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customization-and-troubleshooting

customization-and-troubleshooting

Customize nano-graphrag prompts, JSON repair, entity extraction, DSPy modules, and empty-graph failure recovery.

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name: customization-and-troubleshooting description: "Customize nano-graphrag prompts, JSON repair, entity extraction, DSPy modules, and empty-graph failure recovery." disable-model-invocation: true metadata: disco-role: operating license: MIT

customization-and-troubleshooting

Use this sub-skill when nano-graphrag needs prompt changes, robust JSON parsing, custom entity extraction, DSPy extraction, or recovery from empty graph/entity failures.

Read or run these first

  • Read references/entity-extraction.md when changing entity_extraction_func, entity_extract_max_gleaning, default entity/relation tuple output, merge behavior, or DSPy extraction modules.
  • Read references/prompts-and-json.md when editing nano_graphrag.prompt.PROMPTS, GRAPH_FIELD_SEP, convert_response_to_json_func, or community-report JSON expectations.
  • Read references/troubleshooting.md when symptoms include Leiden.EmptyNetworkError, Processed ... 0 entities, malformed provider output, an Ollama context-window issue, a missing compiled DSPy module, or DSPy BadRequestError fallback.
  • Run scripts/json_repair_probe.py on a raw model response when you need to verify whether nano-graphrag's default convert_response_to_json can recover a meaningful object before changing application code.

Route away when needed

  • For provider credentials, model client wiring, unsupported API kwargs, base URLs, or ordinary OpenAI-compatible/Ollama/Azure/Bedrock setup, use the sibling provider-and-model-integrations sub-skill; return here only when the provider response format breaks extraction or JSON repair.
  • For basic GraphRAG.insert, GraphRAG.query, query modes, chunking lifecycle, and context-only retrieval, use the sibling core-graphrag-workflows sub-skill.
  • For file, vector, graph, Neo4j, HNSW, or service backend setup, use the sibling storage-backends sub-skill unless the backend error is only a secondary symptom of extracting zero entities.

Fast symptom triage

  1. If insertion reports zero entities/relations or Leiden fails on an empty network, inspect entity extraction output format first; do not start by replacing storage.
  2. If the community report or global-map step fails to parse JSON, probe the raw response with the bundled JSON repair script, then decide whether to tighten the prompt or supply a custom convert_response_to_json_func.
  3. If DSPy extraction returns empty lists, check for provider request errors and compiled-module path configuration before assuming the text has no entities.
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