2025最新版微软GraphRAG 2.0.0本地部署教程：基于Ollama快速构建知识图谱

一、前言

微软近期发布了知识图谱工具 GraphRAG 2.0.0，支持基于本地大模型（Ollama）快速构建知识图谱，显著提升了RAG（检索增强生成）的效果。本文手把手教你如何从零部署，并附踩坑记录和性能实测！

二、环境准备

1. 创建虚拟环境

推荐使用 Python 3.12.4（亲测兼容性较佳）：

conda create -n graphrag200 python=3.12.4
conda activate graphrag200

2. 拉取源码

建议通过Git下载最新代码（Windows用户需提前安装Git）：

git clone https://github.com/microsoft/graphrag.git
cd graphrag

    （附：若直接下载压缩包解压，解压完后需创建一个仓库，不然后续会报错）

        创建仓库方法：

git init
git add .
git commit -m "Initial commit"

3. 安装依赖

一键安装所需依赖包：

pip install -e .

4. 创建输入文件夹

用于存放待处理的文档（Windows可以直接手动创建）：

mkdir -p ./graphrag_ollama/input

将数据集放入input目录即可。

三、关键配置修改

1. 初始化项目

执行初始化命令（注意与旧版参数不同）：

python -m graphrag init --root ./graphrag_ollama

2. 修改settings.yaml

核心配置项（需按需调整）：

• 模型设置：使用Ollama本地模型

 注意修改一下圈出的几个地方

测试小文件时，建议把chunks改小：

 修改结果如下：

• ### This config file contains required core defaults that must be set, along with a handful of common optional settings.
  ### For a full list of available settings, see https://microsoft.github.io/graphrag/config/yaml/### LLM settings ###
  ## There are a number of settings to tune the threading and token limits for LLM calls - check the docs.models:default_chat_model:type: openai_chat # or azure_openai_chatapi_base: http://192.168.0.167:11434/v1# api_version: 2024-05-01-previewauth_type: api_key # or azure_managed_identityapi_key: ${GRAPHRAG_API_KEY} # set this in the generated .env file# audience: "https://cognitiveservices.azure.com/.default"# organization: <organization_id>model: deepseek-r1:32b# deployment_name: <azure_model_deployment_name>encoding_model: cl100k_base # automatically set by tiktoken if left undefinedmodel_supports_json: true # recommended if this is available for your model.concurrent_requests: 25 # max number of simultaneous LLM requests allowedasync_mode: threaded # or asyncioretry_strategy: nativemax_retries: -1                   # set to -1 for dynamic retry logic (most optimal setting based on server response)tokens_per_minute: 0              # set to 0 to disable rate limitingrequests_per_minute: 0            # set to 0 to disable rate limitingdefault_embedding_model:type: openai_embedding # or azure_openai_embeddingapi_base: http://192.168.0.167:11434/v1# api_version: 2024-05-01-previewauth_type: api_key # or azure_managed_identityapi_key: ${GRAPHRAG_API_KEY}# audience: "https://cognitiveservices.azure.com/.default"# organization: <organization_id>model: bge-m3:latest# deployment_name: <azure_model_deployment_name>encoding_model: cl100k_base # automatically set by tiktoken if left undefinedmodel_supports_json: true # recommended if this is available for your model.concurrent_requests: 25 # max number of simultaneous LLM requests allowedasync_mode: threaded # or asyncioretry_strategy: nativemax_retries: -1                   # set to -1 for dynamic retry logic (most optimal setting based on server response)tokens_per_minute: 0              # set to 0 to disable rate limitingrequests_per_minute: 0            # set to 0 to disable rate limitingvector_store:default_vector_store:type: lancedbdb_uri: output\lancedbcontainer_name: defaultoverwrite: Trueembed_text:model_id: default_embedding_modelvector_store_id: default_vector_store### Input settings ###input:type: file # or blobfile_type: text # or csvbase_dir: "input"file_encoding: utf-8file_pattern: ".*\\.txt$$"chunks:size: 200overlap: 50group_by_columns: [id]### Output settings ###
  ## If blob storage is specified in the following four sections,
  ## connection_string and container_name must be providedcache:type: file # [file, blob, cosmosdb]base_dir: "cache"reporting:type: file # [file, blob, cosmosdb]base_dir: "logs"output:type: file # [file, blob, cosmosdb]base_dir: "output"### Workflow settings ###extract_graph:model_id: default_chat_modelprompt: "prompts/extract_graph.txt"entity_types: [organization,person,geo,event]max_gleanings: 1summarize_descriptions:model_id: default_chat_modelprompt: "prompts/summarize_descriptions.txt"max_length: 500extract_graph_nlp:text_analyzer:extractor_type: regex_english # [regex_english, syntactic_parser, cfg]extract_claims:enabled: falsemodel_id: default_chat_modelprompt: "prompts/extract_claims.txt"description: "Any claims or facts that could be relevant to information discovery."max_gleanings: 1community_reports:model_id: default_chat_modelgraph_prompt: "prompts/community_report_graph.txt"text_prompt: "prompts/community_report_text.txt"max_length: 2000max_input_length: 8000cluster_graph:max_cluster_size: 10embed_graph:enabled: false # if true, will generate node2vec embeddings for nodesumap:enabled: false # if true, will generate UMAP embeddings for nodes (embed_graph must also be enabled)snapshots:graphml: falseembeddings: false### Query settings ###
  ## The prompt locations are required here, but each search method has a number of optional knobs that can be tuned.
  ## See the config docs: https://microsoft.github.io/graphrag/config/yaml/#querylocal_search:chat_model_id: default_chat_modelembedding_model_id: default_embedding_modelprompt: "prompts/local_search_system_prompt.txt"global_search:chat_model_id: default_chat_modelmap_prompt: "prompts/global_search_map_system_prompt.txt"reduce_prompt: "prompts/global_search_reduce_system_prompt.txt"knowledge_prompt: "prompts/global_search_knowledge_system_prompt.txt"drift_search:chat_model_id: default_chat_modelembedding_model_id: default_embedding_modelprompt: "prompts/drift_search_system_prompt.txt"reduce_prompt: "prompts/drift_search_reduce_prompt.txt"basic_search:chat_model_id: default_chat_modelembedding_model_id: default_embedding_modelprompt: "prompts/basic_search_system_prompt.txt"
  

  四、构建知识图谱

  执行索引命令（算力警告：亲测4090-24G显卡处理2万字需3小时）：

  python -m graphrag index --root ./graphrag_ollama

  五、知识图谱查询

  支持多种查询方式，按需选择：

• 方法	命令示例	用途
  全局查询	python -m graphrag query --method global --query "知识图谱定义"	跨文档综合分析
  局部查询	python -m graphrag query --method local --query "知识图谱定义"	单文档精准检索
  DRIFT查询	python -m graphrag query --method drift --query "知识图谱定义"	动态漂移分析
  基础查询	python -m graphrag query --method basic --query "知识图谱定义"	传统RAG检索

六、注意事项

1. 模型路径：确保Ollama服务已启动，且模型名称与配置一致（如deepseek-r1:32b需提前拉取）。

2. 算力需求：小规模数据集建议使用GPU加速，CPU模式耗时可能成倍增加。

3. 文件编码：输入文档需为UTF-8编码，否则可能报错。

4. 配置备份：修改settings.yaml前建议备份原始文件。

七、总结

GraphRAG 2.0.0大幅优化了知识图谱的构建效率，结合本地模型可实现隐私安全的行业级应用。若遇到部署问题，欢迎在评论区留言交流！

相关资源：

 GraphRAG GitHub仓库

Ollama模型库

原创声明：本文为作者原创，未经授权禁止转载。如需引用请联系作者。

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