Inspiration
There is a subset of problems where it makes sense to consult a structured data source and neither vector-based RAG nor plain-text LLM context is enough. For these domains, a knowledge graph can offer a superior context.
What it does
Chat app that answers questions based on a knowledge graph. Has a pretty robust pipeline that parses user's question, leverages an LLM to create a CYPHER query out of it, validates and re-constructs the CYPHER query, executes the query against the graph DB, and then recombines the DB results with LLM to make a human-readable answer.
How we built it
Langgraph, Neo4j, python, uvicorn, pydantic, vite, js, react, OpenAI
Built With
- javascript
- langgraph
- neo4j
- pydantic
- python
- react
- uvicorn
- vite
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