Remove extra code

AGENTS.md

@@ -1,284 +0,0 @@
-# AGENTS.md
-
-## Project Overview
-
-Pipecat is an open-source Python framework for building real-time voice and multimodal conversational AI agents. The codebase is organized around a pipeline architecture where data flows through connected services (STT → LLM → TTS).
-
-## Development Environment Setup
-
-### Prerequisites
-- **Minimum Python Version:** 3.10
-- **Recommended Python Version:** 3.12
-- **Package Manager:** uv (recommended) or pip
-
-### Setup Commands
-
-```bash
-# Clone the repository
-git clone https://github.com/pipecat-ai/pipecat.git
-cd pipecat
-
-# Install dependencies with uv (recommended)
-uv sync --group dev --all-extras \
-  --no-extra gstreamer \
-  --no-extra krisp \
-  --no-extra local \
-  --no-extra ultravox
-
-# Or with pip
-pip install -e ".[dev]"
-
-# Install pre-commit hooks
-uv run pre-commit install
-
-# Set up environment variables
-cp env.example .env
-```
-
-## Build and Test Commands
-
-### Running Tests
-```bash
-# Run all tests
-uv run pytest
-
-# Run specific test file
-uv run pytest tests/test_name.py
-
-# Run tests with coverage
-uv run pytest --cov=pipecat --cov-report=html
-```
-
-### Code Quality
-```bash
-# Format code (required before commits)
-uv run ruff format
-
-# Lint code
-uv run ruff check
-
-# Type checking
-uv run mypy src/pipecat
-
-# Run pre-commit checks manually
-uv run pre-commit run --all-files
-```
-
-### Documentation
-```bash
-# Build API documentation
-cd docs/api
-./build-docs.sh
-
-# Build docs manually
-sphinx-build -b html . _build/html -W --keep-going
-```
-
-## Code Style Guidelines
-
-### Python Standards
-- **Formatting:** Strict PEP 8 via Ruff
-- **Docstrings:** Google-style format
-- **Type Hints:** Required for all public APIs
-- **Import Organization:** Automated via Ruff
-
-### Docstring Conventions
-- **Classes:** Describe purpose + `__init__` with complete `Args:` section
-- **Dataclasses:** Use `Parameters:` section, no `__init__` docstring
-- **Methods:** Include `Args:` and `Returns:` sections
-- **Properties:** Must have `Returns:` section
-- **Examples:** Use `Examples:` section with `::` syntax
-
-### File Organization
-```
-src/pipecat/           # Main package
-├── processors/        # Frame processors
-├── services/          # AI service integrations
-├── transports/        # Communication layers
-├── frames/            # Data frame definitions
-└── pipeline/          # Pipeline orchestration
-
-examples/foundational/ # Step-by-step tutorials
-tests/                 # Test suite
-```
-
-## Testing Instructions
-
-### Test Structure
-- **Unit Tests:** Test individual components in isolation
-- **Integration Tests:** Test service interactions
-- **Example Tests:** Validate foundational examples work
-
-### Adding Tests
-```bash
-# Test naming convention
-test_<component>_<functionality>.py
-
-# Run specific test pattern
-uv run pytest -k "test_pipeline"
-
-# Run with debugging
-uv run pytest -s -vv tests/test_name.py::test_function
-```
-
-### Pre-commit Requirements
-All commits must pass:
-- Ruff formatting
-- Ruff linting
-- Type checking
-- Basic test suite
-
-## Dependency Management
-
-### Using uv (Recommended)
-```bash
-# Add runtime dependency
-uv add package-name
-
-# Add optional dependency
-uv add --optional service package-name
-
-# Add development dependency
-uv add --group dev package-name
-
-# Update lockfile
-uv lock
-
-# Sync dependencies
-uv sync
-```
-
-### Important Notes
-- **Always commit both `pyproject.toml` and `uv.lock` together**
-- **Never manually edit `uv.lock`** - it's auto-generated
-- **Use extras for optional service dependencies** (e.g., `[openai]`, `[cartesia]`)
-
-## Project Structure Guidelines
-
-### Service Integration
-When adding new AI services:
-1. Create service class in `src/pipecat/services/<provider>/`
-2. Follow existing patterns (e.g., STTService, LLMService)
-3. Add to appropriate extras in `pyproject.toml`
-4. Include tests in `tests/`
-5. Add documentation examples
-
-### Frame Processing
-For custom processors:
-1. Inherit from `FrameProcessor`
-2. Implement `process_frame()` method. ALWAYS explicitly call `await super().process_frame(frame, direction)` at the top of this method.
-3. Handle frame direction (FrameDirection.UPSTREAM/DOWNSTREAM)
-4. Add proper type hints and docstrings
-
-### Transport Implementation
-For new transport layers:
-1. Inherit from `BaseTransport`
-2. Implement required abstract methods
-3. Handle connection lifecycle
-4. Support both input and output streams
-
-## Security Considerations
-
-### API Keys
-- **Never commit API keys** to the repository
-- **Use environment variables** for all secrets
-- **Reference `env.example`** for required variables
-- **Use `.env` files** for local development
-
-### Input Validation
-- **Validate all external inputs** (audio, text, API responses)
-- **Sanitize user data** before processing
-- **Handle rate limiting** for external services
-- **Implement proper timeout handling**
-
-## Performance Guidelines
-
-### Memory Management
-- **Clean up resources** in transport disconnection handlers
-- **Use async context managers** for service connections
-- **Implement proper frame lifecycle** management
-
-### Latency Optimization
-- **Choose appropriate STT services** for latency requirements
-- **Use streaming TTS** when possible
-- **Implement connection pooling** for HTTP services
-- **Consider WebRTC** for real-time applications
-
-## Common Patterns
-
-### Error Handling
-```python
-@transport.event_handler("on_error")
-async def on_error(transport, error):
-    logger.error(f"Transport error: {error}")
-
-    # Shutdown the pipeline
-    await task.queue_frame(EndFrame())
-```
-
-### Service Configuration
-```python
-# Use environment variables for configuration
-service = OpenAILLMService(
-    api_key=os.getenv("OPENAI_API_KEY", ""),
-    model="gpt-4o",
-    params={"temperature": 0.7}
-)
-```
-
-### Pipeline Assembly
-```python
-pipeline = Pipeline([
-    transport.input(),
-    stt_service,
-    context_aggregator.user(),
-    llm_service,
-    tts_service,
-    transport.output(),
-    context_aggregator.assistant(),
-])
-```
-
-## Commit and PR Guidelines
-
-### Commit Message Format
-```
-<type>(<scope>): <description>
-
-[optional body]
-
-[optional footer]
-```
-
-Types: `feat`, `fix`, `docs`, `style`, `refactor`, `test`, `chore`
-
-### PR Requirements
-- **All tests must pass**
-- **Code must be properly formatted** (Ruff)
-- **Include appropriate tests** for new functionality
-- **Update documentation** if needed
-- **Reference related issues** in description
-
-### Review Process
-1. Automated checks must pass
-2. Manual code review by maintainers
-3. Documentation review for user-facing changes
-4. Integration testing for service additions
-
-## Troubleshooting
-
-### Common Issues
-- **Import errors:** Run `uv sync` to ensure dependencies are installed
-- **Test failures:** Check environment variables in `.env`
-- **Format errors:** Run `uv run ruff format` before committing
-- **Type errors:** Ensure all public methods have type hints
-
-### Development Tips
-- **Use foundational examples** as starting points for testing
-- **Check existing services** for integration patterns
-- **Run tests frequently** during development
-- **Use IDE integration** for Ruff formatting
-
-### Getting Help
-- **Documentation:** [docs.pipecat.ai](https://docs.pipecat.ai)
-- **Issues:** [GitHub Issues](https://github.com/pipecat-ai/pipecat/issues)

examples/foundational/07-interruptible.py

@@ -9,7 +9,6 @@ import wave
 
 from dotenv import load_dotenv
 from loguru import logger
-from prerecorded_message_processor import PrerecordedMessageProcessor
 
 from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
 from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
@@ -86,18 +85,12 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     context = LLMContext(messages)
     context_aggregator = LLMContextAggregatorPair(context)
 
-    # Create the prerecorded message processor
-    prerecorded_processor = PrerecordedMessageProcessor(
-        audio_file_path=os.path.join(os.path.dirname(__file__), "assets", "ding1.wav")
-    )
-
     pipeline = Pipeline(
         [
             transport.input(),  # Transport user input
             stt,
             context_aggregator.user(),  # User responses
             llm,  # LLM
-            prerecorded_processor,  # Check for prerecorded message trigger
             tts,  # TTS
             transport.output(),  # Transport bot output
             context_aggregator.assistant(),  # Assistant spoken responses

examples/foundational/prerecorded_message_processor.py

@@ -1,114 +0,0 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-"""Prerecorded message processor for playing audio instead of TTS."""
-
-import wave
-
-from pipecat.frames.frames import (
-    Frame,
-    LLMFullResponseEndFrame,
-    LLMFullResponseStartFrame,
-    LLMTextFrame,
-    OutputAudioRawFrame,
-)
-from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
-
-
-class PrerecordedMessageProcessor(FrameProcessor):
-    """Processor that intercepts specific LLM text and plays prerecorded audio.
-
-    This processor checks incoming LLMTextFrame instances for a specific text pattern.
-    When the pattern "Your pre-recorded message" is detected, it replaces the text
-    with a prerecorded audio message by pushing LLMFullResponseStartFrame, the audio
-    data as OutputAudioRawFrame, and LLMFullResponseEndFrame. Other frames pass through
-    unchanged.
-
-    Parameters:
-        audio_file_path: Path to the WAV file containing the prerecorded message.
-
-    Example::
-
-        processor = PrerecordedMessageProcessor(
-            audio_file_path="path/to/message.wav"
-        )
-
-        pipeline = Pipeline([
-            transport.input(),
-            stt,
-            context_aggregator.user(),
-            llm,
-            processor,  # Insert before TTS
-            tts,
-            transport.output(),
-            context_aggregator.assistant(),
-        ])
-    """
-
-    def __init__(
-        self,
-        *,
-        audio_file_path: str,
-        **kwargs,
-    ):
-        """Initialize the prerecorded message processor.
-
-        Args:
-            audio_file_path: Path to the WAV file containing the prerecorded message.
-            **kwargs: Additional arguments passed to FrameProcessor.
-        """
-        super().__init__(**kwargs)
-        self._audio_file_path = audio_file_path
-        self._audio_data = None
-        self._sample_rate = None
-        self._num_channels = None
-        self._load_audio()
-
-    def _load_audio(self) -> None:
-        """Load the prerecorded audio file into memory."""
-        try:
-            with wave.open(self._audio_file_path, "rb") as wav_file:
-                self._sample_rate = wav_file.getframerate()
-                self._num_channels = wav_file.getnchannels()
-                self._audio_data = wav_file.readframes(wav_file.getnframes())
-        except Exception as e:
-            raise ValueError(f"Failed to load audio file {self._audio_file_path}: {e}")
-
-        # Ensure audio was loaded successfully
-        if self._audio_data is None or self._sample_rate is None or self._num_channels is None:
-            raise ValueError(f"Failed to load audio data from {self._audio_file_path}")
-
-    async def process_frame(self, frame: Frame, direction: FrameDirection) -> None:
-        """Process incoming frames and replace specific text with prerecorded audio.
-
-        Args:
-            frame: The frame to process.
-            direction: Direction of the frame flow.
-        """
-        await super().process_frame(frame, direction)
-
-        # Check if this is an LLMTextFrame with our trigger text
-        if isinstance(frame, LLMTextFrame) and frame.text == "Your pre-recorded message":
-            # Ensure audio data is loaded (should always be true after __init__)
-            if self._audio_data is None or self._sample_rate is None or self._num_channels is None:
-                raise RuntimeError("Audio data not loaded")
-
-            # Push the prerecorded message sequence
-            await self.push_frame(LLMFullResponseStartFrame(), direction)
-            await self.push_frame(frame, direction)  # Keep the text frame for context
-
-            # Push the prerecorded audio
-            audio_frame = OutputAudioRawFrame(
-                audio=self._audio_data,
-                sample_rate=self._sample_rate,
-                num_channels=self._num_channels,
-            )
-            await self.push_frame(audio_frame, direction)
-
-            await self.push_frame(LLMFullResponseEndFrame(), direction)
-        else:
-            # Pass through all other frames unchanged
-            await self.push_frame(frame, direction)