What is the project about?
AnythingLLM is a full-stack application that allows users to chat with documents, resources, or any content using Large Language Models (LLMs). It essentially creates a private, customizable ChatGPT-like experience.
What problem does it solve?
It enables users to leverage the power of LLMs to interact with their own data in a conversational way, without compromising privacy or requiring complex setup. It solves the problem of needing to rely solely on public-facing, general-purpose LLMs, and allows for contextualized conversations based on specific documents. It also addresses multi-user and permissioning needs.
What are the features of the project?
- Chat with documents: Allows users to "chat" with documents, using them as context for LLM responses.
- Workspace organization: Organizes documents into "workspaces," which act like conversation threads with contained documents.
- Multi-modal support: Supports images as input.
- Multi-user support: Supports multiple users with permissions (Docker version).
- AI Agents: Custom AI agents and a no-code AI agent builder.
- Embeddable Chat Widget: Custom embeddable chat widget for websites (Docker version).
- Multiple document types: Supports various document formats (PDF, TXT, DOCX, etc.).
- Cost and time-saving: Includes measures for managing large documents efficiently.
- Developer API: Provides a full API for custom integrations.
- LLM and VectorDB Choice: Allows users to select from various LLMs and Vector Databases.
- Cloud and Local: Can be run locally or deployed to the cloud.
What are the technologies used in the project?
- Frontend: ViteJS + React
- Backend: NodeJS (Express)
- Document Processing: NodeJS (Express)
- Large Language Models (LLMs): Supports various LLMs, including OpenAI, Azure OpenAI, Anthropic, Google Gemini Pro, and many open-source models (llama.cpp compatible).
- Embedder Models: AnythingLLM Native Embedder, OpenAI, Azure OpenAI, LocalAI, Ollama, Cohere.
- Audio Transcription Models: AnythingLLM Built-in, OpenAI.
- TTS (Text-to-Speech): Native Browser Built-in, PiperTTSLocal, OpenAI TTS, ElevenLabs.
- STT (Speech-to-Text): Native Browser Built-in.
- Vector Databases: LanceDB (default), Astra DB, Pinecone, Chroma, Weaviate, Qdrant, Milvus, Zilliz.
- Docker: Docker support for deployment.
What are the benefits of the project?
- Privacy: Allows users to interact with their data privately.
- Customization: Offers high configurability in terms of LLMs, vector databases, and other settings.
- Contextualized Conversations: Enables more relevant and accurate responses by using specific documents as context.
- Efficiency: Provides features for managing large documents effectively.
- Flexibility: Supports various deployment options (local, cloud).
- Extensibility: Offers an API for custom integrations.
- Multi-User Management: Facilitates collaborative use with permission controls.
What are the use cases of the project?
- Personal Knowledge Management: Chatting with personal notes, documents, and research materials.
- Customer Support: Creating a chatbot that can answer questions based on company documentation.
- Research: Analyzing and interacting with research papers, datasets, and other scholarly materials.
- Content Creation: Generating content based on specific source materials.
- Education: Creating interactive learning experiences based on educational resources.
- Internal Documentation: Allowing employees to easily query and interact with internal company documents.
- Website Chatbots: Adding a custom chatbot to a website that uses specific documents as its knowledge base.
