curiousily/ragbase
Completely local RAG. Chat with your PDF documents (with open LLM) and UI to that uses LangChain, Streamlit, Ollama (Llama 3.1), Qdrant and advanced methods like reranking and semantic chunking.
This tool helps you privately and securely chat with your PDF documents, allowing you to ask questions and get answers without your information ever leaving your computer. You provide your PDF files, and the system processes them to create a searchable knowledge base. The output is conversational answers to your questions, drawing directly from your uploaded documents. It's ideal for researchers, analysts, or anyone who needs to extract information from multiple documents while maintaining strict data privacy.
122 stars. No commits in the last 6 months.
Use this if you need to quickly find answers or synthesize information from a collection of private PDF documents without uploading them to external cloud services.
Not ideal if you need to analyze image-heavy PDFs where text extraction is not sufficient, or if you require advanced data manipulation beyond conversational querying.
Stars
122
Forks
43
Language
Python
License
MIT
Category
Last pushed
Jul 26, 2024
Commits (30d)
0
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