Case Study
AI Document Processing Agent
RAG + agentic workflow that extracts structured insights from large PDF corpora and answers queries with citations.
PythonLangGraphFAISSFastAPIPyTorch
Problem Statement
Teams needed accurate, auditable answers across large document sets without manual triage or brittle keyword search.
Dataset / Scale
Thousands of PDFs, average 40-80 pages each, indexed for vector search and chunk level retrieval.
Architecture Diagram
Document loader → chunking → embedding pipeline
Vector store retrieval + hybrid reranking
Agentic orchestration for tool selection
Citations and validation against sources
ML / LLM Pipeline
Ingest → Clean → Chunk → EmbedRetrieve → Rerank → GenerateValidate → Cite → Respond
System Design
- FastAPI inference gateway
- LangGraph orchestration for agent steps
- FAISS vector index with metadata filters
- Batch ingestion for cost efficiency
Engineering Challenges
- Reducing hallucinations with citation constraints
- Balancing recall vs. precision in retrieval
- Latency under 1.5s for common queries
Metrics
- +28% answer accuracy with reranking
- 1.3s median response time
- 98% source-attributed responses
Screenshots
Document ingestion pipeline with chunking status
Agent trace timeline showing tool calls
Response panel with citation highlights