GraphMind

GraphMind is a production-style knowledge graph system that converts unstructured content such as documents, notes, articles, and URLs into connected, queryable intelligence. Instead of only keyword matching, it extracts entities and relationships so users can explore connections like who appears across documents and which organizations they are linked to.

Problem It Solves

Manual research across scattered files does not scale and usually forces repeated reading of the same material. GraphMind automates structured extraction and gives teams relationship-first queries, making research synthesis, due diligence, and competitive intelligence faster and more reliable.

System Architecture

• Frontend: Next.js + TypeScript interface for document ingestion, graph exploration, and semantic query workflows.
• APIs: Node.js gateway and Python/FastAPI services for parsing, enrichment, and graph query orchestration.
• Background Jobs: Celery workers process long-running extraction pipelines using RabbitMQ as the broker and Redis for caching and task state.
• LLM Layer: OpenAI-powered prompts identify entities, normalize metadata, and infer relationship candidates from raw text.
• Cloud Deployment: Deployed to cloud infrastructure with autoscaling workers and managed observability for production reliability.

Why Graph over Basic RAG/Search

RAG and search tools are excellent for retrieving relevant chunks, but they still treat documents as mostly isolated units. GraphMind models the relationships between entities and facts, so insights come from the connections across sources, not only similarity scores within individual chunks.

Interview-Ready Summary

I built GraphMind to solve the scaling problem of manual research across unstructured documents. The platform uses Next.js + TypeScript with Node/Python APIs, Celery/RabbitMQ/Redis background processing, and OpenAI-based extraction, then deploys to cloud infrastructure so users can query relationships directly rather than re-reading source material.