GP Support Agent
Production-grade deterministic RAG + LangGraph agent for healthcare knowledge bases
Overview
A production-grade, deterministic RAG + agent architecture built with LangChain and LangGraph, applied to a GP/healthcare-reception knowledge base. Includes an integrated evaluation harness for grounded response measurement — ensuring agent answers are traceable to source documents rather than hallucinated.
Problem
Healthcare reception and GP support requires grounded, auditable answers — hallucinations are unacceptable. Most RAG systems are non-deterministic and lack evaluation harnesses to verify that responses are actually grounded in source documents.
Solution
A deterministic RAG pipeline backed by LangGraph for agent state management, applied to a curated GP/healthcare knowledge base. Every response is verifiable against source chunks. An evaluation harness measures groundedness, coverage, and faithfulness of agent outputs.
Architecture
- 1Healthcare knowledge base ingestion — document chunking and embedding
- 2LangChain retrieval layer — semantic similarity search over knowledge base
- 3LangGraph agent state machine — deterministic conversation and tool-call orchestration
- 4Grounded response generation — answers cited to source document chunks
- 5Evaluation harness — groundedness, faithfulness, and coverage scoring
- 6Deterministic design — reproducible agent behavior across identical inputs
Technical Challenges
- Ensuring deterministic agent behavior in LangGraph despite LLM temperature variations — solved with structured output schemas and fixed retrieval top-K.
- Designing a groundedness evaluator that distinguishes faithful paraphrasing from hallucination.
- Curating a healthcare knowledge base with enough coverage to handle real GP reception query patterns.
Results
- Functional production-grade RAG + agent system with integrated evaluation harness.
- Demonstrates deterministic agent architecture applicable to any high-stakes knowledge domain.