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Graphrag developer challenge (casalnuovo di napoli)

Casalnuovo di Napoli
Bullaki
Pubblicato il 17 novembre
Descrizione

GraphRAG Developer Challenge – Legal Document Processing (Prototype) 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) Contact: 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.) Deliverable 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).

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