Knowledge Graph RAG (ToPWR)
FastAPI + Neo4j + LangGraph + MCP workflow that turns natural language into Cypher, deployed as a 4-service Docker stack.
A university data assistant built at Solvro to help students and staff navigate institutional information faster. The system combines a React chat UI, a FastAPI application layer, an MCP server, and a Neo4j knowledge graph so users can ask natural-language questions and retrieve structured university data.
Converted user questions into Cypher-backed graph retrieval through a FastAPI backend and an MCP server
Designed a LangGraph pipeline with LLM guardrails for query routing, Cypher generation, and response orchestration
Built a Prefect ETL pipeline ingesting PDF/text documents into the graph via OCR, extraction, and LLM-generated Cypher
Impact
Natural language mapped to graph-backed university data, in production at Solvro
Role
Machine Learning Engineer
Timeline
2025
Key tags
Problem
University information is scattered across departments, pages, and systems, which makes discovery slow for students and staff.
Solution
Built a knowledge-graph retrieval workflow that checks query relevance, generates Cypher, executes graph retrieval, and returns answers through an application API.
Architecture
A React frontend, FastAPI backend, FastMCP server, and Neo4j run as a 4-service Docker stack. LangGraph orchestrates retrieval with guardrails, while a Prefect ETL flow processes documents and populates the knowledge graph.
Challenges
- Modeling university entities and relationships cleanly in a graph schema
- Handling ambiguous user intent without unsafe or misleading queries
- Keeping retrieval tied to structured data while preserving conversational UX
Technology stack
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