llm/qwen), [SambaNova](https://docs.pipecat.ai/server/services/llm/sambanova) [Together AI](https://docs.pipecat.ai/server/services/llm/together) |
|
||||
| Text-to-Speech | [Async](https://docs.pipecat.ai/server/services/tts/asyncai), [AWS](https://docs.pipecat.ai/server/services/tts/aws), [Azure](https://docs.pipecat.ai/server/services/tts/azure), [Cartesia](https://docs.pipecat.ai/server/services/tts/cartesia), [Deepgram](https://docs.pipecat.ai/server/services/tts/deepgram), [ElevenLabs](https://docs.pipecat.ai/server/services/tts/elevenlabs), [Fish](https://docs.pipecat.ai/server/services/tts/fish), [Google](https://docs.pipecat.ai/server/services/tts/google), [Groq](https://docs.pipecat.ai/server/services/tts/groq), [Hume](https://docs.pipecat.ai/server/services/tts/hume), [Inworld](https://docs.pipecat.ai/server/services/tts/inworld), [LMNT](https://docs.pipecat.ai/server/services/tts/lmnt), [MiniMax](https://docs.pipecat.ai/server/services/tts/minimax), [Neuphonic](https://docs.pipecat.ai/server/services/tts/neuphonic), [NVIDIA Riva](https://docs.pipecat.ai/server/services/tts/riva), [OpenAI](https://docs.pipecat.ai/server/services/tts/openai), [Piper](https://docs.pipecat.ai/server/services/tts/piper), [PlayHT](https://docs.pipecat.ai/server/services/tts/playht), [Rime](https://docs.pipecat.ai/server/services/tts/rime), [Sarvam](https://docs.pipecat.ai/server/services/tts/sarvam), [Speechmatics](https://docs.pipecat.ai/server/services/tts/speechmatics), [XTTS](https://docs.pipecat.ai/server/services/tts/xtts) |
|
||||
| Speech-to-Speech | [AWS Nova Sonic](https://docs.pipecat.ai/server/services/s2s/aws), [Gemini Multimodal Live](https://docs.pipecat.ai/server/services/s2s/gemini), [OpenAI Realtime](https://docs.pipecat.ai/server/services/s2s/openai) |
|
||||
| Transport | [Daily (WebRTC)](https://docs.pipecat.ai/server/services/transport/daily), [FastAPI Websocket](https://docs.pipecat.ai/server/services/transport/fastapi-websocket), [SmallWebRTCTransport](https://docs.pipecat.ai/server/services/transport/small-webrtc), [WebSocket Server](https://docs.pipecat.ai/server/services/transport/websocket-server), Local |
|
||||
| Serializers | [Plivo](https://docs.pipecat.ai/server/utilities/serializers/plivo), [Twilio](https://docs.pipecat.ai/server/utilities/serializers/twilio), [Telnyx](https://docs.pipecat.ai/server/utilities/serializers/telnyx) |
|
||||
| Video | [HeyGen](https://docs.pipecat.ai/server/services/video/heygen), [Tavus](https://docs.pipecat.ai/server/services/video/tavus), [Simli](https://docs.pipecat.ai/server/services/video/simli) |
|
||||
| Memory | [mem0](https://docs.pipecat.ai/server/services/memory/mem0) |
|
||||
| Vision & Image | [fal](https://docs.pipecat.ai/server/services/image-generation/fal), [Google Imagen](https://docs.pipecat.ai/server/services/image-generation/fal), [Moondream](https://docs.pipecat.ai/server/services/vision/moondream) |
|
||||
| Audio Processing | [Silero VAD](https://docs.pipecat.ai/server/utilities/audio/silero-vad-analyzer), [Krisp](https://docs.pipecat.ai/server/utilities/audio/krisp-filter), [Koala](https://docs.pipecat.ai/server/utilities/audio/koala-filter), [ai-coustics](https://docs.pipecat.ai/server/utilities/audio/aic-filter) |
|
||||
| Analytics & Metrics | [OpenTelemetry](https://docs.pipecat.ai/server/utilities/opentelemetry), [Sentry](https://docs.pipecat.ai/server/services/analytics/sentry) |
|
||||
|
||||
📚 [View full services documentation →](https://docs.pipecat.ai/server/services/supported-services)
|
||||
|
||||
@@ -142,15 +128,15 @@ You can get started with Pipecat running on your local machine, then move your a
|
||||
|
||||
## 🧪 Code examples
|
||||
|
||||
- [Foundational](https://github.com/pipecat-ai/pipecat/tree/main/examples) — small snippets that build on each other, introducing one or two concepts at a time
|
||||
- [Foundational](https://github.com/pipecat-ai/pipecat/tree/main/examples/foundational) — small snippets that build on each other, introducing one or two concepts at a time
|
||||
- [Example apps](https://github.com/pipecat-ai/pipecat-examples) — complete applications that you can use as starting points for development
|
||||
|
||||
## 🛠️ Contributing to the framework
|
||||
|
||||
### Prerequisites
|
||||
|
||||
**Minimum Python Version:** 3.11
|
||||
**Recommended Python Version:** >= 3.12
|
||||
**Minimum Python Version:** 3.10
|
||||
**Recommended Python Version:** 3.12
|
||||
|
||||
### Setup Steps
|
||||
|
||||
@@ -166,7 +152,9 @@ You can get started with Pipecat running on your local machine, then move your a
|
||||
```bash
|
||||
uv sync --group dev --all-extras \
|
||||
--no-extra gstreamer \
|
||||
--no-extra krisp \
|
||||
--no-extra local \
|
||||
--no-extra ultravox # (ultravox not fully supported on macOS)
|
||||
```
|
||||
|
||||
3. Install the git pre-commit hooks:
|
||||
@@ -177,15 +165,6 @@ You can get started with Pipecat running on your local machine, then move your a
|
||||
|
||||
> **Note**: Some extras (local, gstreamer) require system dependencies. See documentation if you encounter build errors.
|
||||
|
||||
### Claude Code Skills
|
||||
|
||||
Install development workflow skills for contributing to Pipecat with [Claude Code](https://claude.ai/code):
|
||||
|
||||
```
|
||||
claude plugin marketplace add pipecat-ai/pipecat
|
||||
claude plugin install pipecat-dev@pipecat-dev-skills
|
||||
```
|
||||
|
||||
### Running tests
|
||||
|
||||
To run all tests, from the root directory:
|
||||
|
||||
@@ -1 +0,0 @@
|
||||
- Added `MistralSTTService` for real-time speech-to-text using Mistral's Voxtral Realtime API (`voxtral-mini-transcribe-realtime-2602`). Supports streaming transcription with interim results, automatic language detection, and VAD-driven utterance lifecycle.
|
||||
@@ -1 +0,0 @@
|
||||
- Fixed `pipecat-ai[tavus]` not installing the required `daily-python` dependency. Installing the `tavus` extra now correctly pulls in `pipecat-ai[daily]`.
|
||||
@@ -1 +0,0 @@
|
||||
- STT services now reconnect safely when settings change: reconnection is deferred until the current user turn ends (i.e., until `UserStoppedSpeakingFrame` is received) rather than interrupting an active speech session. Audio frames received while the reconnect is in progress are buffered and replayed once the new connection is ready. `CartesiaSTTService` and `DeepgramSTTService` both use this new behavior.
|
||||
@@ -1 +0,0 @@
|
||||
- Fixed audio loss and potential errors when STT settings were updated mid-speech. Previously, `CartesiaSTTService` and `DeepgramSTTService` would immediately disconnect and reconnect when settings changed, dropping any in-flight audio. Reconnection is now deferred until the user stops speaking, and audio arriving during the reconnect window is buffered and replayed.
|
||||
@@ -1 +0,0 @@
|
||||
- Added `buttons` field to `OutputDTMFFrame` and `OutputDTMFUrgentFrame` for sending multi-key DTMF sequences as a `list[KeypadEntry]`. Use `OutputDTMFFrame.from_string("123#")` (or the equivalent on `OutputDTMFUrgentFrame`) to build one from a dial string, and `to_string()` to convert back.
|
||||
@@ -1 +0,0 @@
|
||||
- Added `DailyOutputDTMFFrame` and `DailyOutputDTMFUrgentFrame` frames. In addition to the inherited `buttons`, they accept `session_id`, `digit_duration_ms` and `method`, which are forwarded to Daily's `send_dtmf` as `sessionId`, `digitDurationMs` and `method`.
|
||||
@@ -1 +0,0 @@
|
||||
- Added `DailyTransport.send_dtmf()` to expose the Daily call client's DTMF sending capability, enabling applications to send tones during a call (e.g. IVR navigation).
|
||||
@@ -1,16 +0,0 @@
|
||||
{% for section, _ in sections.items() %}
|
||||
{% if sections[section] %}
|
||||
{% for category, val in definitions.items() if category in sections[section]%}
|
||||
### {{ definitions[category]['name'] }}
|
||||
|
||||
{% for text, values in sections[section][category].items() %}
|
||||
{{ text }}
|
||||
(PR {{ values|join(', ') }})
|
||||
|
||||
{% endfor %}
|
||||
{% endfor %}
|
||||
{% else %}
|
||||
No significant changes.
|
||||
|
||||
{% endif %}
|
||||
{% endfor %}
|
||||
103
docs/TURN_AWARE_TRANSCRIPT_PROCESSOR.md
Normal file
103
docs/TURN_AWARE_TRANSCRIPT_PROCESSOR.md
Normal file
@@ -0,0 +1,103 @@
|
||||
# TurnAwareTranscriptProcessor Example
|
||||
|
||||
## Overview
|
||||
|
||||
The `TurnAwareTranscriptProcessor` combines user and assistant transcript tracking with turn boundary detection. It correctly handles interruptions by only capturing what was actually spoken.
|
||||
|
||||
## Basic Usage
|
||||
|
||||
```python
|
||||
from pipecat.processors.transcript_processor import TurnAwareTranscriptProcessor
|
||||
|
||||
# Create the processor
|
||||
turn_processor = TurnAwareTranscriptProcessor()
|
||||
|
||||
# Register event handlers
|
||||
@turn_processor.event_handler("on_turn_started")
|
||||
async def handle_turn_started(processor, turn_number):
|
||||
print(f"Turn {turn_number} started")
|
||||
|
||||
@turn_processor.event_handler("on_turn_ended")
|
||||
async def handle_turn_ended(processor, turn_number, user_text, assistant_text, was_interrupted):
|
||||
print(f"\nTurn {turn_number} ended:")
|
||||
print(f" User said: {user_text}")
|
||||
print(f" Assistant said: {assistant_text}")
|
||||
print(f" Was interrupted: {was_interrupted}")
|
||||
|
||||
@turn_processor.event_handler("on_transcript_update")
|
||||
async def handle_transcript_update(processor, frame):
|
||||
for msg in frame.messages:
|
||||
print(f"[{msg.role}]: {msg.content}")
|
||||
|
||||
# Add to pipeline
|
||||
pipeline = Pipeline([
|
||||
transport.input(),
|
||||
stt,
|
||||
turn_processor, # Process transcripts and track turns
|
||||
context_aggregator.user(),
|
||||
llm,
|
||||
tts,
|
||||
transport.output(),
|
||||
context_aggregator.assistant(),
|
||||
])
|
||||
```
|
||||
|
||||
## Features
|
||||
|
||||
1. **Turn Boundary Detection**: Automatically detects when turns start and end based on user and bot speaking patterns
|
||||
2. **Interruption Handling**: Correctly captures only what was actually spoken when interruptions occur
|
||||
3. **Real-time Transcripts**: Emits transcript messages for both user and assistant speech
|
||||
4. **Turn Events**: Provides start/end events with accumulated transcripts for each turn
|
||||
|
||||
## Events
|
||||
|
||||
### on_turn_started
|
||||
Emitted when a new turn begins (user starts speaking).
|
||||
|
||||
**Handler signature**: `async def handler(processor, turn_number)`
|
||||
|
||||
### on_turn_ended
|
||||
Emitted when a turn ends with accumulated transcripts.
|
||||
|
||||
**Handler signature**: `async def handler(processor, turn_number, user_transcript, assistant_transcript, was_interrupted)`
|
||||
|
||||
### on_transcript_update
|
||||
Inherited from `BaseTranscriptProcessor`, emitted for individual transcript messages.
|
||||
|
||||
**Handler signature**: `async def handler(processor, frame)`
|
||||
|
||||
## Turn Logic
|
||||
|
||||
- Turns start when the user begins speaking (`UserStartedSpeakingFrame`)
|
||||
- Turns end when:
|
||||
- The user starts speaking again (previous turn ends, new turn starts)
|
||||
- The bot is interrupted (`InterruptionFrame`)
|
||||
- The pipeline ends (`EndFrame`/`CancelFrame`)
|
||||
|
||||
## Integration with OpenTelemetry
|
||||
|
||||
You can use turn events to enrich OpenTelemetry spans:
|
||||
|
||||
```python
|
||||
from pipecat.utils.tracing.turn_trace_observer import TurnTraceObserver
|
||||
|
||||
turn_tracker = TurnTrackingObserver()
|
||||
turn_tracer = TurnTraceObserver(turn_tracker)
|
||||
turn_processor = TurnAwareTranscriptProcessor()
|
||||
|
||||
@turn_processor.event_handler("on_turn_ended")
|
||||
async def add_transcripts_to_span(processor, turn_number, user_text, assistant_text, interrupted):
|
||||
# Get current span and add transcript data
|
||||
from opentelemetry import trace
|
||||
current_span = trace.get_current_span()
|
||||
if current_span:
|
||||
current_span.set_attribute("turn.user_text", user_text)
|
||||
current_span.set_attribute("turn.assistant_text", assistant_text)
|
||||
```
|
||||
|
||||
## Notes
|
||||
|
||||
- The processor handles async frame processing correctly by delaying turn end until frames are processed
|
||||
- Works with word-level timestamps from TTS services like Cartesia
|
||||
- Accumulates both user (`TranscriptionFrame`) and assistant (`TTSTextFrame`) speech
|
||||
- Emits individual transcript messages in addition to turn-level aggregation
|
||||
@@ -1,60 +1,109 @@
|
||||
# Pipecat API Documentation
|
||||
# Pipecat Documentation
|
||||
|
||||
This directory contains the source files for auto-generating Pipecat's API reference documentation.
|
||||
This directory contains the source files for auto-generating Pipecat's server API reference documentation.
|
||||
|
||||
## Setup
|
||||
|
||||
1. Install documentation dependencies:
|
||||
|
||||
```bash
|
||||
pip install -r requirements.txt
|
||||
```
|
||||
|
||||
2. Make the build scripts executable:
|
||||
|
||||
```bash
|
||||
chmod +x build-docs.sh rtd-test.py
|
||||
```
|
||||
|
||||
## Building Documentation
|
||||
|
||||
From this directory:
|
||||
From this directory, you can build the documentation in several ways:
|
||||
|
||||
### Local Build
|
||||
|
||||
```bash
|
||||
# Build docs (warnings shown but don't fail the build)
|
||||
cd docs/api && uv run ./build-docs.sh
|
||||
# Using the build script (automatically opens docs when done)
|
||||
./build-docs.sh
|
||||
|
||||
# Build with strict mode (warnings treated as errors)
|
||||
cd docs/api && uv run ./build-docs.sh --strict
|
||||
# Or directly with sphinx-build
|
||||
sphinx-build -b html . _build/html -W --keep-going
|
||||
```
|
||||
|
||||
The build script will:
|
||||
### ReadTheDocs Test Build
|
||||
|
||||
1. Install documentation dependencies via `uv sync --group docs`
|
||||
2. Clean previous build output
|
||||
3. Run `sphinx-build` to generate HTML documentation
|
||||
4. Open the result in your browser (macOS)
|
||||
To test the documentation build process exactly as it would run on ReadTheDocs:
|
||||
|
||||
```bash
|
||||
./rtd-test.py
|
||||
```
|
||||
|
||||
This script:
|
||||
|
||||
- Creates a fresh virtual environment
|
||||
- Installs all dependencies as specified in requirements files
|
||||
- Handles conflicting dependencies (like grpcio versions for Riva and PlayHT)
|
||||
- Builds the documentation in an isolated environment
|
||||
- Provides detailed logging of the build process
|
||||
|
||||
Use this script to verify your documentation will build correctly on ReadTheDocs before pushing changes.
|
||||
|
||||
## Viewing Documentation
|
||||
|
||||
The built documentation will be available at `_build/html/index.html`. To open:
|
||||
|
||||
```bash
|
||||
# On MacOS
|
||||
open _build/html/index.html
|
||||
|
||||
# On Linux
|
||||
xdg-open _build/html/index.html
|
||||
|
||||
# On Windows
|
||||
start _build/html/index.html
|
||||
```
|
||||
|
||||
## Directory Structure
|
||||
|
||||
```
|
||||
.
|
||||
├── api/ # Auto-generated API documentation (created during build)
|
||||
├── _build/ # Built documentation output
|
||||
├── conf.py # Sphinx configuration (mock imports, extensions, etc.)
|
||||
├── api/ # Auto-generated API documentation
|
||||
├── _build/ # Built documentation
|
||||
├── _static/ # Static files (images, css, etc.)
|
||||
├── conf.py # Sphinx configuration
|
||||
├── index.rst # Main documentation entry point
|
||||
├── requirements-base.txt # Base documentation dependencies
|
||||
├── requirements-riva.txt # Riva-specific dependencies
|
||||
├── requirements-playht.txt # PlayHT-specific dependencies
|
||||
├── build-docs.sh # Local build script
|
||||
└── rtd-test.sh # ReadTheDocs test build script (uses pip, not uv)
|
||||
└── rtd-test.py # ReadTheDocs test build script
|
||||
```
|
||||
|
||||
## How It Works
|
||||
## Notes
|
||||
|
||||
- `conf.py` runs `sphinx-apidoc` during Sphinx's `setup()` phase to generate `.rst` files from Python source
|
||||
- Sphinx autodoc imports each module to extract docstrings
|
||||
- Modules with unavailable dependencies are listed in `autodoc_mock_imports` in `conf.py`
|
||||
- Napoleon extension converts Google-style docstrings to reStructuredText
|
||||
- Documentation is auto-generated from Python docstrings
|
||||
- Service modules are automatically detected and included
|
||||
- The build process matches our ReadTheDocs configuration
|
||||
- Warnings are treated as errors (-W flag) to maintain consistency
|
||||
- The --keep-going flag ensures all errors are reported
|
||||
- Dependencies are split into multiple requirements files to handle version conflicts
|
||||
|
||||
## Troubleshooting
|
||||
|
||||
**Module not appearing in docs:**
|
||||
If you encounter missing service modules:
|
||||
|
||||
1. Check the build output for `autodoc: failed to import` warnings
|
||||
2. If the module has an unresolvable import dependency, add it to `autodoc_mock_imports` in `conf.py`
|
||||
3. Verify the module is importable: `uv run python -c "import pipecat.module.name"`
|
||||
1. Verify the service is installed with its extras: `pip install pipecat-ai[service-name]`
|
||||
2. Check the build logs for import errors
|
||||
3. Ensure the service module is properly initialized in the package
|
||||
4. Run `./rtd-test.py` to test in an isolated environment matching ReadTheDocs
|
||||
|
||||
**Duplicate object warnings:**
|
||||
For dependency conflicts:
|
||||
|
||||
These come from re-export modules or Sphinx discovering the same class through multiple import paths. Usually cosmetic.
|
||||
1. Check the requirements files for version specifications
|
||||
2. Use `rtd-test.py` to verify dependency resolution
|
||||
3. Consider adding service-specific requirements files if needed
|
||||
|
||||
**Docstring formatting warnings:**
|
||||
For more information:
|
||||
|
||||
Docstrings use reStructuredText, not Markdown. Common issues:
|
||||
- Use `Example::` with indented code blocks, not `` ```python ``
|
||||
- Ensure blank lines between directive content and subsequent sections
|
||||
- Use `Parameters:` (not `Attributes:`) for dataclass field documentation to avoid duplicate entries
|
||||
- [ReadTheDocs Configuration](.readthedocs.yaml)
|
||||
- [Sphinx Documentation](https://www.sphinx-doc.org/)
|
||||
|
||||
@@ -1,16 +1,8 @@
|
||||
#!/bin/bash
|
||||
|
||||
# Usage: ./build-docs.sh [--strict]
|
||||
# --strict: Treat warnings as errors (default: warnings only)
|
||||
|
||||
SPHINX_OPTS=""
|
||||
if [ "$1" = "--strict" ]; then
|
||||
SPHINX_OPTS="-W --keep-going"
|
||||
fi
|
||||
|
||||
# Build docs using uv
|
||||
echo "Installing dependencies with uv..."
|
||||
uv sync --group docs --all-extras --no-extra gstreamer --no-extra local_smart_turn --no-extra moondream --no-extra mlx-whisper
|
||||
uv sync --group docs --all-extras --no-extra krisp --no-extra gstreamer --no-extra ultravox --no-extra local_smart_turn --no-extra moondream --no-extra riva --no-extra mlx-whisper
|
||||
|
||||
# Check if sphinx-build is available
|
||||
if ! uv run sphinx-build --version &> /dev/null; then
|
||||
@@ -22,7 +14,8 @@ fi
|
||||
rm -rf _build
|
||||
|
||||
echo "Building documentation..."
|
||||
uv run sphinx-build -b html -d _build/doctrees . _build/html $SPHINX_OPTS
|
||||
# Build docs matching ReadTheDocs configuration
|
||||
uv run sphinx-build -b html -d _build/doctrees . _build/html -W --keep-going
|
||||
|
||||
if [ $? -eq 0 ]; then
|
||||
echo "Documentation built successfully!"
|
||||
@@ -31,4 +24,4 @@ if [ $? -eq 0 ]; then
|
||||
else
|
||||
echo "Documentation build failed!" >&2
|
||||
exit 1
|
||||
fi
|
||||
fi
|
||||
@@ -4,19 +4,6 @@ import sys
|
||||
from datetime import datetime
|
||||
from pathlib import Path
|
||||
|
||||
# Fix Pydantic v2 + Sphinx autodoc incompatibility: ConfigDict(extra="allow") fails
|
||||
# during Sphinx's import because __pydantic_extra__ annotation on BaseModel resolves to
|
||||
# `Dict[str, Any] | None` whose get_origin() is Union, not dict. Patch the check to
|
||||
# accept Union-wrapped dict types (i.e., Optional[Dict[str, Any]]).
|
||||
import pydantic._internal._generate_schema as _pydantic_gs
|
||||
|
||||
_ORIG_DICT_TYPES = _pydantic_gs.DICT_TYPES
|
||||
# Expand the accepted types to include Union (Optional[Dict[str, Any]])
|
||||
import types
|
||||
import typing
|
||||
|
||||
_pydantic_gs.DICT_TYPES = [*_ORIG_DICT_TYPES, typing.Union, types.UnionType]
|
||||
|
||||
# Configure logging
|
||||
logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s")
|
||||
logger = logging.getLogger("sphinx-build")
|
||||
@@ -61,6 +48,8 @@ autodoc_default_options = {
|
||||
# Mock imports for optional dependencies
|
||||
autodoc_mock_imports = [
|
||||
# Krisp - has build issues on some platforms
|
||||
"pipecat_ai_krisp",
|
||||
"krisp",
|
||||
"krisp_audio",
|
||||
# System-specific GUI libraries
|
||||
"_tkinter",
|
||||
@@ -72,6 +61,9 @@ autodoc_mock_imports = [
|
||||
# OpenCV - sometimes has import issues during docs build
|
||||
"cv2",
|
||||
# Heavy ML packages excluded from ReadTheDocs
|
||||
# ultravox dependencies
|
||||
"vllm",
|
||||
"vllm.engine.arg_utils",
|
||||
# local-smart-turn dependencies
|
||||
"coremltools",
|
||||
"coremltools.models",
|
||||
@@ -89,29 +81,19 @@ autodoc_mock_imports = [
|
||||
"einops",
|
||||
"intel_extension_for_pytorch",
|
||||
"huggingface_hub",
|
||||
# riva dependencies
|
||||
"riva",
|
||||
"riva.client",
|
||||
"riva.client.Auth",
|
||||
"riva.client.ASRService",
|
||||
"riva.client.StreamingRecognitionConfig",
|
||||
"riva.client.RecognitionConfig",
|
||||
"riva.client.AudioEncoding",
|
||||
"riva.client.proto.riva_tts_pb2",
|
||||
"riva.client.SpeechSynthesisService",
|
||||
# MLX dependencies (Apple Silicon specific)
|
||||
"mlx",
|
||||
"mlx_whisper", # Note: might need underscore format too
|
||||
# Pydantic v2 compatibility issues in third-party SDKs
|
||||
"hume",
|
||||
"hume.tts",
|
||||
"hume.tts.types",
|
||||
"cartesia",
|
||||
"camb",
|
||||
"sarvamai",
|
||||
"openai.types.beta.realtime",
|
||||
"langchain_core",
|
||||
"langchain_core.messages",
|
||||
# FastAPI - Pydantic v2 compatibility issues during Sphinx autodoc
|
||||
"fastapi",
|
||||
"fastapi.applications",
|
||||
"fastapi.routing",
|
||||
"fastapi.params",
|
||||
"fastapi.middleware",
|
||||
"fastapi.responses",
|
||||
"uvicorn",
|
||||
# Deepgram dependencies
|
||||
"deepgram",
|
||||
]
|
||||
|
||||
# HTML output settings
|
||||
@@ -137,9 +119,8 @@ def import_core_modules():
|
||||
"pipecat.observers",
|
||||
"pipecat.runner",
|
||||
"pipecat.serializers",
|
||||
"pipecat.sync",
|
||||
"pipecat.transcriptions",
|
||||
"pipecat.turns",
|
||||
"pipecat.extensions",
|
||||
"pipecat.utils",
|
||||
]
|
||||
|
||||
@@ -184,6 +165,7 @@ def setup(app):
|
||||
logger.info(f"Source directory: {source_dir}")
|
||||
|
||||
excludes = [
|
||||
str(project_root / "src/pipecat/pipeline/to_be_updated"),
|
||||
str(project_root / "src/pipecat/examples"),
|
||||
str(project_root / "src/pipecat/tests"),
|
||||
"**/test_*.py",
|
||||
|
||||
@@ -30,7 +30,7 @@ Quick Links
|
||||
Runner <api/pipecat.runner>
|
||||
Serializers <api/pipecat.serializers>
|
||||
Services <api/pipecat.services>
|
||||
Sync <api/pipecat.sync>
|
||||
Transcriptions <api/pipecat.transcriptions>
|
||||
Transports <api/pipecat.transports>
|
||||
Turns <api/pipecat.turns>
|
||||
Utils <api/pipecat.utils>
|
||||
Utils <api/pipecat.utils>
|
||||
49
env.example
49
env.example
@@ -31,9 +31,6 @@ AZURE_DALLE_API_KEY=...
|
||||
AZURE_DALLE_ENDPOINT=https://...
|
||||
AZURE_DALLE_MODEL=...
|
||||
|
||||
# Camb.ai
|
||||
CAMB_API_KEY=...
|
||||
|
||||
# Cartesia
|
||||
CARTESIA_API_KEY=...
|
||||
CARTESIA_VOICE_ID=...
|
||||
@@ -43,12 +40,11 @@ CEREBRAS_API_KEY=...
|
||||
|
||||
# Daily
|
||||
DAILY_API_KEY=...
|
||||
DAILY_ROOM_URL=https://...
|
||||
DAILY_SAMPLE_ROOM_URL=https://...
|
||||
|
||||
# Deepgram
|
||||
DEEPGRAM_API_KEY=...
|
||||
SAGEMAKER_STT_ENDPOINT_NAME=...
|
||||
SAGEMAKER_TTS_ENDPOINT_NAME=...
|
||||
SAGEMAKER_ENDPOINT_NAME=...
|
||||
|
||||
# DeepSeek
|
||||
DEEPSEEK_API_KEY=...
|
||||
@@ -77,15 +73,14 @@ GOOGLE_CLOUD_PROJECT_ID=...
|
||||
GOOGLE_CLOUD_LOCATION=...
|
||||
GOOGLE_TEST_CREDENTIALS=...
|
||||
|
||||
# Gradium
|
||||
GRAPDIUM_API_KEY=...
|
||||
# Grok
|
||||
GROK_API_KEY=...
|
||||
|
||||
# Groq
|
||||
GROQ_API_KEY=...
|
||||
|
||||
# Heygen
|
||||
HEYGEN_API_KEY=...
|
||||
HEYGEN_LIVE_AVATAR_API_KEY=...
|
||||
|
||||
# Hume
|
||||
HUME_API_KEY=...
|
||||
@@ -98,13 +93,7 @@ INWORLD_API_KEY=...
|
||||
KRISP_MODEL_PATH=...
|
||||
|
||||
# Krisp Viva
|
||||
KRISP_VIVA_API_KEY=...
|
||||
KRISP_VIVA_FILTER_MODEL_PATH=...
|
||||
KRISP_VIVA_TURN_MODEL_PATH=...
|
||||
|
||||
# LemonSlice
|
||||
LEMONSLICE_API_KEY=...
|
||||
LEMONSLICE_AGENT_ID=...
|
||||
KRISP_VIVA_MODEL_PATH=...
|
||||
|
||||
# LiveKit
|
||||
LIVEKIT_API_KEY=...
|
||||
@@ -121,21 +110,18 @@ MINIMAX_GROUP_ID=...
|
||||
# Mistral
|
||||
MISTRAL_API_KEY=...
|
||||
|
||||
# Nebius
|
||||
NEBIUS_API_KEY=...
|
||||
|
||||
# Neuphonic
|
||||
NEUPHONIC_API_KEY=...
|
||||
|
||||
# Novita
|
||||
NOVITA_API_KEY=...
|
||||
|
||||
# NVIDIA
|
||||
NVIDIA_API_KEY=...
|
||||
|
||||
# OpenAI
|
||||
OPENAI_API_KEY=...
|
||||
|
||||
# OpenPipe
|
||||
OPENPIPE_API_KEY=...
|
||||
|
||||
# OpenRouter
|
||||
OPENROUTER_API_KEY=...
|
||||
|
||||
@@ -148,6 +134,10 @@ KOALA_ACCESS_KEY=...
|
||||
# Piper
|
||||
PIPER_BASE_URL=...
|
||||
|
||||
# PlayHT
|
||||
PLAYHT_USER_ID=...
|
||||
PLAYHT_API_KEY=...
|
||||
|
||||
# Plivo
|
||||
PLIVO_AUTH_ID=...
|
||||
PLIVO_AUTH_TOKEN=...
|
||||
@@ -155,10 +145,6 @@ PLIVO_AUTH_TOKEN=...
|
||||
# Qwen
|
||||
QWEN_API_KEY=...
|
||||
|
||||
# Resemble AI
|
||||
RESEMBLE_API_KEY=
|
||||
RESEMBLE_VOICE_UUID=
|
||||
|
||||
# Rime
|
||||
RIME_API_KEY=...
|
||||
RIME_VOICE_ID=...
|
||||
@@ -176,9 +162,6 @@ SENTRY_DSN=...
|
||||
SIMLI_API_KEY=...
|
||||
SIMLI_FACE_ID=...
|
||||
|
||||
# Smallest
|
||||
SMALLEST_API_KEY=...
|
||||
|
||||
# Smart turn
|
||||
LOCAL_SMART_TURN_MODEL_PATH=...
|
||||
FAL_SMART_TURN_API_KEY=...
|
||||
@@ -204,14 +187,8 @@ TOGETHER_API_KEY=...
|
||||
TWILIO_ACCOUNT_SID=...
|
||||
TWILIO_AUTH_TOKEN=...
|
||||
|
||||
# Ultravox Realtime
|
||||
ULTRAVOX_API_KEY=...
|
||||
|
||||
# WhatsApp
|
||||
WHATSAPP_TOKEN=...
|
||||
WHATSAPP_WEBHOOK_VERIFICATION_TOKEN=...
|
||||
WHATSAPP_PHONE_NUMBER_ID=...
|
||||
WHATSAPP_APP_SECRET=...
|
||||
|
||||
# xAI / Grok
|
||||
XAI_API_KEY=...
|
||||
WHATSAPP_APP_SECRET=...
|
||||
@@ -1,150 +1,31 @@
|
||||
# Pipecat Examples
|
||||
|
||||
This directory contains examples showing how to build voice and multimodal agents with Pipecat.
|
||||
This directory contains examples to help you learn how to build with Pipecat.
|
||||
|
||||
## Setup
|
||||
## Getting Started
|
||||
|
||||
1. Follow the [README](https://github.com/pipecat-ai/pipecat/blob/main/README.md#%EF%B8%8F-contributing-to-the-framework) steps to get your local environment configured.
|
||||
New to Pipecat? Start here:
|
||||
|
||||
> **Run from root directory**: Make sure you are running the steps from the root directory.
|
||||
- **[Quickstart](quickstart/)** - Get your first voice AI bot running in 5 minutes _(coming soon)_
|
||||
- **[Client/Server Web](client-server-web/)** - Learn to build web applications with Pipecat's client SDKs _(coming soon)_
|
||||
- **[Phone Bot with Twilio](phone-bot-twilio/)** - Connect your bot to a phone number _(coming soon)_
|
||||
|
||||
> **Using local audio?**: The `LocalAudioTransport` requires a system dependency for `portaudio`. Install the dependency to use the transport.
|
||||
## Foundational Examples
|
||||
|
||||
2. Copy the [`env.example`](../env.example) file and add API keys for services you plan to use:
|
||||
Single-file examples that introduce core Pipecat concepts one at a time. These examples:
|
||||
|
||||
```bash
|
||||
cp env.example .env
|
||||
# Edit .env with your API keys
|
||||
```
|
||||
- Build on each other progressively
|
||||
- Focus on specific features or integrations
|
||||
- Are used for testing with every Pipecat release
|
||||
|
||||
3. Run any example:
|
||||
See the **[Foundational Examples README](foundational/)** for the complete list.
|
||||
|
||||
```bash
|
||||
uv run python getting-started/01-say-one-thing.py
|
||||
```
|
||||
## More Advanced Examples
|
||||
|
||||
4. Open the web interface at http://localhost:7860/client/ and click "Connect"
|
||||
Ready to explore complex use cases? Visit **[pipecat-examples](https://github.com/pipecat-ai/pipecat-examples)** for:
|
||||
|
||||
## Running examples with other transports
|
||||
|
||||
Most examples support running with other transports, like Twilio or Daily.
|
||||
|
||||
### Daily
|
||||
|
||||
You need to create a Daily account at https://dashboard.daily.co/u/signup. Once signed up, you can create your own room from the dashboard and set the environment variables `DAILY_ROOM_URL` and `DAILY_API_KEY`. Alternatively, you can let the example create a room for you (still needs `DAILY_API_KEY` environment variable). Then, start any example with `-t daily`:
|
||||
|
||||
```bash
|
||||
uv run getting-started/06-voice-agent.py -t daily
|
||||
```
|
||||
|
||||
### Twilio
|
||||
|
||||
It is also possible to run the example through a Twilio phone number. You will need to setup a few things:
|
||||
|
||||
1. Install and run [ngrok](https://ngrok.com/download).
|
||||
|
||||
```bash
|
||||
ngrok http 7860
|
||||
```
|
||||
|
||||
2. Configure your Twilio phone number. One way is to setup a TwiML app and set the request URL to the ngrok URL from step (1). Then, set your phone number to use the new TwiML app.
|
||||
|
||||
Then, run the example with:
|
||||
|
||||
```bash
|
||||
uv run getting-started/06-voice-agent.py -t twilio -x NGROK_HOST_NAME
|
||||
```
|
||||
|
||||
## Directory Structure
|
||||
|
||||
### [`getting-started/`](./getting-started/)
|
||||
|
||||
Progressive introduction to Pipecat, from minimal TTS to a full voice agent with function calling.
|
||||
|
||||
### [`voice/`](./voice/)
|
||||
|
||||
Full STT + LLM + TTS voice agent pipelines showcasing different speech service providers (Deepgram, ElevenLabs, Cartesia, etc.)
|
||||
|
||||
### [`function-calling/`](./function-calling/)
|
||||
|
||||
Function calling with different LLM providers (OpenAI, Anthropic, Google, etc.)
|
||||
|
||||
### [`transcription/`](./transcription/)
|
||||
|
||||
Speech-to-text examples with various STT providers.
|
||||
|
||||
### [`vision/`](./vision/)
|
||||
|
||||
Image description and vision capabilities with different multimodal LLMs.
|
||||
|
||||
### [`realtime/`](./realtime/)
|
||||
|
||||
Realtime and multimodal live APIs (OpenAI Realtime, Gemini Live, AWS Nova Sonic, Ultravox, Grok).
|
||||
|
||||
### [`persistent-context/`](./persistent-context/)
|
||||
|
||||
Maintaining conversation context across sessions with different providers.
|
||||
|
||||
### [`context-summarization/`](./context-summarization/)
|
||||
|
||||
Summarizing conversation context to manage token limits.
|
||||
|
||||
### [`update-settings/`](./update-settings/)
|
||||
|
||||
Changing service settings at runtime, organized by service type:
|
||||
|
||||
- **[`stt/`](./update-settings/stt/)** — Speech-to-text settings
|
||||
- **[`tts/`](./update-settings/tts/)** — Text-to-speech settings
|
||||
- **[`llm/`](./update-settings/llm/)** — LLM settings
|
||||
|
||||
### [`turn-management/`](./turn-management/)
|
||||
|
||||
Turn detection, interruption handling, and user input management.
|
||||
|
||||
### [`thinking-and-mcp/`](./thinking-and-mcp/)
|
||||
|
||||
LLM thinking/reasoning modes and MCP (Model Context Protocol) tool server integration.
|
||||
|
||||
### [`transports/`](./transports/)
|
||||
|
||||
Transport layer examples (WebRTC, Daily, LiveKit).
|
||||
|
||||
### [`video-avatar/`](./video-avatar/)
|
||||
|
||||
Video avatar integrations (Tavus, HeyGen, Simli, LemonSlice).
|
||||
|
||||
### [`video-processing/`](./video-processing/)
|
||||
|
||||
Video processing, mirroring, GStreamer, and custom video tracks.
|
||||
|
||||
### [`audio/`](./audio/)
|
||||
|
||||
Audio recording, background sounds, and sound effects.
|
||||
|
||||
### [`observability/`](./observability/)
|
||||
|
||||
Pipeline monitoring: observers, heartbeats, and Sentry metrics.
|
||||
|
||||
### [`rag/`](./rag/)
|
||||
|
||||
Retrieval-augmented generation, grounding, and long-term memory (Mem0, Gemini).
|
||||
|
||||
### [`features/`](./features/)
|
||||
|
||||
Miscellaneous features: wake phrases, live translation, service switching, voice switching, and more.
|
||||
|
||||
## Advanced Usage
|
||||
|
||||
### Customizing Network Settings
|
||||
|
||||
```bash
|
||||
uv run python <example-name> --host 0.0.0.0 --port 8080
|
||||
```
|
||||
|
||||
### Troubleshooting
|
||||
|
||||
- **No audio/video**: Check browser permissions for microphone and camera
|
||||
- **Connection errors**: Verify API keys in `.env` file
|
||||
- **Port conflicts**: Use `--port` to change the port
|
||||
|
||||
For more examples, visit the [pipecat-examples repository](https://github.com/pipecat-ai/pipecat-examples).
|
||||
- Production-ready applications
|
||||
- Multi-platform client implementations
|
||||
- Telephony integrations
|
||||
- Multimodal and creative applications
|
||||
- Deployment and monitoring examples
|
||||
|
||||
@@ -1,238 +0,0 @@
|
||||
#
|
||||
# Copyright (c) 2024-2026, Daily
|
||||
#
|
||||
# SPDX-License-Identifier: BSD 2-Clause License
|
||||
#
|
||||
|
||||
"""Example demonstrating advanced context summarization configuration.
|
||||
|
||||
This example shows how to customize context summarization with:
|
||||
- A dedicated cheap/fast LLM for generating summaries (Gemini Flash)
|
||||
- A custom summary message template (XML tags)
|
||||
- A custom summarization prompt
|
||||
- A summarization timeout
|
||||
- The on_summary_applied event for observability
|
||||
"""
|
||||
|
||||
import asyncio
|
||||
import os
|
||||
|
||||
from dotenv import load_dotenv
|
||||
from loguru import logger
|
||||
|
||||
from pipecat.adapters.schemas.function_schema import FunctionSchema
|
||||
from pipecat.adapters.schemas.tools_schema import ToolsSchema
|
||||
from pipecat.audio.vad.silero import SileroVADAnalyzer
|
||||
from pipecat.frames.frames import LLMRunFrame
|
||||
from pipecat.pipeline.pipeline import Pipeline
|
||||
from pipecat.pipeline.runner import PipelineRunner
|
||||
from pipecat.pipeline.task import PipelineParams, PipelineTask
|
||||
from pipecat.processors.aggregators.llm_context import LLMContext
|
||||
from pipecat.processors.aggregators.llm_context_summarizer import SummaryAppliedEvent
|
||||
from pipecat.processors.aggregators.llm_response_universal import (
|
||||
LLMAssistantAggregatorParams,
|
||||
LLMContextAggregatorPair,
|
||||
LLMUserAggregatorParams,
|
||||
)
|
||||
from pipecat.runner.types import RunnerArguments
|
||||
from pipecat.runner.utils import create_transport
|
||||
from pipecat.services.cartesia.tts import CartesiaTTSService
|
||||
from pipecat.services.deepgram.stt import DeepgramSTTService
|
||||
from pipecat.services.google.llm import GoogleLLMService
|
||||
from pipecat.services.llm_service import FunctionCallParams
|
||||
from pipecat.services.openai.llm import OpenAILLMService
|
||||
from pipecat.transports.base_transport import BaseTransport, TransportParams
|
||||
from pipecat.transports.daily.transport import DailyParams
|
||||
from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
|
||||
from pipecat.utils.context.llm_context_summarization import (
|
||||
LLMAutoContextSummarizationConfig,
|
||||
LLMContextSummaryConfig,
|
||||
)
|
||||
|
||||
load_dotenv(override=True)
|
||||
|
||||
# We use lambdas to defer transport parameter creation until the transport
|
||||
# type is selected at runtime.
|
||||
transport_params = {
|
||||
"daily": lambda: DailyParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
),
|
||||
"twilio": lambda: FastAPIWebsocketParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
),
|
||||
"webrtc": lambda: TransportParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
),
|
||||
}
|
||||
|
||||
# Custom summarization prompt tailored to the application
|
||||
CUSTOM_SUMMARIZATION_PROMPT = """Summarize this conversation, preserving:
|
||||
- Key decisions and agreements
|
||||
- Important facts and user preferences
|
||||
- Any pending action items or unresolved questions
|
||||
|
||||
Be concise. Use clear, factual statements grouped by topic.
|
||||
Omit greetings, small talk, and resolved tangents."""
|
||||
|
||||
|
||||
# Tool functions for the LLM
|
||||
async def get_current_weather(params: FunctionCallParams):
|
||||
"""Get the current weather."""
|
||||
logger.info("Tool called: get_current_weather")
|
||||
await asyncio.sleep(1) # Simulate some processing
|
||||
await params.result_callback({"conditions": "nice", "temperature": "75"})
|
||||
|
||||
|
||||
async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
logger.info("Starting bot")
|
||||
|
||||
stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
|
||||
|
||||
tts = CartesiaTTSService(
|
||||
api_key=os.getenv("CARTESIA_API_KEY"),
|
||||
settings=CartesiaTTSService.Settings(
|
||||
voice="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
),
|
||||
)
|
||||
|
||||
system_prompt = """You are a helpful LLM in a voice call. Your goal is to demonstrate your
|
||||
capabilities in a succinct way. Your output will be spoken aloud, so avoid
|
||||
special characters that can't easily be spoken, such as emojis or bullet points.
|
||||
Respond to what the user said in a creative and helpful way.
|
||||
You have access to tools to get the current weather - use them when relevant.
|
||||
When you see a <context_summary> block, it contains a compressed summary
|
||||
of earlier conversation. Use it as reference but don't mention it to the user.
|
||||
"""
|
||||
|
||||
# Primary LLM for conversation (could be any provider)
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
settings=OpenAILLMService.Settings(
|
||||
system_instruction=system_prompt,
|
||||
),
|
||||
)
|
||||
|
||||
# Dedicated cheap/fast LLM for summarization only
|
||||
summarization_llm = GoogleLLMService(
|
||||
api_key=os.getenv("GOOGLE_API_KEY"),
|
||||
settings=GoogleLLMService.Settings(
|
||||
model="gemini-2.5-flash",
|
||||
),
|
||||
)
|
||||
|
||||
# Register tool functions
|
||||
llm.register_function("get_current_weather", get_current_weather)
|
||||
|
||||
weather_function = FunctionSchema(
|
||||
name="get_current_weather",
|
||||
description="Get the current weather",
|
||||
properties={
|
||||
"location": {
|
||||
"type": "string",
|
||||
"description": "The city and state, e.g. San Francisco, CA",
|
||||
},
|
||||
"format": {
|
||||
"type": "string",
|
||||
"enum": ["celsius", "fahrenheit"],
|
||||
"description": "The temperature unit to use. Infer this from the user's location.",
|
||||
},
|
||||
},
|
||||
required=["location", "format"],
|
||||
)
|
||||
tools = ToolsSchema(standard_tools=[weather_function])
|
||||
|
||||
context = LLMContext(tools=tools)
|
||||
|
||||
# Create aggregators with custom summarization
|
||||
user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
|
||||
context,
|
||||
user_params=LLMUserAggregatorParams(
|
||||
vad_analyzer=SileroVADAnalyzer(),
|
||||
),
|
||||
assistant_params=LLMAssistantAggregatorParams(
|
||||
enable_auto_context_summarization=True,
|
||||
auto_context_summarization_config=LLMAutoContextSummarizationConfig(
|
||||
# Trigger thresholds (low values to demonstrate quickly)
|
||||
max_context_tokens=1000,
|
||||
max_unsummarized_messages=10,
|
||||
summary_config=LLMContextSummaryConfig(
|
||||
# Summary generation
|
||||
target_context_tokens=800,
|
||||
min_messages_after_summary=2,
|
||||
summarization_prompt=CUSTOM_SUMMARIZATION_PROMPT,
|
||||
# Custom summary format - wrap in XML tags so the system
|
||||
# prompt can identify summaries vs. live conversation
|
||||
summary_message_template="<context_summary>\n{summary}\n</context_summary>",
|
||||
# Use a dedicated cheap LLM for summarization instead of
|
||||
# the primary conversation model
|
||||
llm=summarization_llm,
|
||||
# Cancel summarization if it takes longer than 60 seconds
|
||||
summarization_timeout=60.0,
|
||||
),
|
||||
),
|
||||
),
|
||||
)
|
||||
|
||||
# Listen for summarization events
|
||||
@assistant_aggregator.event_handler("on_summary_applied")
|
||||
async def on_summary_applied(aggregator, summarizer, event: SummaryAppliedEvent):
|
||||
logger.info(
|
||||
f"Context summarized: {event.original_message_count} messages -> "
|
||||
f"{event.new_message_count} messages "
|
||||
f"({event.summarized_message_count} summarized, "
|
||||
f"{event.preserved_message_count} preserved)"
|
||||
)
|
||||
|
||||
pipeline = Pipeline(
|
||||
[
|
||||
transport.input(), # Transport user input
|
||||
stt,
|
||||
user_aggregator, # User responses
|
||||
llm, # LLM
|
||||
tts, # TTS
|
||||
transport.output(), # Transport bot output
|
||||
assistant_aggregator, # Assistant spoken responses
|
||||
]
|
||||
)
|
||||
|
||||
task = PipelineTask(
|
||||
pipeline,
|
||||
params=PipelineParams(
|
||||
enable_metrics=True,
|
||||
enable_usage_metrics=True,
|
||||
),
|
||||
idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
|
||||
)
|
||||
|
||||
@transport.event_handler("on_client_connected")
|
||||
async def on_client_connected(transport, client):
|
||||
logger.info("Client connected")
|
||||
# Kick off the conversation.
|
||||
context.add_message(
|
||||
{"role": "developer", "content": "Please introduce yourself to the user."}
|
||||
)
|
||||
await task.queue_frames([LLMRunFrame()])
|
||||
|
||||
@transport.event_handler("on_client_disconnected")
|
||||
async def on_client_disconnected(transport, client):
|
||||
logger.info("Client disconnected")
|
||||
await task.cancel()
|
||||
|
||||
runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
|
||||
|
||||
await runner.run(task)
|
||||
|
||||
|
||||
async def bot(runner_args: RunnerArguments):
|
||||
"""Main bot entry point compatible with Pipecat Cloud."""
|
||||
transport = await create_transport(runner_args, transport_params)
|
||||
await run_bot(transport, runner_args)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
from pipecat.runner.run import main
|
||||
|
||||
main()
|
||||
@@ -1,199 +0,0 @@
|
||||
#
|
||||
# Copyright (c) 2024-2026, Daily
|
||||
#
|
||||
# SPDX-License-Identifier: BSD 2-Clause License
|
||||
#
|
||||
|
||||
"""Example demonstrating context summarization feature.
|
||||
|
||||
This example shows how to enable and configure context summarization to automatically
|
||||
compress conversation history when token limits are approached. It also demonstrates
|
||||
that summarization correctly handles function calls, preserving incomplete function
|
||||
call sequences.
|
||||
"""
|
||||
|
||||
import asyncio
|
||||
import os
|
||||
|
||||
from dotenv import load_dotenv
|
||||
from loguru import logger
|
||||
|
||||
from pipecat.adapters.schemas.function_schema import FunctionSchema
|
||||
from pipecat.adapters.schemas.tools_schema import ToolsSchema
|
||||
from pipecat.audio.vad.silero import SileroVADAnalyzer
|
||||
from pipecat.frames.frames import LLMRunFrame
|
||||
from pipecat.pipeline.pipeline import Pipeline
|
||||
from pipecat.pipeline.runner import PipelineRunner
|
||||
from pipecat.pipeline.task import PipelineParams, PipelineTask
|
||||
from pipecat.processors.aggregators.llm_context import LLMContext
|
||||
from pipecat.processors.aggregators.llm_context_summarizer import SummaryAppliedEvent
|
||||
from pipecat.processors.aggregators.llm_response_universal import (
|
||||
LLMAssistantAggregatorParams,
|
||||
LLMContextAggregatorPair,
|
||||
LLMUserAggregatorParams,
|
||||
)
|
||||
from pipecat.runner.types import RunnerArguments
|
||||
from pipecat.runner.utils import create_transport
|
||||
from pipecat.services.cartesia.tts import CartesiaTTSService
|
||||
from pipecat.services.deepgram.stt import DeepgramSTTService
|
||||
from pipecat.services.google.llm import GoogleLLMService
|
||||
from pipecat.services.llm_service import FunctionCallParams
|
||||
from pipecat.transports.base_transport import BaseTransport, TransportParams
|
||||
from pipecat.transports.daily.transport import DailyParams
|
||||
from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
|
||||
from pipecat.utils.context.llm_context_summarization import (
|
||||
LLMAutoContextSummarizationConfig,
|
||||
LLMContextSummaryConfig,
|
||||
)
|
||||
|
||||
load_dotenv(override=True)
|
||||
|
||||
# We use lambdas to defer transport parameter creation until the transport
|
||||
# type is selected at runtime.
|
||||
transport_params = {
|
||||
"daily": lambda: DailyParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
),
|
||||
"twilio": lambda: FastAPIWebsocketParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
),
|
||||
"webrtc": lambda: TransportParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
),
|
||||
}
|
||||
|
||||
|
||||
# Tool functions for the LLM
|
||||
async def get_current_weather(params: FunctionCallParams):
|
||||
"""Get the current time in a readable format."""
|
||||
logger.info("Tool called: get_current_weather")
|
||||
await asyncio.sleep(1) # Simulate some processing
|
||||
await params.result_callback({"conditions": "nice", "temperature": "75"})
|
||||
|
||||
|
||||
async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
logger.info("Starting bot")
|
||||
|
||||
stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
|
||||
|
||||
tts = CartesiaTTSService(
|
||||
api_key=os.getenv("CARTESIA_API_KEY"),
|
||||
settings=CartesiaTTSService.Settings(
|
||||
voice="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
),
|
||||
)
|
||||
|
||||
llm = GoogleLLMService(
|
||||
api_key=os.getenv("GOOGLE_API_KEY"),
|
||||
settings=GoogleLLMService.Settings(
|
||||
system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way. You have access to tools to get the current weather - use them when relevant.",
|
||||
),
|
||||
)
|
||||
|
||||
# Register tool functions
|
||||
llm.register_function("get_current_weather", get_current_weather)
|
||||
|
||||
weather_function = FunctionSchema(
|
||||
name="get_current_weather",
|
||||
description="Get the current weather",
|
||||
properties={
|
||||
"location": {
|
||||
"type": "string",
|
||||
"description": "The city and state, e.g. San Francisco, CA",
|
||||
},
|
||||
"format": {
|
||||
"type": "string",
|
||||
"enum": ["celsius", "fahrenheit"],
|
||||
"description": "The temperature unit to use. Infer this from the user's location.",
|
||||
},
|
||||
},
|
||||
required=["location", "format"],
|
||||
)
|
||||
tools = ToolsSchema(standard_tools=[weather_function])
|
||||
|
||||
context = LLMContext(tools=tools)
|
||||
|
||||
# Create aggregators with summarization enabled
|
||||
user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
|
||||
context,
|
||||
user_params=LLMUserAggregatorParams(
|
||||
vad_analyzer=SileroVADAnalyzer(),
|
||||
),
|
||||
assistant_params=LLMAssistantAggregatorParams(
|
||||
enable_auto_context_summarization=True,
|
||||
# Optional: customize context summarization behavior
|
||||
# Using low limits to demonstrate the feature quickly
|
||||
auto_context_summarization_config=LLMAutoContextSummarizationConfig(
|
||||
max_context_tokens=1000, # Trigger summarization at 1000 tokens
|
||||
max_unsummarized_messages=10, # Or when 10 new messages accumulate
|
||||
summary_config=LLMContextSummaryConfig(
|
||||
target_context_tokens=800, # Target context size for the summarization
|
||||
min_messages_after_summary=2, # Keep last 2 messages uncompressed
|
||||
),
|
||||
),
|
||||
),
|
||||
)
|
||||
|
||||
# Listen for summarization events
|
||||
@assistant_aggregator.event_handler("on_summary_applied")
|
||||
async def on_summary_applied(aggregator, summarizer, event: SummaryAppliedEvent):
|
||||
logger.info(
|
||||
f"Context summarized: {event.original_message_count} messages -> "
|
||||
f"{event.new_message_count} messages "
|
||||
f"({event.summarized_message_count} summarized, "
|
||||
f"{event.preserved_message_count} preserved)"
|
||||
)
|
||||
|
||||
pipeline = Pipeline(
|
||||
[
|
||||
transport.input(), # Transport user input
|
||||
stt,
|
||||
user_aggregator, # User responses
|
||||
llm, # LLM
|
||||
tts, # TTS
|
||||
transport.output(), # Transport bot output
|
||||
assistant_aggregator, # Assistant spoken responses
|
||||
]
|
||||
)
|
||||
|
||||
task = PipelineTask(
|
||||
pipeline,
|
||||
params=PipelineParams(
|
||||
enable_metrics=True,
|
||||
enable_usage_metrics=True,
|
||||
),
|
||||
idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
|
||||
)
|
||||
|
||||
@transport.event_handler("on_client_connected")
|
||||
async def on_client_connected(transport, client):
|
||||
logger.info("Client connected")
|
||||
# Kick off the conversation.
|
||||
context.add_message(
|
||||
{"role": "developer", "content": "Please introduce yourself to the user."}
|
||||
)
|
||||
await task.queue_frames([LLMRunFrame()])
|
||||
|
||||
@transport.event_handler("on_client_disconnected")
|
||||
async def on_client_disconnected(transport, client):
|
||||
logger.info("Client disconnected")
|
||||
await task.cancel()
|
||||
|
||||
runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
|
||||
|
||||
await runner.run(task)
|
||||
|
||||
|
||||
async def bot(runner_args: RunnerArguments):
|
||||
"""Main bot entry point compatible with Pipecat Cloud."""
|
||||
transport = await create_transport(runner_args, transport_params)
|
||||
await run_bot(transport, runner_args)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
from pipecat.runner.run import main
|
||||
|
||||
main()
|
||||
@@ -1,173 +0,0 @@
|
||||
#
|
||||
# Copyright (c) 2024-2026, Daily
|
||||
#
|
||||
# SPDX-License-Identifier: BSD 2-Clause License
|
||||
#
|
||||
|
||||
"""Example demonstrating manual context summarization via a function call.
|
||||
|
||||
This example shows how to trigger context summarization on demand rather than
|
||||
automatically. The user can ask the bot to "summarize the conversation" and the
|
||||
bot will call a function that pushes an LLMSummarizeContextFrame into the
|
||||
pipeline, causing the LLM service to compress the conversation history.
|
||||
|
||||
Unlike example 54, automatic summarization is NOT enabled here. Summarization
|
||||
only happens when the user explicitly requests it through the function call.
|
||||
"""
|
||||
|
||||
import os
|
||||
|
||||
from dotenv import load_dotenv
|
||||
from loguru import logger
|
||||
|
||||
from pipecat.adapters.schemas.function_schema import FunctionSchema
|
||||
from pipecat.adapters.schemas.tools_schema import ToolsSchema
|
||||
from pipecat.audio.vad.silero import SileroVADAnalyzer
|
||||
from pipecat.frames.frames import LLMRunFrame, LLMSummarizeContextFrame
|
||||
from pipecat.pipeline.pipeline import Pipeline
|
||||
from pipecat.pipeline.runner import PipelineRunner
|
||||
from pipecat.pipeline.task import PipelineParams, PipelineTask
|
||||
from pipecat.processors.aggregators.llm_context import LLMContext
|
||||
from pipecat.processors.aggregators.llm_response_universal import (
|
||||
LLMContextAggregatorPair,
|
||||
LLMUserAggregatorParams,
|
||||
)
|
||||
from pipecat.runner.types import RunnerArguments
|
||||
from pipecat.runner.utils import create_transport
|
||||
from pipecat.services.cartesia.tts import CartesiaTTSService
|
||||
from pipecat.services.deepgram.stt import DeepgramSTTService
|
||||
from pipecat.services.llm_service import FunctionCallParams
|
||||
from pipecat.services.openai.llm import OpenAILLMService
|
||||
from pipecat.transports.base_transport import BaseTransport, TransportParams
|
||||
from pipecat.transports.daily.transport import DailyParams
|
||||
from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
|
||||
|
||||
load_dotenv(override=True)
|
||||
|
||||
# We use lambdas to defer transport parameter creation until the transport
|
||||
# type is selected at runtime.
|
||||
transport_params = {
|
||||
"daily": lambda: DailyParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
),
|
||||
"twilio": lambda: FastAPIWebsocketParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
),
|
||||
"webrtc": lambda: TransportParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
),
|
||||
}
|
||||
|
||||
|
||||
async def summarize_conversation(params: FunctionCallParams):
|
||||
"""Trigger manual context summarization via a pipeline frame."""
|
||||
logger.info("Tool called: summarize_conversation")
|
||||
await params.result_callback({"status": "summarization_requested"})
|
||||
await params.llm.queue_frame(LLMSummarizeContextFrame())
|
||||
|
||||
|
||||
async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
logger.info("Starting bot")
|
||||
|
||||
stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
|
||||
|
||||
tts = CartesiaTTSService(
|
||||
api_key=os.getenv("CARTESIA_API_KEY"),
|
||||
settings=CartesiaTTSService.Settings(
|
||||
voice="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
),
|
||||
)
|
||||
|
||||
system_prompt = """You are a helpful LLM in a voice call. Your goal is to demonstrate your
|
||||
capabilities in a succinct way. Your output will be spoken aloud, so avoid
|
||||
special characters that can't easily be spoken, such as emojis or bullet points.
|
||||
Respond to what the user said in a creative and helpful way.
|
||||
If the user asks you to summarize the conversation, call the
|
||||
summarize_conversation function. After summarization, briefly acknowledge
|
||||
that the conversation history has been compressed.
|
||||
"""
|
||||
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
settings=OpenAILLMService.Settings(
|
||||
system_instruction=system_prompt,
|
||||
),
|
||||
)
|
||||
|
||||
llm.register_function("summarize_conversation", summarize_conversation)
|
||||
|
||||
summarize_function = FunctionSchema(
|
||||
name="summarize_conversation",
|
||||
description=(
|
||||
"Summarize and compress the conversation history. "
|
||||
"Call this when the user asks you to summarize the conversation "
|
||||
"or when you want to free up context space."
|
||||
),
|
||||
properties={},
|
||||
required=[],
|
||||
)
|
||||
tools = ToolsSchema(standard_tools=[summarize_function])
|
||||
|
||||
context = LLMContext(tools=tools)
|
||||
|
||||
# Automatic summarization is NOT enabled here (enable_auto_context_summarization
|
||||
# defaults to False). The summarizer is still created internally so that
|
||||
# LLMSummarizeContextFrame frames pushed via the function call are handled.
|
||||
user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
|
||||
context,
|
||||
user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
|
||||
)
|
||||
|
||||
pipeline = Pipeline(
|
||||
[
|
||||
transport.input(), # Transport user input
|
||||
stt,
|
||||
user_aggregator, # User responses
|
||||
llm, # LLM
|
||||
tts, # TTS
|
||||
transport.output(), # Transport bot output
|
||||
assistant_aggregator, # Assistant spoken responses
|
||||
]
|
||||
)
|
||||
|
||||
task = PipelineTask(
|
||||
pipeline,
|
||||
params=PipelineParams(
|
||||
enable_metrics=True,
|
||||
enable_usage_metrics=True,
|
||||
),
|
||||
idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
|
||||
)
|
||||
|
||||
@transport.event_handler("on_client_connected")
|
||||
async def on_client_connected(transport, client):
|
||||
logger.info("Client connected")
|
||||
# Kick off the conversation.
|
||||
context.add_message(
|
||||
{"role": "developer", "content": "Please introduce yourself to the user."}
|
||||
)
|
||||
await task.queue_frames([LLMRunFrame()])
|
||||
|
||||
@transport.event_handler("on_client_disconnected")
|
||||
async def on_client_disconnected(transport, client):
|
||||
logger.info("Client disconnected")
|
||||
await task.cancel()
|
||||
|
||||
runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
|
||||
|
||||
await runner.run(task)
|
||||
|
||||
|
||||
async def bot(runner_args: RunnerArguments):
|
||||
"""Main bot entry point compatible with Pipecat Cloud."""
|
||||
transport = await create_transport(runner_args, transport_params)
|
||||
await run_bot(transport, runner_args)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
from pipecat.runner.run import main
|
||||
|
||||
main()
|
||||
@@ -1,199 +0,0 @@
|
||||
#
|
||||
# Copyright (c) 2024-2026, Daily
|
||||
#
|
||||
# SPDX-License-Identifier: BSD 2-Clause License
|
||||
#
|
||||
|
||||
"""Example demonstrating context summarization feature.
|
||||
|
||||
This example shows how to enable and configure context summarization to automatically
|
||||
compress conversation history when token limits are approached. It also demonstrates
|
||||
that summarization correctly handles function calls, preserving incomplete function
|
||||
call sequences.
|
||||
"""
|
||||
|
||||
import asyncio
|
||||
import os
|
||||
|
||||
from dotenv import load_dotenv
|
||||
from loguru import logger
|
||||
|
||||
from pipecat.adapters.schemas.function_schema import FunctionSchema
|
||||
from pipecat.adapters.schemas.tools_schema import ToolsSchema
|
||||
from pipecat.audio.vad.silero import SileroVADAnalyzer
|
||||
from pipecat.frames.frames import LLMRunFrame
|
||||
from pipecat.pipeline.pipeline import Pipeline
|
||||
from pipecat.pipeline.runner import PipelineRunner
|
||||
from pipecat.pipeline.task import PipelineParams, PipelineTask
|
||||
from pipecat.processors.aggregators.llm_context import LLMContext
|
||||
from pipecat.processors.aggregators.llm_context_summarizer import SummaryAppliedEvent
|
||||
from pipecat.processors.aggregators.llm_response_universal import (
|
||||
LLMAssistantAggregatorParams,
|
||||
LLMContextAggregatorPair,
|
||||
LLMUserAggregatorParams,
|
||||
)
|
||||
from pipecat.runner.types import RunnerArguments
|
||||
from pipecat.runner.utils import create_transport
|
||||
from pipecat.services.cartesia.tts import CartesiaTTSService
|
||||
from pipecat.services.deepgram.stt import DeepgramSTTService
|
||||
from pipecat.services.llm_service import FunctionCallParams
|
||||
from pipecat.services.openai.llm import OpenAILLMService
|
||||
from pipecat.transports.base_transport import BaseTransport, TransportParams
|
||||
from pipecat.transports.daily.transport import DailyParams
|
||||
from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
|
||||
from pipecat.utils.context.llm_context_summarization import (
|
||||
LLMAutoContextSummarizationConfig,
|
||||
LLMContextSummaryConfig,
|
||||
)
|
||||
|
||||
load_dotenv(override=True)
|
||||
|
||||
# We use lambdas to defer transport parameter creation until the transport
|
||||
# type is selected at runtime.
|
||||
transport_params = {
|
||||
"daily": lambda: DailyParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
),
|
||||
"twilio": lambda: FastAPIWebsocketParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
),
|
||||
"webrtc": lambda: TransportParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
),
|
||||
}
|
||||
|
||||
|
||||
# Tool functions for the LLM
|
||||
async def get_current_weather(params: FunctionCallParams):
|
||||
"""Get the current time in a readable format."""
|
||||
logger.info("Tool called: get_current_weather")
|
||||
await asyncio.sleep(1) # Simulate some processing
|
||||
await params.result_callback({"conditions": "nice", "temperature": "75"})
|
||||
|
||||
|
||||
async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
logger.info("Starting bot")
|
||||
|
||||
stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
|
||||
|
||||
tts = CartesiaTTSService(
|
||||
api_key=os.getenv("CARTESIA_API_KEY"),
|
||||
settings=CartesiaTTSService.Settings(
|
||||
voice="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
),
|
||||
)
|
||||
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
settings=OpenAILLMService.Settings(
|
||||
system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way. You have access to tools to get the current weather - use them when relevant.",
|
||||
),
|
||||
)
|
||||
|
||||
# Register tool functions
|
||||
llm.register_function("get_current_weather", get_current_weather)
|
||||
|
||||
weather_function = FunctionSchema(
|
||||
name="get_current_weather",
|
||||
description="Get the current weather",
|
||||
properties={
|
||||
"location": {
|
||||
"type": "string",
|
||||
"description": "The city and state, e.g. San Francisco, CA",
|
||||
},
|
||||
"format": {
|
||||
"type": "string",
|
||||
"enum": ["celsius", "fahrenheit"],
|
||||
"description": "The temperature unit to use. Infer this from the user's location.",
|
||||
},
|
||||
},
|
||||
required=["location", "format"],
|
||||
)
|
||||
tools = ToolsSchema(standard_tools=[weather_function])
|
||||
|
||||
context = LLMContext(tools=tools)
|
||||
|
||||
# Create aggregators with summarization enabled
|
||||
user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
|
||||
context,
|
||||
user_params=LLMUserAggregatorParams(
|
||||
vad_analyzer=SileroVADAnalyzer(),
|
||||
),
|
||||
assistant_params=LLMAssistantAggregatorParams(
|
||||
enable_auto_context_summarization=True,
|
||||
# Optional: customize context summarization behavior
|
||||
# Using low limits to demonstrate the feature quickly
|
||||
auto_context_summarization_config=LLMAutoContextSummarizationConfig(
|
||||
max_context_tokens=1000, # Trigger summarization at 1000 tokens
|
||||
max_unsummarized_messages=10, # Or when 10 new messages accumulate
|
||||
summary_config=LLMContextSummaryConfig(
|
||||
target_context_tokens=800, # Target context size for the summarization
|
||||
min_messages_after_summary=2, # Keep last 2 messages uncompressed
|
||||
),
|
||||
),
|
||||
),
|
||||
)
|
||||
|
||||
# Listen for summarization events
|
||||
@assistant_aggregator.event_handler("on_summary_applied")
|
||||
async def on_summary_applied(aggregator, summarizer, event: SummaryAppliedEvent):
|
||||
logger.info(
|
||||
f"Context summarized: {event.original_message_count} messages -> "
|
||||
f"{event.new_message_count} messages "
|
||||
f"({event.summarized_message_count} summarized, "
|
||||
f"{event.preserved_message_count} preserved)"
|
||||
)
|
||||
|
||||
pipeline = Pipeline(
|
||||
[
|
||||
transport.input(), # Transport user input
|
||||
stt,
|
||||
user_aggregator, # User responses
|
||||
llm, # LLM
|
||||
tts, # TTS
|
||||
transport.output(), # Transport bot output
|
||||
assistant_aggregator, # Assistant spoken responses
|
||||
]
|
||||
)
|
||||
|
||||
task = PipelineTask(
|
||||
pipeline,
|
||||
params=PipelineParams(
|
||||
enable_metrics=True,
|
||||
enable_usage_metrics=True,
|
||||
),
|
||||
idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
|
||||
)
|
||||
|
||||
@transport.event_handler("on_client_connected")
|
||||
async def on_client_connected(transport, client):
|
||||
logger.info("Client connected")
|
||||
# Kick off the conversation.
|
||||
context.add_message(
|
||||
{"role": "developer", "content": "Please introduce yourself to the user."}
|
||||
)
|
||||
await task.queue_frames([LLMRunFrame()])
|
||||
|
||||
@transport.event_handler("on_client_disconnected")
|
||||
async def on_client_disconnected(transport, client):
|
||||
logger.info("Client disconnected")
|
||||
await task.cancel()
|
||||
|
||||
runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
|
||||
|
||||
await runner.run(task)
|
||||
|
||||
|
||||
async def bot(runner_args: RunnerArguments):
|
||||
"""Main bot entry point compatible with Pipecat Cloud."""
|
||||
transport = await create_transport(runner_args, transport_params)
|
||||
await run_bot(transport, runner_args)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
from pipecat.runner.run import main
|
||||
|
||||
main()
|
||||
@@ -1,173 +0,0 @@
|
||||
#
|
||||
# Copyright (c) 2024-2026, Daily
|
||||
#
|
||||
# SPDX-License-Identifier: BSD 2-Clause License
|
||||
#
|
||||
|
||||
|
||||
import os
|
||||
|
||||
from dotenv import load_dotenv
|
||||
from loguru import logger
|
||||
|
||||
from pipecat.audio.vad.silero import SileroVADAnalyzer
|
||||
from pipecat.frames.frames import LLMRunFrame
|
||||
from pipecat.pipeline.parallel_pipeline import ParallelPipeline
|
||||
from pipecat.pipeline.pipeline import Pipeline
|
||||
from pipecat.pipeline.runner import PipelineRunner
|
||||
from pipecat.pipeline.task import PipelineParams, PipelineTask
|
||||
from pipecat.processors.aggregators.llm_context import LLMContext
|
||||
from pipecat.processors.aggregators.llm_response_universal import (
|
||||
LLMContextAggregatorPair,
|
||||
LLMUserAggregatorParams,
|
||||
)
|
||||
from pipecat.processors.audio.vad_processor import VADProcessor
|
||||
from pipecat.runner.types import RunnerArguments
|
||||
from pipecat.runner.utils import create_transport
|
||||
from pipecat.services.cartesia.tts import CartesiaTTSService
|
||||
from pipecat.services.deepgram.stt import DeepgramSTTService
|
||||
from pipecat.services.groq.llm import GroqLLMService
|
||||
from pipecat.services.openai.llm import OpenAILLMService
|
||||
from pipecat.transports.base_transport import BaseTransport, TransportParams
|
||||
from pipecat.transports.daily.transport import DailyParams
|
||||
from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
|
||||
from pipecat.turns.user_turn_processor import UserTurnProcessor
|
||||
from pipecat.turns.user_turn_strategies import ExternalUserTurnStrategies
|
||||
|
||||
load_dotenv(override=True)
|
||||
|
||||
|
||||
# We use lambdas to defer transport parameter creation until the transport
|
||||
# type is selected at runtime.
|
||||
transport_params = {
|
||||
"daily": lambda: DailyParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
),
|
||||
"twilio": lambda: FastAPIWebsocketParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
),
|
||||
"webrtc": lambda: TransportParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
),
|
||||
}
|
||||
|
||||
|
||||
async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
logger.info(f"Starting bot")
|
||||
|
||||
stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
|
||||
|
||||
tts = CartesiaTTSService(
|
||||
api_key=os.getenv("CARTESIA_API_KEY"),
|
||||
settings=CartesiaTTSService.Settings(
|
||||
voice="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
),
|
||||
)
|
||||
|
||||
openai_llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
settings=OpenAILLMService.Settings(
|
||||
system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
|
||||
),
|
||||
)
|
||||
|
||||
groq_llm = GroqLLMService(
|
||||
api_key=os.getenv("GROQ_API_KEY"),
|
||||
settings=GroqLLMService.Settings(
|
||||
system_instruction="You are a very helpful assistant. Your goal is to demonstrate your capabilities in detail in a creative and helpful way.",
|
||||
),
|
||||
)
|
||||
|
||||
openai_context = LLMContext()
|
||||
groq_context = LLMContext()
|
||||
|
||||
# We use an external VADProcessor because the UserTurnProcessor is shared
|
||||
# across multiple parallel aggregators. The VADProcessor emits
|
||||
# VADUserStartedSpeakingFrame and VADUserStoppedSpeakingFrame which the
|
||||
# UserTurnProcessor needs to manage turn lifecycle.
|
||||
vad_processor = VADProcessor(vad_analyzer=SileroVADAnalyzer())
|
||||
|
||||
# We use this external user turn processor. This processor will push
|
||||
# UserStartedSpeakingFrame and UserStoppedSpeakingFrame as well as
|
||||
# interruptions. This can be used in advanced cases when there are multiple
|
||||
# aggregators in the pipeline.
|
||||
user_turn_processor = UserTurnProcessor()
|
||||
|
||||
# We use external user turn strategies for both aggregators since the turn
|
||||
# management is done by the common UserTurnProcessor.
|
||||
openai_context_aggregator = LLMContextAggregatorPair(
|
||||
openai_context,
|
||||
user_params=LLMUserAggregatorParams(user_turn_strategies=ExternalUserTurnStrategies()),
|
||||
)
|
||||
groq_context_aggregator = LLMContextAggregatorPair(
|
||||
groq_context,
|
||||
user_params=LLMUserAggregatorParams(user_turn_strategies=ExternalUserTurnStrategies()),
|
||||
)
|
||||
|
||||
pipeline = Pipeline(
|
||||
[
|
||||
transport.input(), # Transport user input
|
||||
stt, # STT
|
||||
vad_processor,
|
||||
user_turn_processor,
|
||||
ParallelPipeline(
|
||||
[
|
||||
openai_context_aggregator.user(), # User responses
|
||||
openai_llm, # LLM
|
||||
tts, # TTS (bot will speak the chosen language)
|
||||
transport.output(), # Transport bot output
|
||||
openai_context_aggregator.assistant(), # Assistant spoken responses
|
||||
],
|
||||
[
|
||||
groq_context_aggregator.user(), # User responses
|
||||
groq_llm, # LLM
|
||||
groq_context_aggregator.assistant(), # Assistant responses
|
||||
],
|
||||
),
|
||||
]
|
||||
)
|
||||
|
||||
task = PipelineTask(
|
||||
pipeline,
|
||||
params=PipelineParams(
|
||||
enable_metrics=True,
|
||||
enable_usage_metrics=True,
|
||||
),
|
||||
idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
|
||||
)
|
||||
|
||||
@transport.event_handler("on_client_connected")
|
||||
async def on_client_connected(transport, client):
|
||||
logger.info(f"Client connected")
|
||||
# Kick off the conversation.
|
||||
openai_context.add_message(
|
||||
{"role": "developer", "content": "Please introduce yourself to the user."}
|
||||
)
|
||||
groq_context.add_message(
|
||||
{"role": "developer", "content": "Please introduce yourself to the user."}
|
||||
)
|
||||
await task.queue_frames([LLMRunFrame()])
|
||||
|
||||
@transport.event_handler("on_client_disconnected")
|
||||
async def on_client_disconnected(transport, client):
|
||||
logger.info(f"Client disconnected")
|
||||
await task.cancel()
|
||||
|
||||
runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
|
||||
|
||||
await runner.run(task)
|
||||
|
||||
|
||||
async def bot(runner_args: RunnerArguments):
|
||||
"""Main bot entry point compatible with Pipecat Cloud."""
|
||||
transport = await create_transport(runner_args, transport_params)
|
||||
await run_bot(transport, runner_args)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
from pipecat.runner.run import main
|
||||
|
||||
main()
|
||||
@@ -1,184 +0,0 @@
|
||||
#
|
||||
# Copyright (c) 2024-2026, Daily
|
||||
#
|
||||
# SPDX-License-Identifier: BSD 2-Clause License
|
||||
#
|
||||
|
||||
"""RTVIObserver ignored sources example.
|
||||
|
||||
This example shows how to suppress RTVI messages from a specific pipeline
|
||||
processor so that secondary branches don't leak events to the client.
|
||||
|
||||
"""
|
||||
|
||||
import os
|
||||
|
||||
from dotenv import load_dotenv
|
||||
from loguru import logger
|
||||
|
||||
from pipecat.audio.vad.silero import SileroVADAnalyzer
|
||||
from pipecat.frames.frames import LLMRunFrame
|
||||
from pipecat.pipeline.parallel_pipeline import ParallelPipeline
|
||||
from pipecat.pipeline.pipeline import Pipeline
|
||||
from pipecat.pipeline.runner import PipelineRunner
|
||||
from pipecat.pipeline.task import PipelineParams, PipelineTask
|
||||
from pipecat.processors.aggregators.llm_context import LLMContext
|
||||
from pipecat.processors.aggregators.llm_response_universal import (
|
||||
LLMContextAggregatorPair,
|
||||
LLMUserAggregatorParams,
|
||||
)
|
||||
from pipecat.processors.audio.vad_processor import VADProcessor
|
||||
from pipecat.processors.frameworks.rtvi import RTVIObserverParams
|
||||
from pipecat.runner.types import RunnerArguments
|
||||
from pipecat.runner.utils import create_transport
|
||||
from pipecat.services.cartesia.tts import CartesiaTTSService
|
||||
from pipecat.services.deepgram.stt import DeepgramSTTService
|
||||
from pipecat.services.openai.llm import OpenAILLMService
|
||||
from pipecat.transports.base_transport import BaseTransport, TransportParams
|
||||
from pipecat.transports.daily.transport import DailyParams
|
||||
from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
|
||||
from pipecat.turns.user_turn_processor import UserTurnProcessor
|
||||
from pipecat.turns.user_turn_strategies import ExternalUserTurnStrategies
|
||||
|
||||
load_dotenv(override=True)
|
||||
|
||||
# We use lambdas to defer transport parameter creation until the transport
|
||||
# type is selected at runtime.
|
||||
transport_params = {
|
||||
"daily": lambda: DailyParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
),
|
||||
"twilio": lambda: FastAPIWebsocketParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
),
|
||||
"webrtc": lambda: TransportParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
),
|
||||
}
|
||||
|
||||
|
||||
async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
logger.info("Starting bot")
|
||||
|
||||
stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
|
||||
|
||||
tts = CartesiaTTSService(
|
||||
api_key=os.getenv("CARTESIA_API_KEY"),
|
||||
settings=CartesiaTTSService.Settings(
|
||||
voice="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
),
|
||||
)
|
||||
|
||||
# Main LLM — drives the conversation. Its RTVI events reach the client.
|
||||
main_llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
settings=OpenAILLMService.Settings(
|
||||
system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
|
||||
),
|
||||
)
|
||||
|
||||
# Evaluator LLM — silently grades the user's message in the background.
|
||||
# Its RTVI events will be suppressed so the client is unaware of this branch.
|
||||
evaluator_llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
name="EvaluatorLLM",
|
||||
settings=OpenAILLMService.Settings(
|
||||
system_instruction="You are a silent quality evaluator. When given a user message, respond with a single JSON object: {'score': <1-5>, 'reason': '<brief reason>'}. Do not respond conversationally.",
|
||||
),
|
||||
)
|
||||
|
||||
main_context = LLMContext()
|
||||
evaluator_context = LLMContext()
|
||||
|
||||
# We use an external VADProcessor because the UserTurnProcessor is shared
|
||||
# across multiple parallel aggregators. The VADProcessor emits
|
||||
# VADUserStartedSpeakingFrame and VADUserStoppedSpeakingFrame which the
|
||||
# UserTurnProcessor needs to manage turn lifecycle.
|
||||
vad_processor = VADProcessor(vad_analyzer=SileroVADAnalyzer())
|
||||
|
||||
# We use this external user turn processor. This processor will push
|
||||
# UserStartedSpeakingFrame and UserStoppedSpeakingFrame as well as
|
||||
# interruptions. This can be used in advanced cases when there are multiple
|
||||
# aggregators in the pipeline.
|
||||
user_turn_processor = UserTurnProcessor()
|
||||
|
||||
# We use external user turn strategies for both aggregators since the turn
|
||||
# management is done by the common UserTurnProcessor.
|
||||
main_context_aggregator = LLMContextAggregatorPair(
|
||||
main_context,
|
||||
user_params=LLMUserAggregatorParams(user_turn_strategies=ExternalUserTurnStrategies()),
|
||||
)
|
||||
evaluator_context_aggregator = LLMContextAggregatorPair(
|
||||
evaluator_context,
|
||||
user_params=LLMUserAggregatorParams(user_turn_strategies=ExternalUserTurnStrategies()),
|
||||
)
|
||||
|
||||
pipeline = Pipeline(
|
||||
[
|
||||
transport.input(), # Transport user input
|
||||
stt, # STT
|
||||
vad_processor,
|
||||
user_turn_processor,
|
||||
ParallelPipeline(
|
||||
# Main branch: speaks to the user.
|
||||
[
|
||||
main_context_aggregator.user(),
|
||||
main_llm,
|
||||
tts,
|
||||
transport.output(),
|
||||
main_context_aggregator.assistant(),
|
||||
],
|
||||
# Evaluator branch: silent background scoring, no audio output.
|
||||
[
|
||||
evaluator_context_aggregator.user(),
|
||||
evaluator_llm,
|
||||
evaluator_context_aggregator.assistant(),
|
||||
],
|
||||
),
|
||||
]
|
||||
)
|
||||
|
||||
task = PipelineTask(
|
||||
pipeline,
|
||||
params=PipelineParams(
|
||||
enable_metrics=True,
|
||||
enable_usage_metrics=True,
|
||||
),
|
||||
rtvi_observer_params=RTVIObserverParams(ignored_sources=[evaluator_llm]),
|
||||
idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
|
||||
)
|
||||
|
||||
@transport.event_handler("on_client_connected")
|
||||
async def on_client_connected(transport, client):
|
||||
logger.info("Client connected")
|
||||
main_context.add_message(
|
||||
{"role": "developer", "content": "Please introduce yourself to the user."}
|
||||
)
|
||||
evaluator_context.add_message(
|
||||
{"role": "developer", "content": "Ready to evaluate user messages."}
|
||||
)
|
||||
await task.queue_frames([LLMRunFrame()])
|
||||
|
||||
@transport.event_handler("on_client_disconnected")
|
||||
async def on_client_disconnected(transport, client):
|
||||
logger.info("Client disconnected")
|
||||
await task.cancel()
|
||||
|
||||
runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
|
||||
|
||||
await runner.run(task)
|
||||
|
||||
|
||||
async def bot(runner_args: RunnerArguments):
|
||||
"""Main bot entry point compatible with Pipecat Cloud."""
|
||||
transport = await create_transport(runner_args, transport_params)
|
||||
await run_bot(transport, runner_args)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
from pipecat.runner.run import main
|
||||
|
||||
main()
|
||||
@@ -1,142 +0,0 @@
|
||||
#
|
||||
# Copyright (c) 2024-2026, Daily
|
||||
#
|
||||
# SPDX-License-Identifier: BSD 2-Clause License
|
||||
#
|
||||
|
||||
|
||||
import os
|
||||
|
||||
from dotenv import load_dotenv
|
||||
from loguru import logger
|
||||
|
||||
from pipecat.audio.vad.silero import SileroVADAnalyzer
|
||||
from pipecat.frames.frames import TTSSpeakFrame
|
||||
from pipecat.pipeline.pipeline import Pipeline
|
||||
from pipecat.pipeline.runner import PipelineRunner
|
||||
from pipecat.pipeline.task import PipelineParams, PipelineTask
|
||||
from pipecat.processors.aggregators.llm_context import LLMContext
|
||||
from pipecat.processors.aggregators.llm_response_universal import (
|
||||
LLMContextAggregatorPair,
|
||||
LLMUserAggregatorParams,
|
||||
)
|
||||
from pipecat.runner.types import RunnerArguments
|
||||
from pipecat.runner.utils import create_transport
|
||||
from pipecat.services.cartesia.tts import CartesiaTTSService
|
||||
from pipecat.services.deepgram.stt import DeepgramSTTService
|
||||
from pipecat.services.openai.llm import OpenAILLMService
|
||||
from pipecat.transports.base_transport import BaseTransport, TransportParams
|
||||
from pipecat.transports.daily.transport import DailyParams
|
||||
from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
|
||||
from pipecat.turns.user_start import TranscriptionUserTurnStartStrategy
|
||||
from pipecat.turns.user_turn_strategies import UserTurnStrategies
|
||||
|
||||
load_dotenv(override=True)
|
||||
|
||||
|
||||
# We use lambdas to defer transport parameter creation until the transport
|
||||
# type is selected at runtime.
|
||||
transport_params = {
|
||||
"daily": lambda: DailyParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
),
|
||||
"twilio": lambda: FastAPIWebsocketParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
),
|
||||
"webrtc": lambda: TransportParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
),
|
||||
}
|
||||
|
||||
|
||||
async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
logger.info(f"Starting bot")
|
||||
|
||||
stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
|
||||
|
||||
tts = CartesiaTTSService(
|
||||
api_key=os.getenv("CARTESIA_API_KEY"),
|
||||
settings=CartesiaTTSService.Settings(
|
||||
voice="d4db5fb9-f44b-4bd1-85fa-192e0f0d75f9", # Spanish-speaking Lady
|
||||
),
|
||||
)
|
||||
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
settings=OpenAILLMService.Settings(
|
||||
system_instruction="You are a live translation assistant. Your sole purpose is to translate English text into Spanish. When you receive English text from the user, immediately translate it into natural, fluent Spanish. Do not add explanations, commentary, or extra information—only provide the Spanish translation of the text you receive.",
|
||||
),
|
||||
)
|
||||
|
||||
context = LLMContext()
|
||||
|
||||
# We use the TranscriptionUserTurnStartStrategy to start a new user turn
|
||||
# every time a transcription is received. We disable interruptions, so the
|
||||
# user can continue speaking while the bot is transcribing, without
|
||||
# interrupting the bot.
|
||||
user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
|
||||
context,
|
||||
user_params=LLMUserAggregatorParams(
|
||||
user_turn_strategies=UserTurnStrategies(
|
||||
start=[TranscriptionUserTurnStartStrategy(enable_interruptions=False)],
|
||||
),
|
||||
vad_analyzer=SileroVADAnalyzer(),
|
||||
),
|
||||
)
|
||||
|
||||
pipeline = Pipeline(
|
||||
[
|
||||
transport.input(), # Transport user input
|
||||
stt, # STT
|
||||
user_aggregator, # User responses
|
||||
llm, # LLM
|
||||
tts, # TTS (bot will speak the chosen language)
|
||||
transport.output(), # Transport bot output
|
||||
assistant_aggregator, # Assistant spoken responses
|
||||
]
|
||||
)
|
||||
|
||||
task = PipelineTask(
|
||||
pipeline,
|
||||
params=PipelineParams(
|
||||
enable_metrics=True,
|
||||
enable_usage_metrics=True,
|
||||
),
|
||||
idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
|
||||
)
|
||||
|
||||
@transport.event_handler("on_client_connected")
|
||||
async def on_client_connected(transport, client):
|
||||
logger.info(f"Client connected")
|
||||
await task.queue_frames(
|
||||
[
|
||||
TTSSpeakFrame(
|
||||
text="Hello, welcome to live translation. Everything you say will be automatically translated to Spanish. Let's begin!",
|
||||
append_to_context=True,
|
||||
),
|
||||
]
|
||||
)
|
||||
|
||||
@transport.event_handler("on_client_disconnected")
|
||||
async def on_client_disconnected(transport, client):
|
||||
logger.info(f"Client disconnected")
|
||||
await task.cancel()
|
||||
|
||||
runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
|
||||
|
||||
await runner.run(task)
|
||||
|
||||
|
||||
async def bot(runner_args: RunnerArguments):
|
||||
"""Main bot entry point compatible with Pipecat Cloud."""
|
||||
transport = await create_transport(runner_args, transport_params)
|
||||
await run_bot(transport, runner_args)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
from pipecat.runner.run import main
|
||||
|
||||
main()
|
||||
@@ -1,147 +0,0 @@
|
||||
#
|
||||
# Copyright (c) 2024-2026, Daily
|
||||
#
|
||||
# SPDX-License-Identifier: BSD 2-Clause License
|
||||
#
|
||||
|
||||
import os
|
||||
|
||||
from dotenv import load_dotenv
|
||||
from loguru import logger
|
||||
|
||||
from pipecat.audio.vad.silero import SileroVADAnalyzer
|
||||
from pipecat.frames.frames import LLMRunFrame
|
||||
from pipecat.pipeline.pipeline import Pipeline
|
||||
from pipecat.pipeline.runner import PipelineRunner
|
||||
from pipecat.pipeline.task import PipelineParams, PipelineTask
|
||||
from pipecat.processors.aggregators.llm_context import LLMContext
|
||||
from pipecat.processors.aggregators.llm_response_universal import (
|
||||
LLMContextAggregatorPair,
|
||||
LLMUserAggregatorParams,
|
||||
)
|
||||
from pipecat.runner.types import RunnerArguments
|
||||
from pipecat.runner.utils import create_transport
|
||||
from pipecat.services.cartesia.tts import CartesiaTTSService
|
||||
from pipecat.services.deepgram.stt import DeepgramSTTService
|
||||
from pipecat.services.openai.llm import OpenAILLMService
|
||||
from pipecat.transports.base_transport import BaseTransport, TransportParams
|
||||
from pipecat.transports.daily.transport import DailyParams
|
||||
from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
|
||||
from pipecat.turns.user_start import WakePhraseUserTurnStartStrategy
|
||||
from pipecat.turns.user_turn_strategies import (
|
||||
UserTurnStrategies,
|
||||
default_user_turn_start_strategies,
|
||||
)
|
||||
|
||||
load_dotenv(override=True)
|
||||
|
||||
# We use lambdas to defer transport parameter creation until the transport
|
||||
# type is selected at runtime.
|
||||
transport_params = {
|
||||
"daily": lambda: DailyParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
),
|
||||
"twilio": lambda: FastAPIWebsocketParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
),
|
||||
"webrtc": lambda: TransportParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
),
|
||||
}
|
||||
|
||||
|
||||
async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
logger.info(f"Starting bot")
|
||||
|
||||
stt = DeepgramSTTService(
|
||||
api_key=os.getenv("DEEPGRAM_API_KEY"),
|
||||
settings=DeepgramSTTService.Settings(
|
||||
keyterm=["pipecat"],
|
||||
),
|
||||
)
|
||||
|
||||
tts = CartesiaTTSService(
|
||||
api_key=os.getenv("CARTESIA_API_KEY"),
|
||||
settings=CartesiaTTSService.Settings(
|
||||
voice="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
),
|
||||
)
|
||||
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
settings=OpenAILLMService.Settings(
|
||||
system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
|
||||
),
|
||||
)
|
||||
|
||||
context = LLMContext()
|
||||
user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
|
||||
context,
|
||||
user_params=LLMUserAggregatorParams(
|
||||
user_turn_strategies=UserTurnStrategies(
|
||||
start=[
|
||||
WakePhraseUserTurnStartStrategy(
|
||||
phrases=["pipecat"],
|
||||
# Timeout before wake phrase must be spoken again
|
||||
timeout=5.0,
|
||||
),
|
||||
*default_user_turn_start_strategies(),
|
||||
]
|
||||
),
|
||||
vad_analyzer=SileroVADAnalyzer(),
|
||||
),
|
||||
)
|
||||
|
||||
pipeline = Pipeline(
|
||||
[
|
||||
transport.input(), # Transport user input
|
||||
stt,
|
||||
user_aggregator, # User responses
|
||||
llm, # LLM
|
||||
tts, # TTS
|
||||
transport.output(), # Transport bot output
|
||||
assistant_aggregator, # Assistant spoken responses
|
||||
]
|
||||
)
|
||||
|
||||
task = PipelineTask(
|
||||
pipeline,
|
||||
params=PipelineParams(
|
||||
enable_metrics=True,
|
||||
enable_usage_metrics=True,
|
||||
),
|
||||
idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
|
||||
)
|
||||
|
||||
@transport.event_handler("on_client_connected")
|
||||
async def on_client_connected(transport, client):
|
||||
logger.info(f"Client connected")
|
||||
# Kick off the conversation.
|
||||
context.add_message(
|
||||
{"role": "developer", "content": "Please introduce yourself to the user."}
|
||||
)
|
||||
await task.queue_frames([LLMRunFrame()])
|
||||
|
||||
@transport.event_handler("on_client_disconnected")
|
||||
async def on_client_disconnected(transport, client):
|
||||
logger.info(f"Client disconnected")
|
||||
await task.cancel()
|
||||
|
||||
runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
|
||||
|
||||
await runner.run(task)
|
||||
|
||||
|
||||
async def bot(runner_args: RunnerArguments):
|
||||
"""Main bot entry point compatible with Pipecat Cloud."""
|
||||
transport = await create_transport(runner_args, transport_params)
|
||||
await run_bot(transport, runner_args)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
from pipecat.runner.run import main
|
||||
|
||||
main()
|
||||
70
examples/foundational/01-say-one-thing-piper.py
Normal file
70
examples/foundational/01-say-one-thing-piper.py
Normal file
@@ -0,0 +1,70 @@
|
||||
#
|
||||
# Copyright (c) 2024–2025, Daily
|
||||
#
|
||||
# SPDX-License-Identifier: BSD 2-Clause License
|
||||
#
|
||||
|
||||
import os
|
||||
|
||||
import aiohttp
|
||||
from dotenv import load_dotenv
|
||||
from loguru import logger
|
||||
|
||||
from pipecat.frames.frames import EndFrame, TTSSpeakFrame
|
||||
from pipecat.pipeline.pipeline import Pipeline
|
||||
from pipecat.pipeline.runner import PipelineRunner
|
||||
from pipecat.pipeline.task import PipelineTask
|
||||
from pipecat.runner.types import RunnerArguments
|
||||
from pipecat.runner.utils import create_transport
|
||||
from pipecat.services.piper.tts import PiperTTSService
|
||||
from pipecat.transports.base_transport import BaseTransport, TransportParams
|
||||
from pipecat.transports.daily.transport import DailyParams
|
||||
from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
|
||||
|
||||
load_dotenv(override=True)
|
||||
|
||||
|
||||
# We store functions so objects (e.g. SileroVADAnalyzer) don't get
|
||||
# instantiated. The function will be called when the desired transport gets
|
||||
# selected.
|
||||
transport_params = {
|
||||
"daily": lambda: DailyParams(audio_out_enabled=True),
|
||||
"twilio": lambda: FastAPIWebsocketParams(audio_out_enabled=True),
|
||||
"webrtc": lambda: TransportParams(audio_out_enabled=True),
|
||||
}
|
||||
|
||||
|
||||
async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
logger.info(f"Starting bot")
|
||||
|
||||
# Create an HTTP session
|
||||
async with aiohttp.ClientSession() as session:
|
||||
tts = PiperTTSService(
|
||||
base_url=os.getenv("PIPER_BASE_URL"), aiohttp_session=session, sample_rate=24000
|
||||
)
|
||||
|
||||
task = PipelineTask(
|
||||
Pipeline([tts, transport.output()]),
|
||||
idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
|
||||
)
|
||||
|
||||
# Register an event handler so we can play the audio when the client joins
|
||||
@transport.event_handler("on_client_connected")
|
||||
async def on_client_connected(transport, client):
|
||||
await task.queue_frames([TTSSpeakFrame(f"Hello there!"), EndFrame()])
|
||||
|
||||
runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
|
||||
|
||||
await runner.run(task)
|
||||
|
||||
|
||||
async def bot(runner_args: RunnerArguments):
|
||||
"""Main bot entry point compatible with Pipecat Cloud."""
|
||||
transport = await create_transport(runner_args, transport_params)
|
||||
await run_bot(transport, runner_args)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
from pipecat.runner.run import main
|
||||
|
||||
main()
|
||||
71
examples/foundational/01-say-one-thing-rime.py
Normal file
71
examples/foundational/01-say-one-thing-rime.py
Normal file
@@ -0,0 +1,71 @@
|
||||
#
|
||||
# Copyright (c) 2024–2025, Daily
|
||||
#
|
||||
# SPDX-License-Identifier: BSD 2-Clause License
|
||||
#
|
||||
|
||||
import os
|
||||
|
||||
import aiohttp
|
||||
from dotenv import load_dotenv
|
||||
from loguru import logger
|
||||
|
||||
from pipecat.frames.frames import EndFrame, TTSSpeakFrame
|
||||
from pipecat.pipeline.pipeline import Pipeline
|
||||
from pipecat.pipeline.runner import PipelineRunner
|
||||
from pipecat.pipeline.task import PipelineTask
|
||||
from pipecat.runner.types import RunnerArguments
|
||||
from pipecat.runner.utils import create_transport
|
||||
from pipecat.services.rime.tts import RimeHttpTTSService
|
||||
from pipecat.transports.base_transport import BaseTransport, TransportParams
|
||||
from pipecat.transports.daily.transport import DailyParams
|
||||
from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
|
||||
|
||||
load_dotenv(override=True)
|
||||
|
||||
# We store functions so objects (e.g. SileroVADAnalyzer) don't get
|
||||
# instantiated. The function will be called when the desired transport gets
|
||||
# selected.
|
||||
transport_params = {
|
||||
"daily": lambda: DailyParams(audio_out_enabled=True),
|
||||
"twilio": lambda: FastAPIWebsocketParams(audio_out_enabled=True),
|
||||
"webrtc": lambda: TransportParams(audio_out_enabled=True),
|
||||
}
|
||||
|
||||
|
||||
async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
logger.info(f"Starting bot")
|
||||
|
||||
# Create an HTTP session
|
||||
async with aiohttp.ClientSession() as session:
|
||||
tts = RimeHttpTTSService(
|
||||
api_key=os.getenv("RIME_API_KEY", ""),
|
||||
voice_id="rex",
|
||||
aiohttp_session=session,
|
||||
)
|
||||
|
||||
task = PipelineTask(
|
||||
Pipeline([tts, transport.output()]),
|
||||
idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
|
||||
)
|
||||
|
||||
# Register an event handler so we can play the audio when the client joins
|
||||
@transport.event_handler("on_client_connected")
|
||||
async def on_client_connected(transport, client):
|
||||
await task.queue_frames([TTSSpeakFrame(f"Hello there!"), EndFrame()])
|
||||
|
||||
runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
|
||||
|
||||
await runner.run(task)
|
||||
|
||||
|
||||
async def bot(runner_args: RunnerArguments):
|
||||
"""Main bot entry point compatible with Pipecat Cloud."""
|
||||
transport = await create_transport(runner_args, transport_params)
|
||||
await run_bot(transport, runner_args)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
from pipecat.runner.run import main
|
||||
|
||||
main()
|
||||
@@ -1,5 +1,5 @@
|
||||
#
|
||||
# Copyright (c) 2024-2026, Daily
|
||||
# Copyright (c) 2024–2025, Daily
|
||||
#
|
||||
# SPDX-License-Identifier: BSD 2-Clause License
|
||||
#
|
||||
@@ -23,8 +23,9 @@ from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
|
||||
load_dotenv(override=True)
|
||||
|
||||
|
||||
# We use lambdas to defer transport parameter creation until the transport
|
||||
# type is selected at runtime.
|
||||
# We store functions so objects (e.g. SileroVADAnalyzer) don't get
|
||||
# instantiated. The function will be called when the desired transport gets
|
||||
# selected.
|
||||
transport_params = {
|
||||
"daily": lambda: DailyParams(audio_out_enabled=True),
|
||||
"twilio": lambda: FastAPIWebsocketParams(audio_out_enabled=True),
|
||||
@@ -37,9 +38,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
|
||||
tts = CartesiaTTSService(
|
||||
api_key=os.getenv("CARTESIA_API_KEY"),
|
||||
settings=CartesiaTTSService.Settings(
|
||||
voice="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
),
|
||||
voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
)
|
||||
|
||||
task = PipelineTask(
|
||||
@@ -1,5 +1,5 @@
|
||||
#
|
||||
# Copyright (c) 2024-2026, Daily
|
||||
# Copyright (c) 2024–2025, Daily
|
||||
#
|
||||
# SPDX-License-Identifier: BSD 2-Clause License
|
||||
#
|
||||
@@ -29,9 +29,7 @@ async def main():
|
||||
|
||||
tts = CartesiaTTSService(
|
||||
api_key=os.getenv("CARTESIA_API_KEY"),
|
||||
settings=CartesiaTTSService.Settings(
|
||||
voice="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
),
|
||||
voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
)
|
||||
|
||||
pipeline = Pipeline([tts, transport.output()])
|
||||
62
examples/foundational/01b-livekit-audio.py
Normal file
62
examples/foundational/01b-livekit-audio.py
Normal file
@@ -0,0 +1,62 @@
|
||||
#
|
||||
# Copyright (c) 2024–2025, Daily
|
||||
#
|
||||
# SPDX-License-Identifier: BSD 2-Clause License
|
||||
#
|
||||
|
||||
import asyncio
|
||||
import os
|
||||
import sys
|
||||
|
||||
from dotenv import load_dotenv
|
||||
from loguru import logger
|
||||
|
||||
from pipecat.frames.frames import TTSSpeakFrame
|
||||
from pipecat.pipeline.pipeline import Pipeline
|
||||
from pipecat.pipeline.runner import PipelineRunner
|
||||
from pipecat.pipeline.task import PipelineTask
|
||||
from pipecat.runner.livekit import configure
|
||||
from pipecat.services.cartesia.tts import CartesiaTTSService
|
||||
from pipecat.transports.livekit.transport import LiveKitParams, LiveKitTransport
|
||||
|
||||
load_dotenv(override=True)
|
||||
|
||||
logger.remove(0)
|
||||
logger.add(sys.stderr, level="DEBUG")
|
||||
|
||||
|
||||
async def main():
|
||||
(url, token, room_name) = await configure()
|
||||
|
||||
transport = LiveKitTransport(
|
||||
url=url,
|
||||
token=token,
|
||||
room_name=room_name,
|
||||
params=LiveKitParams(audio_out_enabled=True),
|
||||
)
|
||||
|
||||
tts = CartesiaTTSService(
|
||||
api_key=os.getenv("CARTESIA_API_KEY"),
|
||||
voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
)
|
||||
|
||||
runner = PipelineRunner()
|
||||
|
||||
task = PipelineTask(Pipeline([tts, transport.output()]))
|
||||
|
||||
# Register an event handler so we can play the audio when the
|
||||
# participant joins.
|
||||
@transport.event_handler("on_first_participant_joined")
|
||||
async def on_first_participant_joined(transport, participant_id):
|
||||
await asyncio.sleep(1)
|
||||
await task.queue_frame(
|
||||
TTSSpeakFrame(
|
||||
"Hello there! How are you doing today? Would you like to talk about the weather?"
|
||||
)
|
||||
)
|
||||
|
||||
await runner.run(task)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(main())
|
||||
65
examples/foundational/01c-fastpitch.py
Normal file
65
examples/foundational/01c-fastpitch.py
Normal file
@@ -0,0 +1,65 @@
|
||||
#
|
||||
# Copyright (c) 2024–2025, Daily
|
||||
#
|
||||
# SPDX-License-Identifier: BSD 2-Clause License
|
||||
#
|
||||
|
||||
import os
|
||||
|
||||
from dotenv import load_dotenv
|
||||
from loguru import logger
|
||||
|
||||
from pipecat.frames.frames import EndFrame, TTSSpeakFrame
|
||||
from pipecat.pipeline.pipeline import Pipeline
|
||||
from pipecat.pipeline.runner import PipelineRunner
|
||||
from pipecat.pipeline.task import PipelineTask
|
||||
from pipecat.runner.types import RunnerArguments
|
||||
from pipecat.runner.utils import create_transport
|
||||
from pipecat.services.riva.tts import FastPitchTTSService
|
||||
from pipecat.transports.base_transport import BaseTransport, TransportParams
|
||||
from pipecat.transports.daily.transport import DailyParams
|
||||
from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
|
||||
|
||||
load_dotenv(override=True)
|
||||
|
||||
|
||||
# We store functions so objects (e.g. SileroVADAnalyzer) don't get
|
||||
# instantiated. The function will be called when the desired transport gets
|
||||
# selected.
|
||||
transport_params = {
|
||||
"daily": lambda: DailyParams(audio_out_enabled=True),
|
||||
"twilio": lambda: FastAPIWebsocketParams(audio_out_enabled=True),
|
||||
"webrtc": lambda: TransportParams(audio_out_enabled=True),
|
||||
}
|
||||
|
||||
|
||||
async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
logger.info(f"Starting bot")
|
||||
|
||||
tts = FastPitchTTSService(api_key=os.getenv("NVIDIA_API_KEY"))
|
||||
|
||||
task = PipelineTask(
|
||||
Pipeline([tts, transport.output()]),
|
||||
idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
|
||||
)
|
||||
|
||||
# Register an event handler so we can play the audio when the client joins
|
||||
@transport.event_handler("on_client_connected")
|
||||
async def on_client_connected(transport, client):
|
||||
await task.queue_frames([TTSSpeakFrame(f"Hello there!"), EndFrame()])
|
||||
|
||||
runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
|
||||
|
||||
await runner.run(task)
|
||||
|
||||
|
||||
async def bot(runner_args: RunnerArguments):
|
||||
"""Main bot entry point compatible with Pipecat Cloud."""
|
||||
transport = await create_transport(runner_args, transport_params)
|
||||
await run_bot(transport, runner_args)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
from pipecat.runner.run import main
|
||||
|
||||
main()
|
||||
@@ -1,5 +1,5 @@
|
||||
#
|
||||
# Copyright (c) 2024-2026, Daily
|
||||
# Copyright (c) 2024–2025, Daily
|
||||
#
|
||||
# SPDX-License-Identifier: BSD 2-Clause License
|
||||
#
|
||||
@@ -25,8 +25,9 @@ from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
|
||||
load_dotenv(override=True)
|
||||
|
||||
|
||||
# We use lambdas to defer transport parameter creation until the transport
|
||||
# type is selected at runtime.
|
||||
# We store functions so objects (e.g. SileroVADAnalyzer) don't get
|
||||
# instantiated. The function will be called when the desired transport gets
|
||||
# selected.
|
||||
transport_params = {
|
||||
"daily": lambda: DailyParams(audio_out_enabled=True),
|
||||
"twilio": lambda: FastAPIWebsocketParams(audio_out_enabled=True),
|
||||
@@ -39,17 +40,17 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
|
||||
tts = CartesiaTTSService(
|
||||
api_key=os.getenv("CARTESIA_API_KEY"),
|
||||
settings=CartesiaTTSService.Settings(
|
||||
voice="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
),
|
||||
voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
)
|
||||
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
settings=OpenAILLMService.Settings(
|
||||
system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
|
||||
),
|
||||
)
|
||||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
|
||||
|
||||
messages = [
|
||||
{
|
||||
"role": "system",
|
||||
"content": "You are an LLM in a WebRTC session, and this is a 'hello world' demo. Say hello to the world.",
|
||||
}
|
||||
]
|
||||
|
||||
task = PipelineTask(
|
||||
Pipeline([llm, tts, transport.output()]),
|
||||
@@ -59,9 +60,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
# Register an event handler so we can play the audio when the client joins
|
||||
@transport.event_handler("on_client_connected")
|
||||
async def on_client_connected(transport, client):
|
||||
context = LLMContext()
|
||||
context.add_message({"role": "developer", "content": "Say hello to the world."})
|
||||
await task.queue_frames([LLMContextFrame(context), EndFrame()])
|
||||
await task.queue_frames([LLMContextFrame(LLMContext(messages)), EndFrame()])
|
||||
|
||||
runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
|
||||
|
||||
83
examples/foundational/03-still-frame.py
Normal file
83
examples/foundational/03-still-frame.py
Normal file
@@ -0,0 +1,83 @@
|
||||
#
|
||||
# Copyright (c) 2024–2025, Daily
|
||||
#
|
||||
# SPDX-License-Identifier: BSD 2-Clause License
|
||||
#
|
||||
|
||||
import os
|
||||
|
||||
import aiohttp
|
||||
from dotenv import load_dotenv
|
||||
from loguru import logger
|
||||
|
||||
from pipecat.frames.frames import TextFrame
|
||||
from pipecat.pipeline.pipeline import Pipeline
|
||||
from pipecat.pipeline.runner import PipelineRunner
|
||||
from pipecat.pipeline.task import PipelineTask
|
||||
from pipecat.runner.types import RunnerArguments
|
||||
from pipecat.runner.utils import create_transport
|
||||
from pipecat.services.fal.image import FalImageGenService
|
||||
from pipecat.transports.base_transport import BaseTransport, TransportParams
|
||||
from pipecat.transports.daily.transport import DailyParams
|
||||
|
||||
load_dotenv(override=True)
|
||||
|
||||
|
||||
# We store functions so objects (e.g. SileroVADAnalyzer) don't get
|
||||
# instantiated. The function will be called when the desired transport gets
|
||||
# selected.
|
||||
transport_params = {
|
||||
"daily": lambda: DailyParams(
|
||||
video_out_enabled=True,
|
||||
video_out_width=1024,
|
||||
video_out_height=1024,
|
||||
),
|
||||
"webrtc": lambda: TransportParams(
|
||||
video_out_enabled=True,
|
||||
video_out_width=1024,
|
||||
video_out_height=1024,
|
||||
),
|
||||
}
|
||||
|
||||
|
||||
async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
logger.info(f"Starting bot")
|
||||
|
||||
# Create an HTTP session
|
||||
async with aiohttp.ClientSession() as session:
|
||||
imagegen = FalImageGenService(
|
||||
params=FalImageGenService.InputParams(image_size="square_hd"),
|
||||
aiohttp_session=session,
|
||||
key=os.getenv("FAL_KEY"),
|
||||
)
|
||||
|
||||
task = PipelineTask(
|
||||
Pipeline([imagegen, transport.output()]),
|
||||
idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
|
||||
)
|
||||
|
||||
# Register an event handler so we can play the audio when the client joins
|
||||
@transport.event_handler("on_client_connected")
|
||||
async def on_client_connected(transport, client):
|
||||
await task.queue_frame(TextFrame("a cat in the style of picasso"))
|
||||
|
||||
@transport.event_handler("on_client_disconnected")
|
||||
async def on_client_disconnected(transport, client):
|
||||
logger.info(f"Client disconnected")
|
||||
await task.cancel()
|
||||
|
||||
runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
|
||||
|
||||
await runner.run(task)
|
||||
|
||||
|
||||
async def bot(runner_args: RunnerArguments):
|
||||
"""Main bot entry point compatible with Pipecat Cloud."""
|
||||
transport = await create_transport(runner_args, transport_params)
|
||||
await run_bot(transport, runner_args)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
from pipecat.runner.run import main
|
||||
|
||||
main()
|
||||
@@ -1,5 +1,5 @@
|
||||
#
|
||||
# Copyright (c) 2024-2026, Daily
|
||||
# Copyright (c) 2024–2025, Daily
|
||||
#
|
||||
# SPDX-License-Identifier: BSD 2-Clause License
|
||||
#
|
||||
@@ -37,9 +37,7 @@ async def main():
|
||||
)
|
||||
|
||||
imagegen = FalImageGenService(
|
||||
settings=FalImageGenService.Settings(
|
||||
image_size="square_hd",
|
||||
),
|
||||
params=FalImageGenService.InputParams(image_size="square_hd"),
|
||||
aiohttp_session=session,
|
||||
key=os.getenv("FAL_KEY"),
|
||||
)
|
||||
@@ -1,5 +1,5 @@
|
||||
#
|
||||
# Copyright (c) 2024-2026, Daily
|
||||
# Copyright (c) 2024–2025, Daily
|
||||
#
|
||||
# SPDX-License-Identifier: BSD 2-Clause License
|
||||
#
|
||||
@@ -22,8 +22,9 @@ from pipecat.transports.daily.transport import DailyParams
|
||||
load_dotenv(override=True)
|
||||
|
||||
|
||||
# We use lambdas to defer transport parameter creation until the transport
|
||||
# type is selected at runtime.
|
||||
# We store functions so objects (e.g. SileroVADAnalyzer) don't get
|
||||
# instantiated. The function will be called when the desired transport gets
|
||||
# selected.
|
||||
transport_params = {
|
||||
"daily": lambda: DailyParams(
|
||||
video_out_enabled=True,
|
||||
@@ -1,5 +1,5 @@
|
||||
#
|
||||
# Copyright (c) 2024-2026, Daily
|
||||
# Copyright (c) 2024–2025, Daily
|
||||
#
|
||||
# SPDX-License-Identifier: BSD 2-Clause License
|
||||
#
|
||||
@@ -8,6 +8,7 @@ import argparse
|
||||
import asyncio
|
||||
import os
|
||||
from contextlib import asynccontextmanager
|
||||
from typing import Dict
|
||||
|
||||
import uvicorn
|
||||
from dotenv import load_dotenv
|
||||
@@ -16,16 +17,16 @@ from fastapi.responses import RedirectResponse
|
||||
from loguru import logger
|
||||
from pipecat_ai_small_webrtc_prebuilt.frontend import SmallWebRTCPrebuiltUI
|
||||
|
||||
from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
|
||||
from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
|
||||
from pipecat.audio.vad.silero import SileroVADAnalyzer
|
||||
from pipecat.audio.vad.vad_analyzer import VADParams
|
||||
from pipecat.frames.frames import LLMRunFrame
|
||||
from pipecat.pipeline.pipeline import Pipeline
|
||||
from pipecat.pipeline.runner import PipelineRunner
|
||||
from pipecat.pipeline.task import PipelineParams, PipelineTask
|
||||
from pipecat.processors.aggregators.llm_context import LLMContext
|
||||
from pipecat.processors.aggregators.llm_response_universal import (
|
||||
LLMContextAggregatorPair,
|
||||
LLMUserAggregatorParams,
|
||||
)
|
||||
from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
|
||||
from pipecat.services.cartesia.tts import CartesiaTTSService
|
||||
from pipecat.services.deepgram.stt import DeepgramSTTService
|
||||
from pipecat.services.openai.llm import OpenAILLMService
|
||||
@@ -38,7 +39,7 @@ load_dotenv(override=True)
|
||||
app = FastAPI()
|
||||
|
||||
# Store connections by pc_id
|
||||
pcs_map: dict[str, SmallWebRTCConnection] = {}
|
||||
pcs_map: Dict[str, SmallWebRTCConnection] = {}
|
||||
|
||||
ice_servers = [
|
||||
IceServer(
|
||||
@@ -59,6 +60,8 @@ async def run_example(webrtc_connection: SmallWebRTCConnection):
|
||||
params=TransportParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
)
|
||||
|
||||
@@ -66,33 +69,30 @@ async def run_example(webrtc_connection: SmallWebRTCConnection):
|
||||
|
||||
tts = CartesiaTTSService(
|
||||
api_key=os.getenv("CARTESIA_API_KEY"),
|
||||
settings=CartesiaTTSService.Settings(
|
||||
voice="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
),
|
||||
voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
)
|
||||
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
settings=OpenAILLMService.Settings(
|
||||
system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
|
||||
),
|
||||
)
|
||||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
|
||||
|
||||
context = LLMContext()
|
||||
user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
|
||||
context,
|
||||
user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
|
||||
)
|
||||
messages = [
|
||||
{
|
||||
"role": "system",
|
||||
"content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
|
||||
},
|
||||
]
|
||||
|
||||
context = LLMContext(messages)
|
||||
context_aggregator = LLMContextAggregatorPair(context)
|
||||
|
||||
pipeline = Pipeline(
|
||||
[
|
||||
transport.input(), # Transport user input
|
||||
stt,
|
||||
user_aggregator, # User responses
|
||||
context_aggregator.user(), # User responses
|
||||
llm, # LLM
|
||||
tts, # TTS
|
||||
transport.output(), # Transport bot output
|
||||
assistant_aggregator, # Assistant spoken responses
|
||||
context_aggregator.assistant(), # Assistant spoken responses
|
||||
]
|
||||
)
|
||||
|
||||
@@ -108,9 +108,7 @@ async def run_example(webrtc_connection: SmallWebRTCConnection):
|
||||
async def on_client_connected(transport, client):
|
||||
logger.info(f"Client connected")
|
||||
# Kick off the conversation.
|
||||
context.add_message(
|
||||
{"role": "developer", "content": "Please introduce yourself to the user."}
|
||||
)
|
||||
messages.append({"role": "system", "content": "Please introduce yourself to the user."})
|
||||
await task.queue_frames([LLMRunFrame()])
|
||||
|
||||
@transport.event_handler("on_client_disconnected")
|
||||
@@ -1,5 +1,5 @@
|
||||
#
|
||||
# Copyright (c) 2024-2026, Daily
|
||||
# Copyright (c) 2024–2025, Daily
|
||||
#
|
||||
# SPDX-License-Identifier: BSD 2-Clause License
|
||||
#
|
||||
@@ -12,16 +12,16 @@ import aiohttp
|
||||
from dotenv import load_dotenv
|
||||
from loguru import logger
|
||||
|
||||
from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
|
||||
from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
|
||||
from pipecat.audio.vad.silero import SileroVADAnalyzer
|
||||
from pipecat.audio.vad.vad_analyzer import VADParams
|
||||
from pipecat.frames.frames import LLMRunFrame
|
||||
from pipecat.pipeline.pipeline import Pipeline
|
||||
from pipecat.pipeline.runner import PipelineRunner
|
||||
from pipecat.pipeline.task import PipelineParams, PipelineTask
|
||||
from pipecat.processors.aggregators.llm_context import LLMContext
|
||||
from pipecat.processors.aggregators.llm_response_universal import (
|
||||
LLMContextAggregatorPair,
|
||||
LLMUserAggregatorParams,
|
||||
)
|
||||
from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
|
||||
from pipecat.runner.daily import configure
|
||||
from pipecat.services.cartesia.tts import CartesiaTTSService
|
||||
from pipecat.services.openai.llm import OpenAILLMService
|
||||
@@ -45,37 +45,36 @@ async def main():
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
transcription_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
)
|
||||
|
||||
tts = CartesiaTTSService(
|
||||
api_key=os.getenv("CARTESIA_API_KEY"),
|
||||
settings=CartesiaTTSService.Settings(
|
||||
voice="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
),
|
||||
voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
)
|
||||
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
settings=OpenAILLMService.Settings(
|
||||
system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
|
||||
),
|
||||
)
|
||||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
|
||||
|
||||
context = LLMContext()
|
||||
user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
|
||||
context,
|
||||
user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
|
||||
)
|
||||
messages = [
|
||||
{
|
||||
"role": "system",
|
||||
"content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
|
||||
},
|
||||
]
|
||||
|
||||
context = LLMContext(messages)
|
||||
context_aggregator = LLMContextAggregatorPair(context)
|
||||
|
||||
pipeline = Pipeline(
|
||||
[
|
||||
transport.input(), # Transport user input
|
||||
user_aggregator, # User responses
|
||||
context_aggregator.user(), # User responses
|
||||
llm, # LLM
|
||||
tts, # TTS
|
||||
transport.output(), # Transport bot output
|
||||
assistant_aggregator, # Assistant spoken responses
|
||||
context_aggregator.assistant(), # Assistant spoken responses
|
||||
]
|
||||
)
|
||||
|
||||
@@ -91,9 +90,7 @@ async def main():
|
||||
async def on_first_participant_joined(transport, participant):
|
||||
await transport.capture_participant_transcription(participant["id"])
|
||||
# Kick off the conversation.
|
||||
context.add_message(
|
||||
{"role": "developer", "content": "Please introduce yourself to the user."}
|
||||
)
|
||||
messages.append({"role": "system", "content": "Please introduce yourself to the user."})
|
||||
await task.queue_frames([LLMRunFrame()])
|
||||
|
||||
@transport.event_handler("on_participant_left")
|
||||
@@ -1,5 +1,5 @@
|
||||
#
|
||||
# Copyright (c) 2024-2026, Daily
|
||||
# Copyright (c) 2024–2025, Daily
|
||||
#
|
||||
# SPDX-License-Identifier: BSD 2-Clause License
|
||||
#
|
||||
@@ -12,7 +12,10 @@ import sys
|
||||
from dotenv import load_dotenv
|
||||
from loguru import logger
|
||||
|
||||
from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
|
||||
from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
|
||||
from pipecat.audio.vad.silero import SileroVADAnalyzer
|
||||
from pipecat.audio.vad.vad_analyzer import VADParams
|
||||
from pipecat.frames.frames import (
|
||||
InterruptionFrame,
|
||||
TranscriptionFrame,
|
||||
@@ -24,10 +27,7 @@ from pipecat.pipeline.pipeline import Pipeline
|
||||
from pipecat.pipeline.runner import PipelineRunner
|
||||
from pipecat.pipeline.task import PipelineParams, PipelineTask
|
||||
from pipecat.processors.aggregators.llm_context import LLMContext
|
||||
from pipecat.processors.aggregators.llm_response_universal import (
|
||||
LLMContextAggregatorPair,
|
||||
LLMUserAggregatorParams,
|
||||
)
|
||||
from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
|
||||
from pipecat.runner.livekit import configure
|
||||
from pipecat.services.cartesia.tts import CartesiaTTSService
|
||||
from pipecat.services.deepgram.stt import DeepgramSTTService
|
||||
@@ -50,40 +50,42 @@ async def main():
|
||||
params=LiveKitParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
)
|
||||
|
||||
stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
|
||||
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
settings=OpenAILLMService.Settings(
|
||||
system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
|
||||
),
|
||||
)
|
||||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
|
||||
|
||||
tts = CartesiaTTSService(
|
||||
api_key=os.getenv("CARTESIA_API_KEY"),
|
||||
settings=CartesiaTTSService.Settings(
|
||||
voice="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
),
|
||||
voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
)
|
||||
|
||||
context = LLMContext()
|
||||
user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
|
||||
context,
|
||||
user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
|
||||
)
|
||||
messages = [
|
||||
{
|
||||
"role": "system",
|
||||
"content": "You are a helpful LLM in a WebRTC call. "
|
||||
"Your goal is to demonstrate your capabilities in a succinct way. "
|
||||
"Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. "
|
||||
"Respond to what the user said in a creative and helpful way.",
|
||||
},
|
||||
]
|
||||
|
||||
context = LLMContext(messages)
|
||||
context_aggregator = LLMContextAggregatorPair(context)
|
||||
|
||||
pipeline = Pipeline(
|
||||
[
|
||||
transport.input(), # Transport user input
|
||||
stt,
|
||||
user_aggregator, # User responses
|
||||
context_aggregator.user(), # User responses
|
||||
llm, # LLM
|
||||
tts, # TTS
|
||||
transport.output(), # Transport bot output
|
||||
assistant_aggregator, # Assistant spoken responses
|
||||
context_aggregator.assistant(), # Assistant spoken responses
|
||||
]
|
||||
)
|
||||
|
||||
@@ -1,5 +1,5 @@
|
||||
#
|
||||
# Copyright (c) 2024-2026, Daily
|
||||
# Copyright (c) 2024–2025, Daily
|
||||
#
|
||||
# SPDX-License-Identifier: BSD 2-Clause License
|
||||
#
|
||||
@@ -16,14 +16,14 @@ from pipecat.frames.frames import (
|
||||
Frame,
|
||||
LLMContextFrame,
|
||||
LLMFullResponseStartFrame,
|
||||
OutputImageRawFrame,
|
||||
TextFrame,
|
||||
)
|
||||
from pipecat.pipeline.pipeline import Pipeline
|
||||
from pipecat.pipeline.runner import PipelineRunner
|
||||
from pipecat.pipeline.sync_parallel_pipeline import FrameOrder, SyncParallelPipeline
|
||||
from pipecat.pipeline.sync_parallel_pipeline import SyncParallelPipeline
|
||||
from pipecat.pipeline.task import PipelineTask
|
||||
from pipecat.processors.aggregators.llm_context import LLMContext
|
||||
from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
|
||||
from pipecat.processors.aggregators.sentence import SentenceAggregator
|
||||
from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
|
||||
from pipecat.runner.types import RunnerArguments
|
||||
@@ -31,7 +31,6 @@ from pipecat.runner.utils import create_transport
|
||||
from pipecat.services.cartesia.tts import CartesiaHttpTTSService
|
||||
from pipecat.services.fal.image import FalImageGenService
|
||||
from pipecat.services.openai.llm import OpenAILLMService
|
||||
from pipecat.services.tts_service import TextAggregationMode
|
||||
from pipecat.transports.base_transport import BaseTransport, TransportParams
|
||||
from pipecat.transports.daily.transport import DailyParams
|
||||
|
||||
@@ -46,18 +45,6 @@ class MonthFrame(DataFrame):
|
||||
return f"{self.name}(month: {self.month})"
|
||||
|
||||
|
||||
class MarkImageForPlaybackSync(FrameProcessor):
|
||||
"""Marks output image frames to be synchronized with audio playback."""
|
||||
|
||||
async def process_frame(self, frame: Frame, direction: FrameDirection):
|
||||
await super().process_frame(frame, direction)
|
||||
|
||||
if isinstance(frame, OutputImageRawFrame):
|
||||
frame.sync_with_audio = True
|
||||
|
||||
await self.push_frame(frame, direction)
|
||||
|
||||
|
||||
class MonthPrepender(FrameProcessor):
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
@@ -79,8 +66,9 @@ class MonthPrepender(FrameProcessor):
|
||||
await self.push_frame(frame, direction)
|
||||
|
||||
|
||||
# We use lambdas to defer transport parameter creation until the transport
|
||||
# type is selected at runtime.
|
||||
# We store functions so objects (e.g. SileroVADAnalyzer) don't get
|
||||
# instantiated. The function will be called when the desired transport gets
|
||||
# selected.
|
||||
transport_params = {
|
||||
"daily": lambda: DailyParams(
|
||||
audio_out_enabled=True,
|
||||
@@ -112,19 +100,11 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
|
||||
tts = CartesiaHttpTTSService(
|
||||
api_key=os.getenv("CARTESIA_API_KEY"),
|
||||
settings=CartesiaHttpTTSService.Settings(
|
||||
voice="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
),
|
||||
# No need to aggregate by sentences (the default), as we already know we're getting full sentences
|
||||
# (Otherwise the service will unnecessarily wait for follow-up input to confirm the sentence is complete,
|
||||
# which, sadly, actually breaks the synchronization mechanism)
|
||||
text_aggregation_mode=TextAggregationMode.TOKEN,
|
||||
voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
)
|
||||
|
||||
imagegen = FalImageGenService(
|
||||
settings=FalImageGenService.Settings(
|
||||
image_size="square_hd",
|
||||
),
|
||||
params=FalImageGenService.InputParams(image_size="square_hd"),
|
||||
aiohttp_session=session,
|
||||
key=os.getenv("FAL_KEY"),
|
||||
)
|
||||
@@ -137,26 +117,17 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
# that, each pipeline runs concurrently and `SyncParallelPipeline` will
|
||||
# wait for the input frame to be processed.
|
||||
#
|
||||
# We use `FrameOrder.PIPELINE` so that each synchronized batch of output
|
||||
# frames is pushed in the order the pipelines are listed: image first,
|
||||
# then audio. This ensures the transport receives the image before the
|
||||
# audio frames it should accompany.
|
||||
#
|
||||
# Note that `SyncParallelPipeline` requires the last processor in each
|
||||
# of the pipelines to be synchronous. In this case, we use
|
||||
# `FalImageGenService` and `CartesiaHttpTTSService` which make HTTP
|
||||
# `CartesiaHttpTTSService` and `FalImageGenService` which make HTTP
|
||||
# requests and wait for the response.
|
||||
pipeline = Pipeline(
|
||||
[
|
||||
llm, # LLM
|
||||
sentence_aggregator, # Aggregates LLM output into full sentences
|
||||
SyncParallelPipeline( # Run pipelines in parallel aggregating the result
|
||||
[
|
||||
imagegen, # Generate image
|
||||
MarkImageForPlaybackSync(), # Mark image as needing sync w/audio during playback
|
||||
],
|
||||
[month_prepender, tts], # Create "Month: sentence" and output audio
|
||||
frame_order=FrameOrder.PIPELINE,
|
||||
[imagegen], # Generate image
|
||||
),
|
||||
transport.output(), # Transport output
|
||||
]
|
||||
@@ -179,7 +150,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
]:
|
||||
messages = [
|
||||
{
|
||||
"role": "user",
|
||||
"role": "system",
|
||||
"content": f"Describe a nature photograph suitable for use in a calendar, for the month of {month}. Include only the image description with no preamble. Limit the description to one sentence, please.",
|
||||
}
|
||||
]
|
||||
199
examples/foundational/05a-local-sync-speech-and-image.py
Normal file
199
examples/foundational/05a-local-sync-speech-and-image.py
Normal file
@@ -0,0 +1,199 @@
|
||||
#
|
||||
# Copyright (c) 2024–2025, Daily
|
||||
#
|
||||
# SPDX-License-Identifier: BSD 2-Clause License
|
||||
#
|
||||
|
||||
import asyncio
|
||||
import os
|
||||
import sys
|
||||
import tkinter as tk
|
||||
|
||||
import aiohttp
|
||||
from dotenv import load_dotenv
|
||||
from loguru import logger
|
||||
|
||||
from pipecat.frames.frames import (
|
||||
Frame,
|
||||
LLMContextFrame,
|
||||
OutputAudioRawFrame,
|
||||
TextFrame,
|
||||
TTSAudioRawFrame,
|
||||
URLImageRawFrame,
|
||||
)
|
||||
from pipecat.pipeline.pipeline import Pipeline
|
||||
from pipecat.pipeline.runner import PipelineRunner
|
||||
from pipecat.pipeline.sync_parallel_pipeline import SyncParallelPipeline
|
||||
from pipecat.pipeline.task import PipelineTask
|
||||
from pipecat.processors.aggregators.llm_context import LLMContext
|
||||
from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
|
||||
from pipecat.processors.aggregators.sentence import SentenceAggregator
|
||||
from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
|
||||
from pipecat.services.cartesia.tts import CartesiaHttpTTSService
|
||||
from pipecat.services.fal.image import FalImageGenService
|
||||
from pipecat.services.openai.llm import OpenAILLMService
|
||||
from pipecat.transports.local.tk import TkLocalTransport, TkTransportParams
|
||||
|
||||
load_dotenv(override=True)
|
||||
|
||||
logger.remove(0)
|
||||
logger.add(sys.stderr, level="DEBUG")
|
||||
|
||||
|
||||
async def main():
|
||||
async with aiohttp.ClientSession() as session:
|
||||
tk_root = tk.Tk()
|
||||
tk_root.title("Calendar")
|
||||
|
||||
runner = PipelineRunner()
|
||||
|
||||
async def get_month_data(month):
|
||||
messages = [
|
||||
{
|
||||
"role": "system",
|
||||
"content": f"Describe a nature photograph suitable for use in a calendar, for the month of {month}. Include only the image description with no preamble. Limit the description to one sentence, please.",
|
||||
}
|
||||
]
|
||||
|
||||
class ImageDescription(FrameProcessor):
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
self.text = ""
|
||||
|
||||
async def process_frame(self, frame: Frame, direction: FrameDirection):
|
||||
await super().process_frame(frame, direction)
|
||||
|
||||
if isinstance(frame, TextFrame):
|
||||
self.text = frame.text
|
||||
await self.push_frame(frame, direction)
|
||||
|
||||
class AudioGrabber(FrameProcessor):
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
self.audio = bytearray()
|
||||
self.frame = None
|
||||
|
||||
async def process_frame(self, frame: Frame, direction: FrameDirection):
|
||||
await super().process_frame(frame, direction)
|
||||
|
||||
if isinstance(frame, TTSAudioRawFrame):
|
||||
self.audio.extend(frame.audio)
|
||||
self.frame = OutputAudioRawFrame(
|
||||
bytes(self.audio), frame.sample_rate, frame.num_channels
|
||||
)
|
||||
await self.push_frame(frame, direction)
|
||||
|
||||
class ImageGrabber(FrameProcessor):
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
self.frame = None
|
||||
|
||||
async def process_frame(self, frame: Frame, direction: FrameDirection):
|
||||
await super().process_frame(frame, direction)
|
||||
|
||||
if isinstance(frame, URLImageRawFrame):
|
||||
self.frame = frame
|
||||
await self.push_frame(frame, direction)
|
||||
|
||||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
|
||||
|
||||
tts = CartesiaHttpTTSService(
|
||||
api_key=os.getenv("CARTESIA_API_KEY"),
|
||||
voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
)
|
||||
|
||||
imagegen = FalImageGenService(
|
||||
params=FalImageGenService.InputParams(image_size="square_hd"),
|
||||
aiohttp_session=session,
|
||||
key=os.getenv("FAL_KEY"),
|
||||
)
|
||||
|
||||
sentence_aggregator = SentenceAggregator()
|
||||
|
||||
description = ImageDescription()
|
||||
|
||||
audio_grabber = AudioGrabber()
|
||||
|
||||
image_grabber = ImageGrabber()
|
||||
|
||||
# With `SyncParallelPipeline` we synchronize audio and images by
|
||||
# pushing them basically in order (e.g. I1 A1 A1 A1 I2 A2 A2 A2 A2
|
||||
# I3 A3). To do that, each pipeline runs concurrently and
|
||||
# `SyncParallelPipeline` will wait for the input frame to be
|
||||
# processed.
|
||||
#
|
||||
# Note that `SyncParallelPipeline` requires the last processor in
|
||||
# each of the pipelines to be synchronous. In this case, we use
|
||||
# `CartesiaHttpTTSService` and `FalImageGenService` which make HTTP
|
||||
# requests and wait for the response.
|
||||
pipeline = Pipeline(
|
||||
[
|
||||
llm, # LLM
|
||||
sentence_aggregator, # Aggregates LLM output into full sentences
|
||||
description, # Store sentence
|
||||
SyncParallelPipeline(
|
||||
[tts, audio_grabber], # Generate and store audio for the given sentence
|
||||
[imagegen, image_grabber], # Generate and storeimage for the given sentence
|
||||
),
|
||||
]
|
||||
)
|
||||
|
||||
task = PipelineTask(pipeline)
|
||||
await task.queue_frame(LLMContextFrame(LLMContext(messages)))
|
||||
await task.stop_when_done()
|
||||
|
||||
await runner.run(task)
|
||||
|
||||
return {
|
||||
"month": month,
|
||||
"text": description.text,
|
||||
"image": image_grabber.frame,
|
||||
"audio": audio_grabber.frame,
|
||||
}
|
||||
|
||||
transport = TkLocalTransport(
|
||||
tk_root,
|
||||
TkTransportParams(
|
||||
audio_out_enabled=True,
|
||||
video_out_enabled=True,
|
||||
video_out_width=1024,
|
||||
video_out_height=1024,
|
||||
),
|
||||
)
|
||||
|
||||
pipeline = Pipeline([transport.output()])
|
||||
|
||||
task = PipelineTask(pipeline)
|
||||
|
||||
# We only specify a few months as we create tasks all at once and we
|
||||
# might get rate limited otherwise.
|
||||
months: list[str] = [
|
||||
"January",
|
||||
"February",
|
||||
]
|
||||
|
||||
# We create one task per month. This will be executed concurrently.
|
||||
month_tasks = [asyncio.create_task(get_month_data(month)) for month in months]
|
||||
|
||||
# Now we wait for each month task in the order they're completed. The
|
||||
# benefit is we'll have as little delay as possible before the first
|
||||
# month, and likely no delay between months, but the months won't
|
||||
# display in order.
|
||||
async def show_images(month_tasks):
|
||||
for month_data_task in asyncio.as_completed(month_tasks):
|
||||
data = await month_data_task
|
||||
await task.queue_frames([data["image"], data["audio"]])
|
||||
|
||||
await runner.stop_when_done()
|
||||
|
||||
async def run_tk():
|
||||
while not task.has_finished():
|
||||
tk_root.update()
|
||||
tk_root.update_idletasks()
|
||||
await asyncio.sleep(0.1)
|
||||
|
||||
await asyncio.gather(runner.run(task), show_images(month_tasks), run_tk())
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(main())
|
||||
157
examples/foundational/06-listen-and-respond.py
Normal file
157
examples/foundational/06-listen-and-respond.py
Normal file
@@ -0,0 +1,157 @@
|
||||
#
|
||||
# Copyright (c) 2024–2025, Daily
|
||||
#
|
||||
# SPDX-License-Identifier: BSD 2-Clause License
|
||||
#
|
||||
|
||||
import os
|
||||
|
||||
from dotenv import load_dotenv
|
||||
from loguru import logger
|
||||
|
||||
from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
|
||||
from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
|
||||
from pipecat.audio.vad.silero import SileroVADAnalyzer
|
||||
from pipecat.audio.vad.vad_analyzer import VADParams
|
||||
from pipecat.frames.frames import Frame, LLMRunFrame, MetricsFrame
|
||||
from pipecat.metrics.metrics import (
|
||||
LLMUsageMetricsData,
|
||||
ProcessingMetricsData,
|
||||
TTFBMetricsData,
|
||||
TTSUsageMetricsData,
|
||||
)
|
||||
from pipecat.pipeline.pipeline import Pipeline
|
||||
from pipecat.pipeline.runner import PipelineRunner
|
||||
from pipecat.pipeline.task import PipelineParams, PipelineTask
|
||||
from pipecat.processors.aggregators.llm_context import LLMContext
|
||||
from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
|
||||
from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
|
||||
from pipecat.runner.types import RunnerArguments
|
||||
from pipecat.runner.utils import create_transport
|
||||
from pipecat.services.cartesia.tts import CartesiaTTSService
|
||||
from pipecat.services.deepgram.stt import DeepgramSTTService
|
||||
from pipecat.services.openai.llm import OpenAILLMService
|
||||
from pipecat.transports.base_transport import BaseTransport, TransportParams
|
||||
from pipecat.transports.daily.transport import DailyParams
|
||||
from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
|
||||
|
||||
load_dotenv(override=True)
|
||||
|
||||
|
||||
class MetricsLogger(FrameProcessor):
|
||||
async def process_frame(self, frame: Frame, direction: FrameDirection):
|
||||
await super().process_frame(frame, direction)
|
||||
|
||||
if isinstance(frame, MetricsFrame):
|
||||
for d in frame.data:
|
||||
if isinstance(d, TTFBMetricsData):
|
||||
print(f"!!! MetricsFrame: {frame}, ttfb: {d.value}")
|
||||
elif isinstance(d, ProcessingMetricsData):
|
||||
print(f"!!! MetricsFrame: {frame}, processing: {d.value}")
|
||||
elif isinstance(d, LLMUsageMetricsData):
|
||||
tokens = d.value
|
||||
print(
|
||||
f"!!! MetricsFrame: {frame}, tokens: {tokens.prompt_tokens}, characters: {tokens.completion_tokens}"
|
||||
)
|
||||
elif isinstance(d, TTSUsageMetricsData):
|
||||
print(f"!!! MetricsFrame: {frame}, characters: {d.value}")
|
||||
await self.push_frame(frame, direction)
|
||||
|
||||
|
||||
# We store functions so objects (e.g. SileroVADAnalyzer) don't get
|
||||
# instantiated. The function will be called when the desired transport gets
|
||||
# selected.
|
||||
transport_params = {
|
||||
"daily": lambda: DailyParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
"twilio": lambda: FastAPIWebsocketParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
"webrtc": lambda: TransportParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
}
|
||||
|
||||
|
||||
async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
logger.info(f"Starting bot")
|
||||
|
||||
stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
|
||||
|
||||
tts = CartesiaTTSService(
|
||||
api_key=os.getenv("CARTESIA_API_KEY"),
|
||||
voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
)
|
||||
|
||||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
|
||||
|
||||
ml = MetricsLogger()
|
||||
|
||||
messages = [
|
||||
{
|
||||
"role": "system",
|
||||
"content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
|
||||
},
|
||||
]
|
||||
|
||||
context = LLMContext(messages)
|
||||
context_aggregator = LLMContextAggregatorPair(context)
|
||||
|
||||
pipeline = Pipeline(
|
||||
[
|
||||
transport.input(),
|
||||
stt,
|
||||
context_aggregator.user(),
|
||||
llm,
|
||||
tts,
|
||||
ml,
|
||||
transport.output(),
|
||||
context_aggregator.assistant(),
|
||||
]
|
||||
)
|
||||
|
||||
task = PipelineTask(
|
||||
pipeline,
|
||||
params=PipelineParams(
|
||||
enable_metrics=True,
|
||||
enable_usage_metrics=True,
|
||||
),
|
||||
idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
|
||||
)
|
||||
|
||||
@transport.event_handler("on_client_connected")
|
||||
async def on_client_connected(transport, client):
|
||||
logger.info(f"Client connected")
|
||||
# Kick off the conversation.
|
||||
messages.append({"role": "system", "content": "Please introduce yourself to the user."})
|
||||
await task.queue_frames([LLMRunFrame()])
|
||||
|
||||
@transport.event_handler("on_client_disconnected")
|
||||
async def on_client_disconnected(transport, client):
|
||||
logger.info(f"Client disconnected")
|
||||
await task.cancel()
|
||||
|
||||
runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
|
||||
await runner.run(task)
|
||||
|
||||
|
||||
async def bot(runner_args: RunnerArguments):
|
||||
"""Main bot entry point compatible with Pipecat Cloud."""
|
||||
transport = await create_transport(runner_args, transport_params)
|
||||
await run_bot(transport, runner_args)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
from pipecat.runner.run import main
|
||||
|
||||
main()
|
||||
@@ -1,5 +1,5 @@
|
||||
#
|
||||
# Copyright (c) 2024-2026, Daily
|
||||
# Copyright (c) 2024–2025, Daily
|
||||
#
|
||||
# SPDX-License-Identifier: BSD 2-Clause License
|
||||
#
|
||||
@@ -10,7 +10,10 @@ from dotenv import load_dotenv
|
||||
from loguru import logger
|
||||
from PIL import Image
|
||||
|
||||
from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
|
||||
from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
|
||||
from pipecat.audio.vad.silero import SileroVADAnalyzer
|
||||
from pipecat.audio.vad.vad_analyzer import VADParams
|
||||
from pipecat.frames.frames import (
|
||||
BotStartedSpeakingFrame,
|
||||
BotStoppedSpeakingFrame,
|
||||
@@ -22,10 +25,7 @@ from pipecat.pipeline.pipeline import Pipeline
|
||||
from pipecat.pipeline.runner import PipelineRunner
|
||||
from pipecat.pipeline.task import PipelineParams, PipelineTask
|
||||
from pipecat.processors.aggregators.llm_context import LLMContext
|
||||
from pipecat.processors.aggregators.llm_response_universal import (
|
||||
LLMContextAggregatorPair,
|
||||
LLMUserAggregatorParams,
|
||||
)
|
||||
from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
|
||||
from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
|
||||
from pipecat.runner.types import RunnerArguments
|
||||
from pipecat.runner.utils import create_transport
|
||||
@@ -73,8 +73,9 @@ class ImageSyncAggregator(FrameProcessor):
|
||||
await self.push_frame(frame, direction)
|
||||
|
||||
|
||||
# We use lambdas to defer transport parameter creation until the transport
|
||||
# type is selected at runtime.
|
||||
# We store functions so objects (e.g. SileroVADAnalyzer) don't get
|
||||
# instantiated. The function will be called when the desired transport gets
|
||||
# selected.
|
||||
transport_params = {
|
||||
"daily": lambda: DailyParams(
|
||||
audio_in_enabled=True,
|
||||
@@ -82,6 +83,8 @@ transport_params = {
|
||||
video_out_enabled=True,
|
||||
video_out_width=1024,
|
||||
video_out_height=1024,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
"webrtc": lambda: TransportParams(
|
||||
audio_in_enabled=True,
|
||||
@@ -89,6 +92,8 @@ transport_params = {
|
||||
video_out_enabled=True,
|
||||
video_out_width=1024,
|
||||
video_out_height=1024,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
}
|
||||
|
||||
@@ -100,39 +105,36 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
|
||||
tts = CartesiaTTSService(
|
||||
api_key=os.getenv("CARTESIA_API_KEY"),
|
||||
settings=CartesiaTTSService.Settings(
|
||||
voice="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
),
|
||||
voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
)
|
||||
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
settings=OpenAILLMService.Settings(
|
||||
system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
|
||||
),
|
||||
)
|
||||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
|
||||
|
||||
context = LLMContext()
|
||||
user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
|
||||
context,
|
||||
user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
|
||||
)
|
||||
messages = [
|
||||
{
|
||||
"role": "system",
|
||||
"content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
|
||||
},
|
||||
]
|
||||
|
||||
context = LLMContext(messages)
|
||||
context_aggregator = LLMContextAggregatorPair(context)
|
||||
|
||||
image_sync_aggregator = ImageSyncAggregator(
|
||||
os.path.join(os.path.dirname(__file__), "..", "assets", "speaking.png"),
|
||||
os.path.join(os.path.dirname(__file__), "..", "assets", "waiting.png"),
|
||||
os.path.join(os.path.dirname(__file__), "assets", "speaking.png"),
|
||||
os.path.join(os.path.dirname(__file__), "assets", "waiting.png"),
|
||||
)
|
||||
|
||||
pipeline = Pipeline(
|
||||
[
|
||||
transport.input(),
|
||||
stt,
|
||||
user_aggregator,
|
||||
context_aggregator.user(),
|
||||
llm,
|
||||
tts,
|
||||
image_sync_aggregator,
|
||||
transport.output(),
|
||||
assistant_aggregator,
|
||||
context_aggregator.assistant(),
|
||||
]
|
||||
)
|
||||
|
||||
@@ -1,5 +1,5 @@
|
||||
#
|
||||
# Copyright (c) 2024-2026, Daily
|
||||
# Copyright (c) 2024–2025, Daily
|
||||
#
|
||||
# SPDX-License-Identifier: BSD 2-Clause License
|
||||
#
|
||||
@@ -9,20 +9,20 @@ import os
|
||||
from dotenv import load_dotenv
|
||||
from loguru import logger
|
||||
|
||||
from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
|
||||
from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
|
||||
from pipecat.audio.vad.silero import SileroVADAnalyzer
|
||||
from pipecat.audio.vad.vad_analyzer import VADParams
|
||||
from pipecat.frames.frames import LLMRunFrame
|
||||
from pipecat.pipeline.pipeline import Pipeline
|
||||
from pipecat.pipeline.runner import PipelineRunner
|
||||
from pipecat.pipeline.task import PipelineParams, PipelineTask
|
||||
from pipecat.processors.aggregators.llm_context import LLMContext
|
||||
from pipecat.processors.aggregators.llm_response_universal import (
|
||||
LLMContextAggregatorPair,
|
||||
LLMUserAggregatorParams,
|
||||
)
|
||||
from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
|
||||
from pipecat.runner.types import RunnerArguments
|
||||
from pipecat.runner.utils import create_transport
|
||||
from pipecat.services.cartesia.stt import CartesiaSTTService
|
||||
from pipecat.services.cartesia.tts import CartesiaTTSService
|
||||
from pipecat.services.cartesia.tts import CartesiaHttpTTSService
|
||||
from pipecat.services.openai.llm import OpenAILLMService
|
||||
from pipecat.transports.base_transport import BaseTransport, TransportParams
|
||||
from pipecat.transports.daily.transport import DailyParams
|
||||
@@ -30,20 +30,28 @@ from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
|
||||
|
||||
load_dotenv(override=True)
|
||||
|
||||
# We use lambdas to defer transport parameter creation until the transport
|
||||
# type is selected at runtime.
|
||||
|
||||
# We store functions so objects (e.g. SileroVADAnalyzer) don't get
|
||||
# instantiated. The function will be called when the desired transport gets
|
||||
# selected.
|
||||
transport_params = {
|
||||
"daily": lambda: DailyParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
"twilio": lambda: FastAPIWebsocketParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
"webrtc": lambda: TransportParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
}
|
||||
|
||||
@@ -53,35 +61,32 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
|
||||
stt = CartesiaSTTService(api_key=os.getenv("CARTESIA_API_KEY"))
|
||||
|
||||
tts = CartesiaTTSService(
|
||||
tts = CartesiaHttpTTSService(
|
||||
api_key=os.getenv("CARTESIA_API_KEY"),
|
||||
settings=CartesiaTTSService.Settings(
|
||||
voice="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
),
|
||||
voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
)
|
||||
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
settings=OpenAILLMService.Settings(
|
||||
system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
|
||||
),
|
||||
)
|
||||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
|
||||
|
||||
context = LLMContext()
|
||||
user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
|
||||
context,
|
||||
user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
|
||||
)
|
||||
messages = [
|
||||
{
|
||||
"role": "system",
|
||||
"content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
|
||||
},
|
||||
]
|
||||
|
||||
context = LLMContext(messages)
|
||||
context_aggregator = LLMContextAggregatorPair(context)
|
||||
|
||||
pipeline = Pipeline(
|
||||
[
|
||||
transport.input(), # Transport user input
|
||||
stt,
|
||||
user_aggregator, # User responses
|
||||
context_aggregator.user(), # User responses
|
||||
llm, # LLM
|
||||
tts, # TTS
|
||||
transport.output(), # Transport bot output
|
||||
assistant_aggregator, # Assistant spoken responses
|
||||
context_aggregator.assistant(), # Assistant spoken responses
|
||||
]
|
||||
)
|
||||
|
||||
@@ -98,9 +103,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
async def on_client_connected(transport, client):
|
||||
logger.info(f"Client connected")
|
||||
# Kick off the conversation.
|
||||
context.add_message(
|
||||
{"role": "developer", "content": "Please introduce yourself to the user."}
|
||||
)
|
||||
messages.append({"role": "system", "content": "Please introduce yourself to the user."})
|
||||
await task.queue_frames([LLMRunFrame()])
|
||||
|
||||
@transport.event_handler("on_client_disconnected")
|
||||
@@ -1,5 +1,5 @@
|
||||
#
|
||||
# Copyright (c) 2024-2026, Daily
|
||||
# Copyright (c) 2024–2025, Daily
|
||||
#
|
||||
# SPDX-License-Identifier: BSD 2-Clause License
|
||||
#
|
||||
@@ -9,17 +9,16 @@ import os
|
||||
from dotenv import load_dotenv
|
||||
from loguru import logger
|
||||
|
||||
from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
|
||||
from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
|
||||
from pipecat.audio.vad.silero import SileroVADAnalyzer
|
||||
from pipecat.audio.vad.vad_analyzer import VADParams
|
||||
from pipecat.frames.frames import LLMRunFrame
|
||||
from pipecat.observers.loggers.transcription_log_observer import TranscriptionLogObserver
|
||||
from pipecat.pipeline.pipeline import Pipeline
|
||||
from pipecat.pipeline.runner import PipelineRunner
|
||||
from pipecat.pipeline.task import PipelineParams, PipelineTask
|
||||
from pipecat.processors.aggregators.llm_context import LLMContext
|
||||
from pipecat.processors.aggregators.llm_response_universal import (
|
||||
LLMContextAggregatorPair,
|
||||
LLMUserAggregatorParams,
|
||||
)
|
||||
from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
|
||||
from pipecat.runner.types import RunnerArguments
|
||||
from pipecat.runner.utils import create_transport
|
||||
from pipecat.services.cartesia.tts import CartesiaTTSService
|
||||
@@ -28,26 +27,30 @@ from pipecat.services.openai.llm import OpenAILLMService
|
||||
from pipecat.transports.base_transport import BaseTransport, TransportParams
|
||||
from pipecat.transports.daily.transport import DailyParams
|
||||
from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
|
||||
from pipecat.turns.user_start import MinWordsUserTurnStartStrategy
|
||||
from pipecat.turns.user_turn_strategies import UserTurnStrategies
|
||||
|
||||
load_dotenv(override=True)
|
||||
|
||||
|
||||
# We use lambdas to defer transport parameter creation until the transport
|
||||
# type is selected at runtime.
|
||||
# We store functions so objects (e.g. SileroVADAnalyzer) don't get
|
||||
# instantiated. The function will be called when the desired transport gets
|
||||
# selected.
|
||||
transport_params = {
|
||||
"daily": lambda: DailyParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
"twilio": lambda: FastAPIWebsocketParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
"webrtc": lambda: TransportParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
}
|
||||
|
||||
@@ -59,38 +62,30 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
|
||||
tts = CartesiaTTSService(
|
||||
api_key=os.getenv("CARTESIA_API_KEY"),
|
||||
settings=CartesiaTTSService.Settings(
|
||||
voice="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
),
|
||||
voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
)
|
||||
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
settings=OpenAILLMService.Settings(
|
||||
system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
|
||||
),
|
||||
)
|
||||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
|
||||
|
||||
context = LLMContext()
|
||||
user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
|
||||
context,
|
||||
user_params=LLMUserAggregatorParams(
|
||||
user_turn_strategies=UserTurnStrategies(
|
||||
start=[MinWordsUserTurnStartStrategy(min_words=3)],
|
||||
),
|
||||
vad_analyzer=SileroVADAnalyzer(),
|
||||
),
|
||||
)
|
||||
messages = [
|
||||
{
|
||||
"role": "system",
|
||||
"content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
|
||||
},
|
||||
]
|
||||
|
||||
context = LLMContext(messages)
|
||||
context_aggregator = LLMContextAggregatorPair(context)
|
||||
|
||||
pipeline = Pipeline(
|
||||
[
|
||||
transport.input(), # Transport user input
|
||||
stt,
|
||||
user_aggregator, # User responses
|
||||
context_aggregator.user(), # User responses
|
||||
llm, # LLM
|
||||
tts, # TTS
|
||||
transport.output(), # Transport bot output
|
||||
assistant_aggregator, # Assistant spoken responses
|
||||
context_aggregator.assistant(), # Assistant spoken responses
|
||||
]
|
||||
)
|
||||
|
||||
@@ -101,16 +96,13 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
enable_usage_metrics=True,
|
||||
),
|
||||
idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
|
||||
observers=[TranscriptionLogObserver()],
|
||||
)
|
||||
|
||||
@transport.event_handler("on_client_connected")
|
||||
async def on_client_connected(transport, client):
|
||||
logger.info(f"Client connected")
|
||||
# Kick off the conversation.
|
||||
context.add_message(
|
||||
{"role": "developer", "content": "Please introduce yourself to the user."}
|
||||
)
|
||||
messages.append({"role": "system", "content": "Please introduce yourself to the user."})
|
||||
await task.queue_frames([LLMRunFrame()])
|
||||
|
||||
@transport.event_handler("on_client_disconnected")
|
||||
@@ -1,5 +1,5 @@
|
||||
#
|
||||
# Copyright (c) 2024-2026, Daily
|
||||
# Copyright (c) 2024–2025, Daily
|
||||
#
|
||||
# SPDX-License-Identifier: BSD 2-Clause License
|
||||
#
|
||||
@@ -15,12 +15,13 @@ from pipecat.pipeline.pipeline import Pipeline
|
||||
from pipecat.pipeline.runner import PipelineRunner
|
||||
from pipecat.pipeline.task import PipelineParams, PipelineTask
|
||||
from pipecat.processors.aggregators.llm_context import LLMContext
|
||||
from pipecat.processors.aggregators.llm_response_universal import (
|
||||
LLMContextAggregatorPair,
|
||||
from pipecat.processors.aggregators.llm_response import (
|
||||
LLMUserAggregatorParams,
|
||||
)
|
||||
from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
|
||||
from pipecat.runner.types import RunnerArguments
|
||||
from pipecat.runner.utils import create_transport
|
||||
from pipecat.services.openai.base_llm import BaseOpenAILLMService
|
||||
from pipecat.services.openai.llm import OpenAILLMService
|
||||
from pipecat.services.speechmatics.stt import SpeechmaticsSTTService
|
||||
from pipecat.services.speechmatics.tts import SpeechmaticsTTSService
|
||||
@@ -28,12 +29,12 @@ from pipecat.transcriptions.language import Language
|
||||
from pipecat.transports.base_transport import BaseTransport, TransportParams
|
||||
from pipecat.transports.daily.transport import DailyParams
|
||||
from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
|
||||
from pipecat.turns.user_turn_strategies import ExternalUserTurnStrategies
|
||||
|
||||
load_dotenv(override=True)
|
||||
|
||||
# We use lambdas to defer transport parameter creation until the transport
|
||||
# type is selected at runtime.
|
||||
# We store functions so objects (e.g. SileroVADAnalyzer) don't get
|
||||
# instantiated. The function will be called when the desired transport gets
|
||||
# selected.
|
||||
transport_params = {
|
||||
"daily": lambda: DailyParams(
|
||||
audio_in_enabled=True,
|
||||
@@ -75,7 +76,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
|
||||
4. Text-to-Speech (TTS)
|
||||
- Low latency streaming audio synthesis
|
||||
- Multiple voice options available including `sarah`, `theo`, `megan` and `jack`
|
||||
- Multiple voice options available including `sarah`, `theo`, and `megan`
|
||||
|
||||
5. Configuration Options
|
||||
- `operating_point` parameter defaults to `ENHANCED` for optimal accuracy
|
||||
@@ -92,10 +93,12 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
async with aiohttp.ClientSession() as session:
|
||||
stt = SpeechmaticsSTTService(
|
||||
api_key=os.getenv("SPEECHMATICS_API_KEY"),
|
||||
settings=SpeechmaticsSTTService.Settings(
|
||||
params=SpeechmaticsSTTService.InputParams(
|
||||
language=Language.EN,
|
||||
turn_detection_mode=SpeechmaticsSTTService.TurnDetectionMode.ADAPTIVE,
|
||||
# focus_speakers=["S1"],
|
||||
enable_vad=True,
|
||||
enable_diarization=True,
|
||||
focus_speakers=["S1"],
|
||||
end_of_utterance_silence_trigger=0.5,
|
||||
speaker_active_format="<{speaker_id}>{text}</{speaker_id}>",
|
||||
speaker_passive_format="<PASSIVE><{speaker_id}>{text}</{speaker_id}></PASSIVE>",
|
||||
),
|
||||
@@ -103,35 +106,46 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
|
||||
tts = SpeechmaticsTTSService(
|
||||
api_key=os.getenv("SPEECHMATICS_API_KEY"),
|
||||
settings=SpeechmaticsTTSService.Settings(
|
||||
voice="sarah",
|
||||
),
|
||||
voice_id="sarah",
|
||||
aiohttp_session=session,
|
||||
)
|
||||
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
settings=OpenAILLMService.Settings(
|
||||
temperature=0.75,
|
||||
system_instruction="You are a helpful British assistant called Sarah in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Always include punctuation in your responses. Give very short replies - do not give longer replies unless strictly necessary. Respond to what the user said in a concise, funny, creative and helpful way. Use `<Sn/>` tags to identify different speakers - do not use tags in your replies. Do not respond to speakers within `<PASSIVE/>` tags unless explicitly asked to.",
|
||||
),
|
||||
params=BaseOpenAILLMService.InputParams(temperature=0.75),
|
||||
)
|
||||
|
||||
context = LLMContext()
|
||||
user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
|
||||
messages = [
|
||||
{
|
||||
"role": "system",
|
||||
"content": (
|
||||
"You are a helpful British assistant called Sarah. "
|
||||
"Your goal is to demonstrate your capabilities in a succinct way. "
|
||||
"Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. "
|
||||
"Always include punctuation in your responses. "
|
||||
"Give very short replies - do not give longer replies unless strictly necessary. "
|
||||
"Respond to what the user said in a concise, funny, creative and helpful way. "
|
||||
"Use `<Sn/>` tags to identify different speakers - do not use tags in your replies. "
|
||||
"Do not respond to speakers within `<PASSIVE/>` tags unless explicitly asked to. "
|
||||
),
|
||||
},
|
||||
]
|
||||
|
||||
context = LLMContext(messages)
|
||||
context_aggregator = LLMContextAggregatorPair(
|
||||
context,
|
||||
user_params=LLMUserAggregatorParams(user_turn_strategies=ExternalUserTurnStrategies()),
|
||||
user_params=LLMUserAggregatorParams(aggregation_timeout=0.005),
|
||||
)
|
||||
|
||||
pipeline = Pipeline(
|
||||
[
|
||||
transport.input(), # Transport user input
|
||||
stt,
|
||||
user_aggregator, # User responses
|
||||
context_aggregator.user(), # User responses
|
||||
llm, # LLM
|
||||
tts, # TTS
|
||||
transport.output(), # Transport bot output
|
||||
assistant_aggregator, # Assistant spoken responses
|
||||
context_aggregator.assistant(), # Assistant spoken responses
|
||||
]
|
||||
)
|
||||
|
||||
@@ -148,7 +162,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
async def on_client_connected(transport, client):
|
||||
logger.info(f"Client connected")
|
||||
# Kick off the conversation.
|
||||
context.add_message({"role": "developer", "content": "Say a short hello to the user."})
|
||||
messages.append({"role": "system", "content": "Say a short hello to the user."})
|
||||
await task.queue_frames([LLMRunFrame()])
|
||||
|
||||
@transport.event_handler("on_client_disconnected")
|
||||
@@ -1,5 +1,5 @@
|
||||
#
|
||||
# Copyright (c) 2024-2026, Daily
|
||||
# Copyright (c) 2024–2025, Daily
|
||||
#
|
||||
# SPDX-License-Identifier: BSD 2-Clause License
|
||||
#
|
||||
@@ -10,18 +10,22 @@ import aiohttp
|
||||
from dotenv import load_dotenv
|
||||
from loguru import logger
|
||||
|
||||
from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
|
||||
from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
|
||||
from pipecat.audio.vad.silero import SileroVADAnalyzer
|
||||
from pipecat.audio.vad.vad_analyzer import VADParams
|
||||
from pipecat.frames.frames import LLMRunFrame
|
||||
from pipecat.pipeline.pipeline import Pipeline
|
||||
from pipecat.pipeline.runner import PipelineRunner
|
||||
from pipecat.pipeline.task import PipelineParams, PipelineTask
|
||||
from pipecat.processors.aggregators.llm_context import LLMContext
|
||||
from pipecat.processors.aggregators.llm_response_universal import (
|
||||
LLMContextAggregatorPair,
|
||||
from pipecat.processors.aggregators.llm_response import (
|
||||
LLMUserAggregatorParams,
|
||||
)
|
||||
from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
|
||||
from pipecat.runner.types import RunnerArguments
|
||||
from pipecat.runner.utils import create_transport
|
||||
from pipecat.services.openai.base_llm import BaseOpenAILLMService
|
||||
from pipecat.services.openai.llm import OpenAILLMService
|
||||
from pipecat.services.speechmatics.stt import SpeechmaticsSTTService
|
||||
from pipecat.services.speechmatics.tts import SpeechmaticsTTSService
|
||||
@@ -32,20 +36,27 @@ from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
|
||||
|
||||
load_dotenv(override=True)
|
||||
|
||||
# We use lambdas to defer transport parameter creation until the transport
|
||||
# type is selected at runtime.
|
||||
# We store functions so objects (e.g. SileroVADAnalyzer) don't get
|
||||
# instantiated. The function will be called when the desired transport gets
|
||||
# selected.
|
||||
transport_params = {
|
||||
"daily": lambda: DailyParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
"twilio": lambda: FastAPIWebsocketParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
"webrtc": lambda: TransportParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
}
|
||||
|
||||
@@ -64,7 +75,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
|
||||
TTS Features:
|
||||
- Low latency streaming audio synthesis
|
||||
- Multiple voice options available including `sarah`, `theo`, `megan` and `jack`
|
||||
- Multiple voice options available including `sarah`, `theo`, and `megan`
|
||||
|
||||
For more information:
|
||||
- STT: https://docs.speechmatics.com/rt-api-ref
|
||||
@@ -75,43 +86,55 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
async with aiohttp.ClientSession() as session:
|
||||
stt = SpeechmaticsSTTService(
|
||||
api_key=os.getenv("SPEECHMATICS_API_KEY"),
|
||||
settings=SpeechmaticsSTTService.Settings(
|
||||
params=SpeechmaticsSTTService.InputParams(
|
||||
language=Language.EN,
|
||||
enable_diarization=True,
|
||||
end_of_utterance_silence_trigger=0.5,
|
||||
speaker_active_format="<{speaker_id}>{text}</{speaker_id}>",
|
||||
),
|
||||
)
|
||||
|
||||
tts = SpeechmaticsTTSService(
|
||||
api_key=os.getenv("SPEECHMATICS_API_KEY"),
|
||||
settings=SpeechmaticsTTSService.Settings(
|
||||
voice="sarah",
|
||||
),
|
||||
voice_id="sarah",
|
||||
aiohttp_session=session,
|
||||
)
|
||||
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
settings=OpenAILLMService.Settings(
|
||||
temperature=0.75,
|
||||
system_instruction="You are a helpful British assistant called Sarah in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Always include punctuation in your responses. Give very short replies - do not give longer replies unless strictly necessary. Respond to what the user said in a concise, funny, creative and helpful way. Use `<Sn/>` tags to identify different speakers - do not use tags in your replies. Do not respond to speakers within `<PASSIVE/>` tags unless explicitly asked to.",
|
||||
),
|
||||
params=BaseOpenAILLMService.InputParams(temperature=0.75),
|
||||
)
|
||||
|
||||
context = LLMContext()
|
||||
user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
|
||||
messages = [
|
||||
{
|
||||
"role": "system",
|
||||
"content": (
|
||||
"You are a helpful British assistant called Sarah. "
|
||||
"Your goal is to demonstrate your capabilities in a succinct way. "
|
||||
"Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. "
|
||||
"Always include punctuation in your responses. "
|
||||
"Give very short replies - do not give longer replies unless strictly necessary. "
|
||||
"Respond to what the user said in a concise, funny, creative and helpful way. "
|
||||
"Use `<Sn/>` tags to identify different speakers - do not use tags in your replies."
|
||||
),
|
||||
},
|
||||
]
|
||||
|
||||
context = LLMContext(messages)
|
||||
context_aggregator = LLMContextAggregatorPair(
|
||||
context,
|
||||
user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
|
||||
user_params=LLMUserAggregatorParams(aggregation_timeout=0.005),
|
||||
)
|
||||
|
||||
pipeline = Pipeline(
|
||||
[
|
||||
transport.input(), # Transport user input
|
||||
stt, # STT
|
||||
user_aggregator, # User responses
|
||||
context_aggregator.user(), # User responses
|
||||
llm, # LLM
|
||||
tts, # TTS
|
||||
transport.output(), # Transport bot output
|
||||
assistant_aggregator, # Assistant spoken responses
|
||||
context_aggregator.assistant(), # Assistant spoken responses
|
||||
]
|
||||
)
|
||||
|
||||
@@ -128,7 +151,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
async def on_client_connected(transport, client):
|
||||
logger.info(f"Client connected")
|
||||
# Kick off the conversation.
|
||||
context.add_message({"role": "developer", "content": "Say a short hello to the user."})
|
||||
messages.append({"role": "system", "content": "Say a short hello to the user."})
|
||||
await task.queue_frames([LLMRunFrame()])
|
||||
|
||||
@transport.event_handler("on_client_disconnected")
|
||||
@@ -1,5 +1,5 @@
|
||||
#
|
||||
# Copyright (c) 2024-2026, Daily
|
||||
# Copyright (c) 2024–2025, Daily
|
||||
#
|
||||
# SPDX-License-Identifier: BSD 2-Clause License
|
||||
#
|
||||
@@ -10,22 +10,21 @@ import os
|
||||
from dotenv import load_dotenv
|
||||
from loguru import logger
|
||||
|
||||
from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
|
||||
from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
|
||||
from pipecat.audio.vad.silero import SileroVADAnalyzer
|
||||
from pipecat.audio.vad.vad_analyzer import VADParams
|
||||
from pipecat.frames.frames import LLMRunFrame
|
||||
from pipecat.pipeline.pipeline import Pipeline
|
||||
from pipecat.pipeline.runner import PipelineRunner
|
||||
from pipecat.pipeline.task import PipelineParams, PipelineTask
|
||||
from pipecat.processors.aggregators.llm_context import LLMContext
|
||||
from pipecat.processors.aggregators.llm_response_universal import (
|
||||
LLMContextAggregatorPair,
|
||||
LLMUserAggregatorParams,
|
||||
)
|
||||
from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
|
||||
from pipecat.runner.types import RunnerArguments
|
||||
from pipecat.runner.utils import create_transport
|
||||
from pipecat.services.cartesia.tts import CartesiaTTSService
|
||||
from pipecat.services.openai.llm import OpenAILLMService
|
||||
from pipecat.services.soniox.stt import SonioxSTTService
|
||||
from pipecat.transcriptions.language import Language
|
||||
from pipecat.transports.base_transport import BaseTransport, TransportParams
|
||||
from pipecat.transports.daily.transport import DailyParams
|
||||
from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
|
||||
@@ -36,14 +35,20 @@ transport_params = {
|
||||
"daily": lambda: DailyParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
"twilio": lambda: FastAPIWebsocketParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
"webrtc": lambda: TransportParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
}
|
||||
|
||||
@@ -53,43 +58,34 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
|
||||
stt = SonioxSTTService(
|
||||
api_key=os.getenv("SONIOX_API_KEY"),
|
||||
settings=SonioxSTTService.Settings(
|
||||
# Add language hints to use a specific language
|
||||
# Add strict mode to enforce the language hints
|
||||
language_hints=[Language.EN],
|
||||
language_hints_strict=True,
|
||||
),
|
||||
)
|
||||
|
||||
tts = CartesiaTTSService(
|
||||
api_key=os.getenv("CARTESIA_API_KEY"),
|
||||
settings=CartesiaTTSService.Settings(
|
||||
voice="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
),
|
||||
voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
)
|
||||
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
settings=OpenAILLMService.Settings(
|
||||
system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
|
||||
),
|
||||
)
|
||||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
|
||||
|
||||
context = LLMContext()
|
||||
user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
|
||||
context,
|
||||
user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
|
||||
)
|
||||
messages = [
|
||||
{
|
||||
"role": "system",
|
||||
"content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
|
||||
},
|
||||
]
|
||||
|
||||
context = LLMContext(messages)
|
||||
context_aggregator = LLMContextAggregatorPair(context)
|
||||
|
||||
pipeline = Pipeline(
|
||||
[
|
||||
transport.input(), # Transport user input
|
||||
stt,
|
||||
user_aggregator, # User responses
|
||||
context_aggregator.user(), # User responses
|
||||
llm, # LLM
|
||||
tts, # TTS
|
||||
transport.output(), # Transport bot output
|
||||
assistant_aggregator, # Assistant spoken responses
|
||||
context_aggregator.assistant(), # Assistant spoken responses
|
||||
]
|
||||
)
|
||||
task = PipelineTask(
|
||||
@@ -105,9 +101,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
async def on_client_connected(transport, client):
|
||||
logger.info(f"Client connected")
|
||||
# Kick off the conversation.
|
||||
context.add_message(
|
||||
{"role": "developer", "content": "Please introduce yourself to the user."}
|
||||
)
|
||||
messages.append({"role": "system", "content": "Please introduce yourself to the user."})
|
||||
await task.queue_frames([LLMRunFrame()])
|
||||
|
||||
@transport.event_handler("on_client_disconnected")
|
||||
138
examples/foundational/07ab-interruptible-inworld-http.py
Normal file
138
examples/foundational/07ab-interruptible-inworld-http.py
Normal file
@@ -0,0 +1,138 @@
|
||||
#
|
||||
# Copyright (c) 2024–2025, Daily
|
||||
#
|
||||
# SPDX-License-Identifier: BSD 2-Clause License
|
||||
#
|
||||
|
||||
|
||||
import os
|
||||
|
||||
import aiohttp
|
||||
from dotenv import load_dotenv
|
||||
from loguru import logger
|
||||
|
||||
from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
|
||||
from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
|
||||
from pipecat.audio.vad.silero import SileroVADAnalyzer
|
||||
from pipecat.audio.vad.vad_analyzer import VADParams
|
||||
from pipecat.frames.frames import LLMRunFrame
|
||||
from pipecat.pipeline.pipeline import Pipeline
|
||||
from pipecat.pipeline.runner import PipelineRunner
|
||||
from pipecat.pipeline.task import PipelineParams, PipelineTask
|
||||
from pipecat.processors.aggregators.llm_context import LLMContext
|
||||
from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
|
||||
from pipecat.runner.types import RunnerArguments
|
||||
from pipecat.runner.utils import create_transport
|
||||
from pipecat.services.deepgram.stt import DeepgramSTTService
|
||||
from pipecat.services.inworld.tts import InworldTTSService
|
||||
from pipecat.services.openai.llm import OpenAILLMService
|
||||
from pipecat.transports.base_transport import BaseTransport, TransportParams
|
||||
from pipecat.transports.daily.transport import DailyParams
|
||||
from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
|
||||
|
||||
load_dotenv(override=True)
|
||||
|
||||
# We store functions so objects (e.g. SileroVADAnalyzer) don't get
|
||||
# instantiated. The function will be called when the desired transport gets
|
||||
# selected.
|
||||
transport_params = {
|
||||
"daily": lambda: DailyParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
"twilio": lambda: FastAPIWebsocketParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
"webrtc": lambda: TransportParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
}
|
||||
|
||||
|
||||
async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
logger.info(f"Starting bot")
|
||||
|
||||
# Create an HTTP session
|
||||
async with aiohttp.ClientSession() as session:
|
||||
stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
|
||||
|
||||
# Inworld TTS Service - Unified streaming and non-streaming
|
||||
# Set streaming=True for real-time audio, streaming=False for complete audio generation
|
||||
streaming = True # Toggle this to switch between modes
|
||||
|
||||
tts = InworldTTSService(
|
||||
api_key=os.getenv("INWORLD_API_KEY", ""),
|
||||
aiohttp_session=session,
|
||||
voice_id="Ashley",
|
||||
model="inworld-tts-1",
|
||||
streaming=streaming, # True: real-time chunks, False: complete audio then playback
|
||||
)
|
||||
|
||||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
|
||||
|
||||
messages = [
|
||||
{
|
||||
"role": "system",
|
||||
"content": "You are very knowledgable about dogs. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
|
||||
},
|
||||
]
|
||||
|
||||
context = LLMContext(messages)
|
||||
context_aggregator = LLMContextAggregatorPair(context)
|
||||
|
||||
pipeline = Pipeline(
|
||||
[
|
||||
transport.input(), # Transport user input
|
||||
stt, # STT
|
||||
context_aggregator.user(), # User responses
|
||||
llm, # LLM
|
||||
tts, # TTS
|
||||
transport.output(), # Transport bot output
|
||||
context_aggregator.assistant(), # Assistant spoken responses
|
||||
]
|
||||
)
|
||||
|
||||
task = PipelineTask(
|
||||
pipeline,
|
||||
params=PipelineParams(
|
||||
enable_metrics=True,
|
||||
enable_usage_metrics=True,
|
||||
),
|
||||
idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
|
||||
)
|
||||
|
||||
@transport.event_handler("on_client_connected")
|
||||
async def on_client_connected(transport, client):
|
||||
logger.info(f"Client connected")
|
||||
# Kick off the conversation.
|
||||
messages.append({"role": "system", "content": "Please introduce yourself to the user."})
|
||||
await task.queue_frames([LLMRunFrame()])
|
||||
|
||||
@transport.event_handler("on_client_disconnected")
|
||||
async def on_client_disconnected(transport, client):
|
||||
logger.info(f"Client disconnected")
|
||||
await task.cancel()
|
||||
|
||||
runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
|
||||
|
||||
await runner.run(task)
|
||||
|
||||
|
||||
async def bot(runner_args: RunnerArguments):
|
||||
"""Main bot entry point compatible with Pipecat Cloud."""
|
||||
transport = await create_transport(runner_args, transport_params)
|
||||
await run_bot(transport, runner_args)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
from pipecat.runner.run import main
|
||||
|
||||
main()
|
||||
@@ -1,5 +1,5 @@
|
||||
#
|
||||
# Copyright (c) 2024-2026, Daily
|
||||
# Copyright (c) 2024–2025, Daily
|
||||
#
|
||||
# SPDX-License-Identifier: BSD 2-Clause License
|
||||
#
|
||||
@@ -11,16 +11,16 @@ import aiohttp
|
||||
from dotenv import load_dotenv
|
||||
from loguru import logger
|
||||
|
||||
from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
|
||||
from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
|
||||
from pipecat.audio.vad.silero import SileroVADAnalyzer
|
||||
from pipecat.audio.vad.vad_analyzer import VADParams
|
||||
from pipecat.frames.frames import LLMRunFrame
|
||||
from pipecat.pipeline.pipeline import Pipeline
|
||||
from pipecat.pipeline.runner import PipelineRunner
|
||||
from pipecat.pipeline.task import PipelineParams, PipelineTask
|
||||
from pipecat.processors.aggregators.llm_context import LLMContext
|
||||
from pipecat.processors.aggregators.llm_response_universal import (
|
||||
LLMContextAggregatorPair,
|
||||
LLMUserAggregatorParams,
|
||||
)
|
||||
from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
|
||||
from pipecat.runner.types import RunnerArguments
|
||||
from pipecat.runner.utils import create_transport
|
||||
from pipecat.services.asyncai.tts import AsyncAIHttpTTSService
|
||||
@@ -33,20 +33,27 @@ from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
|
||||
load_dotenv(override=True)
|
||||
|
||||
|
||||
# We use lambdas to defer transport parameter creation until the transport
|
||||
# type is selected at runtime.
|
||||
# We store functions so objects (e.g. SileroVADAnalyzer) don't get
|
||||
# instantiated. The function will be called when the desired transport gets
|
||||
# selected.
|
||||
transport_params = {
|
||||
"daily": lambda: DailyParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
"twilio": lambda: FastAPIWebsocketParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
"webrtc": lambda: TransportParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
}
|
||||
|
||||
@@ -60,34 +67,31 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
|
||||
tts = AsyncAIHttpTTSService(
|
||||
api_key=os.getenv("ASYNCAI_API_KEY", ""),
|
||||
settings=AsyncAIHttpTTSService.Settings(
|
||||
voice="e0f39dc4-f691-4e78-bba5-5c636692cc04",
|
||||
),
|
||||
voice_id=os.getenv("ASYNCAI_VOICE_ID", "e0f39dc4-f691-4e78-bba5-5c636692cc04"),
|
||||
aiohttp_session=session,
|
||||
)
|
||||
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
settings=OpenAILLMService.Settings(
|
||||
system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
|
||||
),
|
||||
)
|
||||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
|
||||
|
||||
context = LLMContext()
|
||||
user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
|
||||
context,
|
||||
user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
|
||||
)
|
||||
messages = [
|
||||
{
|
||||
"role": "system",
|
||||
"content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
|
||||
},
|
||||
]
|
||||
|
||||
context = LLMContext(messages)
|
||||
context_aggregator = LLMContextAggregatorPair(context)
|
||||
|
||||
pipeline = Pipeline(
|
||||
[
|
||||
transport.input(), # Transport user input
|
||||
stt,
|
||||
user_aggregator, # User responses
|
||||
context_aggregator.user(), # User responses
|
||||
llm, # LLM
|
||||
tts, # TTS
|
||||
transport.output(), # Transport bot output
|
||||
assistant_aggregator, # Assistant spoken responses
|
||||
context_aggregator.assistant(), # Assistant spoken responses
|
||||
]
|
||||
)
|
||||
|
||||
@@ -104,9 +108,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
async def on_client_connected(transport, client):
|
||||
logger.info(f"Client connected")
|
||||
# Kick off the conversation.
|
||||
context.add_message(
|
||||
{"role": "developer", "content": "Please introduce yourself to the user."}
|
||||
)
|
||||
messages.append({"role": "system", "content": "Please introduce yourself to the user."})
|
||||
await task.queue_frames([LLMRunFrame()])
|
||||
|
||||
@transport.event_handler("on_client_disconnected")
|
||||
@@ -1,5 +1,5 @@
|
||||
#
|
||||
# Copyright (c) 2024-2026, Daily
|
||||
# Copyright (c) 2024–2025, Daily
|
||||
#
|
||||
# SPDX-License-Identifier: BSD 2-Clause License
|
||||
#
|
||||
@@ -10,16 +10,16 @@ import os
|
||||
from dotenv import load_dotenv
|
||||
from loguru import logger
|
||||
|
||||
from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
|
||||
from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
|
||||
from pipecat.audio.vad.silero import SileroVADAnalyzer
|
||||
from pipecat.audio.vad.vad_analyzer import VADParams
|
||||
from pipecat.frames.frames import LLMRunFrame
|
||||
from pipecat.pipeline.pipeline import Pipeline
|
||||
from pipecat.pipeline.runner import PipelineRunner
|
||||
from pipecat.pipeline.task import PipelineParams, PipelineTask
|
||||
from pipecat.processors.aggregators.llm_context import LLMContext
|
||||
from pipecat.processors.aggregators.llm_response_universal import (
|
||||
LLMContextAggregatorPair,
|
||||
LLMUserAggregatorParams,
|
||||
)
|
||||
from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
|
||||
from pipecat.runner.types import RunnerArguments
|
||||
from pipecat.runner.utils import create_transport
|
||||
from pipecat.services.asyncai.tts import AsyncAITTSService
|
||||
@@ -32,20 +32,27 @@ from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
|
||||
load_dotenv(override=True)
|
||||
|
||||
|
||||
# We use lambdas to defer transport parameter creation until the transport
|
||||
# type is selected at runtime.
|
||||
# We store functions so objects (e.g. SileroVADAnalyzer) don't get
|
||||
# instantiated. The function will be called when the desired transport gets
|
||||
# selected.
|
||||
transport_params = {
|
||||
"daily": lambda: DailyParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
"twilio": lambda: FastAPIWebsocketParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
"webrtc": lambda: TransportParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
}
|
||||
|
||||
@@ -57,33 +64,30 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
|
||||
tts = AsyncAITTSService(
|
||||
api_key=os.getenv("ASYNCAI_API_KEY", ""),
|
||||
settings=AsyncAITTSService.Settings(
|
||||
voice="e0f39dc4-f691-4e78-bba5-5c636692cc04",
|
||||
),
|
||||
voice_id=os.getenv("ASYNCAI_VOICE_ID", "e0f39dc4-f691-4e78-bba5-5c636692cc04"),
|
||||
)
|
||||
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
settings=OpenAILLMService.Settings(
|
||||
system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
|
||||
),
|
||||
)
|
||||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
|
||||
|
||||
context = LLMContext()
|
||||
user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
|
||||
context,
|
||||
user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
|
||||
)
|
||||
messages = [
|
||||
{
|
||||
"role": "system",
|
||||
"content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
|
||||
},
|
||||
]
|
||||
|
||||
context = LLMContext(messages)
|
||||
context_aggregator = LLMContextAggregatorPair(context)
|
||||
|
||||
pipeline = Pipeline(
|
||||
[
|
||||
transport.input(), # Transport user input
|
||||
stt,
|
||||
user_aggregator, # User responses
|
||||
context_aggregator.user(), # User responses
|
||||
llm, # LLM
|
||||
tts, # TTS
|
||||
transport.output(), # Transport bot output
|
||||
assistant_aggregator, # Assistant spoken responses
|
||||
context_aggregator.assistant(), # Assistant spoken responses
|
||||
]
|
||||
)
|
||||
|
||||
@@ -100,9 +104,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
async def on_client_connected(transport, client):
|
||||
logger.info(f"Client connected")
|
||||
# Kick off the conversation.
|
||||
context.add_message(
|
||||
{"role": "developer", "content": "Please introduce yourself to the user."}
|
||||
)
|
||||
messages.append({"role": "system", "content": "Please introduce yourself to the user."})
|
||||
await task.queue_frames([LLMRunFrame()])
|
||||
|
||||
@transport.event_handler("on_client_disconnected")
|
||||
@@ -1,5 +1,5 @@
|
||||
#
|
||||
# Copyright (c) 2024-2026, Daily
|
||||
# Copyright (c) 2024–2025, Daily
|
||||
#
|
||||
# SPDX-License-Identifier: BSD 2-Clause License
|
||||
#
|
||||
@@ -13,15 +13,15 @@ from dotenv import load_dotenv
|
||||
from loguru import logger
|
||||
|
||||
from pipecat.audio.filters.aic_filter import AICFilter
|
||||
from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
|
||||
from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
|
||||
from pipecat.audio.vad.vad_analyzer import VADParams
|
||||
from pipecat.frames.frames import LLMRunFrame
|
||||
from pipecat.pipeline.pipeline import Pipeline
|
||||
from pipecat.pipeline.runner import PipelineRunner
|
||||
from pipecat.pipeline.task import PipelineParams, PipelineTask
|
||||
from pipecat.processors.aggregators.llm_context import LLMContext
|
||||
from pipecat.processors.aggregators.llm_response_universal import (
|
||||
LLMContextAggregatorPair,
|
||||
LLMUserAggregatorParams,
|
||||
)
|
||||
from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
|
||||
from pipecat.processors.audio.audio_buffer_processor import AudioBufferProcessor
|
||||
from pipecat.runner.types import RunnerArguments
|
||||
from pipecat.runner.utils import create_transport
|
||||
@@ -35,38 +35,53 @@ from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
|
||||
load_dotenv(override=True)
|
||||
|
||||
|
||||
# Create audio buffer processor so we can hear the audio fitler results.
|
||||
audiobuffer = AudioBufferProcessor(
|
||||
num_channels=2, # 1 for mono, 2 for stereo (user left, bot right)
|
||||
enable_turn_audio=False, # Enable per-turn audio recording
|
||||
)
|
||||
|
||||
|
||||
def _create_aic_filter() -> AICFilter:
|
||||
license_key = os.getenv("AICOUSTICS_LICENSE_KEY", "")
|
||||
|
||||
return AICFilter(
|
||||
license_key=license_key,
|
||||
model_id="quail-vf-2.0-l-16khz",
|
||||
enhancement_level=0.5,
|
||||
)
|
||||
|
||||
|
||||
aic_filter = _create_aic_filter()
|
||||
aic_vad_analyzer = aic_filter.create_vad_analyzer(
|
||||
speech_hold_duration=0.05, minimum_speech_duration=0.0, sensitivity=6.0
|
||||
)
|
||||
|
||||
# We use lambdas to defer transport parameter creation until the transport
|
||||
# type is selected at runtime.
|
||||
# We store functions so objects (e.g. SileroVADAnalyzer) don't get
|
||||
# instantiated. The function will be called when the desired transport gets
|
||||
# selected.
|
||||
transport_params = {
|
||||
"daily": lambda: DailyParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
audio_in_filter=aic_filter,
|
||||
),
|
||||
"twilio": lambda: FastAPIWebsocketParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
audio_in_filter=aic_filter,
|
||||
),
|
||||
"webrtc": lambda: TransportParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
audio_in_filter=aic_filter,
|
||||
),
|
||||
"daily": lambda: (
|
||||
lambda aic: DailyParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
vad_analyzer=aic.create_vad_analyzer(lookback_buffer_size=6.0, sensitivity=6.0),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
audio_in_filter=aic,
|
||||
)
|
||||
)(_create_aic_filter()),
|
||||
"twilio": lambda: (
|
||||
lambda aic: FastAPIWebsocketParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
vad_analyzer=aic.create_vad_analyzer(lookback_buffer_size=6.0, sensitivity=6.0),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
audio_in_filter=aic,
|
||||
)
|
||||
)(_create_aic_filter()),
|
||||
"webrtc": lambda: (
|
||||
lambda aic: TransportParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
vad_analyzer=aic.create_vad_analyzer(lookback_buffer_size=6.0, sensitivity=6.0),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
audio_in_filter=aic,
|
||||
)
|
||||
)(_create_aic_filter()),
|
||||
}
|
||||
|
||||
|
||||
@@ -77,40 +92,31 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
|
||||
tts = CartesiaTTSService(
|
||||
api_key=os.getenv("CARTESIA_API_KEY"),
|
||||
settings=CartesiaTTSService.Settings(
|
||||
voice="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
),
|
||||
voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
)
|
||||
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
settings=OpenAILLMService.Settings(
|
||||
system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
|
||||
),
|
||||
)
|
||||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
|
||||
|
||||
context = LLMContext()
|
||||
user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
|
||||
context,
|
||||
user_params=LLMUserAggregatorParams(vad_analyzer=aic_vad_analyzer),
|
||||
)
|
||||
messages = [
|
||||
{
|
||||
"role": "system",
|
||||
"content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
|
||||
},
|
||||
]
|
||||
|
||||
# Create audio buffer processor so we can hear the audio fitler results.
|
||||
audiobuffer = AudioBufferProcessor(
|
||||
num_channels=2, # 1 for mono, 2 for stereo (user left, bot right)
|
||||
enable_turn_audio=False, # Enable per-turn audio recording
|
||||
)
|
||||
context = LLMContext(messages)
|
||||
context_aggregator = LLMContextAggregatorPair(context)
|
||||
|
||||
pipeline = Pipeline(
|
||||
[
|
||||
transport.input(), # Transport user input
|
||||
stt, # STT
|
||||
user_aggregator, # User responses
|
||||
context_aggregator.user(), # User responses
|
||||
llm, # LLM
|
||||
tts, # TTS
|
||||
transport.output(), # Transport bot output
|
||||
audiobuffer, # write audio data to a file
|
||||
assistant_aggregator, # Assistant spoken responses
|
||||
context_aggregator.assistant(), # Assistant spoken responses
|
||||
]
|
||||
)
|
||||
|
||||
@@ -128,9 +134,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
logger.info(f"Client connected")
|
||||
await audiobuffer.start_recording()
|
||||
# Kick off the conversation.
|
||||
context.add_message(
|
||||
{"role": "developer", "content": "Please introduce yourself to the user."}
|
||||
)
|
||||
messages.append({"role": "system", "content": "Please introduce yourself to the user."})
|
||||
await task.queue_frames([LLMRunFrame()])
|
||||
|
||||
@audiobuffer.event_handler("on_audio_data")
|
||||
@@ -1,5 +1,5 @@
|
||||
#
|
||||
# Copyright (c) 2024-2026, Daily
|
||||
# Copyright (c) 2024–2025, Daily
|
||||
#
|
||||
# SPDX-License-Identifier: BSD 2-Clause License
|
||||
#
|
||||
@@ -9,7 +9,10 @@ import os
|
||||
from dotenv import load_dotenv
|
||||
from loguru import logger
|
||||
|
||||
from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
|
||||
from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
|
||||
from pipecat.audio.vad.silero import SileroVADAnalyzer
|
||||
from pipecat.audio.vad.vad_analyzer import VADParams
|
||||
from pipecat.frames.frames import LLMRunFrame, TTSTextFrame
|
||||
from pipecat.observers.loggers.debug_log_observer import DebugLogObserver, FrameEndpoint
|
||||
from pipecat.pipeline.pipeline import Pipeline
|
||||
@@ -18,8 +21,8 @@ from pipecat.pipeline.task import PipelineParams, PipelineTask
|
||||
from pipecat.processors.aggregators.llm_context import LLMContext
|
||||
from pipecat.processors.aggregators.llm_response_universal import (
|
||||
LLMContextAggregatorPair,
|
||||
LLMUserAggregatorParams,
|
||||
)
|
||||
from pipecat.processors.frameworks.rtvi import RTVIConfig, RTVIObserver, RTVIProcessor
|
||||
from pipecat.runner.types import RunnerArguments
|
||||
from pipecat.runner.utils import create_transport
|
||||
from pipecat.services.deepgram.stt import DeepgramSTTService
|
||||
@@ -33,20 +36,27 @@ from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
|
||||
load_dotenv(override=True)
|
||||
|
||||
|
||||
# We use lambdas to defer transport parameter creation until the transport
|
||||
# type is selected at runtime.
|
||||
# We store functions so objects (e.g. SileroVADAnalyzer) don't get
|
||||
# instantiated. The function will be called when the desired transport gets
|
||||
# selected.
|
||||
transport_params = {
|
||||
"daily": lambda: DailyParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
"twilio": lambda: FastAPIWebsocketParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
"webrtc": lambda: TransportParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
}
|
||||
|
||||
@@ -59,33 +69,33 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
tts = HumeTTSService(
|
||||
api_key=os.getenv("HUME_API_KEY"),
|
||||
# Replace with your Hume voice ID
|
||||
settings=HumeTTSService.Settings(
|
||||
voice="f898a92e-685f-43fa-985b-a46920f0650b",
|
||||
),
|
||||
voice_id="f898a92e-685f-43fa-985b-a46920f0650b",
|
||||
)
|
||||
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
settings=OpenAILLMService.Settings(
|
||||
system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
|
||||
),
|
||||
)
|
||||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
|
||||
|
||||
context = LLMContext()
|
||||
user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
|
||||
context,
|
||||
user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
|
||||
)
|
||||
messages = [
|
||||
{
|
||||
"role": "system",
|
||||
"content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
|
||||
},
|
||||
]
|
||||
|
||||
context = LLMContext(messages)
|
||||
context_aggregator = LLMContextAggregatorPair(context)
|
||||
|
||||
rtvi = RTVIProcessor(config=RTVIConfig(config=[]))
|
||||
|
||||
pipeline = Pipeline(
|
||||
[
|
||||
transport.input(), # Transport user input
|
||||
rtvi,
|
||||
stt,
|
||||
user_aggregator, # User responses
|
||||
context_aggregator.user(), # User responses
|
||||
llm, # LLM
|
||||
tts, # TTS (HumeTTSService with word timestamps)
|
||||
transport.output(), # Transport bot output
|
||||
assistant_aggregator, # Assistant spoken responses
|
||||
context_aggregator.assistant(), # Assistant spoken responses
|
||||
]
|
||||
)
|
||||
|
||||
@@ -98,6 +108,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
),
|
||||
idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
|
||||
observers=[
|
||||
RTVIObserver(rtvi),
|
||||
DebugLogObserver(
|
||||
frame_types={
|
||||
TTSTextFrame: (BaseOutputTransport, FrameEndpoint.SOURCE),
|
||||
@@ -106,6 +117,10 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
],
|
||||
)
|
||||
|
||||
@rtvi.event_handler("on_client_ready")
|
||||
async def on_client_ready(rtvi):
|
||||
await rtvi.set_bot_ready()
|
||||
|
||||
@transport.event_handler("on_client_connected")
|
||||
async def on_client_connected(transport, client):
|
||||
logger.info(f"Client connected")
|
||||
@@ -113,9 +128,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
"💡 Word timestamps are enabled! Watch the console for TTSTextFrame logs showing each word with its PTS."
|
||||
)
|
||||
# Kick off the conversation.
|
||||
context.add_message(
|
||||
{"role": "developer", "content": "Please introduce yourself to the user."}
|
||||
)
|
||||
messages.append({"role": "system", "content": "Please introduce yourself to the user."})
|
||||
await task.queue_frames([LLMRunFrame()])
|
||||
|
||||
@transport.event_handler("on_client_disconnected")
|
||||
@@ -1,5 +1,5 @@
|
||||
#
|
||||
# Copyright (c) 2024-2026, Daily
|
||||
# Copyright (c) 2024–2025, Daily
|
||||
#
|
||||
# SPDX-License-Identifier: BSD 2-Clause License
|
||||
#
|
||||
@@ -8,23 +8,23 @@
|
||||
import os
|
||||
|
||||
from dotenv import load_dotenv
|
||||
from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder
|
||||
from langchain_community.chat_message_histories import ChatMessageHistory
|
||||
from langchain_core.chat_history import BaseChatMessageHistory
|
||||
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
|
||||
from langchain_core.runnables.history import RunnableWithMessageHistory
|
||||
from langchain_openai import ChatOpenAI
|
||||
from loguru import logger
|
||||
|
||||
from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
|
||||
from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
|
||||
from pipecat.audio.vad.silero import SileroVADAnalyzer
|
||||
from pipecat.audio.vad.vad_analyzer import VADParams
|
||||
from pipecat.frames.frames import LLMMessagesUpdateFrame
|
||||
from pipecat.pipeline.pipeline import Pipeline
|
||||
from pipecat.pipeline.runner import PipelineRunner
|
||||
from pipecat.pipeline.task import PipelineParams, PipelineTask
|
||||
from pipecat.processors.aggregators.llm_context import LLMContext
|
||||
from pipecat.processors.aggregators.llm_response_universal import (
|
||||
LLMContextAggregatorPair,
|
||||
LLMUserAggregatorParams,
|
||||
)
|
||||
from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
|
||||
from pipecat.processors.frameworks.langchain import LangchainProcessor
|
||||
from pipecat.runner.types import RunnerArguments
|
||||
from pipecat.runner.utils import create_transport
|
||||
@@ -46,20 +46,27 @@ def get_session_history(session_id: str) -> BaseChatMessageHistory:
|
||||
return message_store[session_id]
|
||||
|
||||
|
||||
# We use lambdas to defer transport parameter creation until the transport
|
||||
# type is selected at runtime.
|
||||
# We store functions so objects (e.g. SileroVADAnalyzer) don't get
|
||||
# instantiated. The function will be called when the desired transport gets
|
||||
# selected.
|
||||
transport_params = {
|
||||
"daily": lambda: DailyParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
"twilio": lambda: FastAPIWebsocketParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
"webrtc": lambda: TransportParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
}
|
||||
|
||||
@@ -71,16 +78,15 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
|
||||
tts = CartesiaTTSService(
|
||||
api_key=os.getenv("CARTESIA_API_KEY"),
|
||||
settings=CartesiaTTSService.Settings(
|
||||
voice="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
),
|
||||
voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
)
|
||||
|
||||
prompt = ChatPromptTemplate.from_messages(
|
||||
[
|
||||
(
|
||||
"system",
|
||||
"You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
|
||||
"Be nice and helpful. Answer very briefly and without special characters like `#` or `*`. "
|
||||
"Your response will be synthesized to voice and those characters will create unnatural sounds.",
|
||||
),
|
||||
MessagesPlaceholder("chat_history"),
|
||||
("human", "{input}"),
|
||||
@@ -96,20 +102,17 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
lc = LangchainProcessor(history_chain)
|
||||
|
||||
context = LLMContext()
|
||||
user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
|
||||
context,
|
||||
user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
|
||||
)
|
||||
context_aggregator = LLMContextAggregatorPair(context)
|
||||
|
||||
pipeline = Pipeline(
|
||||
[
|
||||
transport.input(), # Transport user input
|
||||
stt,
|
||||
user_aggregator, # User responses
|
||||
context_aggregator.user(), # User responses
|
||||
lc, # Langchain
|
||||
tts, # TTS
|
||||
transport.output(), # Transport bot output
|
||||
assistant_aggregator, # Assistant spoken responses
|
||||
context_aggregator.assistant(), # Assistant spoken responses
|
||||
]
|
||||
)
|
||||
|
||||
@@ -1,5 +1,5 @@
|
||||
#
|
||||
# Copyright (c) 2024-2026, Daily
|
||||
# Copyright (c) 2024–2025, Daily
|
||||
#
|
||||
# SPDX-License-Identifier: BSD 2-Clause License
|
||||
#
|
||||
@@ -10,7 +10,6 @@ import os
|
||||
from dotenv import load_dotenv
|
||||
from loguru import logger
|
||||
|
||||
from pipecat.audio.vad.silero import SileroVADAnalyzer
|
||||
from pipecat.frames.frames import LLMRunFrame
|
||||
from pipecat.pipeline.pipeline import Pipeline
|
||||
from pipecat.pipeline.runner import PipelineRunner
|
||||
@@ -18,7 +17,6 @@ from pipecat.pipeline.task import PipelineParams, PipelineTask
|
||||
from pipecat.processors.aggregators.llm_response_universal import (
|
||||
LLMContext,
|
||||
LLMContextAggregatorPair,
|
||||
LLMUserAggregatorParams,
|
||||
)
|
||||
from pipecat.runner.types import RunnerArguments
|
||||
from pipecat.runner.utils import create_transport
|
||||
@@ -28,13 +26,13 @@ from pipecat.services.openai.llm import OpenAILLMService
|
||||
from pipecat.transports.base_transport import BaseTransport, TransportParams
|
||||
from pipecat.transports.daily.transport import DailyParams
|
||||
from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
|
||||
from pipecat.turns.user_turn_strategies import ExternalUserTurnStrategies
|
||||
|
||||
load_dotenv(override=True)
|
||||
|
||||
|
||||
# We use lambdas to defer transport parameter creation until the transport
|
||||
# type is selected at runtime.
|
||||
# We store functions so objects (e.g. SileroVADAnalyzer) don't get
|
||||
# instantiated. The function will be called when the desired transport gets
|
||||
# selected.
|
||||
transport_params = {
|
||||
"daily": lambda: DailyParams(
|
||||
audio_in_enabled=True,
|
||||
@@ -56,43 +54,32 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
|
||||
stt = DeepgramFluxSTTService(
|
||||
api_key=os.getenv("DEEPGRAM_API_KEY"),
|
||||
settings=DeepgramFluxSTTService.Settings(
|
||||
min_confidence=0.3,
|
||||
),
|
||||
params=DeepgramFluxSTTService.InputParams(min_confidence=0.3),
|
||||
)
|
||||
|
||||
tts = DeepgramTTSService(
|
||||
api_key=os.getenv("DEEPGRAM_API_KEY"),
|
||||
settings=DeepgramTTSService.Settings(
|
||||
voice="aura-2-andromeda-en",
|
||||
),
|
||||
)
|
||||
tts = DeepgramTTSService(api_key=os.getenv("DEEPGRAM_API_KEY"), voice="aura-2-andromeda-en")
|
||||
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
settings=OpenAILLMService.Settings(
|
||||
system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
|
||||
),
|
||||
)
|
||||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
|
||||
|
||||
context = LLMContext()
|
||||
user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
|
||||
context,
|
||||
user_params=LLMUserAggregatorParams(
|
||||
user_turn_strategies=ExternalUserTurnStrategies(),
|
||||
vad_analyzer=SileroVADAnalyzer(),
|
||||
),
|
||||
)
|
||||
messages = [
|
||||
{
|
||||
"role": "system",
|
||||
"content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
|
||||
},
|
||||
]
|
||||
|
||||
context = LLMContext(messages)
|
||||
context_aggregator = LLMContextAggregatorPair(context)
|
||||
|
||||
pipeline = Pipeline(
|
||||
[
|
||||
transport.input(), # Transport user input
|
||||
stt, # STT
|
||||
user_aggregator, # User responses
|
||||
context_aggregator.user(), # User responses
|
||||
llm, # LLM
|
||||
tts, # TTS
|
||||
transport.output(), # Transport bot output
|
||||
assistant_aggregator, # Assistant spoken responses
|
||||
context_aggregator.assistant(), # Assistant spoken responses
|
||||
]
|
||||
)
|
||||
|
||||
@@ -109,9 +96,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
async def on_client_connected(transport, client):
|
||||
logger.info(f"Client connected")
|
||||
# Kick off the conversation.
|
||||
context.add_message(
|
||||
{"role": "developer", "content": "Please introduce yourself to the user."}
|
||||
)
|
||||
messages.append({"role": "system", "content": "Please introduce yourself to the user."})
|
||||
await task.queue_frames([LLMRunFrame()])
|
||||
|
||||
@transport.event_handler("on_client_disconnected")
|
||||
@@ -1,5 +1,5 @@
|
||||
#
|
||||
# Copyright (c) 2024-2026, Daily
|
||||
# Copyright (c) 2024–2025, Daily
|
||||
#
|
||||
# SPDX-License-Identifier: BSD 2-Clause License
|
||||
#
|
||||
@@ -11,16 +11,16 @@ import aiohttp
|
||||
from dotenv import load_dotenv
|
||||
from loguru import logger
|
||||
|
||||
from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
|
||||
from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
|
||||
from pipecat.audio.vad.silero import SileroVADAnalyzer
|
||||
from pipecat.audio.vad.vad_analyzer import VADParams
|
||||
from pipecat.frames.frames import LLMRunFrame
|
||||
from pipecat.pipeline.pipeline import Pipeline
|
||||
from pipecat.pipeline.runner import PipelineRunner
|
||||
from pipecat.pipeline.task import PipelineParams, PipelineTask
|
||||
from pipecat.processors.aggregators.llm_context import LLMContext
|
||||
from pipecat.processors.aggregators.llm_response_universal import (
|
||||
LLMContextAggregatorPair,
|
||||
LLMUserAggregatorParams,
|
||||
)
|
||||
from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
|
||||
from pipecat.runner.types import RunnerArguments
|
||||
from pipecat.runner.utils import create_transport
|
||||
from pipecat.services.deepgram.stt import DeepgramSTTService
|
||||
@@ -33,20 +33,27 @@ from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
|
||||
load_dotenv(override=True)
|
||||
|
||||
|
||||
# We use lambdas to defer transport parameter creation until the transport
|
||||
# type is selected at runtime.
|
||||
# We store functions so objects (e.g. SileroVADAnalyzer) don't get
|
||||
# instantiated. The function will be called when the desired transport gets
|
||||
# selected.
|
||||
transport_params = {
|
||||
"daily": lambda: DailyParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
"twilio": lambda: FastAPIWebsocketParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
"webrtc": lambda: TransportParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
}
|
||||
|
||||
@@ -59,34 +66,31 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
|
||||
tts = DeepgramHttpTTSService(
|
||||
api_key=os.getenv("DEEPGRAM_API_KEY"),
|
||||
settings=DeepgramHttpTTSService.Settings(
|
||||
voice="aura-2-andromeda-en",
|
||||
),
|
||||
voice="aura-2-andromeda-en",
|
||||
aiohttp_session=session,
|
||||
)
|
||||
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
settings=OpenAILLMService.Settings(
|
||||
system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
|
||||
),
|
||||
)
|
||||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
|
||||
|
||||
context = LLMContext()
|
||||
user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
|
||||
context,
|
||||
user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
|
||||
)
|
||||
messages = [
|
||||
{
|
||||
"role": "system",
|
||||
"content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
|
||||
},
|
||||
]
|
||||
|
||||
context = LLMContext(messages)
|
||||
context_aggregator = LLMContextAggregatorPair(context)
|
||||
|
||||
pipeline = Pipeline(
|
||||
[
|
||||
transport.input(), # Transport user input
|
||||
stt, # STT
|
||||
user_aggregator, # User responses
|
||||
context_aggregator.user(), # User responses
|
||||
llm, # LLM
|
||||
tts, # TTS
|
||||
transport.output(), # Transport bot output
|
||||
assistant_aggregator, # Assistant spoken responses
|
||||
context_aggregator.assistant(), # Assistant spoken responses
|
||||
]
|
||||
)
|
||||
|
||||
@@ -103,9 +107,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
async def on_client_connected(transport, client):
|
||||
logger.info(f"Client connected")
|
||||
# Kick off the conversation.
|
||||
context.add_message(
|
||||
{"role": "developer", "content": "Please introduce yourself to the user."}
|
||||
)
|
||||
messages.append({"role": "system", "content": "Please introduce yourself to the user."})
|
||||
await task.queue_frames([LLMRunFrame()])
|
||||
|
||||
@transport.event_handler("on_client_disconnected")
|
||||
@@ -1,5 +1,5 @@
|
||||
#
|
||||
# Copyright (c) 2024-2026, Daily
|
||||
# Copyright (c) 2024–2025, Daily
|
||||
#
|
||||
# SPDX-License-Identifier: BSD 2-Clause License
|
||||
#
|
||||
@@ -10,21 +10,21 @@ import os
|
||||
from dotenv import load_dotenv
|
||||
from loguru import logger
|
||||
|
||||
from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
|
||||
from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
|
||||
from pipecat.audio.vad.silero import SileroVADAnalyzer
|
||||
from pipecat.audio.vad.vad_analyzer import VADParams
|
||||
from pipecat.frames.frames import LLMRunFrame
|
||||
from pipecat.pipeline.pipeline import Pipeline
|
||||
from pipecat.pipeline.runner import PipelineRunner
|
||||
from pipecat.pipeline.task import PipelineParams, PipelineTask
|
||||
from pipecat.processors.aggregators.llm_context import LLMContext
|
||||
from pipecat.processors.aggregators.llm_response_universal import (
|
||||
LLMContextAggregatorPair,
|
||||
LLMUserAggregatorParams,
|
||||
)
|
||||
from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
|
||||
from pipecat.runner.types import RunnerArguments
|
||||
from pipecat.runner.utils import create_transport
|
||||
from pipecat.services.aws.llm import AWSBedrockLLMService, AWSBedrockLLMSettings
|
||||
from pipecat.services.deepgram.sagemaker.stt import DeepgramSageMakerSTTService
|
||||
from pipecat.services.deepgram.sagemaker.tts import DeepgramSageMakerTTSService
|
||||
from pipecat.services.aws.llm import AWSBedrockLLMService
|
||||
from pipecat.services.deepgram.stt_sagemaker import DeepgramSageMakerSTTService
|
||||
from pipecat.services.deepgram.tts import DeepgramTTSService
|
||||
from pipecat.transports.base_transport import BaseTransport, TransportParams
|
||||
from pipecat.transports.daily.transport import DailyParams
|
||||
from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
|
||||
@@ -32,20 +32,27 @@ from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
|
||||
load_dotenv(override=True)
|
||||
|
||||
|
||||
# We use lambdas to defer transport parameter creation until the transport
|
||||
# type is selected at runtime.
|
||||
# We store functions so objects (e.g. SileroVADAnalyzer) don't get
|
||||
# instantiated. The function will be called when the desired transport gets
|
||||
# selected.
|
||||
transport_params = {
|
||||
"daily": lambda: DailyParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
"twilio": lambda: FastAPIWebsocketParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
"webrtc": lambda: TransportParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
}
|
||||
|
||||
@@ -58,46 +65,37 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
# - AWS credentials configured (via environment variables or AWS CLI)
|
||||
# - A deployed SageMaker endpoint with Deepgram model
|
||||
stt = DeepgramSageMakerSTTService(
|
||||
endpoint_name=os.getenv("SAGEMAKER_STT_ENDPOINT_NAME"),
|
||||
endpoint_name=os.getenv("SAGEMAKER_ENDPOINT_NAME"),
|
||||
region=os.getenv("AWS_REGION"),
|
||||
)
|
||||
|
||||
# Initialize Deepgram SageMaker TTS Service
|
||||
# This requires:
|
||||
# - AWS credentials configured (via environment variables or AWS CLI)
|
||||
# - A deployed SageMaker endpoint with Deepgram TTS model
|
||||
tts = DeepgramSageMakerTTSService(
|
||||
endpoint_name=os.getenv("SAGEMAKER_TTS_ENDPOINT_NAME"),
|
||||
region=os.getenv("AWS_REGION"),
|
||||
settings=DeepgramSageMakerTTSService.Settings(
|
||||
voice="aura-2-andromeda-en",
|
||||
),
|
||||
)
|
||||
tts = DeepgramTTSService(api_key=os.getenv("DEEPGRAM_API_KEY"), voice="aura-2-andromeda-en")
|
||||
|
||||
llm = AWSBedrockLLMService(
|
||||
aws_region=os.getenv("AWS_REGION"),
|
||||
settings=AWSBedrockLLMSettings(
|
||||
model="us.amazon.nova-pro-v1:0",
|
||||
temperature=0.8,
|
||||
system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
|
||||
),
|
||||
model="us.amazon.nova-pro-v1:0",
|
||||
params=AWSBedrockLLMService.InputParams(temperature=0.8),
|
||||
)
|
||||
|
||||
context = LLMContext()
|
||||
user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
|
||||
context,
|
||||
user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
|
||||
)
|
||||
messages = [
|
||||
{
|
||||
"role": "system",
|
||||
"content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
|
||||
},
|
||||
]
|
||||
|
||||
context = LLMContext(messages)
|
||||
context_aggregator = LLMContextAggregatorPair(context)
|
||||
|
||||
pipeline = Pipeline(
|
||||
[
|
||||
transport.input(), # Transport user input
|
||||
stt, # STT
|
||||
user_aggregator, # User responses
|
||||
context_aggregator.user(), # User responses
|
||||
llm, # LLM
|
||||
tts, # TTS
|
||||
transport.output(), # Transport bot output
|
||||
assistant_aggregator, # Assistant spoken responses
|
||||
context_aggregator.assistant(), # Assistant spoken responses
|
||||
]
|
||||
)
|
||||
|
||||
@@ -114,9 +112,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
async def on_client_connected(transport, client):
|
||||
logger.info(f"Client connected")
|
||||
# Kick off the conversation.
|
||||
context.add_message(
|
||||
{"role": "developer", "content": "Please introduce yourself to the user."}
|
||||
)
|
||||
messages.append({"role": "system", "content": "Please introduce yourself to the user."})
|
||||
await task.queue_frames([LLMRunFrame()])
|
||||
|
||||
@transport.event_handler("on_client_disconnected")
|
||||
@@ -1,5 +1,5 @@
|
||||
#
|
||||
# Copyright (c) 2024-2026, Daily
|
||||
# Copyright (c) 2024–2025, Daily
|
||||
#
|
||||
# SPDX-License-Identifier: BSD 2-Clause License
|
||||
#
|
||||
@@ -7,18 +7,21 @@
|
||||
|
||||
import os
|
||||
|
||||
from deepgram import LiveOptions
|
||||
from dotenv import load_dotenv
|
||||
from loguru import logger
|
||||
|
||||
from pipecat.frames.frames import LLMRunFrame
|
||||
from pipecat.frames.frames import (
|
||||
InterruptionFrame,
|
||||
LLMRunFrame,
|
||||
UserStartedSpeakingFrame,
|
||||
UserStoppedSpeakingFrame,
|
||||
)
|
||||
from pipecat.pipeline.pipeline import Pipeline
|
||||
from pipecat.pipeline.runner import PipelineRunner
|
||||
from pipecat.pipeline.task import PipelineParams, PipelineTask
|
||||
from pipecat.processors.aggregators.llm_context import LLMContext
|
||||
from pipecat.processors.aggregators.llm_response_universal import (
|
||||
LLMContextAggregatorPair,
|
||||
LLMUserAggregatorParams,
|
||||
)
|
||||
from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
|
||||
from pipecat.runner.types import RunnerArguments
|
||||
from pipecat.runner.utils import create_transport
|
||||
from pipecat.services.deepgram.stt import DeepgramSTTService
|
||||
@@ -27,13 +30,13 @@ from pipecat.services.openai.llm import OpenAILLMService
|
||||
from pipecat.transports.base_transport import BaseTransport, TransportParams
|
||||
from pipecat.transports.daily.transport import DailyParams
|
||||
from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
|
||||
from pipecat.turns.user_turn_strategies import ExternalUserTurnStrategies
|
||||
|
||||
load_dotenv(override=True)
|
||||
|
||||
|
||||
# We use lambdas to defer transport parameter creation until the transport
|
||||
# type is selected at runtime.
|
||||
# We store functions so objects (e.g. SileroVADAnalyzer) don't get
|
||||
# instantiated. The function will be called when the desired transport gets
|
||||
# selected.
|
||||
transport_params = {
|
||||
"daily": lambda: DailyParams(
|
||||
audio_in_enabled=True,
|
||||
@@ -55,41 +58,32 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
|
||||
stt = DeepgramSTTService(
|
||||
api_key=os.getenv("DEEPGRAM_API_KEY"),
|
||||
settings=DeepgramSTTService.Settings(
|
||||
vad_events=True,
|
||||
utterance_end_ms="1000",
|
||||
),
|
||||
live_options=LiveOptions(vad_events=True, utterance_end_ms="1000"),
|
||||
)
|
||||
|
||||
tts = DeepgramTTSService(
|
||||
api_key=os.getenv("DEEPGRAM_API_KEY"),
|
||||
settings=DeepgramTTSService.Settings(
|
||||
voice="aura-2-andromeda-en",
|
||||
),
|
||||
)
|
||||
tts = DeepgramTTSService(api_key=os.getenv("DEEPGRAM_API_KEY"), voice="aura-2-andromeda-en")
|
||||
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
settings=OpenAILLMService.Settings(
|
||||
system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
|
||||
),
|
||||
)
|
||||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
|
||||
|
||||
context = LLMContext()
|
||||
user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
|
||||
context,
|
||||
user_params=LLMUserAggregatorParams(user_turn_strategies=ExternalUserTurnStrategies()),
|
||||
)
|
||||
messages = [
|
||||
{
|
||||
"role": "system",
|
||||
"content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
|
||||
},
|
||||
]
|
||||
|
||||
context = LLMContext(messages)
|
||||
context_aggregator = LLMContextAggregatorPair(context)
|
||||
|
||||
pipeline = Pipeline(
|
||||
[
|
||||
transport.input(), # Transport user input
|
||||
stt, # STT
|
||||
user_aggregator, # User responses
|
||||
context_aggregator.user(), # User responses
|
||||
llm, # LLM
|
||||
tts, # TTS
|
||||
transport.output(), # Transport bot output
|
||||
assistant_aggregator, # Assistant spoken responses
|
||||
context_aggregator.assistant(), # Assistant spoken responses
|
||||
]
|
||||
)
|
||||
|
||||
@@ -102,13 +96,19 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
|
||||
)
|
||||
|
||||
@stt.event_handler("on_speech_started")
|
||||
async def on_speech_started(stt, *args, **kwargs):
|
||||
await task.queue_frames([InterruptionFrame(), UserStartedSpeakingFrame()])
|
||||
|
||||
@stt.event_handler("on_utterance_end")
|
||||
async def on_utterance_end(stt, *args, **kwargs):
|
||||
await task.queue_frames([UserStoppedSpeakingFrame()])
|
||||
|
||||
@transport.event_handler("on_client_connected")
|
||||
async def on_client_connected(transport, client):
|
||||
logger.info(f"Client connected")
|
||||
# Kick off the conversation.
|
||||
context.add_message(
|
||||
{"role": "developer", "content": "Please introduce yourself to the user."}
|
||||
)
|
||||
messages.append({"role": "system", "content": "Please introduce yourself to the user."})
|
||||
await task.queue_frames([LLMRunFrame()])
|
||||
|
||||
@transport.event_handler("on_client_disconnected")
|
||||
@@ -1,5 +1,5 @@
|
||||
#
|
||||
# Copyright (c) 2024-2026, Daily
|
||||
# Copyright (c) 2024–2025, Daily
|
||||
#
|
||||
# SPDX-License-Identifier: BSD 2-Clause License
|
||||
#
|
||||
@@ -10,16 +10,16 @@ import os
|
||||
from dotenv import load_dotenv
|
||||
from loguru import logger
|
||||
|
||||
from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
|
||||
from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
|
||||
from pipecat.audio.vad.silero import SileroVADAnalyzer
|
||||
from pipecat.audio.vad.vad_analyzer import VADParams
|
||||
from pipecat.frames.frames import LLMRunFrame
|
||||
from pipecat.pipeline.pipeline import Pipeline
|
||||
from pipecat.pipeline.runner import PipelineRunner
|
||||
from pipecat.pipeline.task import PipelineParams, PipelineTask
|
||||
from pipecat.processors.aggregators.llm_context import LLMContext
|
||||
from pipecat.processors.aggregators.llm_response_universal import (
|
||||
LLMContextAggregatorPair,
|
||||
LLMUserAggregatorParams,
|
||||
)
|
||||
from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
|
||||
from pipecat.runner.types import RunnerArguments
|
||||
from pipecat.runner.utils import create_transport
|
||||
from pipecat.services.deepgram.stt import DeepgramSTTService
|
||||
@@ -32,20 +32,27 @@ from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
|
||||
load_dotenv(override=True)
|
||||
|
||||
|
||||
# We use lambdas to defer transport parameter creation until the transport
|
||||
# type is selected at runtime.
|
||||
# We store functions so objects (e.g. SileroVADAnalyzer) don't get
|
||||
# instantiated. The function will be called when the desired transport gets
|
||||
# selected.
|
||||
transport_params = {
|
||||
"daily": lambda: DailyParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
"twilio": lambda: FastAPIWebsocketParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
"webrtc": lambda: TransportParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
}
|
||||
|
||||
@@ -55,35 +62,29 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
|
||||
stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
|
||||
|
||||
tts = DeepgramTTSService(
|
||||
api_key=os.getenv("DEEPGRAM_API_KEY"),
|
||||
settings=DeepgramTTSService.Settings(
|
||||
voice="aura-2-andromeda-en",
|
||||
),
|
||||
)
|
||||
tts = DeepgramTTSService(api_key=os.getenv("DEEPGRAM_API_KEY"), voice="aura-2-andromeda-en")
|
||||
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
settings=OpenAILLMService.Settings(
|
||||
system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
|
||||
),
|
||||
)
|
||||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
|
||||
|
||||
context = LLMContext()
|
||||
user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
|
||||
context,
|
||||
user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
|
||||
)
|
||||
messages = [
|
||||
{
|
||||
"role": "system",
|
||||
"content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
|
||||
},
|
||||
]
|
||||
|
||||
context = LLMContext(messages)
|
||||
context_aggregator = LLMContextAggregatorPair(context)
|
||||
|
||||
pipeline = Pipeline(
|
||||
[
|
||||
transport.input(), # Transport user input
|
||||
stt, # STT
|
||||
user_aggregator, # User responses
|
||||
context_aggregator.user(), # User responses
|
||||
llm, # LLM
|
||||
tts, # TTS
|
||||
transport.output(), # Transport bot output
|
||||
assistant_aggregator, # Assistant spoken responses
|
||||
context_aggregator.assistant(), # Assistant spoken responses
|
||||
]
|
||||
)
|
||||
|
||||
@@ -100,9 +101,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
async def on_client_connected(transport, client):
|
||||
logger.info(f"Client connected")
|
||||
# Kick off the conversation.
|
||||
context.add_message(
|
||||
{"role": "developer", "content": "Please introduce yourself to the user."}
|
||||
)
|
||||
messages.append({"role": "system", "content": "Please introduce yourself to the user."})
|
||||
await task.queue_frames([LLMRunFrame()])
|
||||
|
||||
@transport.event_handler("on_client_disconnected")
|
||||
@@ -1,5 +1,5 @@
|
||||
#
|
||||
# Copyright (c) 2024-2026, Daily
|
||||
# Copyright (c) 2024–2025, Daily
|
||||
#
|
||||
# SPDX-License-Identifier: BSD 2-Clause License
|
||||
#
|
||||
@@ -11,16 +11,16 @@ import aiohttp
|
||||
from dotenv import load_dotenv
|
||||
from loguru import logger
|
||||
|
||||
from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
|
||||
from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
|
||||
from pipecat.audio.vad.silero import SileroVADAnalyzer
|
||||
from pipecat.audio.vad.vad_analyzer import VADParams
|
||||
from pipecat.frames.frames import LLMRunFrame
|
||||
from pipecat.pipeline.pipeline import Pipeline
|
||||
from pipecat.pipeline.runner import PipelineRunner
|
||||
from pipecat.pipeline.task import PipelineParams, PipelineTask
|
||||
from pipecat.processors.aggregators.llm_context import LLMContext
|
||||
from pipecat.processors.aggregators.llm_response_universal import (
|
||||
LLMContextAggregatorPair,
|
||||
LLMUserAggregatorParams,
|
||||
)
|
||||
from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
|
||||
from pipecat.runner.types import RunnerArguments
|
||||
from pipecat.runner.utils import create_transport
|
||||
from pipecat.services.elevenlabs.stt import ElevenLabsSTTService
|
||||
@@ -33,20 +33,27 @@ from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
|
||||
load_dotenv(override=True)
|
||||
|
||||
|
||||
# We use lambdas to defer transport parameter creation until the transport
|
||||
# type is selected at runtime.
|
||||
# We store functions so objects (e.g. SileroVADAnalyzer) don't get
|
||||
# instantiated. The function will be called when the desired transport gets
|
||||
# selected.
|
||||
transport_params = {
|
||||
"daily": lambda: DailyParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
"twilio": lambda: FastAPIWebsocketParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
"webrtc": lambda: TransportParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
}
|
||||
|
||||
@@ -63,34 +70,31 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
|
||||
tts = ElevenLabsHttpTTSService(
|
||||
api_key=os.getenv("ELEVENLABS_API_KEY", ""),
|
||||
voice_id=os.getenv("ELEVENLABS_VOICE_ID", ""),
|
||||
aiohttp_session=session,
|
||||
settings=ElevenLabsHttpTTSService.Settings(
|
||||
voice=os.getenv("ELEVENLABS_VOICE_ID", ""),
|
||||
),
|
||||
)
|
||||
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
settings=OpenAILLMService.Settings(
|
||||
system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
|
||||
),
|
||||
)
|
||||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
|
||||
|
||||
context = LLMContext()
|
||||
user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
|
||||
context,
|
||||
user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
|
||||
)
|
||||
messages = [
|
||||
{
|
||||
"role": "system",
|
||||
"content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
|
||||
},
|
||||
]
|
||||
|
||||
context = LLMContext(messages)
|
||||
context_aggregator = LLMContextAggregatorPair(context)
|
||||
|
||||
pipeline = Pipeline(
|
||||
[
|
||||
transport.input(), # Transport user input
|
||||
stt,
|
||||
user_aggregator, # User responses
|
||||
context_aggregator.user(), # User responses
|
||||
llm, # LLM
|
||||
tts, # TTS
|
||||
transport.output(), # Transport bot output
|
||||
assistant_aggregator, # Assistant spoken responses
|
||||
context_aggregator.assistant(), # Assistant spoken responses
|
||||
]
|
||||
)
|
||||
|
||||
@@ -107,9 +111,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
async def on_client_connected(transport, client):
|
||||
logger.info(f"Client connected")
|
||||
# Kick off the conversation.
|
||||
context.add_message(
|
||||
{"role": "developer", "content": "Please introduce yourself to the user."}
|
||||
)
|
||||
messages.append({"role": "system", "content": "Please introduce yourself to the user."})
|
||||
await task.queue_frames([LLMRunFrame()])
|
||||
|
||||
@transport.event_handler("on_client_disconnected")
|
||||
@@ -1,5 +1,5 @@
|
||||
#
|
||||
# Copyright (c) 2024-2026, Daily
|
||||
# Copyright (c) 2024–2025, Daily
|
||||
#
|
||||
# SPDX-License-Identifier: BSD 2-Clause License
|
||||
#
|
||||
@@ -10,16 +10,16 @@ import os
|
||||
from dotenv import load_dotenv
|
||||
from loguru import logger
|
||||
|
||||
from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
|
||||
from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
|
||||
from pipecat.audio.vad.silero import SileroVADAnalyzer
|
||||
from pipecat.audio.vad.vad_analyzer import VADParams
|
||||
from pipecat.frames.frames import LLMRunFrame
|
||||
from pipecat.pipeline.pipeline import Pipeline
|
||||
from pipecat.pipeline.runner import PipelineRunner
|
||||
from pipecat.pipeline.task import PipelineParams, PipelineTask
|
||||
from pipecat.processors.aggregators.llm_context import LLMContext
|
||||
from pipecat.processors.aggregators.llm_response_universal import (
|
||||
LLMContextAggregatorPair,
|
||||
LLMUserAggregatorParams,
|
||||
)
|
||||
from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
|
||||
from pipecat.runner.types import RunnerArguments
|
||||
from pipecat.runner.utils import create_transport
|
||||
from pipecat.services.elevenlabs.stt import ElevenLabsRealtimeSTTService
|
||||
@@ -32,20 +32,27 @@ from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
|
||||
load_dotenv(override=True)
|
||||
|
||||
|
||||
# We use lambdas to defer transport parameter creation until the transport
|
||||
# type is selected at runtime.
|
||||
# We store functions so objects (e.g. SileroVADAnalyzer) don't get
|
||||
# instantiated. The function will be called when the desired transport gets
|
||||
# selected.
|
||||
transport_params = {
|
||||
"daily": lambda: DailyParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
"twilio": lambda: FastAPIWebsocketParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
"webrtc": lambda: TransportParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
}
|
||||
|
||||
@@ -57,33 +64,30 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
|
||||
tts = ElevenLabsTTSService(
|
||||
api_key=os.getenv("ELEVENLABS_API_KEY", ""),
|
||||
settings=ElevenLabsTTSService.Settings(
|
||||
voice=os.getenv("ELEVENLABS_VOICE_ID", ""),
|
||||
),
|
||||
voice_id=os.getenv("ELEVENLABS_VOICE_ID", ""),
|
||||
)
|
||||
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
settings=OpenAILLMService.Settings(
|
||||
system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
|
||||
),
|
||||
)
|
||||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
|
||||
|
||||
context = LLMContext()
|
||||
user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
|
||||
context,
|
||||
user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
|
||||
)
|
||||
messages = [
|
||||
{
|
||||
"role": "system",
|
||||
"content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
|
||||
},
|
||||
]
|
||||
|
||||
context = LLMContext(messages)
|
||||
context_aggregator = LLMContextAggregatorPair(context)
|
||||
|
||||
pipeline = Pipeline(
|
||||
[
|
||||
transport.input(), # Transport user input
|
||||
stt,
|
||||
user_aggregator, # User responses
|
||||
context_aggregator.user(), # User responses
|
||||
llm, # LLM
|
||||
tts, # TTS
|
||||
transport.output(), # Transport bot output
|
||||
assistant_aggregator, # Assistant spoken responses
|
||||
context_aggregator.assistant(), # Assistant spoken responses
|
||||
]
|
||||
)
|
||||
|
||||
@@ -100,9 +104,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
async def on_client_connected(transport, client):
|
||||
logger.info(f"Client connected")
|
||||
# Kick off the conversation.
|
||||
context.add_message(
|
||||
{"role": "developer", "content": "Please introduce yourself to the user."}
|
||||
)
|
||||
messages.append({"role": "system", "content": "Please introduce yourself to the user."})
|
||||
await task.queue_frames([LLMRunFrame()])
|
||||
|
||||
@transport.event_handler("on_client_disconnected")
|
||||
129
examples/foundational/07e-interruptible-playht-http.py
Normal file
129
examples/foundational/07e-interruptible-playht-http.py
Normal file
@@ -0,0 +1,129 @@
|
||||
#
|
||||
# Copyright (c) 2024–2025, Daily
|
||||
#
|
||||
# SPDX-License-Identifier: BSD 2-Clause License
|
||||
#
|
||||
|
||||
|
||||
import os
|
||||
|
||||
from dotenv import load_dotenv
|
||||
from loguru import logger
|
||||
|
||||
from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
|
||||
from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
|
||||
from pipecat.audio.vad.silero import SileroVADAnalyzer
|
||||
from pipecat.audio.vad.vad_analyzer import VADParams
|
||||
from pipecat.frames.frames import LLMRunFrame
|
||||
from pipecat.pipeline.pipeline import Pipeline
|
||||
from pipecat.pipeline.runner import PipelineRunner
|
||||
from pipecat.pipeline.task import PipelineParams, PipelineTask
|
||||
from pipecat.processors.aggregators.llm_context import LLMContext
|
||||
from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
|
||||
from pipecat.runner.types import RunnerArguments
|
||||
from pipecat.runner.utils import create_transport
|
||||
from pipecat.services.deepgram.stt import DeepgramSTTService
|
||||
from pipecat.services.openai.llm import OpenAILLMService
|
||||
from pipecat.services.playht.tts import PlayHTHttpTTSService
|
||||
from pipecat.transports.base_transport import BaseTransport, TransportParams
|
||||
from pipecat.transports.daily.transport import DailyParams
|
||||
from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
|
||||
|
||||
load_dotenv(override=True)
|
||||
|
||||
# We store functions so objects (e.g. SileroVADAnalyzer) don't get
|
||||
# instantiated. The function will be called when the desired transport gets
|
||||
# selected.
|
||||
transport_params = {
|
||||
"daily": lambda: DailyParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
"twilio": lambda: FastAPIWebsocketParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
"webrtc": lambda: TransportParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
}
|
||||
|
||||
|
||||
async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
logger.info(f"Starting bot")
|
||||
|
||||
stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
|
||||
|
||||
tts = PlayHTHttpTTSService(
|
||||
user_id=os.getenv("PLAYHT_USER_ID"),
|
||||
api_key=os.getenv("PLAYHT_API_KEY"),
|
||||
voice_url="s3://voice-cloning-zero-shot/d9ff78ba-d016-47f6-b0ef-dd630f59414e/female-cs/manifest.json",
|
||||
)
|
||||
|
||||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
|
||||
|
||||
messages = [
|
||||
{
|
||||
"role": "system",
|
||||
"content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
|
||||
},
|
||||
]
|
||||
|
||||
context = LLMContext(messages)
|
||||
context_aggregator = LLMContextAggregatorPair(context)
|
||||
|
||||
pipeline = Pipeline(
|
||||
[
|
||||
transport.input(), # Transport user input
|
||||
stt,
|
||||
context_aggregator.user(), # User responses
|
||||
llm, # LLM
|
||||
tts, # TTS
|
||||
transport.output(), # Transport bot output
|
||||
context_aggregator.assistant(), # Assistant spoken responses
|
||||
]
|
||||
)
|
||||
|
||||
task = PipelineTask(
|
||||
pipeline,
|
||||
params=PipelineParams(
|
||||
enable_metrics=True,
|
||||
enable_usage_metrics=True,
|
||||
),
|
||||
idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
|
||||
)
|
||||
|
||||
@transport.event_handler("on_client_connected")
|
||||
async def on_client_connected(transport, client):
|
||||
logger.info(f"Client connected")
|
||||
# Kick off the conversation.
|
||||
messages.append({"role": "system", "content": "Please introduce yourself to the user."})
|
||||
await task.queue_frames([LLMRunFrame()])
|
||||
|
||||
@transport.event_handler("on_client_disconnected")
|
||||
async def on_client_disconnected(transport, client):
|
||||
logger.info(f"Client disconnected")
|
||||
await task.cancel()
|
||||
|
||||
runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
|
||||
|
||||
await runner.run(task)
|
||||
|
||||
|
||||
async def bot(runner_args: RunnerArguments):
|
||||
"""Main bot entry point compatible with Pipecat Cloud."""
|
||||
transport = await create_transport(runner_args, transport_params)
|
||||
await run_bot(transport, runner_args)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
from pipecat.runner.run import main
|
||||
|
||||
main()
|
||||
131
examples/foundational/07e-interruptible-playht.py
Normal file
131
examples/foundational/07e-interruptible-playht.py
Normal file
@@ -0,0 +1,131 @@
|
||||
#
|
||||
# Copyright (c) 2024–2025, Daily
|
||||
#
|
||||
# SPDX-License-Identifier: BSD 2-Clause License
|
||||
#
|
||||
|
||||
|
||||
import os
|
||||
|
||||
from dotenv import load_dotenv
|
||||
from loguru import logger
|
||||
|
||||
from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
|
||||
from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
|
||||
from pipecat.audio.vad.silero import SileroVADAnalyzer
|
||||
from pipecat.audio.vad.vad_analyzer import VADParams
|
||||
from pipecat.frames.frames import LLMRunFrame
|
||||
from pipecat.pipeline.pipeline import Pipeline
|
||||
from pipecat.pipeline.runner import PipelineRunner
|
||||
from pipecat.pipeline.task import PipelineParams, PipelineTask
|
||||
from pipecat.processors.aggregators.llm_context import LLMContext
|
||||
from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
|
||||
from pipecat.runner.types import RunnerArguments
|
||||
from pipecat.runner.utils import create_transport
|
||||
from pipecat.services.deepgram.stt import DeepgramSTTService
|
||||
from pipecat.services.openai.llm import OpenAILLMService
|
||||
from pipecat.services.playht.tts import PlayHTTTSService
|
||||
from pipecat.transcriptions.language import Language
|
||||
from pipecat.transports.base_transport import BaseTransport, TransportParams
|
||||
from pipecat.transports.daily.transport import DailyParams
|
||||
from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
|
||||
|
||||
load_dotenv(override=True)
|
||||
|
||||
# We store functions so objects (e.g. SileroVADAnalyzer) don't get
|
||||
# instantiated. The function will be called when the desired transport gets
|
||||
# selected.
|
||||
transport_params = {
|
||||
"daily": lambda: DailyParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
"twilio": lambda: FastAPIWebsocketParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
"webrtc": lambda: TransportParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
}
|
||||
|
||||
|
||||
async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
logger.info(f"Starting bot")
|
||||
|
||||
stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
|
||||
|
||||
tts = PlayHTTTSService(
|
||||
user_id=os.getenv("PLAYHT_USER_ID"),
|
||||
api_key=os.getenv("PLAYHT_API_KEY"),
|
||||
voice_url="s3://voice-cloning-zero-shot/e46b4027-b38d-4d24-b292-38fbca2be0ef/original/manifest.json",
|
||||
params=PlayHTTTSService.InputParams(language=Language.EN),
|
||||
)
|
||||
|
||||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
|
||||
|
||||
messages = [
|
||||
{
|
||||
"role": "system",
|
||||
"content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
|
||||
},
|
||||
]
|
||||
|
||||
context = LLMContext(messages)
|
||||
context_aggregator = LLMContextAggregatorPair(context)
|
||||
|
||||
pipeline = Pipeline(
|
||||
[
|
||||
transport.input(), # Transport user input
|
||||
stt,
|
||||
context_aggregator.user(), # User responses
|
||||
llm, # LLM
|
||||
tts, # TTS
|
||||
transport.output(), # Transport bot output
|
||||
context_aggregator.assistant(), # Assistant spoken responses
|
||||
]
|
||||
)
|
||||
|
||||
task = PipelineTask(
|
||||
pipeline,
|
||||
params=PipelineParams(
|
||||
enable_metrics=True,
|
||||
enable_usage_metrics=True,
|
||||
),
|
||||
idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
|
||||
)
|
||||
|
||||
@transport.event_handler("on_client_connected")
|
||||
async def on_client_connected(transport, client):
|
||||
logger.info(f"Client connected")
|
||||
# Kick off the conversation.
|
||||
messages.append({"role": "system", "content": "Please introduce yourself to the user."})
|
||||
await task.queue_frames([LLMRunFrame()])
|
||||
|
||||
@transport.event_handler("on_client_disconnected")
|
||||
async def on_client_disconnected(transport, client):
|
||||
logger.info(f"Client disconnected")
|
||||
await task.cancel()
|
||||
|
||||
runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
|
||||
|
||||
await runner.run(task)
|
||||
|
||||
|
||||
async def bot(runner_args: RunnerArguments):
|
||||
"""Main bot entry point compatible with Pipecat Cloud."""
|
||||
transport = await create_transport(runner_args, transport_params)
|
||||
await run_bot(transport, runner_args)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
from pipecat.runner.run import main
|
||||
|
||||
main()
|
||||
@@ -1,5 +1,5 @@
|
||||
#
|
||||
# Copyright (c) 2024-2026, Daily
|
||||
# Copyright (c) 2024–2025, Daily
|
||||
#
|
||||
# SPDX-License-Identifier: BSD 2-Clause License
|
||||
#
|
||||
@@ -10,16 +10,16 @@ import os
|
||||
from dotenv import load_dotenv
|
||||
from loguru import logger
|
||||
|
||||
from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
|
||||
from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
|
||||
from pipecat.audio.vad.silero import SileroVADAnalyzer
|
||||
from pipecat.audio.vad.vad_analyzer import VADParams
|
||||
from pipecat.frames.frames import LLMRunFrame
|
||||
from pipecat.pipeline.pipeline import Pipeline
|
||||
from pipecat.pipeline.runner import PipelineRunner
|
||||
from pipecat.pipeline.task import PipelineParams, PipelineTask
|
||||
from pipecat.processors.aggregators.llm_context import LLMContext
|
||||
from pipecat.processors.aggregators.llm_response_universal import (
|
||||
LLMContextAggregatorPair,
|
||||
LLMUserAggregatorParams,
|
||||
)
|
||||
from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
|
||||
from pipecat.runner.types import RunnerArguments
|
||||
from pipecat.runner.utils import create_transport
|
||||
from pipecat.services.azure.llm import AzureLLMService
|
||||
@@ -31,20 +31,27 @@ from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
|
||||
|
||||
load_dotenv(override=True)
|
||||
|
||||
# We use lambdas to defer transport parameter creation until the transport
|
||||
# type is selected at runtime.
|
||||
# We store functions so objects (e.g. SileroVADAnalyzer) don't get
|
||||
# instantiated. The function will be called when the desired transport gets
|
||||
# selected.
|
||||
transport_params = {
|
||||
"daily": lambda: DailyParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
"twilio": lambda: FastAPIWebsocketParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
"webrtc": lambda: TransportParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
}
|
||||
|
||||
@@ -65,27 +72,28 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
llm = AzureLLMService(
|
||||
api_key=os.getenv("AZURE_CHATGPT_API_KEY"),
|
||||
endpoint=os.getenv("AZURE_CHATGPT_ENDPOINT"),
|
||||
settings=AzureLLMService.Settings(
|
||||
model=os.getenv("AZURE_CHATGPT_MODEL"),
|
||||
system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
|
||||
),
|
||||
model=os.getenv("AZURE_CHATGPT_MODEL"),
|
||||
)
|
||||
|
||||
context = LLMContext()
|
||||
user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
|
||||
context,
|
||||
user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
|
||||
)
|
||||
messages = [
|
||||
{
|
||||
"role": "system",
|
||||
"content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
|
||||
},
|
||||
]
|
||||
|
||||
context = LLMContext(messages)
|
||||
context_aggregator = LLMContextAggregatorPair(context)
|
||||
|
||||
pipeline = Pipeline(
|
||||
[
|
||||
transport.input(), # Transport user input
|
||||
stt, # STT
|
||||
user_aggregator, # User responses
|
||||
context_aggregator.user(), # User responses
|
||||
llm, # LLM
|
||||
tts, # TTS
|
||||
transport.output(), # Transport bot output
|
||||
assistant_aggregator, # Assistant spoken responses
|
||||
context_aggregator.assistant(), # Assistant spoken responses
|
||||
]
|
||||
)
|
||||
|
||||
@@ -102,9 +110,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
async def on_client_connected(transport, client):
|
||||
logger.info(f"Client connected")
|
||||
# Kick off the conversation.
|
||||
context.add_message(
|
||||
{"role": "developer", "content": "Please introduce yourself to the user."}
|
||||
)
|
||||
messages.append({"role": "system", "content": "Please introduce yourself to the user."})
|
||||
await task.queue_frames([LLMRunFrame()])
|
||||
|
||||
@transport.event_handler("on_client_disconnected")
|
||||
@@ -1,5 +1,5 @@
|
||||
#
|
||||
# Copyright (c) 2024-2026, Daily
|
||||
# Copyright (c) 2024–2025, Daily
|
||||
#
|
||||
# SPDX-License-Identifier: BSD 2-Clause License
|
||||
#
|
||||
@@ -10,16 +10,16 @@ import os
|
||||
from dotenv import load_dotenv
|
||||
from loguru import logger
|
||||
|
||||
from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
|
||||
from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
|
||||
from pipecat.audio.vad.silero import SileroVADAnalyzer
|
||||
from pipecat.audio.vad.vad_analyzer import VADParams
|
||||
from pipecat.frames.frames import LLMRunFrame
|
||||
from pipecat.pipeline.pipeline import Pipeline
|
||||
from pipecat.pipeline.runner import PipelineRunner
|
||||
from pipecat.pipeline.task import PipelineParams, PipelineTask
|
||||
from pipecat.processors.aggregators.llm_context import LLMContext
|
||||
from pipecat.processors.aggregators.llm_response_universal import (
|
||||
LLMContextAggregatorPair,
|
||||
LLMUserAggregatorParams,
|
||||
)
|
||||
from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
|
||||
from pipecat.runner.types import RunnerArguments
|
||||
from pipecat.runner.utils import create_transport
|
||||
from pipecat.services.azure.llm import AzureLLMService
|
||||
@@ -31,20 +31,27 @@ from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
|
||||
|
||||
load_dotenv(override=True)
|
||||
|
||||
# We use lambdas to defer transport parameter creation until the transport
|
||||
# type is selected at runtime.
|
||||
# We store functions so objects (e.g. SileroVADAnalyzer) don't get
|
||||
# instantiated. The function will be called when the desired transport gets
|
||||
# selected.
|
||||
transport_params = {
|
||||
"daily": lambda: DailyParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
"twilio": lambda: FastAPIWebsocketParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
"webrtc": lambda: TransportParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
}
|
||||
|
||||
@@ -65,27 +72,28 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
llm = AzureLLMService(
|
||||
api_key=os.getenv("AZURE_CHATGPT_API_KEY"),
|
||||
endpoint=os.getenv("AZURE_CHATGPT_ENDPOINT"),
|
||||
settings=AzureLLMService.Settings(
|
||||
model=os.getenv("AZURE_CHATGPT_MODEL"),
|
||||
system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
|
||||
),
|
||||
model=os.getenv("AZURE_CHATGPT_MODEL"),
|
||||
)
|
||||
|
||||
context = LLMContext()
|
||||
user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
|
||||
context,
|
||||
user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
|
||||
)
|
||||
messages = [
|
||||
{
|
||||
"role": "system",
|
||||
"content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
|
||||
},
|
||||
]
|
||||
|
||||
context = LLMContext(messages)
|
||||
context_aggregator = LLMContextAggregatorPair(context)
|
||||
|
||||
pipeline = Pipeline(
|
||||
[
|
||||
transport.input(), # Transport user input
|
||||
stt, # STT
|
||||
user_aggregator, # User responses
|
||||
context_aggregator.user(), # User responses
|
||||
llm, # LLM
|
||||
tts, # TTS
|
||||
transport.output(), # Transport bot output
|
||||
assistant_aggregator, # Assistant spoken responses
|
||||
context_aggregator.assistant(), # Assistant spoken responses
|
||||
]
|
||||
)
|
||||
|
||||
@@ -102,9 +110,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
async def on_client_connected(transport, client):
|
||||
logger.info(f"Client connected")
|
||||
# Kick off the conversation.
|
||||
context.add_message(
|
||||
{"role": "developer", "content": "Please introduce yourself to the user."}
|
||||
)
|
||||
messages.append({"role": "system", "content": "Please introduce yourself to the user."})
|
||||
await task.queue_frames([LLMRunFrame()])
|
||||
|
||||
@transport.event_handler("on_client_disconnected")
|
||||
@@ -1,5 +1,5 @@
|
||||
#
|
||||
# Copyright (c) 2024-2026, Daily
|
||||
# Copyright (c) 2024–2025, Daily
|
||||
#
|
||||
# SPDX-License-Identifier: BSD 2-Clause License
|
||||
#
|
||||
@@ -10,16 +10,16 @@ import os
|
||||
from dotenv import load_dotenv
|
||||
from loguru import logger
|
||||
|
||||
from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
|
||||
from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
|
||||
from pipecat.audio.vad.silero import SileroVADAnalyzer
|
||||
from pipecat.audio.vad.vad_analyzer import VADParams
|
||||
from pipecat.frames.frames import LLMRunFrame
|
||||
from pipecat.pipeline.pipeline import Pipeline
|
||||
from pipecat.pipeline.runner import PipelineRunner
|
||||
from pipecat.pipeline.task import PipelineParams, PipelineTask
|
||||
from pipecat.processors.aggregators.llm_context import LLMContext
|
||||
from pipecat.processors.aggregators.llm_response_universal import (
|
||||
LLMContextAggregatorPair,
|
||||
LLMUserAggregatorParams,
|
||||
)
|
||||
from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
|
||||
from pipecat.runner.types import RunnerArguments
|
||||
from pipecat.runner.utils import create_transport
|
||||
from pipecat.services.openai.llm import OpenAILLMService
|
||||
@@ -31,20 +31,27 @@ from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
|
||||
|
||||
load_dotenv(override=True)
|
||||
|
||||
# We use lambdas to defer transport parameter creation until the transport
|
||||
# type is selected at runtime.
|
||||
# We store functions so objects (e.g. SileroVADAnalyzer) don't get
|
||||
# instantiated. The function will be called when the desired transport gets
|
||||
# selected.
|
||||
transport_params = {
|
||||
"daily": lambda: DailyParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
"twilio": lambda: FastAPIWebsocketParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
"webrtc": lambda: TransportParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
}
|
||||
|
||||
@@ -54,41 +61,33 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
|
||||
stt = OpenAISTTService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
settings=OpenAISTTService.Settings(
|
||||
model="gpt-4o-transcribe",
|
||||
prompt="Expect words related to dogs, such as breed names.",
|
||||
),
|
||||
model="gpt-4o-transcribe",
|
||||
prompt="Expect words related to dogs, such as breed names.",
|
||||
)
|
||||
|
||||
tts = OpenAITTSService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
settings=OpenAITTSService.Settings(
|
||||
voice="ballad",
|
||||
),
|
||||
)
|
||||
tts = OpenAITTSService(api_key=os.getenv("OPENAI_API_KEY"), voice="ballad")
|
||||
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
settings=OpenAILLMService.Settings(
|
||||
system_instruction="You are very knowledgable about dogs. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
|
||||
),
|
||||
)
|
||||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
|
||||
|
||||
context = LLMContext()
|
||||
user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
|
||||
context,
|
||||
user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
|
||||
)
|
||||
messages = [
|
||||
{
|
||||
"role": "system",
|
||||
"content": "You are very knowledgable about dogs. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
|
||||
},
|
||||
]
|
||||
|
||||
context = LLMContext(messages)
|
||||
context_aggregator = LLMContextAggregatorPair(context)
|
||||
|
||||
pipeline = Pipeline(
|
||||
[
|
||||
transport.input(), # Transport user input
|
||||
stt, # STT
|
||||
user_aggregator, # User responses
|
||||
context_aggregator.user(), # User responses
|
||||
llm, # LLM
|
||||
tts, # TTS
|
||||
transport.output(), # Transport bot output
|
||||
assistant_aggregator, # Assistant spoken responses
|
||||
context_aggregator.assistant(), # Assistant spoken responses
|
||||
]
|
||||
)
|
||||
|
||||
@@ -106,9 +105,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
async def on_client_connected(transport, client):
|
||||
logger.info(f"Client connected")
|
||||
# Kick off the conversation.
|
||||
context.add_message(
|
||||
{"role": "developer", "content": "Please introduce yourself to the user."}
|
||||
)
|
||||
messages.append({"role": "system", "content": "Please introduce yourself to the user."})
|
||||
await task.queue_frames([LLMRunFrame()])
|
||||
|
||||
@transport.event_handler("on_client_disconnected")
|
||||
@@ -1,49 +1,58 @@
|
||||
#
|
||||
# Copyright (c) 2024-2026, Daily
|
||||
# Copyright (c) 2024–2025, Daily
|
||||
#
|
||||
# SPDX-License-Identifier: BSD 2-Clause License
|
||||
#
|
||||
|
||||
|
||||
import os
|
||||
import time
|
||||
|
||||
from dotenv import load_dotenv
|
||||
from loguru import logger
|
||||
|
||||
from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
|
||||
from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
|
||||
from pipecat.audio.vad.silero import SileroVADAnalyzer
|
||||
from pipecat.audio.vad.vad_analyzer import VADParams
|
||||
from pipecat.frames.frames import LLMRunFrame
|
||||
from pipecat.pipeline.pipeline import Pipeline
|
||||
from pipecat.pipeline.runner import PipelineRunner
|
||||
from pipecat.pipeline.task import PipelineParams, PipelineTask
|
||||
from pipecat.processors.aggregators.llm_context import LLMContext
|
||||
from pipecat.processors.aggregators.llm_response_universal import (
|
||||
LLMContextAggregatorPair,
|
||||
LLMUserAggregatorParams,
|
||||
)
|
||||
from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
|
||||
from pipecat.runner.types import RunnerArguments
|
||||
from pipecat.runner.utils import create_transport
|
||||
from pipecat.services.cartesia.tts import CartesiaTTSService
|
||||
from pipecat.services.deepgram.stt import DeepgramSTTService
|
||||
from pipecat.services.openai.responses.llm import OpenAIResponsesHttpLLMService
|
||||
from pipecat.services.openpipe.llm import OpenPipeLLMService
|
||||
from pipecat.transports.base_transport import BaseTransport, TransportParams
|
||||
from pipecat.transports.daily.transport import DailyParams
|
||||
from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
|
||||
|
||||
load_dotenv(override=True)
|
||||
|
||||
# We use lambdas to defer transport parameter creation until the transport
|
||||
# type is selected at runtime.
|
||||
# We store functions so objects (e.g. SileroVADAnalyzer) don't get
|
||||
# instantiated. The function will be called when the desired transport gets
|
||||
# selected.
|
||||
transport_params = {
|
||||
"daily": lambda: DailyParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
"twilio": lambda: FastAPIWebsocketParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
"webrtc": lambda: TransportParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
}
|
||||
|
||||
@@ -55,33 +64,35 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
|
||||
tts = CartesiaTTSService(
|
||||
api_key=os.getenv("CARTESIA_API_KEY"),
|
||||
settings=CartesiaTTSService.Settings(
|
||||
voice="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
),
|
||||
voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
)
|
||||
|
||||
llm = OpenAIResponsesHttpLLMService(
|
||||
timestamp = int(time.time())
|
||||
llm = OpenPipeLLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
settings=OpenAIResponsesHttpLLMService.Settings(
|
||||
system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
|
||||
),
|
||||
openpipe_api_key=os.getenv("OPENPIPE_API_KEY"),
|
||||
tags={"conversation_id": f"pipecat-{timestamp}"},
|
||||
)
|
||||
|
||||
context = LLMContext()
|
||||
user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
|
||||
context,
|
||||
user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
|
||||
)
|
||||
messages = [
|
||||
{
|
||||
"role": "system",
|
||||
"content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
|
||||
},
|
||||
]
|
||||
|
||||
context = LLMContext(messages)
|
||||
context_aggregator = LLMContextAggregatorPair(context)
|
||||
|
||||
pipeline = Pipeline(
|
||||
[
|
||||
transport.input(), # Transport user input
|
||||
stt,
|
||||
user_aggregator, # User responses
|
||||
context_aggregator.user(), # User responses
|
||||
llm, # LLM
|
||||
tts, # TTS
|
||||
transport.output(), # Transport bot output
|
||||
assistant_aggregator, # Assistant spoken responses
|
||||
context_aggregator.assistant(), # Assistant spoken responses
|
||||
]
|
||||
)
|
||||
|
||||
@@ -98,9 +109,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
async def on_client_connected(transport, client):
|
||||
logger.info(f"Client connected")
|
||||
# Kick off the conversation.
|
||||
context.add_message(
|
||||
{"role": "developer", "content": "Please introduce yourself to the user."}
|
||||
)
|
||||
messages.append({"role": "system", "content": "Please introduce yourself to the user."})
|
||||
await task.queue_frames([LLMRunFrame()])
|
||||
|
||||
@transport.event_handler("on_client_disconnected")
|
||||
@@ -1,5 +1,5 @@
|
||||
#
|
||||
# Copyright (c) 2024-2026, Daily
|
||||
# Copyright (c) 2024–2025, Daily
|
||||
#
|
||||
# SPDX-License-Identifier: BSD 2-Clause License
|
||||
#
|
||||
@@ -11,16 +11,16 @@ import aiohttp
|
||||
from dotenv import load_dotenv
|
||||
from loguru import logger
|
||||
|
||||
from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
|
||||
from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
|
||||
from pipecat.audio.vad.silero import SileroVADAnalyzer
|
||||
from pipecat.audio.vad.vad_analyzer import VADParams
|
||||
from pipecat.frames.frames import LLMRunFrame
|
||||
from pipecat.pipeline.pipeline import Pipeline
|
||||
from pipecat.pipeline.runner import PipelineRunner
|
||||
from pipecat.pipeline.task import PipelineParams, PipelineTask
|
||||
from pipecat.processors.aggregators.llm_context import LLMContext
|
||||
from pipecat.processors.aggregators.llm_response_universal import (
|
||||
LLMContextAggregatorPair,
|
||||
LLMUserAggregatorParams,
|
||||
)
|
||||
from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
|
||||
from pipecat.runner.types import RunnerArguments
|
||||
from pipecat.runner.utils import create_transport
|
||||
from pipecat.services.deepgram.stt import DeepgramSTTService
|
||||
@@ -32,20 +32,27 @@ from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
|
||||
|
||||
load_dotenv(override=True)
|
||||
|
||||
# We use lambdas to defer transport parameter creation until the transport
|
||||
# type is selected at runtime.
|
||||
# We store functions so objects (e.g. SileroVADAnalyzer) don't get
|
||||
# instantiated. The function will be called when the desired transport gets
|
||||
# selected.
|
||||
transport_params = {
|
||||
"daily": lambda: DailyParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
"twilio": lambda: FastAPIWebsocketParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
"webrtc": lambda: TransportParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
}
|
||||
|
||||
@@ -59,34 +66,31 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
|
||||
tts = XTTSService(
|
||||
aiohttp_session=session,
|
||||
settings=XTTSService.Settings(
|
||||
voice="Claribel Dervla",
|
||||
),
|
||||
voice_id="Claribel Dervla",
|
||||
base_url="http://localhost:8000",
|
||||
)
|
||||
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
settings=OpenAILLMService.Settings(
|
||||
system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
|
||||
),
|
||||
)
|
||||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
|
||||
|
||||
context = LLMContext()
|
||||
user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
|
||||
context,
|
||||
user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
|
||||
)
|
||||
messages = [
|
||||
{
|
||||
"role": "system",
|
||||
"content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
|
||||
},
|
||||
]
|
||||
|
||||
context = LLMContext(messages)
|
||||
context_aggregator = LLMContextAggregatorPair(context)
|
||||
|
||||
pipeline = Pipeline(
|
||||
[
|
||||
transport.input(), # Transport user input
|
||||
stt,
|
||||
user_aggregator, # User responses
|
||||
context_aggregator.user(), # User responses
|
||||
llm, # LLM
|
||||
tts, # TTS
|
||||
transport.output(), # Transport bot output
|
||||
assistant_aggregator, # Assistant spoken responses
|
||||
context_aggregator.assistant(), # Assistant spoken responses
|
||||
]
|
||||
)
|
||||
|
||||
@@ -103,9 +107,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
async def on_client_connected(transport, client):
|
||||
logger.info(f"Client connected")
|
||||
# Kick off the conversation.
|
||||
context.add_message(
|
||||
{"role": "developer", "content": "Please introduce yourself to the user."}
|
||||
)
|
||||
messages.append({"role": "system", "content": "Please introduce yourself to the user."})
|
||||
await task.queue_frames([LLMRunFrame()])
|
||||
|
||||
@transport.event_handler("on_client_disconnected")
|
||||
@@ -1,5 +1,5 @@
|
||||
#
|
||||
# Copyright (c) 2024-2026, Daily
|
||||
# Copyright (c) 2024–2025, Daily
|
||||
#
|
||||
# SPDX-License-Identifier: BSD 2-Clause License
|
||||
#
|
||||
@@ -10,20 +10,20 @@ import os
|
||||
from dotenv import load_dotenv
|
||||
from loguru import logger
|
||||
|
||||
from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
|
||||
from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
|
||||
from pipecat.audio.vad.silero import SileroVADAnalyzer
|
||||
from pipecat.audio.vad.vad_analyzer import VADParams
|
||||
from pipecat.frames.frames import LLMRunFrame
|
||||
from pipecat.pipeline.pipeline import Pipeline
|
||||
from pipecat.pipeline.runner import PipelineRunner
|
||||
from pipecat.pipeline.task import PipelineParams, PipelineTask
|
||||
from pipecat.processors.aggregators.llm_context import LLMContext
|
||||
from pipecat.processors.aggregators.llm_response_universal import (
|
||||
LLMContextAggregatorPair,
|
||||
LLMUserAggregatorParams,
|
||||
)
|
||||
from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
|
||||
from pipecat.runner.types import RunnerArguments
|
||||
from pipecat.runner.utils import create_transport
|
||||
from pipecat.services.cartesia.tts import CartesiaTTSService
|
||||
from pipecat.services.gladia.config import LanguageConfig
|
||||
from pipecat.services.gladia.config import GladiaInputParams, LanguageConfig
|
||||
from pipecat.services.gladia.stt import GladiaSTTService
|
||||
from pipecat.services.openai.llm import OpenAILLMService
|
||||
from pipecat.transcriptions.language import Language
|
||||
@@ -33,20 +33,27 @@ from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
|
||||
|
||||
load_dotenv(override=True)
|
||||
|
||||
# We use lambdas to defer transport parameter creation until the transport
|
||||
# type is selected at runtime.
|
||||
# We store functions so objects (e.g. SileroVADAnalyzer) don't get
|
||||
# instantiated. The function will be called when the desired transport gets
|
||||
# selected.
|
||||
transport_params = {
|
||||
"daily": lambda: DailyParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
"twilio": lambda: FastAPIWebsocketParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
"webrtc": lambda: TransportParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
}
|
||||
|
||||
@@ -57,7 +64,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
stt = GladiaSTTService(
|
||||
api_key=os.getenv("GLADIA_API_KEY", ""),
|
||||
region=os.getenv("GLADIA_REGION"),
|
||||
settings=GladiaSTTService.Settings(
|
||||
params=GladiaInputParams(
|
||||
language_config=LanguageConfig(
|
||||
languages=[Language.EN],
|
||||
)
|
||||
@@ -66,33 +73,30 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
|
||||
tts = CartesiaTTSService(
|
||||
api_key=os.getenv("CARTESIA_API_KEY", ""),
|
||||
settings=CartesiaTTSService.Settings(
|
||||
voice="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
),
|
||||
voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
)
|
||||
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY", ""),
|
||||
settings=OpenAILLMService.Settings(
|
||||
system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
|
||||
),
|
||||
)
|
||||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY", ""))
|
||||
|
||||
context = LLMContext()
|
||||
user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
|
||||
context,
|
||||
user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
|
||||
)
|
||||
messages = [
|
||||
{
|
||||
"role": "system",
|
||||
"content": f"You are a helpful LLM. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
|
||||
},
|
||||
]
|
||||
|
||||
context = LLMContext(messages)
|
||||
context_aggregator = LLMContextAggregatorPair(context)
|
||||
|
||||
pipeline = Pipeline(
|
||||
[
|
||||
transport.input(), # Transport user input
|
||||
stt, # STT
|
||||
user_aggregator, # User responses
|
||||
context_aggregator.user(), # User responses
|
||||
llm, # LLM
|
||||
tts, # TTS
|
||||
transport.output(), # Transport bot output
|
||||
assistant_aggregator, # Assistant spoken responses
|
||||
context_aggregator.assistant(), # Assistant spoken responses
|
||||
]
|
||||
)
|
||||
|
||||
@@ -109,9 +113,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
async def on_client_connected(transport, client):
|
||||
logger.info(f"Client connected")
|
||||
# Kick off the conversation.
|
||||
context.add_message(
|
||||
{"role": "developer", "content": "Please introduce yourself to the user."}
|
||||
)
|
||||
messages.append({"role": "system", "content": "Please introduce yourself to the user."})
|
||||
await task.queue_frames([LLMRunFrame()])
|
||||
|
||||
@transport.event_handler("on_client_disconnected")
|
||||
@@ -1,29 +1,30 @@
|
||||
#
|
||||
# Copyright (c) 2024-2026, Daily
|
||||
# Copyright (c) 2024–2025, Daily
|
||||
#
|
||||
# SPDX-License-Identifier: BSD 2-Clause License
|
||||
#
|
||||
|
||||
|
||||
import os
|
||||
|
||||
from dotenv import load_dotenv
|
||||
from loguru import logger
|
||||
|
||||
from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
|
||||
from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
|
||||
from pipecat.audio.vad.silero import SileroVADAnalyzer
|
||||
from pipecat.audio.vad.vad_analyzer import VADParams
|
||||
from pipecat.frames.frames import LLMRunFrame
|
||||
from pipecat.pipeline.pipeline import Pipeline
|
||||
from pipecat.pipeline.runner import PipelineRunner
|
||||
from pipecat.pipeline.task import PipelineParams, PipelineTask
|
||||
from pipecat.processors.aggregators.llm_context import LLMContext
|
||||
from pipecat.processors.aggregators.llm_response_universal import (
|
||||
LLMContextAggregatorPair,
|
||||
LLMUserAggregatorParams,
|
||||
)
|
||||
from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
|
||||
from pipecat.runner.types import RunnerArguments
|
||||
from pipecat.runner.utils import create_transport
|
||||
from pipecat.services.deepgram.stt import DeepgramSTTService
|
||||
from pipecat.services.lmnt.tts import LmntTTSService
|
||||
from pipecat.services.openai.llm import OpenAILLMService
|
||||
from pipecat.services.piper.tts import PiperTTSService
|
||||
from pipecat.transports.base_transport import BaseTransport, TransportParams
|
||||
from pipecat.transports.daily.transport import DailyParams
|
||||
from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
|
||||
@@ -37,14 +38,20 @@ transport_params = {
|
||||
"daily": lambda: DailyParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
"twilio": lambda: FastAPIWebsocketParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
"webrtc": lambda: TransportParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
}
|
||||
|
||||
@@ -54,34 +61,29 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
|
||||
stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
|
||||
|
||||
tts = PiperTTSService(
|
||||
settings=PiperTTSService.Settings(
|
||||
voice="en_US-ryan-high",
|
||||
),
|
||||
)
|
||||
tts = LmntTTSService(api_key=os.getenv("LMNT_API_KEY"), voice_id="morgan")
|
||||
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
settings=OpenAILLMService.Settings(
|
||||
system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
|
||||
),
|
||||
)
|
||||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
|
||||
|
||||
context = LLMContext()
|
||||
user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
|
||||
context,
|
||||
user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
|
||||
)
|
||||
messages = [
|
||||
{
|
||||
"role": "system",
|
||||
"content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
|
||||
},
|
||||
]
|
||||
|
||||
context = LLMContext(messages)
|
||||
context_aggregator = LLMContextAggregatorPair(context)
|
||||
|
||||
pipeline = Pipeline(
|
||||
[
|
||||
transport.input(), # Transport user input
|
||||
stt,
|
||||
user_aggregator, # User responses
|
||||
context_aggregator.user(), # User respones
|
||||
llm, # LLM
|
||||
tts, # TTS
|
||||
transport.output(), # Transport bot output
|
||||
assistant_aggregator, # Assistant spoken responses
|
||||
context_aggregator.assistant(), # Assistant spoken responses
|
||||
]
|
||||
)
|
||||
|
||||
@@ -98,9 +100,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
async def on_client_connected(transport, client):
|
||||
logger.info(f"Client connected")
|
||||
# Kick off the conversation.
|
||||
context.add_message(
|
||||
{"role": "developer", "content": "Please introduce yourself to the user."}
|
||||
)
|
||||
messages.append({"role": "system", "content": "Please introduce yourself to the user."})
|
||||
await task.queue_frames([LLMRunFrame()])
|
||||
|
||||
@transport.event_handler("on_client_disconnected")
|
||||
@@ -1,5 +1,5 @@
|
||||
#
|
||||
# Copyright (c) 2024-2026, Daily
|
||||
# Copyright (c) 2024–2025, Daily
|
||||
#
|
||||
# SPDX-License-Identifier: BSD 2-Clause License
|
||||
#
|
||||
@@ -10,16 +10,17 @@ import os
|
||||
from dotenv import load_dotenv
|
||||
from loguru import logger
|
||||
|
||||
from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
|
||||
from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
|
||||
from pipecat.audio.vad.silero import SileroVADAnalyzer
|
||||
from pipecat.audio.vad.vad_analyzer import VADParams
|
||||
from pipecat.frames.frames import LLMRunFrame
|
||||
from pipecat.pipeline.pipeline import Pipeline
|
||||
from pipecat.pipeline.runner import PipelineRunner
|
||||
from pipecat.pipeline.task import PipelineParams, PipelineTask
|
||||
from pipecat.processors.aggregators.llm_context import LLMContext
|
||||
from pipecat.processors.aggregators.llm_response_universal import (
|
||||
LLMContextAggregatorPair,
|
||||
LLMUserAggregatorParams,
|
||||
)
|
||||
from pipecat.processors.aggregators.llm_response import LLMUserAggregatorParams
|
||||
from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
|
||||
from pipecat.runner.types import RunnerArguments
|
||||
from pipecat.runner.utils import create_transport
|
||||
from pipecat.services.groq.llm import GroqLLMService
|
||||
@@ -31,20 +32,27 @@ from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
|
||||
|
||||
load_dotenv(override=True)
|
||||
|
||||
# We use lambdas to defer transport parameter creation until the transport
|
||||
# type is selected at runtime.
|
||||
# We store functions so objects (e.g. SileroVADAnalyzer) don't get
|
||||
# instantiated. The function will be called when the desired transport gets
|
||||
# selected.
|
||||
transport_params = {
|
||||
"daily": lambda: DailyParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
"twilio": lambda: FastAPIWebsocketParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
"webrtc": lambda: TransportParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
}
|
||||
|
||||
@@ -55,30 +63,32 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
stt = GroqSTTService(api_key=os.getenv("GROQ_API_KEY"))
|
||||
|
||||
llm = GroqLLMService(
|
||||
api_key=os.getenv("GROQ_API_KEY"),
|
||||
settings=GroqLLMService.Settings(
|
||||
model="llama-3.1-8b-instant",
|
||||
system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
|
||||
),
|
||||
api_key=os.getenv("GROQ_API_KEY"), model="meta-llama/llama-4-maverick-17b-128e-instruct"
|
||||
)
|
||||
|
||||
tts = GroqTTSService(api_key=os.getenv("GROQ_API_KEY"))
|
||||
|
||||
context = LLMContext()
|
||||
user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
|
||||
context,
|
||||
user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
|
||||
messages = [
|
||||
{
|
||||
"role": "system",
|
||||
"content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
|
||||
},
|
||||
]
|
||||
|
||||
context = LLMContext(messages)
|
||||
context_aggregator = LLMContextAggregatorPair(
|
||||
context, user_params=LLMUserAggregatorParams(aggregation_timeout=0.05)
|
||||
)
|
||||
|
||||
pipeline = Pipeline(
|
||||
[
|
||||
transport.input(), # Transport user input
|
||||
stt,
|
||||
user_aggregator, # User responses
|
||||
context_aggregator.user(), # User responses
|
||||
llm, # LLM
|
||||
tts, # TTS
|
||||
transport.output(), # Transport bot output
|
||||
assistant_aggregator, # Assistant spoken responses
|
||||
context_aggregator.assistant(), # Assistant spoken responses
|
||||
]
|
||||
)
|
||||
|
||||
@@ -95,9 +105,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
async def on_client_connected(transport, client):
|
||||
logger.info(f"Client connected")
|
||||
# Kick off the conversation.
|
||||
context.add_message(
|
||||
{"role": "developer", "content": "Please introduce yourself to the user."}
|
||||
)
|
||||
messages.append({"role": "system", "content": "Please introduce yourself to the user."})
|
||||
await task.queue_frames([LLMRunFrame()])
|
||||
|
||||
@transport.event_handler("on_client_disconnected")
|
||||
@@ -1,5 +1,5 @@
|
||||
#
|
||||
# Copyright (c) 2024-2026, Daily
|
||||
# Copyright (c) 2024–2025, Daily
|
||||
#
|
||||
# SPDX-License-Identifier: BSD 2-Clause License
|
||||
#
|
||||
@@ -9,15 +9,12 @@ from dotenv import load_dotenv
|
||||
from loguru import logger
|
||||
|
||||
from pipecat.audio.vad.silero import SileroVADAnalyzer
|
||||
from pipecat.frames.frames import LLMMessagesAppendFrame
|
||||
from pipecat.frames.frames import LLMMessagesAppendFrame, LLMRunFrame
|
||||
from pipecat.pipeline.pipeline import Pipeline
|
||||
from pipecat.pipeline.runner import PipelineRunner
|
||||
from pipecat.pipeline.task import PipelineParams, PipelineTask
|
||||
from pipecat.processors.aggregators.llm_context import LLMContext
|
||||
from pipecat.processors.aggregators.llm_response_universal import (
|
||||
LLMContextAggregatorPair,
|
||||
LLMUserAggregatorParams,
|
||||
)
|
||||
from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
|
||||
from pipecat.processors.frameworks.strands_agents import StrandsAgentsProcessor
|
||||
from pipecat.runner.types import RunnerArguments
|
||||
from pipecat.runner.utils import create_transport
|
||||
@@ -38,20 +35,24 @@ except ImportError:
|
||||
|
||||
load_dotenv(override=True)
|
||||
|
||||
# We use lambdas to defer transport parameter creation until the transport
|
||||
# type is selected at runtime.
|
||||
# We store functions so objects (e.g. SileroVADAnalyzer) don't get
|
||||
# instantiated. The function will be called when the desired transport gets
|
||||
# selected.
|
||||
transport_params = {
|
||||
"daily": lambda: DailyParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(),
|
||||
),
|
||||
"twilio": lambda: FastAPIWebsocketParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(),
|
||||
),
|
||||
"webrtc": lambda: TransportParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(),
|
||||
),
|
||||
}
|
||||
|
||||
@@ -70,9 +71,9 @@ def build_agent(model_id: str, max_tokens: int):
|
||||
@tool
|
||||
def check_weather(location: str) -> str:
|
||||
if location.lower() == "san francisco":
|
||||
return "The weather in San Francisco is sunny and 75 degrees."
|
||||
return "The weather in San Francisco is sunny and 30 degrees."
|
||||
elif location.lower() == "sydney":
|
||||
return "The weather in Sydney is cloudy and 60 degrees."
|
||||
return "The weather in Sydney is cloudy and 20 degrees."
|
||||
else:
|
||||
return "I'm not sure about the weather in that location."
|
||||
|
||||
@@ -95,16 +96,13 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
|
||||
tts = AWSPollyTTSService(
|
||||
region="us-west-2", # only specific regions support generative TTS
|
||||
settings=AWSPollyTTSService.Settings(
|
||||
voice="Joanna",
|
||||
engine="generative",
|
||||
rate="1.1",
|
||||
),
|
||||
voice_id="Joanna",
|
||||
params=AWSPollyTTSService.InputParams(engine="generative", rate="1.1"),
|
||||
)
|
||||
|
||||
# Create Strands agent processor
|
||||
try:
|
||||
agent = build_agent(model_id="us.anthropic.claude-sonnet-4-6", max_tokens=8000)
|
||||
agent = build_agent(model_id="us.anthropic.claude-3-5-haiku-20241022-v1:0", max_tokens=8000)
|
||||
llm = StrandsAgentsProcessor(agent=agent)
|
||||
logger.info("Successfully created Strands agent for NAB customer service coaching")
|
||||
except Exception as e:
|
||||
@@ -116,20 +114,17 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
|
||||
# Setup context aggregators for message handling
|
||||
context = LLMContext()
|
||||
user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
|
||||
context,
|
||||
user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
|
||||
)
|
||||
context_aggregator = LLMContextAggregatorPair(context)
|
||||
|
||||
pipeline = Pipeline(
|
||||
[
|
||||
transport.input(), # Transport user input
|
||||
stt, # Speech-to-text
|
||||
user_aggregator, # User responses
|
||||
context_aggregator.user(), # User responses
|
||||
llm, # Strands Agents processor
|
||||
tts, # Text-to-speech
|
||||
transport.output(), # Transport bot output
|
||||
assistant_aggregator, # Assistant spoken responses
|
||||
context_aggregator.assistant(), # Assistant spoken responses
|
||||
]
|
||||
)
|
||||
|
||||
@@ -151,8 +146,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
LLMMessagesAppendFrame(
|
||||
messages=[
|
||||
{
|
||||
"role": "developer",
|
||||
"content": f"Greet the user and introduce yourself. Don't use emojis.",
|
||||
"role": "user",
|
||||
"content": f"Greet the user and introduce yourself.",
|
||||
}
|
||||
],
|
||||
run_llm=True,
|
||||
@@ -1,5 +1,5 @@
|
||||
#
|
||||
# Copyright (c) 2024-2026, Daily
|
||||
# Copyright (c) 2024–2025, Daily
|
||||
#
|
||||
# SPDX-License-Identifier: BSD 2-Clause License
|
||||
#
|
||||
@@ -8,16 +8,16 @@
|
||||
from dotenv import load_dotenv
|
||||
from loguru import logger
|
||||
|
||||
from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
|
||||
from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
|
||||
from pipecat.audio.vad.silero import SileroVADAnalyzer
|
||||
from pipecat.audio.vad.vad_analyzer import VADParams
|
||||
from pipecat.frames.frames import LLMRunFrame
|
||||
from pipecat.pipeline.pipeline import Pipeline
|
||||
from pipecat.pipeline.runner import PipelineRunner
|
||||
from pipecat.pipeline.task import PipelineParams, PipelineTask
|
||||
from pipecat.processors.aggregators.llm_context import LLMContext
|
||||
from pipecat.processors.aggregators.llm_response_universal import (
|
||||
LLMContextAggregatorPair,
|
||||
LLMUserAggregatorParams,
|
||||
)
|
||||
from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
|
||||
from pipecat.runner.types import RunnerArguments
|
||||
from pipecat.runner.utils import create_transport
|
||||
from pipecat.services.aws.llm import AWSBedrockLLMService
|
||||
@@ -29,20 +29,27 @@ from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
|
||||
|
||||
load_dotenv(override=True)
|
||||
|
||||
# We use lambdas to defer transport parameter creation until the transport
|
||||
# type is selected at runtime.
|
||||
# We store functions so objects (e.g. SileroVADAnalyzer) don't get
|
||||
# instantiated. The function will be called when the desired transport gets
|
||||
# selected.
|
||||
transport_params = {
|
||||
"daily": lambda: DailyParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
"twilio": lambda: FastAPIWebsocketParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
"webrtc": lambda: TransportParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
}
|
||||
|
||||
@@ -54,37 +61,35 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
|
||||
tts = AWSPollyTTSService(
|
||||
region="us-west-2", # only specific regions support generative TTS
|
||||
settings=AWSPollyTTSService.Settings(
|
||||
voice="Joanna",
|
||||
engine="generative",
|
||||
rate="1.1",
|
||||
),
|
||||
voice_id="Joanna",
|
||||
params=AWSPollyTTSService.InputParams(engine="generative", rate="1.1"),
|
||||
)
|
||||
|
||||
llm = AWSBedrockLLMService(
|
||||
aws_region="us-west-2",
|
||||
settings=AWSBedrockLLMService.Settings(
|
||||
model="us.anthropic.claude-sonnet-4-6",
|
||||
temperature=0.8,
|
||||
# system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
|
||||
),
|
||||
model="us.anthropic.claude-haiku-4-5-20251001-v1:0",
|
||||
params=AWSBedrockLLMService.InputParams(temperature=0.8),
|
||||
)
|
||||
|
||||
context = LLMContext()
|
||||
user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
|
||||
context,
|
||||
user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
|
||||
)
|
||||
messages = [
|
||||
{
|
||||
"role": "system",
|
||||
"content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
|
||||
},
|
||||
]
|
||||
|
||||
context = LLMContext(messages)
|
||||
context_aggregator = LLMContextAggregatorPair(context)
|
||||
|
||||
pipeline = Pipeline(
|
||||
[
|
||||
transport.input(), # Transport user input
|
||||
stt, # STT
|
||||
user_aggregator, # User responses
|
||||
context_aggregator.user(), # User responses
|
||||
llm, # LLM
|
||||
tts, # TTS
|
||||
transport.output(), # Transport bot output
|
||||
assistant_aggregator, # Assistant spoken responses
|
||||
context_aggregator.assistant(), # Assistant spoken responses
|
||||
]
|
||||
)
|
||||
|
||||
@@ -101,9 +106,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
async def on_client_connected(transport, client):
|
||||
logger.info(f"Client connected")
|
||||
# Kick off the conversation.
|
||||
context.add_message(
|
||||
{"role": "developer", "content": "Please introduce yourself to the user."}
|
||||
)
|
||||
messages.append({"role": "user", "content": "Please introduce yourself to the user."})
|
||||
await task.queue_frames([LLMRunFrame()])
|
||||
|
||||
@transport.event_handler("on_client_disconnected")
|
||||
@@ -1,5 +1,5 @@
|
||||
#
|
||||
# Copyright (c) 2024-2026, Daily
|
||||
# Copyright (c) 2024–2025, Daily
|
||||
#
|
||||
# SPDX-License-Identifier: BSD 2-Clause License
|
||||
#
|
||||
@@ -13,6 +13,9 @@ Features showcased:
|
||||
- Gemini LLM for conversation and image generation
|
||||
- Google TTS and STT
|
||||
|
||||
Run with:
|
||||
python examples/foundational/07n-interruptible-gemini-image.py
|
||||
|
||||
Make sure to set your environment variables:
|
||||
export GOOGLE_API_KEY=your_api_key_here
|
||||
"""
|
||||
@@ -22,16 +25,16 @@ import os
|
||||
from dotenv import load_dotenv
|
||||
from loguru import logger
|
||||
|
||||
from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
|
||||
from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
|
||||
from pipecat.audio.vad.silero import SileroVADAnalyzer
|
||||
from pipecat.audio.vad.vad_analyzer import VADParams
|
||||
from pipecat.frames.frames import LLMRunFrame
|
||||
from pipecat.pipeline.pipeline import Pipeline
|
||||
from pipecat.pipeline.runner import PipelineRunner
|
||||
from pipecat.pipeline.task import PipelineParams, PipelineTask
|
||||
from pipecat.processors.aggregators.llm_context import LLMContext
|
||||
from pipecat.processors.aggregators.llm_response_universal import (
|
||||
LLMContextAggregatorPair,
|
||||
LLMUserAggregatorParams,
|
||||
)
|
||||
from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
|
||||
from pipecat.runner.types import RunnerArguments
|
||||
from pipecat.runner.utils import create_transport
|
||||
from pipecat.services.google.llm import GoogleLLMService
|
||||
@@ -40,11 +43,13 @@ from pipecat.services.google.tts import GoogleTTSService
|
||||
from pipecat.transcriptions.language import Language
|
||||
from pipecat.transports.base_transport import BaseTransport, TransportParams
|
||||
from pipecat.transports.daily.transport import DailyParams
|
||||
from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
|
||||
|
||||
load_dotenv(override=True)
|
||||
|
||||
# We use lambdas to defer transport parameter creation until the transport
|
||||
# type is selected at runtime.
|
||||
# We store functions so objects (e.g. SileroVADAnalyzer) don't get
|
||||
# instantiated. The function will be called when the desired transport gets
|
||||
# selected.
|
||||
transport_params = {
|
||||
"daily": lambda: DailyParams(
|
||||
audio_in_enabled=True,
|
||||
@@ -52,6 +57,8 @@ transport_params = {
|
||||
video_out_enabled=True,
|
||||
video_out_width=1024,
|
||||
video_out_height=1024,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
"webrtc": lambda: TransportParams(
|
||||
audio_in_enabled=True,
|
||||
@@ -59,6 +66,8 @@ transport_params = {
|
||||
video_out_enabled=True,
|
||||
video_out_width=1024,
|
||||
video_out_height=1024,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
}
|
||||
|
||||
@@ -67,44 +76,40 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
logger.info(f"Starting bot")
|
||||
|
||||
stt = GoogleSTTService(
|
||||
params=GoogleSTTService.InputParams(languages=Language.EN_US),
|
||||
credentials=os.getenv("GOOGLE_TEST_CREDENTIALS"),
|
||||
settings=GoogleSTTService.Settings(
|
||||
languages=[Language.EN_US],
|
||||
),
|
||||
)
|
||||
|
||||
tts = GoogleTTSService(
|
||||
voice_id="en-US-Chirp3-HD-Charon",
|
||||
params=GoogleTTSService.InputParams(language=Language.EN_US),
|
||||
credentials=os.getenv("GOOGLE_TEST_CREDENTIALS"),
|
||||
settings=GoogleTTSService.Settings(
|
||||
voice="en-US-Chirp3-HD-Charon",
|
||||
language=Language.EN_US,
|
||||
),
|
||||
)
|
||||
|
||||
llm = GoogleLLMService(
|
||||
api_key=os.getenv("GOOGLE_API_KEY"),
|
||||
settings=GoogleLLMService.Settings(
|
||||
model="gemini-2.5-flash-image",
|
||||
# model="gemini-3-pro-image-preview", # A more powerful model, but slower,
|
||||
system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
|
||||
),
|
||||
model="gemini-2.5-flash-image",
|
||||
)
|
||||
|
||||
context = LLMContext()
|
||||
user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
|
||||
context,
|
||||
user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
|
||||
)
|
||||
messages = [
|
||||
{
|
||||
"role": "system",
|
||||
"content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
|
||||
},
|
||||
]
|
||||
|
||||
context = LLMContext(messages)
|
||||
context_aggregator = LLMContextAggregatorPair(context)
|
||||
|
||||
pipeline = Pipeline(
|
||||
[
|
||||
transport.input(), # Transport user input
|
||||
stt, # STT
|
||||
user_aggregator, # User responses
|
||||
context_aggregator.user(), # User responses
|
||||
llm, # LLM
|
||||
tts, # Gemini TTS
|
||||
transport.output(), # Transport bot output
|
||||
assistant_aggregator, # Assistant spoken responses
|
||||
context_aggregator.assistant(), # Assistant spoken responses
|
||||
]
|
||||
)
|
||||
|
||||
@@ -121,9 +126,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
async def on_client_connected(transport, client):
|
||||
logger.info(f"Client connected")
|
||||
# Kick off the conversation with a styled introduction
|
||||
context.add_message(
|
||||
{"role": "developer", "content": "Please introduce yourself to the user."}
|
||||
)
|
||||
messages.append({"role": "system", "content": "Please introduce yourself to the user."})
|
||||
await task.queue_frames([LLMRunFrame()])
|
||||
|
||||
@transport.event_handler("on_client_disconnected")
|
||||
Some files were not shown because too many files have changed in this diff Show More
Reference in New Issue
Block a user