An open-source chatbot console for creating and managing unlimited custom chatbots powered by GPT and open-source LLMs.
OpenChat is an open-source chatbot console that simplifies the creation and management of custom chatbots using large language models like GPT. It solves the complexity of AI model deployment by providing a user-friendly interface to build, customize, and embed chatbots with data from PDFs, websites, and codebases. The platform acts as a central hub for managing multiple AI assistants with features like unlimited memory and offline support.
Developers, businesses, and AI enthusiasts who need to deploy customized chatbots for customer support, internal tools, or coding assistance without dealing with complex LLM infrastructure.
Developers choose OpenChat for its streamlined setup, support for multiple data sources, and self-hosted capability, offering full control over chatbot deployment and data privacy compared to proprietary SaaS solutions.
LLMs custom-chatbots console ⚡
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Supports PDFs, websites, and entire codebases as data sources via vector databases, allowing chatbots to handle large files like 400-page PDFs seamlessly, as highlighted in the features.
Enables creation and management of unlimited chatbots from a single console, making it efficient for deploying multiple specialized AI assistants without additional overhead.
Provides JavaScript widgets to embed chatbots on any website or internal tool with minimal effort, as demonstrated in the roadmap and feature list.
Committed to expanding features like offline LLMs and more integrations, evidenced by the detailed public roadmap including Slack, Vertex AI, and open-source model support.
The project is undergoing significant rewrites (e.g., backend to Django, frontend to Next.js), with noted breaking changes like the Qdrant vector store transition, leading to potential instability.
Currently primarily supports GPT models; open-source drivers like Llama2 are in progress, restricting options for users needing alternative or offline models immediately.
Setup requires Docker, environment variables for API keys, and vector database configuration (Pinecone or Qdrant), which can be daunting for quick or simple deployments.