This is an example implementation of a Large Language Model (LLM) transceiver that enables real-time bidirectional communication between a client and a server using WebRTC connections facilitated by the aiortc library. This code serves as a starting point for building your own LLM-based communication system that supports text, file transfers, and media streams (audio and video).
The repository contains both client and server Python code that establishes WebRTC connections and enables communication between a user and an LLM. The code allows users to submit prompts, receive responses from the LLM, and stream outputs in real-time.
- True WebRTC Functionality: Utilizes the aiortc library to establish real WebRTC connections.
- Bidirectional Communication: Supports sending and receiving text messages, files, and media streams (audio and video).
- Data Channels: Employs data channels for efficient and reliable data transfer.
- Media Streams: Manages media tracks for audio and video communication.
- Thread Safety: Implements thread safety using asyncio.Lock.
- LLM Integration Placeholders: Includes placeholders for integrating with an LLM of your choice.
- Python 3.7 or higher
aiortclibraryopencv-python(for video handling)numpyasyncio- Other dependencies as specified in requirements.txt
- Clone the Repository
git clone https://github.com/YOUR-USERNAME/LLM-Transceiver-Example.git
cd LLM-Transceiver-Example
- Install Required Dependencies Create a virtual environment (optional but recommended):
python3 -m venv venv
source venv/bin/activate # On Windows use `venv\Scripts\activate`
Install dependencies:
Copy code
pip install -r requirements.txt
Ensure requirements.txt includes all necessary packages:
aiortc
opencv-python
numpy
- Set Up the Signaling Server Important: This example requires a signaling server for the WebRTC connection setup between the client and the server. Implementing a signaling server is necessary but is considered out of scope for this transceiver example. You can use existing signaling mechanisms provided by aiortc, such as TcpSocketSignaling, or implement your own signaling server.
For testing purposes, you can use the built-in signaling classes in aiortc.
If using TcpSocketSignaling, you can start a simple signaling server:
python -m aiortc.contrib.signaling --mode server --host localhost --port 1234
- Configure Server Code
- Open
server.pyand replace placeholders with actual paths or configurations as needed. - Ensure that the LLM integration placeholders (
process_text_with_llm,process_file_with_llm) are appropriately implemented or leave them as is for testing.
- Run the Server Script In a new terminal window:
python server.py
- Configure Client Code
- Open
client.pyand replace placeholders with actual paths or configurations as needed. - Adjust media sources if necessary (e.g., change /dev/video0 to the appropriate video device or media file).
- Run the Client Script In another terminal window:
python client.py
The client will attempt to connect to the signaling server and establish a WebRTC connection with the server.
client.py: Contains the client-side code that establishes a WebRTC connection to the server, sends text messages and files, and handles incoming media streams.server.py: Contains the server-side code that accepts WebRTC connections from clients, processes incoming data (with placeholders for LLM integration), and sends responses back to the client.requirements.txt: Lists the Python dependencies required to run the code.
- Bidirectional Communication: Enables real-time exchange of text, files, and media streams between client and server.
- WebRTC Data Channels: Utilizes data channels for efficient data transfer.
- Media Handling: Supports sending and receiving audio and video streams.
- Thread Safety: Uses asyncio.Lock to ensure thread-safe operations.
- LLM Integration Placeholders: Server code includes placeholder methods for integrating with an LLM.
- Signaling Server Not Included: A signaling server is required but not included in this example. You need to set up a signaling mechanism for the WebRTC connection.
- LLM Processing Not Implemented: The actual LLM processing logic is not implemented. You need to replace placeholder methods with your own LLM integration code.
- Basic File Transfer Protocol: The file transfer protocol used over the data channel is simplistic and may not handle large files efficiently.
- Implement a Signaling Server
- Set up a signaling server to facilitate the exchange of SDP offers/answers and ICE candidates.
- You can use the built-in TcpSocketSignaling for testing or implement your own signaling server using WebSockets or HTTP.
- Integrate LLM Processing
- Replace the placeholder methods in the server code (process_text_with_llm, process_file_with_llm) with actual LLM integration.
- Ensure that the LLM processing is asynchronous if it involves I/O operations.
- Enhance File Transfer Protocol
- Improve the file transfer mechanism to handle larger files and include error checking.
- Consider implementing chunk acknowledgments and error correction.
- Implement Security Enhancements
- Use SSL/TLS encryption for secure communication, especially if transmitting sensitive data.
- Implement authentication and authorization mechanisms as needed.
- Test and Refine
- Test the client and server interaction thoroughly.
- Optimize for performance and scalability.
If you'd like to contribute to this repository or have suggestions for improvement, please feel free to open an issue or pull request!
This project is licensed under the MIT License - see the LICENSE file for details.
aiortc: A Python library for Web Real-Time Communication (WebRTC).
OpenAI: For providing guidance on building LLM applications.
This code is provided as an example and is intended for educational purposes. It may require modifications to suit your specific use case and should not be used in production without proper testing and security reviews.
For any questions or support, please open an issue on the GitHub repository.
Happy coding!