Live Immigration Knowledge Graph Build
Budget: ₹150,000 – ₹250,000 INR
The assignment centres on taking live immigration feeds from government databases and shaping them into a continually updated knowledge graph, with a clear upgrade path toward Graph-RAG so an LLM can later query the graph directly.
Phase 1 – Knowledge graph
Data will arrive as real-time or near-real-time streams. I already have authorised access to the government endpoints; your job is to design and code the ingestion, normalisation, and storage layers. A graph database such as Neo4j, TigerGraph, or Amazon Neptune is preferred, but I am open to any engine that supports ACID guarantees and fast traversals. The graph must refresh automatically as new records appear and expose a REST/GraphQL interface for downstream services.
The entities and relationships that must be modelled are:
• Visa applications
• Border crossings
• Residency permits
• Visa change procedures
• Validity periods
• Status transitions
• Eligibility rules
Phase 2 – Graph-RAG enablement
Once the schema is stable, we will add a retrieval layer (LangChain or similar) so that a large language model can run natural-language questions against the graph. Clean embeddings, context windows, and response ranking will all be part of this stage.
Key expectations
• Clean, well-documented code (Python).
• Container-ready deployment scripts (Docker + Compose or Helm).
• Continuous ingestion tests that confirm freshness and integrity.
• A short README explaining how to spin up the stack locally and how to execute sample queries.
• For Phase 2, a demo notebook or endpoint that shows at least three successful Graph-RAG queries returning correct, reference-verified answers.
If you thrive on data engineering, graph schemas, and cutting-edge RAG workflows, this project should be a good fit.
Phase 1 – Knowledge graph
Data will arrive as real-time or near-real-time streams. I already have authorised access to the government endpoints; your job is to design and code the ingestion, normalisation, and storage layers. A graph database such as Neo4j, TigerGraph, or Amazon Neptune is preferred, but I am open to any engine that supports ACID guarantees and fast traversals. The graph must refresh automatically as new records appear and expose a REST/GraphQL interface for downstream services.
The entities and relationships that must be modelled are:
• Visa applications
• Border crossings
• Residency permits
• Visa change procedures
• Validity periods
• Status transitions
• Eligibility rules
Phase 2 – Graph-RAG enablement
Once the schema is stable, we will add a retrieval layer (LangChain or similar) so that a large language model can run natural-language questions against the graph. Clean embeddings, context windows, and response ranking will all be part of this stage.
Key expectations
• Clean, well-documented code (Python).
• Container-ready deployment scripts (Docker + Compose or Helm).
• Continuous ingestion tests that confirm freshness and integrity.
• A short README explaining how to spin up the stack locally and how to execute sample queries.
• For Phase 2, a demo notebook or endpoint that shows at least three successful Graph-RAG queries returning correct, reference-verified answers.
If you thrive on data engineering, graph schemas, and cutting-edge RAG workflows, this project should be a good fit.