SEC Filings QA Agent

Overview

Built a semantic Q&A system for SEC filings that lets users ask natural language questions over regulatory financial documents such as 10-K, 8-K, and DEF 14A reports.
The system combines retrieval-augmented generation (RAG) with vector search (ChromaDB), contextual embeddings, and a lightweight Streamlit interface to deliver fast, accurate, and attributed answers across multiple companies’ filings.

It’s designed for deep financial research — enabling both analysts and engineers to query dense corporate disclosures with simple queries like “What are Apple’s risk factors in the latest 10-K?” or “How has Tesla described climate-related risks?”.

Why This Project

Traditional analysis of SEC filings is labor-intensive: filings often exceed hundreds of pages, and pulling insights manually can take hours. By integrating large language models with retrieval systems, this project automates the heavy lifting: it extracts context from long documents and grounds responses in the exact source text. This reduces ambiguity, improves accuracy, and scales document understanding far beyond keyword search. :contentReference[oaicite:0]{index=0}

Key Capabilities

• Semantic Question Answering

  • Users can ask complex natural language questions about financial reports.
  • Responses are grounded in the context of relevant filings, improving relevance and trustworthiness.

• RAG Pipeline Integration

  • Documents are chunked and embedded using sentence-transformers.
  • A ChromaDB vector store enables fast retrieval of semantically relevant text passages.

• Metadata-Driven Attribution

  • Answers include contextual metadata like ticker, date, section, and filing type, helping users verify responses against original sources.

• Interactive UI

  • Streamlit-based interface for quick explorations, chain queries, and interactive research.

How It Works

Use Cases

• Corporate Financial Research
  Quickly analyze risk disclosures, executive compensation, or segment performance across years and companies.

• Investor Insights
  Surface high-impact information from filings before key events like earnings or shareholder meetings.

• Education & Data Exploration
  Enable finance students and researchers to ask interpretive questions on regulatory filings without manual reading.

Design Highlights

• Attribution-Focused Answers
  Source metadata travels with the text chunks to ensure that answers link back to precise parts of filings.

• Conversational Memory
  Supports follow-up questions that build on context from previous queries.

• Modular & Extensible
  Each phase of the pipeline (ingestion, preprocessing, retrieval, LLM calling) is modular, making custom extensions straightforward.

Sample Questions

• “What are Apple’s risk factors in the latest 10-K?”
• “Compare R&D spending of Tesla and Microsoft.”
• “Describe climate-related risk disclosures for JPMorgan.”
• “How was executive compensation updated for UNH?” :contentReference[oaicite:2]{index=2}

Status

This project is fully functional and production-ready, with scope to extend UI filters, improve model accuracy, and add advanced search capabilities.