sienlonglim/LangChain

This project implements RAG using OpenAI's embedding models and LangChain's Python library

16
/ 100
Experimental

Supports multi-source data ingestion (PDFs, Word docs, YouTube, Wikipedia, web pages) with configurable retrieval strategies and query types (restricted vs. creative). Architecture uses document chunking, OpenAI embeddings, and FAISS/Chroma vector stores for semantic search, deployed as an interactive Streamlit application with cost tracking via callback logging. Implements object-oriented design with resource caching and YAML configuration for flexible pipeline management.

No commits in the last 6 months.

No License Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 6 / 25
Maturity 1 / 25
Community 9 / 25

How are scores calculated?

Stars

16

Forks

2

Language

Jupyter Notebook

License

Last pushed

Jan 25, 2024

Commits (30d)

0

Get this data via API

curl "https://pt-edge.onrender.com/api/v1/quality/vector-db/sienlonglim/LangChain"

Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.