Built an end-to-end agentic RAG system that goes beyond simple retrieval — the LangGraph agent reasons over multiple sources, self-corrects, and provides grounded citations.
Architecture
- Retriever Agent — hybrid search (dense + sparse) across Qdrant vector store
- Reasoner Agent — multi-step chain-of-thought with GPT-4 / Claude
- Validator Agent — fact-checks responses against source documents
- Orchestrator — LangGraph state machine managing agent handoffs and retries
Key Decisions
- Chose LangGraph over vanilla LangChain for explicit control over agent state transitions
- Qdrant over Pinecone for self-hosted deployment and cost control
- Streaming responses via Server-Sent Events for perceived latency improvement
Evaluation
Built a custom evaluation harness using RAGAS metrics — faithfulness, answer relevancy, and context precision tracked per-query in MLflow.