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hush/conte
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fix/self-r
| Author | SHA1 | Date | |
|---|---|---|---|
|
b873198a59 | ||
|
|
5b696bd4ae |
@@ -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"
|
||||
]
|
||||
}
|
||||
]
|
||||
}
|
||||
@@ -1,6 +1,6 @@
|
||||
# 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**.
|
||||
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.
|
||||
|
||||
---
|
||||
@@ -28,9 +28,9 @@ This skill analyzes all changes introduced in your branch and performs the follo
|
||||
|
||||
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"
|
||||
- “Clean up my branch code”
|
||||
- “Refactor the changes in my branch”
|
||||
- “Review and improve my branch code”
|
||||
- `/cleanup`
|
||||
|
||||
---
|
||||
|
||||
@@ -3,20 +3,21 @@ 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.
|
||||
Document a Python module and its classes using Google-style docstrings following project conventions. The class name is provided as an argument.
|
||||
|
||||
## Instructions
|
||||
|
||||
1. Determine what to document based on the argument:
|
||||
1. First, find the class in the codebase:
|
||||
```
|
||||
Search for "class ClassName" in src/pipecat/
|
||||
```
|
||||
|
||||
**If a module path is provided** (e.g. `src/pipecat/audio/vad/vad_analyzer.py`):
|
||||
- Use that file directly
|
||||
2. If multiple files contain that class name:
|
||||
- List all matches with their file paths
|
||||
- Ask the user which one they want to document
|
||||
- Wait for confirmation before proceeding
|
||||
|
||||
**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:
|
||||
3. 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
|
||||
|
||||
|
||||
@@ -157,11 +157,7 @@ After processing all mapped pairs, check for two kinds of gaps:
|
||||
|
||||
**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:
|
||||
If the user wants a new page, create it using this template structure:
|
||||
```
|
||||
---
|
||||
title: "Service Name"
|
||||
@@ -211,53 +207,6 @@ pip install "pipecat-ai[package-name]"
|
||||
[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:
|
||||
@@ -272,9 +221,6 @@ After all edits are complete, print a summary:
|
||||
### 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)
|
||||
|
||||
@@ -301,6 +247,4 @@ Before finishing, verify:
|
||||
- [ ] 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
|
||||
|
||||
3
.github/workflows/coverage.yaml
vendored
3
.github/workflows/coverage.yaml
vendored
@@ -37,12 +37,11 @@ jobs:
|
||||
uv sync --group dev \
|
||||
--extra anthropic \
|
||||
--extra aws \
|
||||
--extra deepgram \
|
||||
--extra google \
|
||||
--extra langchain \
|
||||
--extra livekit \
|
||||
--extra local-smart-turn-v3 \
|
||||
--extra piper \
|
||||
--extra sagemaker \
|
||||
--extra tracing \
|
||||
--extra websocket
|
||||
|
||||
|
||||
3
.github/workflows/tests.yaml
vendored
3
.github/workflows/tests.yaml
vendored
@@ -41,12 +41,11 @@ jobs:
|
||||
uv sync --group dev \
|
||||
--extra anthropic \
|
||||
--extra aws \
|
||||
--extra deepgram \
|
||||
--extra google \
|
||||
--extra langchain \
|
||||
--extra livekit \
|
||||
--extra local-smart-turn-v3 \
|
||||
--extra piper \
|
||||
--extra sagemaker \
|
||||
--extra tracing \
|
||||
--extra websocket
|
||||
|
||||
|
||||
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"
|
||||
383
CHANGELOG.md
383
CHANGELOG.md
@@ -7,389 +7,6 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
|
||||
|
||||
<!-- towncrier release notes start -->
|
||||
|
||||
## [0.0.104] - 2026-03-02
|
||||
|
||||
### Added
|
||||
|
||||
- Added `TextAggregationMetricsData` metric measuring the time from the first
|
||||
LLM token to the first complete sentence, representing the latency cost of
|
||||
sentence aggregation in the TTS pipeline.
|
||||
(PR [#3696](https://github.com/pipecat-ai/pipecat/pull/3696))
|
||||
|
||||
- Added support for using strongly-typed objects instead of dicts for updating
|
||||
service settings at runtime.
|
||||
|
||||
Instead of, say:
|
||||
|
||||
```python
|
||||
await task.queue_frame(
|
||||
STTUpdateSettingsFrame(settings={"language": Language.ES})
|
||||
)
|
||||
```
|
||||
|
||||
you'd do:
|
||||
|
||||
```python
|
||||
await task.queue_frame(
|
||||
STTUpdateSettingsFrame(delta=DeepgramSTTSettings(language=Language.ES))
|
||||
)
|
||||
```
|
||||
|
||||
Each service now vends strongly-typed classes like `DeepgramSTTSettings`
|
||||
representing the service's runtime-updatable settings.
|
||||
(PR [#3714](https://github.com/pipecat-ai/pipecat/pull/3714))
|
||||
|
||||
- Added support for specifying private endpoints for Azure Speech-to-Text,
|
||||
enabling use in private networks behind firewalls.
|
||||
(PR [#3764](https://github.com/pipecat-ai/pipecat/pull/3764))
|
||||
|
||||
- Added `LemonSliceTransport` and `LemonSliceApi` to support adding real-time
|
||||
LemonSlice Avatars to any Daily room.
|
||||
(PR [#3791](https://github.com/pipecat-ai/pipecat/pull/3791))
|
||||
|
||||
- Added `output_medium` parameter to `AgentInputParams` and
|
||||
`OneShotInputParams` in Ultravox service to control initial output medium
|
||||
(text or voice) at call creation time.
|
||||
(PR [#3806](https://github.com/pipecat-ai/pipecat/pull/3806))
|
||||
|
||||
- Added `TurnMetricsData` as a generic metrics class for turn detection, with
|
||||
e2e processing time measurement. `KrispVivaTurn` now emits `TurnMetricsData`
|
||||
with `e2e_processing_time_ms` tracking the interval from VAD
|
||||
speech-to-silence transition to turn completion.
|
||||
(PR [#3809](https://github.com/pipecat-ai/pipecat/pull/3809))
|
||||
|
||||
- Added `on_audio_context_interrupted()` and `on_audio_context_completed()`
|
||||
callbacks to `AudioContextTTSService`. Subclasses can override these to
|
||||
perform provider-specific cleanup instead of overriding
|
||||
`_handle_interruption()`.
|
||||
(PR [#3814](https://github.com/pipecat-ai/pipecat/pull/3814))
|
||||
|
||||
- Added `on_summary_applied` event to `LLMContextSummarizer` for observability,
|
||||
providing message counts before and after context summarization.
|
||||
(PR [#3855](https://github.com/pipecat-ai/pipecat/pull/3855))
|
||||
|
||||
- Added `summary_message_template` to `LLMContextSummarizationConfig` for
|
||||
customizing how summaries are formatted when injected into context (e.g.,
|
||||
wrapping in XML tags).
|
||||
(PR [#3855](https://github.com/pipecat-ai/pipecat/pull/3855))
|
||||
|
||||
- Added `summarization_timeout` to `LLMContextSummarizationConfig` (default
|
||||
120s) to prevent hung LLM calls from permanently blocking future
|
||||
summarizations.
|
||||
(PR [#3855](https://github.com/pipecat-ai/pipecat/pull/3855))
|
||||
|
||||
- Added optional `llm` field to `LLMContextSummarizationConfig` for routing
|
||||
summarization to a dedicated LLM service (e.g., a cheaper/faster model)
|
||||
instead of the pipeline's primary model.
|
||||
(PR [#3855](https://github.com/pipecat-ai/pipecat/pull/3855))
|
||||
|
||||
- Add AssemblyAI u3-rt-pro model support with built-in turn detection mode
|
||||
(PR [#3856](https://github.com/pipecat-ai/pipecat/pull/3856))
|
||||
|
||||
- Added `LLMSummarizeContextFrame` to trigger on-demand context summarization
|
||||
from anywhere in the pipeline (e.g. a function call tool). Accepts an
|
||||
optional `config: LLMContextSummaryConfig` to override summary generation
|
||||
settings per request.
|
||||
(PR [#3863](https://github.com/pipecat-ai/pipecat/pull/3863))
|
||||
|
||||
- Added `LLMContextSummaryConfig` (summary generation params:
|
||||
`target_context_tokens`, `min_messages_after_summary`,
|
||||
`summarization_prompt`) and `LLMAutoContextSummarizationConfig` (auto-trigger
|
||||
thresholds: `max_context_tokens`, `max_unsummarized_messages`, plus a nested
|
||||
`summary_config`). These replace the monolithic
|
||||
`LLMContextSummarizationConfig`.
|
||||
(PR [#3863](https://github.com/pipecat-ai/pipecat/pull/3863))
|
||||
|
||||
- Added support for the `speed_alpha` parameter to the `arcana` model in
|
||||
`RimeTTSService`.
|
||||
(PR [#3873](https://github.com/pipecat-ai/pipecat/pull/3873))
|
||||
|
||||
- Added `ClientConnectedFrame`, a new `SystemFrame` pushed by all transports
|
||||
(Daily, LiveKit, FastAPI WebSocket, WebSocket Server, SmallWebRTC, HeyGen,
|
||||
Tavus) when a client connects. Enables observers to track transport readiness
|
||||
timing.
|
||||
(PR [#3881](https://github.com/pipecat-ai/pipecat/pull/3881))
|
||||
|
||||
- Added `StartupTimingObserver` for measuring how long each processor's
|
||||
`start()` method takes during pipeline startup. Also measures transport
|
||||
readiness — the time from `StartFrame` to first client connection — via the
|
||||
`on_transport_timing_report` event.
|
||||
(PR [#3881](https://github.com/pipecat-ai/pipecat/pull/3881))
|
||||
|
||||
- Added `BotConnectedFrame` for SFU transports and `on_transport_timing_report`
|
||||
event to `StartupTimingObserver` with bot and client connection timing.
|
||||
(PR [#3881](https://github.com/pipecat-ai/pipecat/pull/3881))
|
||||
|
||||
- Added optional `direction` parameter to `PipelineTask.queue_frame()` and
|
||||
`PipelineTask.queue_frames()`, allowing frames to be pushed upstream from the
|
||||
end of the pipeline.
|
||||
(PR [#3883](https://github.com/pipecat-ai/pipecat/pull/3883))
|
||||
|
||||
- Added `on_latency_breakdown` event to `UserBotLatencyObserver` providing
|
||||
per-service TTFB, text aggregation, user turn duration, and function call
|
||||
latency metrics for each user-to-bot response cycle.
|
||||
(PR [#3885](https://github.com/pipecat-ai/pipecat/pull/3885))
|
||||
|
||||
- Added `on_first_bot_speech_latency` event to `UserBotLatencyObserver`
|
||||
measuring the time from client connection to first bot speech. An
|
||||
`on_latency_breakdown` is also emitted for this first speech event.
|
||||
(PR [#3885](https://github.com/pipecat-ai/pipecat/pull/3885))
|
||||
|
||||
- Added `broadcast_interruption()` to `FrameProcessor`. This method pushes an
|
||||
`InterruptionFrame` both upstream and downstream directly from the calling
|
||||
processor, avoiding the round-trip through the pipeline task that
|
||||
`push_interruption_task_frame_and_wait()` required.
|
||||
(PR [#3896](https://github.com/pipecat-ai/pipecat/pull/3896))
|
||||
|
||||
### Changed
|
||||
|
||||
- Added `text_aggregation_mode` parameter to `TTSService` and all TTS
|
||||
subclasses with a new `TextAggregationMode` enum (`SENTENCE`, `TOKEN`). All
|
||||
text now flows through text aggregators regardless of mode, enabling pattern
|
||||
detection and tag handling in TOKEN mode.
|
||||
(PR [#3696](https://github.com/pipecat-ai/pipecat/pull/3696))
|
||||
|
||||
- ⚠️ Refactored runtime-updatable service settings to use strongly-typed
|
||||
classes (`TTSSettings`, `STTSettings`, `LLMSettings`, and service-specific
|
||||
subclasses) instead of plain dicts. Each service's `_settings` now holds
|
||||
these strongly-typed objects. For service maintainers, see changes in
|
||||
COMMUNITY_INTEGRATIONS.md.
|
||||
(PR [#3714](https://github.com/pipecat-ai/pipecat/pull/3714))
|
||||
|
||||
- Word timestamp support has been moved from `WordTTSService` into `TTSService`
|
||||
via a new `supports_word_timestamps` parameter. Services that previously
|
||||
extended `WordTTSService`, `AudioContextWordTTSService`, or
|
||||
`WebsocketWordTTSService` now pass `supports_word_timestamps=True` to their
|
||||
parent `__init__` instead.
|
||||
(PR [#3786](https://github.com/pipecat-ai/pipecat/pull/3786))
|
||||
|
||||
- Improved Ultravox TTFB measurement accuracy by using VAD speech end time
|
||||
instead of `UserStoppedSpeakingFrame` timing.
|
||||
(PR [#3806](https://github.com/pipecat-ai/pipecat/pull/3806))
|
||||
|
||||
- Aligned `UltravoxRealtimeLLMService` frame handling with OpenAI/Gemini
|
||||
realtime services: added `InterruptionFrame` handling with metrics cleanup,
|
||||
processing metrics at response boundaries, and improved agent transcript
|
||||
handling for both voice and text output modalities.
|
||||
(PR [#3806](https://github.com/pipecat-ai/pipecat/pull/3806))
|
||||
|
||||
- Updated `OpenAIRealtimeLLMService` default model to `gpt-realtime-1.5`.
|
||||
(PR [#3807](https://github.com/pipecat-ai/pipecat/pull/3807))
|
||||
|
||||
- Added `api_key` parameter to `KrispVivaSDKManager`, `KrispVivaTurn`, and
|
||||
`KrispVivaFilter` for Krisp SDK v1.6.1+ licensing. Falls back to
|
||||
`KRISP_VIVA_API_KEY` environment variable.
|
||||
(PR [#3809](https://github.com/pipecat-ai/pipecat/pull/3809))
|
||||
|
||||
- Bumped `nltk` minimum version from 3.9.1 to 3.9.3 to resolve a security
|
||||
vulnerability.
|
||||
(PR [#3811](https://github.com/pipecat-ai/pipecat/pull/3811))
|
||||
|
||||
- `ServiceSettingsUpdateFrame`s are now `UninterruptibleFrame`s. Generally
|
||||
speaking, you don't want a user interruption to prevent a service setting
|
||||
change from going into effect. Note that you usually don't use
|
||||
`ServiceSettingsUpdateFrame` directly, you use one of its subclasses:
|
||||
- `LLMUpdateSettingsFrame`
|
||||
- `TTSUpdateSettingsFrame`
|
||||
- `STTUpdateSettingsFrame`
|
||||
(PR [#3819](https://github.com/pipecat-ai/pipecat/pull/3819))
|
||||
|
||||
- Updated context summarization to use `user` role instead of `assistant` for
|
||||
summary messages.
|
||||
(PR [#3855](https://github.com/pipecat-ai/pipecat/pull/3855))
|
||||
|
||||
- Rename `AssemblyAISTTService` parameter
|
||||
`min_end_of_turn_silence_when_confident` parameter to `min_turn_silence` (old
|
||||
name still supported with deprecation warning)
|
||||
(PR [#3856](https://github.com/pipecat-ai/pipecat/pull/3856))
|
||||
|
||||
- ⚠️ Renamed `LLMAssistantAggregatorParams` fields:
|
||||
`enable_context_summarization` → `enable_auto_context_summarization` and
|
||||
`context_summarization_config` → `auto_context_summarization_config` (now
|
||||
accepts `LLMAutoContextSummarizationConfig`). The old names still work with a
|
||||
`DeprecationWarning` for one release cycle.
|
||||
(PR [#3863](https://github.com/pipecat-ai/pipecat/pull/3863))
|
||||
|
||||
- `ElevenLabsRealtimeSTTService` now sets `TranscriptionFrame.finalized` to
|
||||
`True` when using `CommitStrategy.MANUAL`.
|
||||
(PR [#3865](https://github.com/pipecat-ai/pipecat/pull/3865))
|
||||
|
||||
- Updated numba version pin from == to >=0.61.2
|
||||
(PR [#3868](https://github.com/pipecat-ai/pipecat/pull/3868))
|
||||
|
||||
- Updated tracing code to use `ServiceSettings` dataclass API
|
||||
(`given_fields()`, attribute access) instead of dict-style access
|
||||
(`.items()`, `in`, subscript).
|
||||
(PR [#3879](https://github.com/pipecat-ai/pipecat/pull/3879))
|
||||
|
||||
- ⚠️ Removed `event` field and `complete()` method from `InterruptionFrame`.
|
||||
Removed `event` field from `InterruptionTaskFrame`. These are no longer
|
||||
needed since `broadcast_interruption()` does not require a round-trip
|
||||
completion signal.
|
||||
(PR [#3896](https://github.com/pipecat-ai/pipecat/pull/3896))
|
||||
|
||||
- Moved `pipecat.services.deepgram.stt_sagemaker` and
|
||||
`pipecat.services.deepgram.tts_sagemaker` to
|
||||
`pipecat.services.deepgram.sagemaker.stt` and
|
||||
`pipecat.services.deepgram.sagemaker.tts`. The old import paths still work
|
||||
but emit a `DeprecationWarning`.
|
||||
(PR [#3902](https://github.com/pipecat-ai/pipecat/pull/3902))
|
||||
|
||||
### Deprecated
|
||||
|
||||
- ⚠️ Deprecated `aggregate_sentences` parameter on `TTSService` and all TTS
|
||||
subclasses. Use `text_aggregation_mode=TextAggregationMode.SENTENCE` or
|
||||
`text_aggregation_mode=TextAggregationMode.TOKEN` instead.
|
||||
(PR [#3696](https://github.com/pipecat-ai/pipecat/pull/3696))
|
||||
|
||||
- Deprecated `set_model()`, `set_voice()`, and `set_language()` on AI services
|
||||
in favor of runtime updates via `TTSUpdateSettingsFrame`,
|
||||
`STTUpdateSettingsFrame`, and `LLMUpdateSettingsFrame`.
|
||||
|
||||
⚠️ Note, too, a subtle behavior change in these deprecated methods. Whereas
|
||||
previously only `set_language()` caused the service to actually react to the
|
||||
update (e.g. by reconnecting to a remote service so it an pick up the
|
||||
change), now all these methods do. This change was made as part of a refactor
|
||||
making them all work the same way under the hood.
|
||||
(PR [#3714](https://github.com/pipecat-ai/pipecat/pull/3714))
|
||||
|
||||
- Dict-based `*UpdateSettingsFrame(settings={...})` is deprecated in favor of
|
||||
passing typed settings delta objects with
|
||||
`*UpdateSettingsFrame(delta={...})`.
|
||||
(PR [#3714](https://github.com/pipecat-ai/pipecat/pull/3714))
|
||||
|
||||
- Deprecated `WordTTSService`, `WebsocketWordTTSService`,
|
||||
`AudioContextWordTTSService`, and `InterruptibleWordTTSService`. Use their
|
||||
non-word counterparts with `supports_word_timestamps=True` instead:
|
||||
- `WordTTSService` → `TTSService(supports_word_timestamps=True)`
|
||||
- `WebsocketWordTTSService` →
|
||||
`WebsocketTTSService(supports_word_timestamps=True)`
|
||||
- `AudioContextWordTTSService` →
|
||||
`AudioContextTTSService(supports_word_timestamps=True)`
|
||||
- `InterruptibleWordTTSService` →
|
||||
`InterruptibleTTSService(supports_word_timestamps=True)`
|
||||
(PR [#3786](https://github.com/pipecat-ai/pipecat/pull/3786))
|
||||
|
||||
- Deprecated `SmartTurnMetricsData` in favor of `TurnMetricsData`.
|
||||
`BaseSmartTurn` now emits `TurnMetricsData` directly.
|
||||
(PR [#3809](https://github.com/pipecat-ai/pipecat/pull/3809))
|
||||
|
||||
- Deprecated `LLMContextSummarizationConfig`. Use
|
||||
`LLMAutoContextSummarizationConfig` with a nested `LLMContextSummaryConfig`
|
||||
instead. The old class emits a `DeprecationWarning`.
|
||||
(PR [#3863](https://github.com/pipecat-ai/pipecat/pull/3863))
|
||||
|
||||
- Deprecated `push_interruption_task_frame_and_wait()` in `FrameProcessor`. Use
|
||||
`broadcast_interruption()` instead. The old method now delegates to
|
||||
`broadcast_interruption()` and logs a deprecation warning.
|
||||
(PR [#3896](https://github.com/pipecat-ai/pipecat/pull/3896))
|
||||
|
||||
### Removed
|
||||
|
||||
- Removed `local-smart-turn-v3` optional extra from `pyproject.toml`. The
|
||||
`transformers` and `onnxruntime` packages are now always installed as core
|
||||
dependencies since they are required by the default turn stop strategy,
|
||||
`TurnAnalyzerUserTurnStopStrategy` which uses `LocalSmartTurnAnalyzerV3`.
|
||||
(PR [#3803](https://github.com/pipecat-ai/pipecat/pull/3803))
|
||||
|
||||
- ⚠️ Removed `PlayHTTTSService` and `PlayHTHttpTTSService`. PlayHT has been
|
||||
shut down and is no longer available.
|
||||
(PR [#3838](https://github.com/pipecat-ai/pipecat/pull/3838))
|
||||
|
||||
### Fixed
|
||||
|
||||
- Added `LLMSpecificMessage` handling in `LLMContextSummarizationUtil` to skip
|
||||
provider-specific messages during context summarization.
|
||||
(PR [#3794](https://github.com/pipecat-ai/pipecat/pull/3794))
|
||||
|
||||
- Treated `response_cancel_not_active` as a non-fatal error in realtime
|
||||
services (`OpenAIRealtimeLLMService`, `GrokRealtimeLLMService`,
|
||||
`OpenAIRealtimeBetaLLMService`) to prevent WebSocket disconnection when
|
||||
cancelling an inactive response.
|
||||
(PR [#3795](https://github.com/pipecat-ai/pipecat/pull/3795))
|
||||
|
||||
- Fixed Poetry compatibility by inlining `local-smart-turn-v3` dependencies
|
||||
(`transformers`, `onnxruntime`) into core dependencies instead of using a
|
||||
self-referential extra.
|
||||
(PR [#3803](https://github.com/pipecat-ai/pipecat/pull/3803))
|
||||
|
||||
- Fixed `SentryMetrics` method signatures to match updated
|
||||
`FrameProcessorMetrics` base class, resolving `TypeError` when using
|
||||
`start_time`/`end_time` keyword arguments.
|
||||
(PR [#3808](https://github.com/pipecat-ai/pipecat/pull/3808))
|
||||
|
||||
- Fixed STT TTFB metrics not being reported for `SonioxSTTService` and
|
||||
`AWSTranscribeSTTService` due to missing `can_generate_metrics()` override.
|
||||
(PR [#3813](https://github.com/pipecat-ai/pipecat/pull/3813))
|
||||
|
||||
- Fixed an issue where `AudioContextTTSService`-based providers (AsyncAI,
|
||||
ElevenLabs, Inworld, Rime) did not close or clean up their server-side audio
|
||||
contexts after normal speech completion, only on interruption.
|
||||
(PR [#3814](https://github.com/pipecat-ai/pipecat/pull/3814))
|
||||
|
||||
- Fixed STT TTFB metrics measuring timeout expiry time instead of actual
|
||||
transcript arrival time.
|
||||
(PR [#3822](https://github.com/pipecat-ai/pipecat/pull/3822))
|
||||
|
||||
- Fixed `InterimTranscriptionFrame` and `TranslationFrame` being
|
||||
unintentionally pushed downstream in `LLMUserAggregator`. They are now
|
||||
consumed like `TranscriptionFrame`.
|
||||
(PR [#3825](https://github.com/pipecat-ai/pipecat/pull/3825))
|
||||
|
||||
- Fixed misleading "Empty audio frame received for STT service" warnings when
|
||||
using audio filters (e.g. `RNNoiseFilter`, `KrispVivaFilter`, `AICFilter`)
|
||||
that buffer audio internally.
|
||||
(PR [#3828](https://github.com/pipecat-ai/pipecat/pull/3828))
|
||||
|
||||
- Fixed issues with `RimeNonJsonTTSService` where trailing punctuation is
|
||||
sometimes vocalized
|
||||
(PR [#3837](https://github.com/pipecat-ai/pipecat/pull/3837))
|
||||
|
||||
- Fixed `TTSSpeakFrame` not committing spoken text to the conversation context
|
||||
when used outside of an LLM response (e.g., bot greetings or injected
|
||||
speech).
|
||||
(PR [#3845](https://github.com/pipecat-ai/pipecat/pull/3845))
|
||||
|
||||
- Removed verbose per-chunk audio logging from `GenesysAudioHookSerializer`
|
||||
that flooded production logs.
|
||||
(PR [#3850](https://github.com/pipecat-ai/pipecat/pull/3850))
|
||||
|
||||
- Add beta feature warning when using custom prompts with AssemblyAI
|
||||
(PR [#3856](https://github.com/pipecat-ai/pipecat/pull/3856))
|
||||
|
||||
- Fixed `LocalSmartTurnAnalyzerV3` producing incorrect end-of-turn predictions
|
||||
at non-16kHz sample rates (e.g. 8kHz Twilio telephony) by adding automatic
|
||||
resampling to 16kHz before Whisper feature extraction.
|
||||
(PR [#3857](https://github.com/pipecat-ai/pipecat/pull/3857))
|
||||
|
||||
- Fixed `PipelineTask` double-inserting `RTVIProcessor` into the frame chain
|
||||
when the user provides both an `RTVIProcessor` in the pipeline and a custom
|
||||
`RTVIObserver` subclass in observers.
|
||||
(PR [#3867](https://github.com/pipecat-ai/pipecat/pull/3867))
|
||||
|
||||
- Fixed turn completion instructions being lost when `LLMMessagesUpdateFrame`
|
||||
replaces the LLM context. When `filter_incomplete_user_turns` is enabled, the
|
||||
turn completion system message is now re-injected after context replacement.
|
||||
(PR [#3888](https://github.com/pipecat-ai/pipecat/pull/3888))
|
||||
|
||||
- Fixed Azure TTS and STT services silently swallowing cancellation errors
|
||||
(invalid API key, network failures, rate limiting) instead of propagating
|
||||
them as `ErrorFrame`s to the pipeline.
|
||||
(PR [#3893](https://github.com/pipecat-ai/pipecat/pull/3893))
|
||||
|
||||
### Performance
|
||||
|
||||
- Switched `GradiumTTSService` from `InterruptibleWordTTSService` to
|
||||
`AudioContextWordTTSService`, eliminating websocket disconnect/reconnect on
|
||||
every interruption by using `client_req_id`-based multiplexing.
|
||||
(PR [#3759](https://github.com/pipecat-ai/pipecat/pull/3759))
|
||||
|
||||
### Other
|
||||
|
||||
- Standardized Sarvam STT/TTS User-Agent header handling to consistently send
|
||||
Pipecat SDK identity in websocket requests.
|
||||
(PR [#3886](https://github.com/pipecat-ai/pipecat/pull/3886))
|
||||
|
||||
## [0.0.103] - 2026-02-20
|
||||
|
||||
### Added
|
||||
|
||||
30
CLAUDE.md
30
CLAUDE.md
@@ -25,7 +25,7 @@ uv run pytest tests/test_name.py
|
||||
uv run pytest tests/test_name.py::test_function_name
|
||||
|
||||
# Preview changelog
|
||||
uv run towncrier build --draft --version Unreleased
|
||||
towncrier build --draft --version Unreleased
|
||||
|
||||
# Lint and format check
|
||||
uv run ruff check
|
||||
@@ -74,7 +74,7 @@ All data flows as **Frame** objects through a pipeline of **FrameProcessors**:
|
||||
- **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`
|
||||
`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.
|
||||
|
||||
@@ -90,26 +90,23 @@ All data flows as **Frame** objects through a pipeline of **FrameProcessors**:
|
||||
|
||||
### 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 |
|
||||
| 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
|
||||
|
||||
@@ -155,3 +152,4 @@ When adding a new service:
|
||||
## 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.
|
||||
|
||||
|
||||
@@ -25,6 +25,7 @@ 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))
|
||||
- **README.md** - Must include:
|
||||
|
||||
- Introduction and explanation of your integration
|
||||
- Installation instructions
|
||||
- Usage instructions with Pipecat Pipeline
|
||||
@@ -109,6 +110,7 @@ Once your PR is submitted, post in the `#community-integrations` Discord channel
|
||||
#### Key requirements:
|
||||
|
||||
- **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
|
||||
@@ -233,79 +235,22 @@ def can_generate_metrics(self) -> bool:
|
||||
|
||||
### Dynamic Settings Updates
|
||||
|
||||
STT, LLM, and TTS services support runtime configuration changes via `*UpdateSettingsFrame`s (e.g. `STTUpdateSettingsFrame`, `TTSUpdateSettingsFrame`, `LLMUpdateSettingsFrame`).
|
||||
|
||||
Each service declares a settings dataclass that extends the appropriate base (`STTSettings`, `TTSSettings`, `LLMSettings`). Fields default to `NOT_GIVEN` so that update objects can represent sparse deltas:
|
||||
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 STTSettings, NOT_GIVEN
|
||||
|
||||
@dataclass
|
||||
class MySTTSettings(STTSettings):
|
||||
"""Settings for my STT service.
|
||||
|
||||
Parameters:
|
||||
region: Cloud region for the service.
|
||||
Args:
|
||||
language: The language to use for speech recognition.
|
||||
"""
|
||||
|
||||
region: str = field(default_factory=lambda: NOT_GIVEN)
|
||||
```
|
||||
|
||||
The service stores its current settings in `self._settings` and declares the type with a class-level annotation for editor support:
|
||||
|
||||
```python
|
||||
class MySTTService(STTService):
|
||||
_settings: MySTTSettings
|
||||
|
||||
def __init__(self, *, model: str, language: str, region: str, **kwargs):
|
||||
# An initial value should be provided for every settings field.
|
||||
# This will be validated at service start.
|
||||
# (If you track sample_rate, it can be a placeholder value like 0; see
|
||||
# "Sample Rate Handling").
|
||||
super().__init__(
|
||||
settings=MySTTSettings(model=model, language=language, region=region), **kwargs
|
||||
)
|
||||
```
|
||||
|
||||
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: STTSettings) -> dict[str, Any]:
|
||||
"""Apply a settings update, reconfiguring the recognizer 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: STTSettings) -> 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
|
||||
|
||||
@@ -315,7 +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()
|
||||
```
|
||||
|
||||
|
||||
@@ -49,12 +49,12 @@ Every pull request that makes a user-facing change should include a changelog en
|
||||
```
|
||||
|
||||
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.)
|
||||
|
||||
@@ -80,6 +80,7 @@ Every pull request that makes a user-facing change should include a changelog en
|
||||
|
||||
```markdown
|
||||
- Updated service configuration:
|
||||
|
||||
- Changed default timeout to 30 seconds
|
||||
- Added retry logic for failed connections
|
||||
```
|
||||
@@ -104,6 +105,7 @@ changelog/1234.changed.2.md
|
||||
|
||||
```markdown
|
||||
- Updated service configuration:
|
||||
|
||||
- Changed default timeout to 30 seconds
|
||||
- Added retry logic for failed connections
|
||||
```
|
||||
|
||||
45
README.md
45
README.md
@@ -55,16 +55,6 @@ 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.
|
||||
|
||||
### 📺️ Pipecat TV Channel
|
||||
|
||||
Catch new features, interviews, and how-tos on our [Pipecat TV](https://www.youtube.com/playlist?list=PLzU2zoMTQIHjqC3v4q2XVSR3hGSzwKFwH) channel.
|
||||
@@ -81,19 +71,19 @@ Catch new features, interviews, and how-tos on our [Pipecat TV](https://www.yout
|
||||
|
||||
## 🧩 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), [Hathora](https://docs.pipecat.ai/server/services/stt/hathora), [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), [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), [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), [Hathora](https://docs.pipecat.ai/server/services/tts/hathora), [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), [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), [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), [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), [SmallWebRTCTransport](https://docs.pipecat.ai/server/services/transport/small-webrtc), [WebSocket Server](https://docs.pipecat.ai/server/services/transport/websocket-server), Local |
|
||||
| Serializers | [Exotel](https://docs.pipecat.ai/server/utilities/serializers/exotel), [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), [Vonage](https://docs.pipecat.ai/server/utilities/serializers/vonage) |
|
||||
| Video | [HeyGen](https://docs.pipecat.ai/server/services/video/heygen), [LemonSlice](https://docs.pipecat.ai/server/services/video/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](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) |
|
||||
| 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), [Hathora](https://docs.pipecat.ai/server/services/stt/hathora), [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), [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), [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), [Hathora](https://docs.pipecat.ai/server/services/tts/hathora), [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), [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), [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), [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), [SmallWebRTCTransport](https://docs.pipecat.ai/server/services/transport/small-webrtc), [WebSocket Server](https://docs.pipecat.ai/server/services/transport/websocket-server), Local |
|
||||
| Serializers | [Exotel](https://docs.pipecat.ai/server/utilities/serializers/exotel), [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), [Vonage](https://docs.pipecat.ai/server/utilities/serializers/vonage) |
|
||||
| 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/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](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)
|
||||
|
||||
@@ -173,15 +163,6 @@ 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
changelog/3759.performance.md
Normal file
1
changelog/3759.performance.md
Normal file
@@ -0,0 +1 @@
|
||||
- Switched `GradiumTTSService` from `InterruptibleWordTTSService` to `AudioContextWordTTSService`, eliminating websocket disconnect/reconnect on every interruption by using `client_req_id`-based multiplexing.
|
||||
1
changelog/3802.fixed.md
Normal file
1
changelog/3802.fixed.md
Normal file
@@ -0,0 +1 @@
|
||||
- Fixed self-referential `pipecat-ai[local-smart-turn-v3]` dependency in `pyproject.toml` that caused Poetry 2.x to fail with a circular dependency error. The underlying packages (`transformers`, `onnxruntime`) are now listed directly in main dependencies.
|
||||
@@ -1 +0,0 @@
|
||||
- ⚠️ Updated `DeepgramSTTService` to use `deepgram-sdk` v6. The `LiveOptions` class was removed from the SDK and is now provided by pipecat directly; import it from `pipecat.services.deepgram.stt` instead of `deepgram`.
|
||||
@@ -1 +0,0 @@
|
||||
- Fixed `DeepgramSTTService` keepalive ping timeout disconnections. The deepgram-sdk v6 removed automatic keepalive; pipecat now sends explicit `KeepAlive` messages every 5 seconds, within the recommended 3–5 second interval before Deepgram's 10-second inactivity timeout.
|
||||
@@ -1,3 +0,0 @@
|
||||
- Support for Voice Focus 2.0 models.
|
||||
- Updated `aic-sdk` to `~=2.1.0` to support Voice Focus 2.0 models.
|
||||
- Cleaned unused `ParameterFixedError` exception handling in `AICFilter` parameter setup.
|
||||
@@ -1 +0,0 @@
|
||||
- Fixed `BufferError: Existing exports of data: object cannot be re-sized` in `AICFilter` caused by holding a `memoryview` on the mutable audio buffer across async yield points.
|
||||
@@ -1 +0,0 @@
|
||||
- `max_context_tokens` and `max_unsummarized_messages` in `LLMAutoContextSummarizationConfig` (and deprecated `LLMContextSummarizationConfig`) can now be set to `None` independently to disable that summarization threshold. At least one must remain set.
|
||||
@@ -1 +0,0 @@
|
||||
- Added optional `timeout_secs` parameter to `register_function()` and `register_direct_function()` for per-tool function call timeout control, overriding the global `function_call_timeout_secs` default.
|
||||
@@ -1 +0,0 @@
|
||||
- Added `cloud-audio-only` recording option to Daily transport's `enable_recording` property.
|
||||
@@ -1,15 +0,0 @@
|
||||
- Wired up `system_instruction` in `BaseOpenAILLMService`, `AnthropicLLMService`, and `AWSBedrockLLMService` so it works as a default system prompt, matching the behavior of the Google services. This enables sharing a single `LLMContext` across multiple LLM services, where each service provides its own system instruction independently.
|
||||
|
||||
```python
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
system_instruction="You are a helpful assistant.",
|
||||
)
|
||||
|
||||
context = LLMContext()
|
||||
|
||||
@transport.event_handler("on_client_connected")
|
||||
async def on_client_connected(transport, client):
|
||||
context.add_message({"role": "user", "content": "Please introduce yourself."})
|
||||
await task.queue_frames([LLMRunFrame()])
|
||||
```
|
||||
@@ -1 +0,0 @@
|
||||
- Updated foundational examples to use `system_instruction` on LLM services instead of adding system messages to `LLMContext`.
|
||||
@@ -42,7 +42,7 @@ This script:
|
||||
|
||||
- Creates a fresh virtual environment
|
||||
- Installs all dependencies as specified in requirements files
|
||||
- Handles conflicting dependencies (like grpcio versions for Riva)
|
||||
- Handles conflicting dependencies (like grpcio versions for Riva and PlayHT)
|
||||
- Builds the documentation in an isolated environment
|
||||
- Provides detailed logging of the build process
|
||||
|
||||
@@ -74,6 +74,7 @@ start _build/html/index.html
|
||||
├── 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.py # ReadTheDocs test build script
|
||||
```
|
||||
|
||||
@@ -104,14 +104,9 @@ 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=...
|
||||
|
||||
# LiveKit
|
||||
LIVEKIT_API_KEY=...
|
||||
LIVEKIT_API_SECRET=...
|
||||
@@ -151,6 +146,10 @@ KOALA_ACCESS_KEY=...
|
||||
# Piper
|
||||
PIPER_BASE_URL=...
|
||||
|
||||
# PlayHT
|
||||
PLAYHT_USER_ID=...
|
||||
PLAYHT_API_KEY=...
|
||||
|
||||
# Plivo
|
||||
PLIVO_AUTH_ID=...
|
||||
PLIVO_AUTH_TOKEN=...
|
||||
|
||||
@@ -42,10 +42,14 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
)
|
||||
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
system_instruction="You are an LLM in a WebRTC session, and this is a 'hello world' demo.",
|
||||
)
|
||||
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()]),
|
||||
@@ -55,9 +59,7 @@ 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": "system", "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)
|
||||
|
||||
|
||||
@@ -70,12 +70,16 @@ async def run_example(webrtc_connection: SmallWebRTCConnection):
|
||||
voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
)
|
||||
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
system_instruction="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.",
|
||||
)
|
||||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
|
||||
|
||||
context = LLMContext()
|
||||
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)
|
||||
user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
|
||||
context,
|
||||
user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
|
||||
@@ -105,7 +109,7 @@ 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": "system", "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")
|
||||
|
||||
@@ -53,13 +53,16 @@ async def main():
|
||||
voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
)
|
||||
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
model="gpt-4o",
|
||||
system_instruction="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.",
|
||||
)
|
||||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
|
||||
|
||||
context = LLMContext()
|
||||
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)
|
||||
user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
|
||||
context,
|
||||
user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
|
||||
@@ -88,9 +91,7 @@ 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": "system", "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")
|
||||
|
||||
@@ -55,17 +55,24 @@ async def main():
|
||||
|
||||
stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
|
||||
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
system_instruction="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.",
|
||||
)
|
||||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
|
||||
|
||||
tts = CartesiaTTSService(
|
||||
api_key=os.getenv("CARTESIA_API_KEY"),
|
||||
voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
)
|
||||
|
||||
context = LLMContext()
|
||||
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)
|
||||
user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
|
||||
context,
|
||||
user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
|
||||
|
||||
@@ -86,14 +86,18 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
)
|
||||
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
system_instruction="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.",
|
||||
)
|
||||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
|
||||
|
||||
ml = MetricsLogger()
|
||||
|
||||
context = LLMContext()
|
||||
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)
|
||||
user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
|
||||
context,
|
||||
user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
|
||||
@@ -125,7 +129,7 @@ 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": "system", "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")
|
||||
|
||||
@@ -103,12 +103,16 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
)
|
||||
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
system_instruction="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.",
|
||||
)
|
||||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
|
||||
|
||||
context = LLMContext()
|
||||
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)
|
||||
user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
|
||||
context,
|
||||
user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
|
||||
|
||||
@@ -59,12 +59,16 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
)
|
||||
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
system_instruction="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.",
|
||||
)
|
||||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
|
||||
|
||||
context = LLMContext()
|
||||
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)
|
||||
user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
|
||||
context,
|
||||
user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
|
||||
@@ -95,7 +99,7 @@ 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": "system", "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")
|
||||
|
||||
@@ -24,7 +24,6 @@ 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.services.tts_service import TextAggregationMode
|
||||
from pipecat.transports.base_transport import BaseTransport, TransportParams
|
||||
from pipecat.transports.daily.transport import DailyParams
|
||||
from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
|
||||
@@ -57,17 +56,18 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
tts = CartesiaTTSService(
|
||||
api_key=os.getenv("CARTESIA_API_KEY"),
|
||||
voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
# Alternatively, you can use TextAggregationMode.TOKEN to stream tokens instead of
|
||||
# sentencesfor faster response times.
|
||||
# text_aggregation_mode=TextAggregationMode.TOKEN,
|
||||
)
|
||||
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
system_instruction="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.",
|
||||
)
|
||||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
|
||||
|
||||
context = LLMContext()
|
||||
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)
|
||||
user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
|
||||
context,
|
||||
user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
|
||||
@@ -98,7 +98,7 @@ 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": "system", "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")
|
||||
|
||||
@@ -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
|
||||
@@ -61,18 +60,19 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
|
||||
tts = DeepgramTTSService(api_key=os.getenv("DEEPGRAM_API_KEY"), voice="aura-2-andromeda-en")
|
||||
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
system_instruction="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.",
|
||||
)
|
||||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
|
||||
|
||||
context = LLMContext()
|
||||
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)
|
||||
user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
|
||||
context,
|
||||
user_params=LLMUserAggregatorParams(
|
||||
user_turn_strategies=ExternalUserTurnStrategies(),
|
||||
vad_analyzer=SileroVADAnalyzer(),
|
||||
),
|
||||
user_params=LLMUserAggregatorParams(user_turn_strategies=ExternalUserTurnStrategies()),
|
||||
)
|
||||
|
||||
pipeline = Pipeline(
|
||||
@@ -100,7 +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": "system", "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")
|
||||
|
||||
@@ -63,12 +63,14 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
aiohttp_session=session,
|
||||
)
|
||||
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
system_instruction="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.",
|
||||
)
|
||||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
|
||||
|
||||
messages = []
|
||||
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)
|
||||
user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
|
||||
@@ -101,9 +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": "system", "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")
|
||||
|
||||
@@ -23,8 +23,8 @@ from pipecat.processors.aggregators.llm_response_universal import (
|
||||
from pipecat.runner.types import RunnerArguments
|
||||
from pipecat.runner.utils import create_transport
|
||||
from pipecat.services.aws.llm import AWSBedrockLLMService
|
||||
from pipecat.services.deepgram.sagemaker.stt import DeepgramSageMakerSTTService
|
||||
from pipecat.services.deepgram.sagemaker.tts import DeepgramSageMakerTTSService
|
||||
from pipecat.services.deepgram.stt_sagemaker import DeepgramSageMakerSTTService
|
||||
from pipecat.services.deepgram.tts_sagemaker import DeepgramSageMakerTTSService
|
||||
from pipecat.transports.base_transport import BaseTransport, TransportParams
|
||||
from pipecat.transports.daily.transport import DailyParams
|
||||
from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
|
||||
@@ -76,10 +76,16 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
aws_region=os.getenv("AWS_REGION"),
|
||||
model="us.amazon.nova-pro-v1:0",
|
||||
params=AWSBedrockLLMService.InputParams(temperature=0.8),
|
||||
system_instruction="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 = [
|
||||
{
|
||||
"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)
|
||||
user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
|
||||
context,
|
||||
user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
|
||||
@@ -110,7 +116,7 @@ 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": "system", "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")
|
||||
|
||||
@@ -61,12 +61,16 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
|
||||
tts = DeepgramTTSService(api_key=os.getenv("DEEPGRAM_API_KEY"), voice="aura-2-andromeda-en")
|
||||
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
system_instruction="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.",
|
||||
)
|
||||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
|
||||
|
||||
context = LLMContext()
|
||||
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)
|
||||
user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
|
||||
context,
|
||||
user_params=LLMUserAggregatorParams(user_turn_strategies=ExternalUserTurnStrategies()),
|
||||
@@ -97,7 +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": "system", "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")
|
||||
|
||||
@@ -57,12 +57,16 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
|
||||
tts = DeepgramTTSService(api_key=os.getenv("DEEPGRAM_API_KEY"), voice="aura-2-andromeda-en")
|
||||
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
system_instruction="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.",
|
||||
)
|
||||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
|
||||
|
||||
context = LLMContext()
|
||||
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)
|
||||
user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
|
||||
context,
|
||||
user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
|
||||
@@ -93,7 +97,7 @@ 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": "system", "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")
|
||||
|
||||
@@ -67,12 +67,16 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
aiohttp_session=session,
|
||||
)
|
||||
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
system_instruction="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.",
|
||||
)
|
||||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
|
||||
|
||||
context = LLMContext()
|
||||
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)
|
||||
user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
|
||||
context,
|
||||
user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
|
||||
@@ -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": "system", "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")
|
||||
|
||||
@@ -60,12 +60,16 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
voice_id=os.getenv("ELEVENLABS_VOICE_ID", ""),
|
||||
)
|
||||
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
system_instruction="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.",
|
||||
)
|
||||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
|
||||
|
||||
context = LLMContext()
|
||||
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)
|
||||
user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
|
||||
context,
|
||||
user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
|
||||
@@ -96,7 +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": "system", "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")
|
||||
|
||||
@@ -4,14 +4,14 @@
|
||||
# SPDX-License-Identifier: BSD 2-Clause License
|
||||
#
|
||||
|
||||
import asyncio
|
||||
|
||||
import os
|
||||
|
||||
from dotenv import load_dotenv
|
||||
from loguru import logger
|
||||
|
||||
from pipecat.audio.vad.silero import SileroVADAnalyzer
|
||||
from pipecat.frames.frames import LLMRunFrame, LLMUpdateSettingsFrame
|
||||
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
|
||||
@@ -22,13 +22,17 @@ from pipecat.processors.aggregators.llm_response_universal import (
|
||||
)
|
||||
from pipecat.runner.types import RunnerArguments
|
||||
from pipecat.runner.utils import create_transport
|
||||
from pipecat.services.aws.nova_sonic.llm import AWSNovaSonicLLMService, AWSNovaSonicLLMSettings
|
||||
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 use lambdas to defer transport parameter creation until the transport
|
||||
# type is selected at runtime.
|
||||
transport_params = {
|
||||
"daily": lambda: DailyParams(
|
||||
audio_in_enabled=True,
|
||||
@@ -48,21 +52,21 @@ transport_params = {
|
||||
async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
logger.info(f"Starting bot")
|
||||
|
||||
llm = AWSNovaSonicLLMService(
|
||||
secret_access_key=os.getenv("AWS_SECRET_ACCESS_KEY"),
|
||||
access_key_id=os.getenv("AWS_ACCESS_KEY_ID"),
|
||||
region=os.getenv("AWS_REGION"),
|
||||
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.",
|
||||
},
|
||||
{
|
||||
"role": "user",
|
||||
"content": "Tell me a fun fact!",
|
||||
},
|
||||
]
|
||||
|
||||
context = LLMContext(messages)
|
||||
@@ -73,11 +77,13 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
|
||||
pipeline = Pipeline(
|
||||
[
|
||||
transport.input(),
|
||||
user_aggregator,
|
||||
llm,
|
||||
transport.output(),
|
||||
assistant_aggregator,
|
||||
transport.input(), # Transport user input
|
||||
stt,
|
||||
user_aggregator, # User responses
|
||||
llm, # LLM
|
||||
tts, # TTS
|
||||
transport.output(), # Transport bot output
|
||||
assistant_aggregator, # Assistant spoken responses
|
||||
]
|
||||
)
|
||||
|
||||
@@ -93,15 +99,10 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
@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()])
|
||||
|
||||
await asyncio.sleep(10)
|
||||
logger.info("Updating AWS Nova Sonic LLM settings: temperature=0.1")
|
||||
await task.queue_frame(
|
||||
LLMUpdateSettingsFrame(delta=AWSNovaSonicLLMSettings(temperature=0.1))
|
||||
)
|
||||
|
||||
@transport.event_handler("on_client_disconnected")
|
||||
async def on_client_disconnected(transport, client):
|
||||
logger.info(f"Client disconnected")
|
||||
@@ -4,14 +4,14 @@
|
||||
# SPDX-License-Identifier: BSD 2-Clause License
|
||||
#
|
||||
|
||||
import asyncio
|
||||
|
||||
import os
|
||||
|
||||
from dotenv import load_dotenv
|
||||
from loguru import logger
|
||||
|
||||
from pipecat.audio.vad.silero import SileroVADAnalyzer
|
||||
from pipecat.frames.frames import LLMRunFrame, TTSUpdateSettingsFrame
|
||||
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
|
||||
@@ -22,9 +22,10 @@ from pipecat.processors.aggregators.llm_response_universal import (
|
||||
)
|
||||
from pipecat.runner.types import RunnerArguments
|
||||
from pipecat.runner.utils import create_transport
|
||||
from pipecat.services.cartesia.tts import CartesiaTTSService, CartesiaTTSSettings, GenerationConfig
|
||||
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
|
||||
@@ -54,17 +55,23 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
|
||||
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
|
||||
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"),
|
||||
system_instruction="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.",
|
||||
)
|
||||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
|
||||
|
||||
context = LLMContext()
|
||||
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)
|
||||
user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
|
||||
context,
|
||||
user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
|
||||
@@ -95,17 +102,9 @@ 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": "system", "content": "Please introduce yourself to the user."})
|
||||
messages.append({"role": "system", "content": "Please introduce yourself to the user."})
|
||||
await task.queue_frames([LLMRunFrame()])
|
||||
|
||||
await asyncio.sleep(10)
|
||||
logger.info("Updating Cartesia TTS settings: speed increased to 1.5")
|
||||
await task.queue_frame(
|
||||
TTSUpdateSettingsFrame(
|
||||
delta=CartesiaTTSSettings(generation_config=GenerationConfig(speed=1.5))
|
||||
)
|
||||
)
|
||||
|
||||
@transport.event_handler("on_client_disconnected")
|
||||
async def on_client_disconnected(transport, client):
|
||||
logger.info(f"Client disconnected")
|
||||
@@ -66,10 +66,16 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
api_key=os.getenv("AZURE_CHATGPT_API_KEY"),
|
||||
endpoint=os.getenv("AZURE_CHATGPT_ENDPOINT"),
|
||||
model=os.getenv("AZURE_CHATGPT_MODEL"),
|
||||
system_instruction="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 = [
|
||||
{
|
||||
"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)
|
||||
user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
|
||||
context,
|
||||
user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
|
||||
@@ -100,7 +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": "system", "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")
|
||||
|
||||
@@ -66,10 +66,16 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
api_key=os.getenv("AZURE_CHATGPT_API_KEY"),
|
||||
endpoint=os.getenv("AZURE_CHATGPT_ENDPOINT"),
|
||||
model=os.getenv("AZURE_CHATGPT_MODEL"),
|
||||
system_instruction="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 = [
|
||||
{
|
||||
"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)
|
||||
user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
|
||||
context,
|
||||
user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
|
||||
@@ -100,7 +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": "system", "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")
|
||||
|
||||
@@ -11,6 +11,7 @@ from dotenv import load_dotenv
|
||||
from loguru import logger
|
||||
|
||||
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
|
||||
@@ -60,12 +61,16 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
|
||||
tts = OpenAITTSService(api_key=os.getenv("OPENAI_API_KEY"), voice="ballad")
|
||||
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
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()
|
||||
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)
|
||||
user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
|
||||
context,
|
||||
user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
|
||||
@@ -97,7 +102,7 @@ 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": "system", "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")
|
||||
|
||||
@@ -66,12 +66,16 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
|
||||
tts = OpenAITTSService(api_key=os.getenv("OPENAI_API_KEY"), voice="ballad")
|
||||
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
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()
|
||||
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)
|
||||
user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
|
||||
context,
|
||||
user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
|
||||
@@ -103,7 +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": "system", "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")
|
||||
|
||||
@@ -65,10 +65,16 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
openpipe_api_key=os.getenv("OPENPIPE_API_KEY"),
|
||||
tags={"conversation_id": f"pipecat-{timestamp}"},
|
||||
system_instruction="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 = [
|
||||
{
|
||||
"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)
|
||||
user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
|
||||
context,
|
||||
user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
|
||||
@@ -99,7 +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": "system", "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")
|
||||
|
||||
@@ -63,12 +63,16 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
base_url="http://localhost:8000",
|
||||
)
|
||||
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
system_instruction="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.",
|
||||
)
|
||||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
|
||||
|
||||
context = LLMContext()
|
||||
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)
|
||||
user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
|
||||
context,
|
||||
user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
|
||||
@@ -99,9 +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": "system", "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")
|
||||
|
||||
@@ -56,12 +56,16 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
|
||||
tts = LmntTTSService(api_key=os.getenv("LMNT_API_KEY"), voice_id="morgan")
|
||||
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
system_instruction="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.",
|
||||
)
|
||||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
|
||||
|
||||
context = LLMContext()
|
||||
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)
|
||||
user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
|
||||
context,
|
||||
user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
|
||||
@@ -92,7 +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": "system", "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")
|
||||
|
||||
@@ -55,14 +55,19 @@ 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"),
|
||||
model="meta-llama/llama-4-maverick-17b-128e-instruct",
|
||||
system_instruction="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.",
|
||||
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()
|
||||
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)
|
||||
user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
|
||||
context,
|
||||
user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
|
||||
@@ -93,7 +98,7 @@ 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": "system", "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")
|
||||
|
||||
@@ -62,10 +62,16 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
aws_region="us-west-2",
|
||||
model="us.anthropic.claude-haiku-4-5-20251001-v1:0",
|
||||
params=AWSBedrockLLMService.InputParams(temperature=0.8),
|
||||
system_instruction="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 = [
|
||||
{
|
||||
"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)
|
||||
user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
|
||||
context,
|
||||
user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
|
||||
@@ -96,7 +102,7 @@ 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": "user", "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")
|
||||
|
||||
@@ -83,11 +83,17 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
llm = GoogleLLMService(
|
||||
api_key=os.getenv("GOOGLE_API_KEY"),
|
||||
model="gemini-2.5-flash-image",
|
||||
# model="gemini-3-pro-image-preview", # A more powerful model, but slower,
|
||||
system_instruction="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.",
|
||||
# model="gemini-3-pro-image-preview", # A more powerful model, but slower
|
||||
)
|
||||
|
||||
context = LLMContext()
|
||||
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)
|
||||
user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
|
||||
context,
|
||||
user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
|
||||
@@ -118,7 +124,7 @@ 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": "system", "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")
|
||||
|
||||
@@ -71,7 +71,13 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
llm = GoogleLLMService(
|
||||
api_key=os.getenv("GOOGLE_API_KEY"),
|
||||
model="gemini-2.5-flash",
|
||||
system_instruction="""You are a helpful AI assistant in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way.
|
||||
)
|
||||
|
||||
# System message that instructs the AI on how to speak
|
||||
messages = [
|
||||
{
|
||||
"role": "system",
|
||||
"content": """You are a helpful AI assistant in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way.
|
||||
|
||||
IMPORTANT: You're using Gemini TTS which supports expressive markup tags. You can use these tags in your responses:
|
||||
- [sigh] - Insert a sigh sound
|
||||
@@ -89,9 +95,10 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
- "The answer is... [long pause] ...42!"
|
||||
|
||||
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()
|
||||
context = LLMContext(messages)
|
||||
user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
|
||||
context,
|
||||
user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
|
||||
@@ -122,7 +129,7 @@ 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(
|
||||
messages.append(
|
||||
{
|
||||
"role": "system",
|
||||
"content": "You are an AI assistant. You can help with a variety of tasks. Introduce yourself and ask the user what they would like to know.",
|
||||
|
||||
@@ -72,10 +72,16 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
# params=GoogleLLMService.InputParams(
|
||||
# thinking=GoogleLLMService.ThinkingConfig(thinking_budget=4096)
|
||||
# ),
|
||||
system_instruction="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 = [
|
||||
{
|
||||
"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)
|
||||
user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
|
||||
context,
|
||||
user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
|
||||
@@ -106,7 +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": "system", "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")
|
||||
|
||||
@@ -72,10 +72,16 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
# params=GoogleLLMService.InputParams(
|
||||
# thinking=GoogleLLMService.ThinkingConfig(thinking_budget=4096)
|
||||
# ),
|
||||
system_instruction="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 = [
|
||||
{
|
||||
"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)
|
||||
user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
|
||||
context,
|
||||
user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
|
||||
@@ -106,7 +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": "system", "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")
|
||||
|
||||
@@ -1,175 +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.assemblyai.models import AssemblyAIConnectionParams
|
||||
from pipecat.services.assemblyai.stt import AssemblyAISTTService
|
||||
from pipecat.services.cartesia.tts import CartesiaTTSService
|
||||
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.
|
||||
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):
|
||||
"""AssemblyAI u3-rt-pro with Built-in Turn Detection
|
||||
|
||||
This example demonstrates using AssemblyAI's u3-rt-pro Speech-to-Text model
|
||||
with AssemblyAI's built-in turn detection for more natural conversation flow.
|
||||
|
||||
Key features:
|
||||
|
||||
1. AssemblyAI Turn Detection
|
||||
- Set `vad_force_turn_endpoint=False` to use AssemblyAI's built-in turn detection
|
||||
- AssemblyAI's model determines when user starts/stops speaking
|
||||
- Uses `ExternalUserTurnStrategies` to delegate turn control to AssemblyAI
|
||||
- More natural turn detection based on speech patterns and pauses
|
||||
|
||||
2. Advanced Turn Detection Tuning
|
||||
- `min_turn_silence`: Minimum silence (ms) when confident about end-of-turn.
|
||||
Lower values = faster responses. Default: 100ms
|
||||
- `max_turn_silence`: Maximum silence (ms) before forcing end-of-turn.
|
||||
Prevents long pauses. Default: 1000ms
|
||||
|
||||
3. Prompt-Based Transcription Enhancement
|
||||
- Use `prompt` parameter to improve accuracy for specific names/terms
|
||||
- Particularly useful for proper nouns, technical terms, domain vocabulary
|
||||
- Example: "Names: Xiomara, Saoirse, Krzystof. Technical terms: API, OAuth."
|
||||
|
||||
4. Speaker Diarization (Optional)
|
||||
- Enable with `speaker_labels=True`
|
||||
- Automatically identifies different speakers in multi-party conversations
|
||||
- TranscriptionFrame includes speaker_id field (e.g., "Speaker A", "Speaker B")
|
||||
|
||||
5. Language Detection (Optional, multilingual model only)
|
||||
- Enable with `language_detection=True`
|
||||
- Automatically detects spoken language
|
||||
- Available with universal-streaming-multilingual model
|
||||
|
||||
For more information: https://www.assemblyai.com/docs/speech-to-text/streaming
|
||||
"""
|
||||
logger.info(f"Starting bot")
|
||||
|
||||
stt = AssemblyAISTTService(
|
||||
api_key=os.getenv("ASSEMBLYAI_API_KEY"),
|
||||
vad_force_turn_endpoint=False, # Use AssemblyAI's built-in turn detection
|
||||
connection_params=AssemblyAIConnectionParams(
|
||||
speech_model="u3-rt-pro",
|
||||
# Optional: Tune turn detection timing (defaults shown below)
|
||||
# min_turn_silence=100, # Default
|
||||
# max_turn_silence=1000, # Default
|
||||
# Optional: Boost accuracy for specific names/terms
|
||||
# prompt="Names: Xiomara, Saoirse, Krzystof. Technical terms: API, OAuth.",
|
||||
# Optional: Enable speaker diarization
|
||||
# speaker_labels=True,
|
||||
),
|
||||
)
|
||||
|
||||
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"),
|
||||
system_instruction="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()
|
||||
user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
|
||||
context,
|
||||
user_params=LLMUserAggregatorParams(
|
||||
user_turn_strategies=ExternalUserTurnStrategies(),
|
||||
vad_analyzer=SileroVADAnalyzer(),
|
||||
),
|
||||
)
|
||||
|
||||
pipeline = Pipeline(
|
||||
[
|
||||
transport.input(), # Transport user input
|
||||
stt, # 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": "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()
|
||||
@@ -62,12 +62,16 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
)
|
||||
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
system_instruction="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.",
|
||||
)
|
||||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
|
||||
|
||||
context = LLMContext()
|
||||
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)
|
||||
user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
|
||||
context,
|
||||
user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
|
||||
@@ -98,7 +102,7 @@ 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": "system", "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")
|
||||
|
||||
@@ -31,8 +31,6 @@ from pipecat.audio.filters.krisp_viva_filter import KrispVivaFilter
|
||||
from pipecat.audio.turn.krisp_viva_turn import KrispVivaTurn
|
||||
from pipecat.audio.vad.silero import SileroVADAnalyzer
|
||||
from pipecat.frames.frames import LLMRunFrame
|
||||
from pipecat.metrics.metrics import TurnMetricsData
|
||||
from pipecat.observers.loggers.metrics_log_observer import MetricsLogObserver
|
||||
from pipecat.pipeline.pipeline import Pipeline
|
||||
from pipecat.pipeline.runner import PipelineRunner
|
||||
from pipecat.pipeline.task import PipelineParams, PipelineTask
|
||||
@@ -43,37 +41,32 @@ from pipecat.processors.aggregators.llm_response_universal import (
|
||||
)
|
||||
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.deepgram.tts import DeepgramTTSService
|
||||
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_stop import TurnAnalyzerUserTurnStopStrategy
|
||||
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.
|
||||
|
||||
krisp_viva_filter = KrispVivaFilter()
|
||||
|
||||
transport_params = {
|
||||
"daily": lambda: DailyParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
audio_in_filter=krisp_viva_filter,
|
||||
audio_in_filter=KrispVivaFilter(),
|
||||
),
|
||||
"twilio": lambda: FastAPIWebsocketParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
audio_in_filter=krisp_viva_filter,
|
||||
audio_in_filter=KrispVivaFilter(),
|
||||
),
|
||||
"webrtc": lambda: TransportParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
audio_in_filter=krisp_viva_filter,
|
||||
audio_in_filter=KrispVivaFilter(),
|
||||
),
|
||||
}
|
||||
|
||||
@@ -83,16 +76,18 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
|
||||
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"
|
||||
)
|
||||
tts = DeepgramTTSService(api_key=os.getenv("DEEPGRAM_API_KEY"), voice="aura-helios-en")
|
||||
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
system_instruction="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.",
|
||||
)
|
||||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
|
||||
|
||||
context = LLMContext()
|
||||
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)
|
||||
user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
|
||||
context,
|
||||
user_params=LLMUserAggregatorParams(
|
||||
@@ -122,14 +117,13 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
enable_usage_metrics=True,
|
||||
),
|
||||
idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
|
||||
observers=[MetricsLogObserver(include_metrics={TurnMetricsData})],
|
||||
)
|
||||
|
||||
@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": "system", "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")
|
||||
|
||||
@@ -60,12 +60,16 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
|
||||
tts = DeepgramTTSService(api_key=os.getenv("DEEPGRAM_API_KEY"), voice="aura-helios-en")
|
||||
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
system_instruction="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.",
|
||||
)
|
||||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
|
||||
|
||||
context = LLMContext()
|
||||
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)
|
||||
user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
|
||||
context,
|
||||
user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
|
||||
@@ -96,7 +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": "system", "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")
|
||||
|
||||
@@ -65,12 +65,16 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
aiohttp_session=session,
|
||||
)
|
||||
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
system_instruction="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.",
|
||||
)
|
||||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
|
||||
|
||||
context = LLMContext()
|
||||
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)
|
||||
user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
|
||||
context,
|
||||
user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
|
||||
@@ -101,9 +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": "system", "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")
|
||||
|
||||
@@ -59,12 +59,16 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
voice_id="luna",
|
||||
)
|
||||
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
system_instruction="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.",
|
||||
)
|
||||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
|
||||
|
||||
context = LLMContext()
|
||||
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)
|
||||
user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
|
||||
context,
|
||||
user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
|
||||
@@ -95,7 +99,7 @@ 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": "system", "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")
|
||||
|
||||
@@ -55,14 +55,19 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
stt = NvidiaSTTService(api_key=os.getenv("NVIDIA_API_KEY"))
|
||||
|
||||
llm = NvidiaLLMService(
|
||||
api_key=os.getenv("NVIDIA_API_KEY"),
|
||||
model="meta/llama-3.3-70b-instruct",
|
||||
system_instruction="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.",
|
||||
api_key=os.getenv("NVIDIA_API_KEY"), model="meta/llama-3.1-405b-instruct"
|
||||
)
|
||||
|
||||
tts = NvidiaTTSService(api_key=os.getenv("NVIDIA_API_KEY"))
|
||||
|
||||
context = LLMContext()
|
||||
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)
|
||||
user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
|
||||
context,
|
||||
user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
|
||||
@@ -93,7 +98,7 @@ 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": "system", "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")
|
||||
|
||||
@@ -60,12 +60,16 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
model="4ce7e917cedd4bc2bb2e6ff3a46acaa1", # Barack Obama
|
||||
)
|
||||
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
system_instruction="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.",
|
||||
)
|
||||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
|
||||
|
||||
context = LLMContext()
|
||||
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)
|
||||
user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
|
||||
context,
|
||||
user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
|
||||
@@ -96,7 +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": "system", "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")
|
||||
|
||||
@@ -64,12 +64,16 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
aiohttp_session=session,
|
||||
)
|
||||
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
system_instruction="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.",
|
||||
)
|
||||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
|
||||
|
||||
context = LLMContext()
|
||||
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)
|
||||
user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
|
||||
context,
|
||||
user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
|
||||
@@ -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": "system", "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")
|
||||
|
||||
@@ -59,12 +59,16 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
voice_id="fc854436-2dac-4d21-aa69-ae17b54e98eb", # Emily
|
||||
)
|
||||
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
system_instruction="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.",
|
||||
)
|
||||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
|
||||
|
||||
context = LLMContext()
|
||||
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)
|
||||
user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
|
||||
context,
|
||||
user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
|
||||
@@ -95,7 +99,7 @@ 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": "system", "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")
|
||||
|
||||
@@ -62,12 +62,16 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
)
|
||||
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
system_instruction="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.",
|
||||
)
|
||||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
|
||||
|
||||
context = LLMContext()
|
||||
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)
|
||||
user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
|
||||
context,
|
||||
user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
|
||||
@@ -98,7 +102,7 @@ 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": "system", "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")
|
||||
|
||||
@@ -47,12 +47,16 @@ async def main():
|
||||
voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
)
|
||||
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
system_instruction="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.",
|
||||
)
|
||||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
|
||||
|
||||
context = LLMContext()
|
||||
messages = [
|
||||
{
|
||||
"role": "system",
|
||||
"content": "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)
|
||||
user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
|
||||
context,
|
||||
user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
|
||||
@@ -78,7 +82,7 @@ async def main():
|
||||
),
|
||||
)
|
||||
|
||||
context.add_message({"role": "system", "content": "Please introduce yourself to the user."})
|
||||
messages.append({"role": "system", "content": "Please introduce yourself to the user."})
|
||||
await task.queue_frames([LLMRunFrame()])
|
||||
|
||||
runner = PipelineRunner()
|
||||
|
||||
@@ -66,12 +66,16 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
params=MiniMaxHttpTTSService.InputParams(language=Language.EN),
|
||||
)
|
||||
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
system_instruction="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.",
|
||||
)
|
||||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
|
||||
|
||||
context = LLMContext()
|
||||
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)
|
||||
user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
|
||||
context,
|
||||
user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
|
||||
@@ -102,9 +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": "system", "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")
|
||||
|
||||
@@ -68,12 +68,16 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
params=SarvamHttpTTSService.InputParams(language=Language.EN),
|
||||
)
|
||||
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
system_instruction="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.",
|
||||
)
|
||||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
|
||||
|
||||
context = LLMContext()
|
||||
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)
|
||||
user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
|
||||
context,
|
||||
user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
|
||||
@@ -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": "system", "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")
|
||||
|
||||
@@ -62,12 +62,16 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
model="bulbul:v2",
|
||||
voice_id="manisha",
|
||||
)
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
system_instruction="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.",
|
||||
)
|
||||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
|
||||
|
||||
context = LLMContext()
|
||||
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)
|
||||
user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
|
||||
context,
|
||||
user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
|
||||
@@ -97,7 +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": "system", "content": "Please introduce yourself to the user."})
|
||||
messages.append({"role": "system", "content": "Please introduce yourself to the user."})
|
||||
await task.queue_frames([LLMRunFrame()])
|
||||
|
||||
# Optionally, you can wait for 30 seconds and then change the voice.
|
||||
|
||||
@@ -64,12 +64,16 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
)
|
||||
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
system_instruction="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.",
|
||||
)
|
||||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
|
||||
|
||||
context = LLMContext()
|
||||
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)
|
||||
user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
|
||||
context,
|
||||
user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
|
||||
@@ -99,7 +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": "system", "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")
|
||||
|
||||
@@ -64,12 +64,16 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
streaming=True,
|
||||
)
|
||||
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
system_instruction="You are a helpful AI demonstrating Inworld AI's TTS. 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 friendly and helpful way.",
|
||||
)
|
||||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
|
||||
|
||||
context = LLMContext()
|
||||
messages = [
|
||||
{
|
||||
"role": "system",
|
||||
"content": "You are a helpful AI demonstrating Inworld AI's TTS. 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 friendly and helpful way.",
|
||||
},
|
||||
]
|
||||
|
||||
context = LLMContext(messages)
|
||||
user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
|
||||
context,
|
||||
user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
|
||||
@@ -107,9 +111,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
async def on_client_connected(transport, client):
|
||||
logger.info("Client connected")
|
||||
# Kick off the conversation.
|
||||
context.add_message(
|
||||
{"role": "system", "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")
|
||||
|
||||
@@ -61,12 +61,16 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
temperature=1.1,
|
||||
)
|
||||
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
system_instruction="You are a helpful AI demonstrating Inworld AI's TTS. 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 friendly and helpful way.",
|
||||
)
|
||||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
|
||||
|
||||
context = LLMContext()
|
||||
messages = [
|
||||
{
|
||||
"role": "system",
|
||||
"content": "You are a helpful AI demonstrating Inworld AI's TTS. 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 friendly and helpful way.",
|
||||
},
|
||||
]
|
||||
|
||||
context = LLMContext(messages)
|
||||
user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
|
||||
context,
|
||||
user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
|
||||
@@ -104,7 +108,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
async def on_client_connected(transport, client):
|
||||
logger.info("Client connected")
|
||||
# Kick off the conversation.
|
||||
context.add_message({"role": "system", "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")
|
||||
|
||||
@@ -64,12 +64,16 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
aiohttp_session=session,
|
||||
)
|
||||
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
system_instruction="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.",
|
||||
)
|
||||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
|
||||
|
||||
context = LLMContext()
|
||||
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)
|
||||
user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
|
||||
context,
|
||||
user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
|
||||
@@ -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": "system", "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")
|
||||
|
||||
@@ -60,12 +60,16 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
voice_id=os.getenv("ASYNCAI_VOICE_ID", "e0f39dc4-f691-4e78-bba5-5c636692cc04"),
|
||||
)
|
||||
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
system_instruction="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.",
|
||||
)
|
||||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
|
||||
|
||||
context = LLMContext()
|
||||
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)
|
||||
user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
|
||||
context,
|
||||
user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
|
||||
@@ -96,7 +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": "system", "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")
|
||||
|
||||
@@ -40,7 +40,7 @@ def _create_aic_filter() -> AICFilter:
|
||||
|
||||
return AICFilter(
|
||||
license_key=license_key,
|
||||
model_id="quail-vf-2.0-l-16khz",
|
||||
model_id="quail-vf-l-16khz",
|
||||
)
|
||||
|
||||
|
||||
@@ -80,12 +80,16 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
)
|
||||
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
system_instruction="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.",
|
||||
)
|
||||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
|
||||
|
||||
context = LLMContext()
|
||||
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)
|
||||
user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
|
||||
context,
|
||||
user_params=LLMUserAggregatorParams(vad_analyzer=aic_vad_analyzer),
|
||||
@@ -124,7 +128,7 @@ 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": "system", "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")
|
||||
|
||||
@@ -62,12 +62,16 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
voice_id="f898a92e-685f-43fa-985b-a46920f0650b",
|
||||
)
|
||||
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
system_instruction="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.",
|
||||
)
|
||||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
|
||||
|
||||
context = LLMContext()
|
||||
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)
|
||||
user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
|
||||
context,
|
||||
user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
|
||||
@@ -109,7 +113,7 @@ 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": "system", "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")
|
||||
|
||||
@@ -66,12 +66,16 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
url="wss://us.api.gradium.ai/api/speech/tts",
|
||||
)
|
||||
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
system_instruction="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.",
|
||||
)
|
||||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
|
||||
|
||||
context = LLMContext()
|
||||
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)
|
||||
user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
|
||||
context,
|
||||
user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
|
||||
@@ -102,7 +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": "system", "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")
|
||||
|
||||
@@ -59,12 +59,18 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
model="mars-flash",
|
||||
)
|
||||
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
system_instruction="You are a helpful voice assistant powered by Camb AI text-to-speech. ",
|
||||
)
|
||||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
|
||||
|
||||
context = LLMContext()
|
||||
messages = [
|
||||
{
|
||||
"role": "system",
|
||||
"content": "You are a helpful voice assistant powered by Camb AI text-to-speech. "
|
||||
"Keep your responses concise and conversational since they will be spoken aloud. "
|
||||
"Avoid special characters, emojis, or bullet points.",
|
||||
},
|
||||
]
|
||||
|
||||
context = LLMContext(messages)
|
||||
user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
|
||||
context,
|
||||
user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
|
||||
@@ -95,7 +101,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
@transport.event_handler("on_client_connected")
|
||||
async def on_client_connected(transport, client):
|
||||
logger.info("Client connected")
|
||||
context.add_message({"role": "system", "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")
|
||||
|
||||
@@ -64,10 +64,16 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
base_url="https://app-362f7ca1-6975-4e18-a605-ab202bf2c315.app.hathora.dev/v1",
|
||||
api_key=os.getenv("HATHORA_API_KEY"),
|
||||
model=None,
|
||||
system_instruction="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 = [
|
||||
{
|
||||
"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(vad_analyzer=SileroVADAnalyzer()),
|
||||
@@ -98,7 +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": "system", "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")
|
||||
|
||||
@@ -56,12 +56,16 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
|
||||
tts = PiperTTSService(voice_id="en_US-ryan-high")
|
||||
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
system_instruction="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.",
|
||||
)
|
||||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
|
||||
|
||||
context = LLMContext()
|
||||
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)
|
||||
user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
|
||||
context,
|
||||
user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
|
||||
@@ -92,7 +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": "system", "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")
|
||||
|
||||
@@ -56,12 +56,16 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
|
||||
tts = KokoroTTSService(voice_id="af_heart")
|
||||
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
system_instruction="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.",
|
||||
)
|
||||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
|
||||
|
||||
context = LLMContext()
|
||||
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)
|
||||
user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
|
||||
context,
|
||||
user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
|
||||
@@ -92,7 +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": "system", "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")
|
||||
|
||||
@@ -62,12 +62,16 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
voice_id=os.getenv("RESEMBLE_VOICE_UUID"),
|
||||
)
|
||||
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
system_instruction="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.",
|
||||
)
|
||||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
|
||||
|
||||
context = LLMContext()
|
||||
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)
|
||||
user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
|
||||
context,
|
||||
user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
|
||||
@@ -98,7 +102,7 @@ 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": "system", "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")
|
||||
|
||||
@@ -98,12 +98,16 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
)
|
||||
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
system_instruction="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.",
|
||||
)
|
||||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
|
||||
|
||||
context = LLMContext()
|
||||
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)
|
||||
user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
|
||||
context,
|
||||
user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
|
||||
@@ -137,7 +141,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
async def on_client_connected(transport, client):
|
||||
logger.info(f"Client connected: {client}")
|
||||
# Kick off the conversation.
|
||||
context.add_message({"role": "system", "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")
|
||||
|
||||
@@ -10,7 +10,7 @@ from dotenv import load_dotenv
|
||||
from loguru import logger
|
||||
|
||||
from pipecat.audio.vad.silero import SileroVADAnalyzer
|
||||
from pipecat.frames.frames import LLMRunFrame, TTSSpeakFrame
|
||||
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
|
||||
@@ -59,14 +59,18 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
)
|
||||
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
system_instruction="You are a helpful assistant. Respond to what the user said in a creative and helpful way. Keep your responses brief.",
|
||||
)
|
||||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
|
||||
|
||||
messages = [
|
||||
{
|
||||
"role": "system",
|
||||
"content": "You are a helpful assistant. Respond to what the user said in a creative and helpful way. Keep your responses brief.",
|
||||
},
|
||||
]
|
||||
|
||||
hey_robot_filter = WakeCheckFilter(["hey robot", "hey, robot"])
|
||||
|
||||
context = LLMContext()
|
||||
context = LLMContext(messages)
|
||||
user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
|
||||
context,
|
||||
user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
|
||||
@@ -98,13 +102,7 @@ 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": "system",
|
||||
"content": "Please introduce yourself. Tell the user they should say 'Hey Robot' before talking to you.",
|
||||
}
|
||||
)
|
||||
await task.queue_frames([LLMRunFrame()])
|
||||
await task.queue_frame(TTSSpeakFrame("Hi! If you want to talk to me, just say 'Hey Robot'"))
|
||||
|
||||
@transport.event_handler("on_client_disconnected")
|
||||
async def on_client_disconnected(transport, client):
|
||||
|
||||
@@ -104,17 +104,21 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
|
||||
stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
|
||||
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
system_instruction="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.",
|
||||
)
|
||||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
|
||||
|
||||
tts = CartesiaTTSService(
|
||||
api_key=os.getenv("CARTESIA_API_KEY"),
|
||||
voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
)
|
||||
|
||||
context = LLMContext()
|
||||
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)
|
||||
user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
|
||||
context,
|
||||
user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
|
||||
|
||||
@@ -56,12 +56,16 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
)
|
||||
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
system_instruction="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. You are also able to describe images.",
|
||||
)
|
||||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
|
||||
|
||||
context = LLMContext()
|
||||
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. You are also able to describe images.",
|
||||
},
|
||||
]
|
||||
|
||||
context = LLMContext(messages)
|
||||
user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
|
||||
context,
|
||||
user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
|
||||
@@ -110,7 +114,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
size=image.size,
|
||||
text=question,
|
||||
)
|
||||
context.add_message(message)
|
||||
messages.append(message)
|
||||
await task.queue_frames([LLMRunFrame()])
|
||||
|
||||
@transport.event_handler("on_client_disconnected")
|
||||
|
||||
@@ -56,12 +56,16 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
)
|
||||
|
||||
llm = AnthropicLLMService(
|
||||
api_key=os.getenv("ANTHROPIC_API_KEY"),
|
||||
system_instruction="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. You are also able to describe images.",
|
||||
)
|
||||
llm = AnthropicLLMService(api_key=os.getenv("ANTHROPIC_API_KEY"))
|
||||
|
||||
context = LLMContext()
|
||||
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. You are also able to describe images.",
|
||||
},
|
||||
]
|
||||
|
||||
context = LLMContext(messages)
|
||||
user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
|
||||
context,
|
||||
user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
|
||||
@@ -110,7 +114,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
size=image.size,
|
||||
text=question,
|
||||
)
|
||||
context.add_message(message)
|
||||
messages.append(message)
|
||||
await task.queue_frames([LLMRunFrame()])
|
||||
|
||||
@transport.event_handler("on_client_disconnected")
|
||||
|
||||
@@ -63,10 +63,16 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
# Here we can't because AWS Bedrock doesn't support it for Claude 3.7,
|
||||
# which we need for image input.
|
||||
params=AWSBedrockLLMService.InputParams(temperature=0.8),
|
||||
system_instruction="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. You are also able to describe images.",
|
||||
)
|
||||
|
||||
context = LLMContext()
|
||||
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. You are also able to describe images.",
|
||||
},
|
||||
]
|
||||
|
||||
context = LLMContext(messages)
|
||||
user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
|
||||
context,
|
||||
user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
|
||||
@@ -115,7 +121,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
size=image.size,
|
||||
text=question,
|
||||
)
|
||||
context.add_message(message)
|
||||
messages.append(message)
|
||||
await task.queue_frames([LLMRunFrame()])
|
||||
|
||||
@transport.event_handler("on_client_disconnected")
|
||||
|
||||
@@ -56,12 +56,16 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
)
|
||||
|
||||
llm = GoogleLLMService(
|
||||
api_key=os.getenv("GOOGLE_API_KEY"),
|
||||
system_instruction="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. You are also able to describe images.",
|
||||
)
|
||||
llm = GoogleLLMService(api_key=os.getenv("GOOGLE_API_KEY"))
|
||||
|
||||
context = LLMContext()
|
||||
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. You are also able to describe images.",
|
||||
},
|
||||
]
|
||||
|
||||
context = LLMContext(messages)
|
||||
user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
|
||||
context,
|
||||
user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
|
||||
@@ -110,7 +114,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
size=image.size,
|
||||
text=question,
|
||||
)
|
||||
context.add_message(message)
|
||||
messages.append(message)
|
||||
await task.queue_frames([LLMRunFrame()])
|
||||
|
||||
@transport.event_handler("on_client_disconnected")
|
||||
|
||||
@@ -16,7 +16,6 @@ from pipecat.pipeline.task import PipelineTask
|
||||
from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
|
||||
from pipecat.runner.types import RunnerArguments
|
||||
from pipecat.runner.utils import create_transport
|
||||
from pipecat.services.assemblyai.models import AssemblyAIConnectionParams
|
||||
from pipecat.services.assemblyai.stt import AssemblyAISTTService
|
||||
from pipecat.transports.base_transport import BaseTransport, TransportParams
|
||||
from pipecat.transports.daily.transport import DailyParams
|
||||
@@ -50,9 +49,6 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
|
||||
stt = AssemblyAISTTService(
|
||||
api_key=os.getenv("ASSEMBLYAI_API_KEY"),
|
||||
connection_params=AssemblyAIConnectionParams(
|
||||
speech_model="u3-rt-pro",
|
||||
),
|
||||
)
|
||||
|
||||
tl = TranscriptionLogger()
|
||||
|
||||
@@ -70,10 +70,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
)
|
||||
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
system_instruction="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.",
|
||||
)
|
||||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
|
||||
|
||||
# You can also register a function_name of None to get all functions
|
||||
# sent to the same callback with an additional function_name parameter.
|
||||
@@ -113,7 +110,14 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
)
|
||||
tools = ToolsSchema(standard_tools=[weather_function, restaurant_function])
|
||||
|
||||
context = LLMContext(tools=tools)
|
||||
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, tools)
|
||||
user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
|
||||
context,
|
||||
user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
|
||||
|
||||
@@ -70,7 +70,6 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
llm = TogetherLLMService(
|
||||
api_key=os.getenv("TOGETHER_API_KEY"),
|
||||
model="meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo",
|
||||
system_instruction="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.",
|
||||
)
|
||||
# You can also register a function_name of None to get all functions
|
||||
# sent to the same callback with an additional function_name parameter.
|
||||
@@ -97,7 +96,14 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
required=["location", "format"],
|
||||
)
|
||||
tools = ToolsSchema(standard_tools=[weather_function])
|
||||
context = LLMContext(tools=tools)
|
||||
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, tools)
|
||||
user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
|
||||
context,
|
||||
user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
|
||||
|
||||
@@ -94,10 +94,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
)
|
||||
|
||||
# Anthropic for vision analysis
|
||||
llm = AnthropicLLMService(
|
||||
api_key=os.getenv("ANTHROPIC_API_KEY"),
|
||||
system_instruction="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. You are able to describe images from the user camera.",
|
||||
)
|
||||
llm = AnthropicLLMService(api_key=os.getenv("ANTHROPIC_API_KEY"))
|
||||
llm.register_function("fetch_user_image", fetch_user_image)
|
||||
|
||||
@llm.event_handler("on_function_calls_started")
|
||||
@@ -121,7 +118,14 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
)
|
||||
tools = ToolsSchema(standard_tools=[fetch_image_function])
|
||||
|
||||
context = LLMContext(tools=tools)
|
||||
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. You are able to describe images from the user camera.",
|
||||
},
|
||||
]
|
||||
|
||||
context = LLMContext(messages, tools)
|
||||
user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
|
||||
context,
|
||||
user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
|
||||
@@ -158,7 +162,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
client_id = get_transport_client_id(transport, client)
|
||||
|
||||
# Kick off the conversation.
|
||||
context.add_message(
|
||||
messages.append(
|
||||
{
|
||||
"role": "system",
|
||||
"content": f"Please introduce yourself to the user. Use '{client_id}' as the user ID during function calls.",
|
||||
|
||||
@@ -101,7 +101,6 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
# Here we can't because AWS Bedrock doesn't support it for Claude 3.7,
|
||||
# which we need for image input.
|
||||
params=AWSBedrockLLMService.InputParams(temperature=0.8),
|
||||
system_instruction="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. You are able to describe images from the user camera.",
|
||||
)
|
||||
llm.register_function("fetch_user_image", fetch_user_image)
|
||||
|
||||
@@ -126,7 +125,14 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
)
|
||||
tools = ToolsSchema(standard_tools=[fetch_image_function])
|
||||
|
||||
context = LLMContext(tools=tools)
|
||||
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. You are able to describe images from the user camera.",
|
||||
},
|
||||
]
|
||||
|
||||
context = LLMContext(messages, tools)
|
||||
user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
|
||||
context,
|
||||
user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
|
||||
@@ -163,7 +169,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
client_id = get_transport_client_id(transport, client)
|
||||
|
||||
# Kick off the conversation.
|
||||
context.add_message(
|
||||
messages.append(
|
||||
{
|
||||
"role": "system",
|
||||
"content": f"Please introduce yourself to the user. Use '{client_id}' as the user ID during function calls.",
|
||||
|
||||
@@ -94,10 +94,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
)
|
||||
|
||||
# Google Gemini model for vision analysis
|
||||
llm = GoogleLLMService(
|
||||
api_key=os.getenv("GOOGLE_API_KEY"),
|
||||
system_instruction="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. You are able to describe images from the user camera.",
|
||||
)
|
||||
llm = GoogleLLMService(api_key=os.getenv("GOOGLE_API_KEY"))
|
||||
llm.register_function("fetch_user_image", fetch_user_image)
|
||||
|
||||
@llm.event_handler("on_function_calls_started")
|
||||
@@ -121,7 +118,14 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
)
|
||||
tools = ToolsSchema(standard_tools=[fetch_image_function])
|
||||
|
||||
context = LLMContext(tools=tools)
|
||||
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. You are able to describe images from the user camera.",
|
||||
},
|
||||
]
|
||||
|
||||
context = LLMContext(messages, tools)
|
||||
user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
|
||||
context,
|
||||
user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
|
||||
@@ -158,7 +162,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
client_id = get_transport_client_id(transport, client)
|
||||
|
||||
# Kick off the conversation.
|
||||
context.add_message(
|
||||
messages.append(
|
||||
{
|
||||
"role": "system",
|
||||
"content": f"Please introduce yourself to the user. Use '{client_id}' as the user ID during function calls.",
|
||||
|
||||
@@ -124,10 +124,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
)
|
||||
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
system_instruction="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. You are able to describe images from the user camera.",
|
||||
)
|
||||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
|
||||
llm.register_function("fetch_user_image", fetch_user_image)
|
||||
|
||||
@llm.event_handler("on_function_calls_started")
|
||||
@@ -151,7 +148,14 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
)
|
||||
tools = ToolsSchema(standard_tools=[fetch_image_function])
|
||||
|
||||
context = LLMContext(tools=tools)
|
||||
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. You are able to describe images from the user camera.",
|
||||
},
|
||||
]
|
||||
|
||||
context = LLMContext(messages, tools)
|
||||
user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
|
||||
context,
|
||||
user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
|
||||
@@ -196,7 +200,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
client_id = get_transport_client_id(transport, client)
|
||||
|
||||
# Kick off the conversation.
|
||||
context.add_message(
|
||||
messages.append(
|
||||
{
|
||||
"role": "system",
|
||||
"content": f"Please introduce yourself to the user. Use '{client_id}' as the user ID during function calls.",
|
||||
|
||||
@@ -94,10 +94,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
)
|
||||
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
system_instruction="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. You are able to describe images from the user camera.",
|
||||
)
|
||||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
|
||||
llm.register_function("fetch_user_image", fetch_user_image)
|
||||
|
||||
@llm.event_handler("on_function_calls_started")
|
||||
@@ -121,7 +118,14 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
)
|
||||
tools = ToolsSchema(standard_tools=[fetch_image_function])
|
||||
|
||||
context = LLMContext(tools=tools)
|
||||
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. You are able to describe images from the user camera.",
|
||||
},
|
||||
]
|
||||
|
||||
context = LLMContext(messages, tools)
|
||||
user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
|
||||
context,
|
||||
user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
|
||||
@@ -157,7 +161,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
client_id = get_transport_client_id(transport, client)
|
||||
|
||||
# Kick off the conversation.
|
||||
context.add_message(
|
||||
messages.append(
|
||||
{
|
||||
"role": "system",
|
||||
"content": f"Please introduce yourself to the user. Use '{client_id}' as the user ID during function calls.",
|
||||
|
||||
@@ -67,10 +67,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
)
|
||||
|
||||
llm = GroqLLMService(
|
||||
api_key=os.getenv("GROQ_API_KEY"),
|
||||
system_instruction="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.",
|
||||
)
|
||||
llm = GroqLLMService(api_key=os.getenv("GROQ_API_KEY"))
|
||||
# You can also register a function_name of None to get all functions
|
||||
# sent to the same callback with an additional function_name parameter.
|
||||
llm.register_function("get_current_weather", fetch_weather_from_api)
|
||||
@@ -96,7 +93,14 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
required=["location"],
|
||||
)
|
||||
tools = ToolsSchema(standard_tools=[weather_function])
|
||||
context = LLMContext(tools=tools)
|
||||
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, tools)
|
||||
user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
|
||||
context,
|
||||
user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
|
||||
|
||||
@@ -67,10 +67,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
)
|
||||
|
||||
llm = GrokLLMService(
|
||||
api_key=os.getenv("GROK_API_KEY"),
|
||||
system_instruction="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.",
|
||||
)
|
||||
llm = GrokLLMService(api_key=os.getenv("GROK_API_KEY"))
|
||||
# You can also register a function_name of None to get all functions
|
||||
# sent to the same callback with an additional function_name parameter.
|
||||
llm.register_function("get_current_weather", fetch_weather_from_api)
|
||||
@@ -92,7 +89,14 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
required=["location", "format"],
|
||||
)
|
||||
tools = ToolsSchema(standard_tools=[weather_function])
|
||||
context = LLMContext(tools=tools)
|
||||
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, tools)
|
||||
user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
|
||||
context,
|
||||
user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
|
||||
|
||||
Some files were not shown because too many files have changed in this diff Show More
Reference in New Issue
Block a user