Agentic Rag Financial Parser
Enterprise RAG ecosystem managing 15,000+ semantic chunks. Features hybrid parsing (LlamaParse/PyMuPDF) and 256-dim MRL embeddings for 512MB RAM environments
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What is Agentic Rag Financial Parser?
The Agentic Rag Financial Parser is an 8-node Agentic RAG system built with LangGraph that processes complex Indian government financial and legal PDFs, including Union Budget documents, Finance Bills, RBI Acts, and the Constitution. The tool achieves 100% extraction accuracy on Union Budget 2026-27 when verified against the Ministry of Finance. It is specifically designed to handle challenging document features like complex tables, merged cells, bilingual content (Hindi and English), and mixed document types.
Key features include Vision-First Extraction using LlamaParse VLM to interpret complex stacked bar charts from PDFs, Agentic Logic with LangGraph that analyzes data structure logically rather than just retrieving information, and Zero Hallucination protection through a Hallucination Guard node that cross-checks extracted figures with original sources. The system uses an 8-node LangGraph StateGraph architecture for document processing.
This tool is designed for financial analysts, government researchers, policy analysts, and anyone working with Indian financial documents who needs accurate extraction of numerical data from complex budget documents. It is particularly useful for professionals who need to analyze Finance Bills with misaligned columns, Hindi headers, and values spanning multiple pages.
Agentic Rag Financial Parser pricing
Pricing model: Freemium
Free - Hosted on Render free tier with 512MB RAM. The tool operates with a budget of ₹0. No paid plans are mentioned. The system uses LlamaParse VLM which may have usage costs beyond free tier allocations.
Agentic Rag Financial Parser pros
- 100% extraction accuracy on Union Budget 2026-27 verified against Ministry of Finance
- Handles complex tables with merged cells accurately
- Processes bilingual documents (Hindi and English)
- Vision-First Extraction using LlamaParse VLM for chart interpretation
- Zero Hallucination with Hallucination Guard node cross-checking
- 8-node LangGraph StateGraph architecture for robust processing
- Handles values spilling over page edges in Finance Bill tables
- Resolves misaligned columns in complex financial tables
- Live production deployment on Render free tier
- Works with Union Budget, Finance Bills, RBI Acts, and Constitution
- No decimal errors across 30+ data points in testing
- 10 out of 10 years extracted flawlessly in tax trend chart evaluation
- Budget of ₹0 using Render Free Tier and 512MB RAM
- Converts chaotic PDFs into clean markdown format
- Production-ready with rigorous evaluation testing
Agentic Rag Financial Parser cons
- Limited to Indian government financial documents only
- Requires LlamaParse API which may have usage costs beyond free tier
- Runs on 512MB RAM which may limit processing large documents
- Render free tier may have downtime or slow response times
- Specialized for Hindi-English bilingual content, may not support other languages
- No documented batch processing for multiple documents
- Limited to PDF format input only
- Open-source project with only 2 GitHub stars, limited community support
Frequently asked questions about Agentic Rag Financial Parser
What documents does the Agentic Rag Financial Parser support?
The tool processes Indian government financial and legal PDFs including Union Budget documents, Finance Bills, RBI Acts, and the Constitution of India. It specializes in extracting data from complex financial documents with tables, merged cells, and bilingual content.
How accurate is the extraction?
The system achieves 100% extraction accuracy on Union Budget 2026-27 when verified against the Ministry of Finance. In testing with a bilingual Tax Trend chart spanning ten years, all 10 out of 10 years were extracted flawlessly with no decimal errors across more than 30 data points.
How does the tool handle complex tables?
The system uses Vision-First Extraction with LlamaParse VLM to directly interpret complex documents. It handles misaligned columns, Hindi headers, values spilling over page edges, and merged cells by converting PDFs to well-structured Markdown before embedding.
Does the tool hallucinate data?
No. The system includes a Hallucination Guard node that cross-checks extracted figures with the original source PDF before providing the final answer, ensuring zero hallucination in the output.
What architecture does the system use?
The tool is built with an 8-node LangGraph StateGraph architecture. Nodes handle specific tasks in the RAG workflow including document parsing, chart interpretation, table extraction, and hallucination checking.
Can it process bilingual documents?
Yes, the system is specifically designed to handle bilingual documents with both Hindi and English content, including Finance Bill tables with Hindi headers and mixed language content.
What is the cost to use this tool?
The tool is free and hosted on Render's free tier with 512MB RAM. It operates with a budget of ₹0. However, LlamaParse VLM usage may incur costs beyond free tier allocations.
What file formats are supported?
The tool currently supports PDF format only, specifically text-based PDFs containing financial documents, tables, and charts from Indian government sources.
Is this suitable for international financial documents?
No, this tool is specifically designed for Indian government financial documents. It is optimized for Union Budget, Finance Bills, RBI Acts, and the Constitution, with special handling for Hindi-English bilingual content.
How do I access the tool?
The tool is live and accessible at https://agentic-rag-financial-parser.onrender.com. The complete source code is also available on GitHub for those who want to self-host or modify it.