计划执行与检查节点
executing-plans
按既定实施计划完成工作,保持过程检查和反馈。
provider-and-model-integrations【效率与协作】Configure nano-graphrag LLM and embedding providers safely, including OpenAI, Azure OpenAI, Bedrock, OpenAI-compatible APIs, Ollama, local embeddings, cache kwargs, and provider pitfalls.
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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.
GraphRAG() uses OpenAI chat models (gpt-4o, gpt-4o-mini) and OpenAI embeddings (text-embedding-3-small).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.best_model_func, async cheap_model_func, and/or an EmbeddingFunc-wrapped embedding_func to GraphRAG.**kwargs, pop hashing_kv, and not forward hashing_kv to provider SDK calls.response_format or max_tokens need adapter cleanup before making the request.embedding_func, wrap_embedding_func_with_attrs, vector shape, dimension, normalization, and batching behavior.--help to print credential-free provider skeletons or inspect a local function shape without calling provider APIs by default.QueryParam details belong to ../core-graphrag-workflows/.../storage-backends/.../customization-and-troubleshooting/; this sub-skill cross-links there when provider output format is the likely cause.