Graphrag Developer Challenge

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EUR 30.000 - 50.000
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Descrizione del lavoro

GraphRAG Developer Challenge – Legal Document Processing (Prototype)

Role Details

  • Role: Senior RAG Systems Developer (Contract / Freelance)
  • Compensation: $600 — paid only if you pass (95% benchmark)
  • Timeline: 3–5 days from materials receipt to live demo
  • Purpose: Technical evaluation for potential long-term hire
  • Frontend / UI: None (backend prototype only)

Objective

We’re seeking an expert in graph-based retrieval (GraphRAG) to build a high-accuracy prototype for legal document reasoning. This is a paid technical test that may lead to a long-term position. The goal is a true GraphRAG system featuring explicit knowledge-graph construction and traversal, multi-hop reasoning, agentic orchestration, and strong focus on retrieval accuracy and explainability.

Materials Provided

  • /docs/ → Pre-processed Markdown legal documents with metadata
  • /sample_questions.json → Example question format
  • /sample_answers_rag.json → Example answer format

Download materials:

(Benchmark uses unseen questions.)

Deliverables

Implement two functions in Python 3.12 (Poetry project):

  • def ingest(document_paths: List[str]) -> None: 'Ingest Markdown docs and build the knowledge graph.'
  • def query(questions: List[str]) -> List[str]: 'Return answers with Vancouver-style citations grounded in retrieved sources.'

Requirements: No UI, no API keys provided. Any stack may be used. query(...) must support parallel execution (~400 questions in ≤60 min) and show a progress indicator. Test thoroughly for correctness and performance before the demo.

Live Demo

In a 60-minute live session you will:

  • Receive ~400 unseen questions.
  • Run query(...) to produce answers.json.
  • Explain your architecture: how the graph is built, traversed, and used to generate grounded answers.
  • Only the developer(s) who wrote the code may present.

Evaluation

Passing requires an overall score above 95%, measured by (LLM as a judge): Faithfulness (grounded, no hallucinations), Relevance (retrieval matches intent), Completeness (covers key legal points), and Clarity (structured, legally coherent writing).