The problem
Support teams answer the same types of questions repeatedly: “What’s my order status?”, “What’s in the knowledge base?”, “Summarize ticket trends.” Doing this with a single generic chatbot is brittle; different questions need different backends—RAG over docs, SQL over DB, or analysis over aggregated data.
The solution
Customer Support AI uses LangGraph to orchestrate three agents: RAG (Pinecone + LLM for knowledge-base answers), SQL (natural language to read-only SQL over PostgreSQL), and Analyzer (data analysis and insights). An orchestrator routes each query to the right agent(s) and returns a unified response.
Without specialized agents
One-size-fits-all chatbot; no clean separation between docs, DB, and analysis—hard to scale and secure.
With Customer Support AI
RAG + SQL + Analyzer agents with automatic routing; security (API key, read-only SQL, CORS); Docker-ready.
What it does
- RAG agent – Retrieves context from Pinecone, generates answers from knowledge base.
- SQL agent – Translates natural language to SQL, runs read-only queries on PostgreSQL with safety checks.
- Analyzer agent – Analyzes data and returns insights (e.g. trends, summaries).
- Orchestrator – Routes queries to the appropriate agent(s) automatically.
- Security – API key auth, input validation, SQL injection prevention, CORS, non-root Docker.
Tech stack
Backend: FastAPI, LangGraph, Pinecone, SQLAlchemy, PostgreSQL. Frontend: React, Vite, Axios. Docker Compose for full stack.