2025新版GraphRAG 2.1.0本地部署教程，使用国内大模型API

前言

最近在部署GraphRAG，踩了不少坑，参考了不少同行的文件，这里简单分享一下，并附部署全过程流程，帮助大家避免踩坑。

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提示：以下是本篇文章正文内容，下面案例可供参考

一、准备工作

1.虚拟环境

这个不用过多赘述，conda创建，或者直接在pycham中默认创建一个。笔者使用的python环境为3.12.8。

2.获得代码

这里笔者是直接去GitHub上面下载压缩包，粘到项目中，对于初学者而言我个人觉得反而更方便一些。

3.安装依赖

安装poetry(如果没有)，再使用poetry安装依赖

pip install poetry
poetry install

4.数据准备

这里随便整一篇即可，我随便找的放到了创建的文件夹graphrag_index下的input中。

二、关键步骤

1.初始化项目

执行初始化命令（注意不同版本命令有出入）：

graphrag init --root ./tets/graphrag_index  #根据自己路径改

运行完上述项目后会得到一个.env文件和一个settings.yaml文件，其他不用管。

2.修改配置

我这里使用的是阿里云百炼的api，对于初学者而言比较省事，有些中转的国外GPT的密匙反而更容易报错，也可以采用ollama，可以参考这位的：https://blog.csdn.net/m0_54356251/article/details/146074188
①修改.env文件，换成自己的api密匙。这里要注意，apikey外面那两个“<>”大概率是要去掉的，不去掉后面运行索引，验证会失败。
②修改settings.yaml文件，如下（标注的地方都要改）：
使用阿里的向量模型还有个问题，我这里使用的text-embedding-v2，原本使用v3到最后会报错，笔者猜测因为最大行数的限制。另外切片也需要修改一下，改小一点不要使用默认：

修改后如下整体如下所示：

### 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: https://dashscope.aliyuncs.com/compatible-mode/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-v3
    # 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: https://dashscope.aliyuncs.com/compatible-mode/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: text-embedding-v2
    # 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

### Input settings ###

input:
  type: file # or blob
  file_type: text # [csv, text, json]
  base_dir: "input"

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

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

output:
  type: file # [file, blob, cosmosdb]
  base_dir: "output"
    
cache:
  type: file # [file, blob, cosmosdb]
  base_dir: "cache"

reporting:
  type: file # [file, blob, cosmosdb]
  base_dir: "logs"

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

### Workflow settings ###

embed_text:
  model_id: default_embedding_model
  vector_store_id: default_vector_store

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]

cluster_graph:
  max_cluster_size: 10

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

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"

3.进行索引

建立索引，运行如下命令：

graphrag index --root ./tets/graphrag_index   

4.查询

可以先进行graphrag query --help查看一下查询有哪些类型，再选择。这里推荐看另外一篇同行的文章 https://blog.csdn.net/m0_54356251/article/details/146074188我这里进行了全局查询如下：

graphrag query --root ./tets/graphrag_index --method global --query "这篇文章讲了什么"

总结

之前运行graphrag踩过各种坑，这是笔者第一次写文章，主要是想分享一下，避免后来人像我一样一步一踩。大家有什么问题欢迎在评论区留言。