Anything LLM

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What is Anything LLM ?

Anything LLM is a full-stack application that enables you to turn any document, resource, or piece of content into context that any LLM can use as references during chatting. This application allows you to pick and choose which LLM or Vector Database you want to use as well as supporting multi-user management and permissions.

An efficient, customizable, and open-source enterprise-ready document chatbot solution.

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Anything LLM Features

• Multi-user instance support and permissioning

• Multiple document type support (PDF, TXT, DOCX, etc)

• Manage documents in your vector database from a simple UI

• Two chat modes conversation and query. Conversation retains previous questions and amendments. Query is simple QA against your documents

• In-chat citations linked to the original document source and text

• Simple technology stack for fast iteration

• 100% Cloud deployment ready.

• “Bring your own LLM” model.

• Extremely efficient cost-saving measures for managing very large documents. You’ll never pay to embed a massive document or transcript more than once. 90% more cost effective than other document chatbot solutions.

• Full Developer API for custom integrations!

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Supported LLMs, Embedders, and Vector Databases

Supported LLMs:

• Any open-source llama.cpp compatible model

• OpenAI

• Azure OpenAI

• Anthropic ClaudeV2

• Google Gemini Pro

• Ollama (chat models)

• LM Studio (all models)

• LocalAi (all models)

Supported Embedding models:

• AnythingLLM Native Embedder (default)

• OpenAI

• Azure OpenAI

• LM Studio (all)

• LocalAi (all)

Supported Vector Databases:

• LanceDB (default)

• Pinecone

• Chroma

• Weaviate

• QDrant

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Anything LLM Overview

AnythingLLM is a full-stack application where you can use commercial off-the-shelf LLMs or popular open source LLMs and vectorDB solutions to build a private ChatGPT with no compromises that you can run locally as well as host remotely and be able to chat intelligently with any documents you provide it.

AnythingLLM divides your documents into objects called workspaces . A Workspace functions a lot like a thread, but with the addition of containerization of your documents. Workspaces can share documents, but they do not talk to each other so you can keep your context for each workspace clean.

Technical Overview

This monorepo consists of three main sections:

• frontend: A viteJS + React frontend that you can run to easily create and manage all your content the LLM can use.

• server: A NodeJS express server to handle all the interactions and do all the vectorDB management and LLM interactions.

• docker: Docker instructions and build process + information for building from source.

• collector: NodeJS express server that process and parses documents from the UI.

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How to setup for development

• yarn setup To fill in the required .env files you’ll need in each of the application sections (from root of repo).

• Go fill those out before proceeding. Ensure server/.env.development is filled or else things won’t work right.

• yarn dev:server To boot the server locally (from root of repo).

• yarn dev:frontend To boot the frontend locally (from root of repo).

• yarn dev:collector To then run the document collector (from root of repo).