OpenChat provides an accessible console for interacting with large language models, simplifying the setup and management of customized chatbots for everyday users. It allows for the creation of unlimited, memory-rich chatbots powered by GPT models, with future support for open-source alternatives and integrations with various data sources and platforms.

How It Works

The system leverages a backend architecture that supports multiple vector stores, including Pinecone and Qdrant, for managing chatbot memory and data ingestion. It processes user-provided data like PDFs and websites, converting them into embeddings for efficient retrieval and contextual interaction with LLMs. The console offers features like website embedding and codebase integration for pair programming.

Quick Start & Requirements

• Install via Docker: git clone the repository and run make install (or make.bat for Windows).
• Prerequisites: Docker, OpenAI API key, and optionally Pinecone or Qdrant credentials.
• Access the console at http://localhost:8000.
• Full documentation: https://github.com/openchatai/OpenChat#readme

Highlighted Details

• Supports GPT-3 and GPT-4 models.
• Enables chatbot customization with PDFs, websites, and codebases.
• Offers unlimited memory capacity per chatbot.
• Allows embedding chatbots as widgets on websites.
• Includes support for Llama 2.

Maintenance & Community

The project is actively developed with contributions from a community of developers. Links to community resources are not explicitly provided in the README.

Licensing & Compatibility

Licensed under the MIT License, permitting commercial use and integration with closed-source projects.

Limitations & Caveats

The recent backend rewrite in Python Django introduces a breaking change related to the Qdrant vector store. Support for offline open-source models and full offline usage is planned but not yet implemented.