LLM Pipeline & Knowledge Graph Architect
Budget: €250 – €750 EUR
I’m building an end-to-end pipeline that ingests technology-and-innovation papers—journal articles, conference proceedings, and patents—feeds them through an LLM stack, and renders an interactive knowledge graph that shows how breakthroughs evolve over time.
Before any code is written, I need a seasoned data scientist to dissect available tools and design a rock-solid architecture where interoperability is the guiding principle. Your analysis will compare ingestion frameworks (e.g., Apache NiFi, Airbyte), vector stores and graph databases (Pinecone, Neo4j, TigerGraph), and orchestration layers that can sit comfortably alongside modern LLM tooling such as LangChain or LlamaIndex.
What I expect from you:
• A concise report that evaluates candidate tools, highlights integration points, and flags any interoperability pain points I should anticipate.
• A reference architecture diagram that links ingestion, LLM processing, vector storage, and graph visualization components.
• A conceptual framework showing how textual entities (methods, findings, inventors, citations) are extracted, linked, and surfaced in the graph.
• A demonstration notebook or pseudo-code illustrating the flow: ingestion → chunking & embedding → entity/relation extraction → graph upsert.
Acceptance will be based on the clarity and feasibility of the architecture, the depth of the interoperability discussion, and the practicality of your sample flow. If you thrive on stitching diverse systems together and love turning raw scientific literature into navigable knowledge, I’d like to review your approach and iterate quickly toward implementation.
Before any code is written, I need a seasoned data scientist to dissect available tools and design a rock-solid architecture where interoperability is the guiding principle. Your analysis will compare ingestion frameworks (e.g., Apache NiFi, Airbyte), vector stores and graph databases (Pinecone, Neo4j, TigerGraph), and orchestration layers that can sit comfortably alongside modern LLM tooling such as LangChain or LlamaIndex.
What I expect from you:
• A concise report that evaluates candidate tools, highlights integration points, and flags any interoperability pain points I should anticipate.
• A reference architecture diagram that links ingestion, LLM processing, vector storage, and graph visualization components.
• A conceptual framework showing how textual entities (methods, findings, inventors, citations) are extracted, linked, and surfaced in the graph.
• A demonstration notebook or pseudo-code illustrating the flow: ingestion → chunking & embedding → entity/relation extraction → graph upsert.
Acceptance will be based on the clarity and feasibility of the architecture, the depth of the interoperability discussion, and the practicality of your sample flow. If you thrive on stitching diverse systems together and love turning raw scientific literature into navigable knowledge, I’d like to review your approach and iterate quickly toward implementation.