Ambuj123-lab

Ambuj123-lab / agentic-rag-financial-parser

Public

Enterprise RAG ecosystem managing 15,000+ semantic chunks. Features hybrid parsing (LlamaParse/PyMuPDF) and 256-dim MRL embeddings for 512MB RAM environments

agentic-rag-financial-parser.onrender.com agentic-rag fastapi genai langgraph llmops
40
18
100% credibility
Found Mar 28, 2026 at 40 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

A user-friendly web chat app for querying Indian financial and legal documents like budgets, tax laws, and schemes with AI-powered insights and optional personal file uploads.

How It Works

1
🌐 Discover the App

You stumble upon this helpful web tool that makes sense of confusing Indian budgets, tax rules, and legal docs through simple chat.

2
🔐 Sign In Easily

Click the Google button to sign in quickly and securely, no hassle or new passwords needed.

3
💬 Ask Your Questions

Type something like 'What changed in the new budget tax slabs?' and watch the AI think step-by-step before giving a smart reply.

4
Upload Your Docs?
📄
Upload PDF

Your file gets checked securely and added just for you, ready to query privately.

➡️
Chat Only

Dive straight into questions about government budgets or laws without extras.

5
📊 Get Clear Answers

See detailed responses with exact page sources from docs and a confidence score so you know how solid the info is.

🎉 Master Your Finances

Now you confidently understand tax rules, pensions, or budgets like a pro, saving hours of reading tiny print.

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Star Growth

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AI-Generated Review

What is agentic-rag-financial-parser?

This Python-based full-stack app delivers an agentic RAG system for querying Indian financial docs like Union Budgets, tax laws, and RBI guidelines. Users upload PDFs or chat against a pre-indexed 15,000+ semantic chunk knowledge base via a React dashboard with Google OAuth login. It handles hybrid parsing and 256-dim MRL embeddings optimized for 512MB RAM enterprise RAG pipelines.

Why is it gaining traction?

The 8-node agentic flow stands out: it classifies queries, cross-questions vagueness, guards hallucinations, and streams answers with sources and confidence scores. Production hooks like secure uploads, Redis caching, Langfuse tracing, and Docker deploys on Render make it plug-and-play for enterprise RAG Reddit discussions or enterprise-rag GitHub forks. Live demo at agentic-rag-financial-parser.onrender.com lets devs test instantly.

Who should use this?

Finance AI builders prototyping enterprise RAG LLM apps for compliance docs. Legal tech teams needing low-RAM pipelines for tax/PF analysis. Indie devs inspired by enterprise RAG Manning books or Tyler Suard challenges, wanting FastAPI + Pinecone starters with auth and HITL chunk review.

Verdict

Strong prototype at 40 stars and 1.0% credibility—excellent docs, live app, and patterns for enterprise GitHub Actions/Copilot workflows, but low maturity means audit for prod. Fork it for custom enterprise RAG systems; skip if needing battle-tested scale.

(198 words)

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