saricmilos / RAG-Langchain

What is RAG-Langchain?

This Python project delivers a deployable LangChain-based RAG chatbot for querying SEC EDGAR filings from big tech like Apple, NVIDIA, Microsoft, and Google—pulling the latest 5 years of 10-Ks, 10-Qs, and more. Users get a FastAPI service at /chat that handles natural questions on risks, financials, and strategy, with source citations and multi-turn conversation memory. It solves the pain of digging through dense PDFs by enabling semantic search grounded in authoritative data.

Why is it gaining traction?

As an advanced RAG LangChain GitHub example, it stands out with agentic tool routing: internal vector store first, Alpha Vantage for live news sentiment, DuckDuckGo as fallback. Developers love the rag LangChain chatbot setup with Pinecone persistence, Docker deployment, and ingestion scripts for PDF RAG LangChain GitHub workflows. It's a practical rag Langchain project GitHub starter that skips boilerplate for rag Langchain Ollama or Python experiments.

Who should use this?

Fintech devs prototyping investment assistants or compliance bots on EDGAR data. Analysts building rag Langchain from scratch GitHub demos for company deep dives. Python teams needing a langchain RAG based chatbot baseline for multimodal RAG Langchain GitHub or rag Langchain tutorial pipelines.

Verdict

Worth forking as a rag Langchain example with real data and API endpoints, but 33 stars and 1.0% credibility signal early-stage maturity—docs are README-focused, no extensive tests. Tweak for production, but it's a fast ramp for financial RAG.

(198 words)