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생산성 및 협업 · SKILL.md

provider-and-model-integrations

provider-and-model-integrations

Configure nano-graphrag LLM and embedding providers safely, including OpenAI, Azure OpenAI, Bedrock, OpenAI-compatible APIs, Ollama, local embeddings, cache kwargs, and provider pitfalls.

Independent GitHub snapshot · Automated classification · Original documentation governs

Skill 문서

GitHub

name: provider-and-model-integrations description: "Configure nano-graphrag LLM and embedding providers safely, including OpenAI, Azure OpenAI, Bedrock, OpenAI-compatible APIs, Ollama, local embeddings, cache kwargs, and provider pitfalls." disable-model-invocation: true metadata: disco-role: operating license: MIT

Provider and model integrations

Use this sub-skill when a user needs to configure nano-graphrag LLM or embedding providers, replace the default OpenAI calls, combine hosted LLMs with local embeddings, tune provider concurrency/token limits, or debug provider-specific failures.

Fast routing

  • Default provider path: GraphRAG() uses OpenAI chat models (gpt-4o, gpt-4o-mini) and OpenAI embeddings (text-embedding-3-small).
  • Built-in switches: use GraphRAG(..., using_azure_openai=True) for Azure OpenAI defaults, or GraphRAG(..., using_amazon_bedrock=True, best_model_id=..., cheap_model_id=...) for Bedrock chat plus Titan embeddings.
  • Custom provider path: pass async best_model_func, async cheap_model_func, and/or an EmbeddingFunc-wrapped embedding_func to GraphRAG.
  • Cache-aware LLM functions must accept **kwargs, pop hashing_kv, and not forward hashing_kv to provider SDK calls.
  • Provider SDKs that reject OpenAI-specific kwargs such as response_format or max_tokens need adapter cleanup before making the request.

Read or run these files

  • Read references/provider-recipes.md when implementing OpenAI, Azure OpenAI, Bedrock, OpenAI-compatible, DeepSeek-style, Ollama, or hosted-LLM-plus-local-embedding recipes.
  • Read references/embedding-functions.md when writing or validating embedding_func, wrap_embedding_func_with_attrs, vector shape, dimension, normalization, and batching behavior.
  • Read references/troubleshooting.md when credentials, unsupported kwargs, malformed JSON, Ollama context, Bedrock region/IAM, rate limits, or provider tuning issues appear.
  • Run scripts/provider_template.py with --help to print credential-free provider skeletons or inspect a local function shape without calling provider APIs by default.

Boundaries

  • Query-mode semantics, insert/query lifecycle, chunking, and QueryParam details belong to ../core-graphrag-workflows/.
  • Storage adapters and vector-store class selection belong to ../storage-backends/.
  • Prompt editing, JSON repair internals, DSPy entity extraction, and empty-graph root-cause analysis belong to ../customization-and-troubleshooting/; this sub-skill cross-links there when provider output format is the likely cause.
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