sienlonglim/LangChain
This project implements RAG using OpenAI's embedding models and LangChain's Python library
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.
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Jan 25, 2024
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