SEC 10-K RAG Q&A Platform
Intelligent SEC filing analysis with RAG-powered Q&A, streaming responses, and grounded answers with citations using OpenAI, Qdrant, and FastAPI
Feature Overview
Comprehensive SEC filing analysis platform with RAG-powered intelligence and streaming Q&A
Core Features
- Company lookup by ticker/name with CIK resolution
- Automated 10-K filing ingestion from EDGAR
- Parsing, sectioning, and intelligent chunking
- OpenAI embeddings with Qdrant vector indexing
- RAG Q&A with strict grounding and citations
- Server-Sent Events (SSE) streaming responses
- Chat session persistence and history
- Metadata filtering by company, year, section
Advanced Features
- Dual-path QA: qualitative narrative + quantitative extraction
- Multi-citation reconciliation for numeric verification
- Idempotent re-indexing with deterministic chunk IDs
- JWT authentication with role-based access control
- Rate limiting and usage monitoring
- Audit logging with trace_id across pipeline
- Admin controls for ingestion management
- Token usage tracking and cost visibility
System Architecture
Production-ready architecture with FastAPI, React, Qdrant, and OpenAI for intelligent SEC filing analysis
FastAPI Backend
Auth, rate limiting, SSE orchestration, and ingestion management
React Frontend
Company search, filing selection, streaming chat UI with citations viewer
PostgreSQL
Companies, filings, chunks, sessions, messages, citations, and job tracking
Qdrant Vector Store
Embeddings with filterable payload for semantic search by company/year/section
OpenAI Integration
Embeddings generation and RAG-powered Q&A with strict grounding
EDGAR API
Automated SEC filing discovery, download, and CIK resolution
RAG Pipeline
End-to-end pipeline from SEC filing ingestion to grounded answers with citations
EDGAR Ingestion
Discover and download 10-K filings by company ticker and year from SEC EDGAR API
Parsing & Sectioning
Normalize text, detect standard 10-K sections, and extract structured content
Chunking & Indexing
Chunk sections with overlap, generate embeddings, and index in Qdrant with metadata
Semantic Retrieval
Query Qdrant with filters for company, year, and section to find relevant context
RAG Q&A
Generate grounded answers with OpenAI using retrieved context and strict citation policy
Streaming Response
Stream answer tokens via SSE with citations and confidence scores in real-time
SSE Streaming Architecture
Real-time response streaming with Server-Sent Events for ChatGPT-like user experience
Server-Sent Events (SSE)
Streaming architecture using SSE for real-time token delivery with lower complexity than WebSockets and better proxy compatibility
Progressive Response
Stream answer tokens as they're generated with message deltas, citations at the end, and real-time UI updates for ChatGPT-like UX
Event Types
Structured event types for different stages: message_delta for tokens, citations for references, final for complete answer, and done signal
Reliability & Observability
Keepalive events prevent timeouts, trace_id enables debugging, audit logging tracks usage, and proper headers ensure stable streaming
Key Benefits
Production-ready SEC filing analysis with enterprise-grade performance, security, and scalability
Fast Retrieval
Typical retrieval time with Qdrant vector search and metadata filtering
Grounded Answers
Strict grounding policy ensures all answers derived from retrieved context
Verifiable Citations
Every answer includes year, section, and snippet citations for verification
Real-Time Streaming
Token-by-token streaming for ChatGPT-like UX with progressive responses
Enterprise Security
JWT authentication, rate limiting, and role-based access control
Scalable Architecture
Handle thousands of filings and millions of chunks with autoscaling
Ready for Production
Complete platform with ingestion pipeline, RAG Q&A, streaming responses, audit logging, and enterprise security features ready to deploy on ECS or Kubernetes
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