Mintplex-Labs / anything-llm
- воскресенье, 11 июня 2023 г. в 00:00:05
A full-stack application that turns any documents into an intelligent chatbot with a sleek UI and easier way to manage your workspaces.
A full-stack application and tool suite that enables you to turn any document, resource, or piece of content into a piece of data that any LLM can use as reference during chatting. This application runs with very minimal overhead as by default the LLM and vectorDB are hosted remotely, but can be swapped for local instances. Currently this project supports Pinecone, ChromaDB & more for vector storage and OpenAI for LLM/chatting.
AnythingLLM aims to be a full-stack application where you can use commercial off-the-shelf LLMs with Long-term-memory solutions or use popular open source LLM and vectorDB solutions.
Anything LLM is a full-stack product 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.
Some cool features of AnythingLLM
conversation
and query
. Conversation retains previous questions and amendments. Query is simple QA against your documentsThis monorepo consists of three main sections:
collector
: Python tools that enable you to quickly convert online resources or local documents into LLM useable format.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.yarn
and node
on your machinepython
3.8+ for running scripts in collector/
.GPT-3.5
, GPT-4
*.yarn setup
from the project root directory.This will fill in the required .env
files you'll need in each of the application sections. Go fill those out before proceeding or else things won't work right.
Next, you will need some content to embed. This could be a Youtube Channel, Medium articles, local text files, word documents, and the list goes on. This is where you will use the collector/
part of the repo.
Go set up and run collector scripts
<issue number>-<short name>