romilandc/langchain-RAG
A RAG implementation on LangChain using Chroma vector db as storage. Take some pdfs, store them in the db, use LLM to inference.
This tool helps you quickly get answers from your PDF documents using a large language model. You provide your own PDFs, and it processes them to allow you to ask questions and receive relevant answers generated by the AI. It's ideal for researchers, analysts, or anyone who needs to extract information efficiently from a collection of documents without manually reading through everything.
No commits in the last 6 months.
Use this if you have a collection of PDF documents and want to quickly find specific information or synthesize answers by querying them with an AI.
Not ideal if you need to perform complex data analysis on structured data within PDFs or require the AI to write entirely new content unrelated to your documents.
Stars
8
Forks
—
Language
Python
License
Apache-2.0
Category
Last pushed
May 01, 2024
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/vector-db/romilandc/langchain-RAG"
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