LM Studio + AnythingLLM: Process Local Documents with RAG Like a Pro!
Summary
This video tutorial demonstrates how to use LM Studio and Anything LLM to query local data using a Retrieval-Augmented Generation (RAG) system. The presenter uses Basecamp API documentation as an example dataset.
The process begins by setting up LM Studio: selecting and running a local Llama model, then starting a server from the developer tab. Next, the user configures Anything LLM to connect with LM Studio as the LLM provider, using default embedding settings and LanceDB for the vector database.
After creating a workspace named "Basecamp RAG," the presenter imports Basecamp API documentation files into Anything LLM. The system processes these documents into the vector database without requiring coding. Once setup is complete, the presenter tests the system by asking for the curl command for "get Todo," and the system correctly retrieves the exact command from the imported documentation.
The tutorial emphasizes that this approach allows users to query any local documents (books, pamphlets, or other files) using their own locally-run language model, providing a private, customizable alternative to cloud-based services.
Details
- Duration: 7m 20s
- URL: LM Studio + AnythingLLM: Process Local Documents with RAG Like a Pro!
Tags
- LMStudio
- AnythingLLM
- RAG
- LocalDocuments
- VectorDatabase
- LlamaModel
- PrivateAI
- DocumentProcessing
- YouTube
- Video
- LocalLLM,LocalAI
