I will build a production rag with langgraph and google vertex ai

I will build a production-oriented RAG system using LangGraph and Google Vertex AI.

Instead of simply connecting an LLM to a vector database, I focus on building a reliable retrieval pipeline that can be evaluated, optimized, and deployed.

Depending on your project, I can implement:

LangGraph RAG workflows

Document ingestion and preprocessing

Chunking and metadata strategies

Vector and hybrid retrieval

Reranking and retrieval optimization

FastAPI backend

Structured LLM responses

Conversation memory

Evaluation with LangSmith and RAGAS

Hallucination and grounding analysis

Docker deployment

Google Cloud / Vertex AI integration

Performance and cost optimization

You will receive clean, documented code and an explanation of the architecture.

I can build a new RAG system from scratch or improve an existing RAG application that suffers from poor retrieval, irrelevant context, hallucinations, latency, or high LLM costs.