Financial Report Document Intelligence & RAG Pipeline
Experimental RAG pipeline chunking SEC 10-K filings with vector embeddings and ChromaDB. Not financial advice.
Project Architecture & Case Study
The Challenge & Scope
Hundreds of pages of dense financial filings overwhelm simple keyword search. Furthermore, standard LLM context windows frequently truncate footnotes and nuanced liquidity disclosures where crucial balance-sheet details reside.
Technical Architecture & Solution
Integrated Python, LangChain, and ChromaDB to construct a structured semantic chunking pipeline. Generated dense vector embeddings and established a citation-grounded QA retrieval chain that directly links answers back to original document line ranges.
Results & Key Deliverables
Delivered a working prototype capable of verifiable, citation-backed QA across annual corporate filings, validating production-grade vector search and chunking heuristics in an experimental environment.
About the Project
An experimental Retrieval-Augmented Generation (RAG) pipeline designed to semantically query risk factors, MD&A sections, and regulatory footnotes across dense corporate SEC 10-K filings. Developed strictly for software engineering and NLP research; does not constitute financial advice or investment recommendations.
Architected, designed, and engineered entirely by Ahmet Mert Yiğitbaşı.
Technologies
- Python 3.10
- LangChain
- ChromaDB
- Vector Database
- RAG Architecture
- Semantic Chunking & Embeddings