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_chat
  api_base: http://192.168.0.167:11434/v1
  # api_version: 2024-05-01-preview
  auth_type: api_key # or azure_managed_identity
  api_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 undefined
  model_supports_json: true # recommended if this is available for your model.
  concurrent_requests: 25 # max number of simultaneous LLM requests allowed
  async_mode: threaded # or asyncio
  retry_strategy: native
  max_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 limiting
  requests_per_minute: 0 # set to 0 to disable rate limiting
  default_embedding_model:
  type: openai_embedding # or azure_openai_embedding
  api_base: http://192.168.0.167:11434/v1
  # api_version: 2024-05-01-preview
  auth_type: api_key # or azure_managed_identity
  api_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 undefined
  model_supports_json: true # recommended if this is available for your model.
  concurrent_requests: 25 # max number of simultaneous LLM requests allowed
  async_mode: threaded # or asyncio
  retry_strategy: native
  max_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 limiting
  requests_per_minute: 0 # set to 0 to disable rate limiting

  vector_store:
  default_vector_store:
  type: lancedb
  db_uri: output\\lancedb
  container_name: default
  overwrite: True

  embed_text:
  model_id: default_embedding_model
  vector_store_id: default_vector_store

  ### Input settings ###

  input:
  type: file # or blob
  file_type: text # or csv
  base_dir: "input"
  file_encoding: utf-8
  file_pattern: ".*\\\\.txt$$"

  chunks:
  size: 200
  overlap: 50
  group_by_columns: [id]

  ### Output settings ###
  ## If blob storage is specified in the following four sections,
  ## connection_string and container_name must be provided

  cache:
  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_model
  prompt: "prompts/extract_graph.txt"
  entity_types: [organization,person,geo,event]
  max_gleanings: 1

  summarize_descriptions:
  model_id: default_chat_model
  prompt: "prompts/summarize_descriptions.txt"
  max_length: 500

  extract_graph_nlp:
  text_analyzer:
  extractor_type: regex_english # [regex_english, syntactic_parser, cfg]

  extract_claims:
  enabled: false
  model_id: default_chat_model
  prompt: "prompts/extract_claims.txt"
  description: "Any claims or facts that could be relevant to information discovery."
  max_gleanings: 1

  community_reports:
  model_id: default_chat_model
  graph_prompt: "prompts/community_report_graph.txt"
  text_prompt: "prompts/community_report_text.txt"
  max_length: 2000
  max_input_length: 8000

  cluster_graph:
  max_cluster_size: 10

  embed_graph:
  enabled: false # if true, will generate node2vec embeddings for nodes

  umap:
  enabled: false # if true, will generate UMAP embeddings for nodes (embed_graph must also be enabled)

  snapshots:
  graphml: false
  embeddings: 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/#query

  local_search:
  chat_model_id: default_chat_model
  embedding_model_id: default_embedding_model
  prompt: "prompts/local_search_system_prompt.txt"

  global_search:
  chat_model_id: default_chat_model
  map_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_model
  embedding_model_id: default_embedding_model
  prompt: "prompts/drift_search_system_prompt.txt"
  reduce_prompt: "prompts/drift_search_reduce_prompt.txt"

  basic_search:
  chat_model_id: default_chat_model
  embedding_model_id: default_embedding_model
  prompt: "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

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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