AnythingLLM

AnythingLLM is a full‑stack, self‑hosted generative AI platform that turns arbitrary documents into interactive knowledge bases. At its core, the application ingests PDFs, Word files, CSVs, code repositories, and any other text‑rich asset, then tokenizes, embeds, and stores the content in a vector database. A lightweight LLM provider layer abstracts over multiple back‑ends (local open‑source models, OpenAI, Azure, AWS Bedrock), enabling developers to swap providers without touching the ingestion or query logic. The user interface is built in React/TypeScript, served by a FastAPI backend that exposes a GraphQL‑like API for chat, agent orchestration, and vector search.

• Backend – Python 3.11, FastAPI + Pydantic for request validation, SQLModel for ORM‑style persistence, and Ray or Celery for background task queues.
• LLM & Embedding – The llm-provider package dynamically loads HuggingFace or OpenAI clients. Embeddings are generated via sentence‑transformers or custom local models, then persisted in a high‑performance vector store (Weaviate, Milvus, or Qdrant).
• Vector DB – The application bundles a lightweight local instance of Qdrant (or can connect to an external cluster), exposing a REST/GRPC interface for similarity search.
• Storage – File system or S3‑compatible object storage backs raw documents and embeddings; SQLite is used for metadata when running locally.
• Frontend – React 18 with Vite, Chakra‑UI for components, and Zustand/Redux Toolkit for state. WebSocket endpoints power real‑time chat streams.
• Containerization – Docker Compose files expose all services; the repo ships with a single‑image anythingllm:latest that bundles backend, vector store, and optional local LLM.

• Document Ingestion API – POST /api/v1/docs/upload accepts multipart files, triggers chunking and embedding pipelines.
• Vector Search API – GET /api/v1/search?q=... returns ranked results with metadata and source snippets.
• Chat & Agent API – POST /api/v1/chat streams LLM responses; agents are defined as JSON workflows that can call external services via webhooks.
• Plugin System – Developers can register new providers or custom vector stores by implementing a small interface and adding the module to plugins/.
• Webhook Support – External services can hook into agent completion events or ingestion callbacks via configurable endpoints.
• Extensibility – The UI exposes a plugin hook that lets developers inject custom panels or chat widgets without recompiling the entire app.

AnythingLLM is designed for both single‑node and multi‑node deployments. The Docker Compose stack runs on any OS that supports Docker, while the underlying services (FastAPI, Qdrant) are stateless and can be horizontally scaled behind a reverse proxy. For large corpora, the vector store can run in a dedicated cluster (e.g., Qdrant on Kubernetes) and be accessed via the same API surface. The local LLM provider can run in a separate GPU container, allowing developers to offload inference while keeping the rest of the stack CPU‑bound.

• Custom Models – Drop a .gguf or HuggingFace checkpoint into the models/ folder; AnythingLLM auto‑detects and exposes it via the LLM API.
• Enterprise Model Gateways – SDK wrappers for OpenAI, Azure OpenAI, and AWS Bedrock let developers authenticate via environment variables or secret managers.
• Agent Scripting – Agents are defined in YAML/JSON; developers can embed custom Python functions or external API calls, enabling workflows like “summarize all docs in this folder” or “generate a release note from code changes.”
• Webhooks & Callbacks – The platform emits events (ingestion_complete, agent_run_start, etc.) that can be consumed by external services or internal micro‑services.

The repository ships with comprehensive docs under docs/, including API reference, plugin guide, and deployment checklist. TypeScript typings are auto‑generated from the FastAPI schema, ensuring type safety across front‑end and back‑end. The community is active on Discord, where developers can request new plugins or report bugs. Licensing is MIT, making it trivial to fork, extend, and redistribute without commercial restrictions.

• Enterprise Knowledge Base – Ingest all internal PDFs, SOPs, and codebases; expose a chat interface for employees to query procedures or code snippets.
• Research Collaboration – Researchers upload papers and datasets; agents auto‑summarize findings or generate literature reviews.
• Customer Support – Deploy a self‑hosted chatbot that pulls from product docs and FAQs, ensuring compliance with data‑privacy regulations.
• DevOps Documentation – Integrate with GitHub or GitLab to automatically index README, CHANGELOG, and CI configs; agents can answer “What does the latest release include?” queries.

• Full Local Control – No data leaves the premises; developers can run any LLM on a