b站教程

Welcome to GraphRAG (microsoft.github.io)

Get Started (microsoft.github.io)官方运行指南

环境配置

python环境

pip3 install graphrag

此处后续补充

模型配置

此处需要LLM也需要embedding，所以既需要Ollama也需要LM Studio

Ollama模型，参考此处本地模型部署。LLMs的本地部署与微调实战 - 欢迎来到我的记录平台 (whtblog.cn)

embedding模型，在LM Studio内下载。LM Studio需要使用代理，用proxifier软件对LM专一代理即可，clash等只能代理网页。具体使用方法参考B站视频比tun模式更智能Proxifier设置多软件网络代理_哔哩哔哩_bilibili

此后即可在LM Studio中进行embedding模型下载部署及服务器启动。

在此界面下载相应的embedding模型，具体选择（后续补充），在服务器启动后记下下边长框内的地址即可，后续需要使用。

除地址外，还需要找到embedding模型的名字如下图，记下需要填。

至此，在运行graphRAG前需要的软件均完成配置，在使用GraphRAG时只需要后台运行Ollama并启动LM Studio的服务器即可。

GraphRAG运行

pip3 install graphrag

直接 pip install 之后再进行操作

mkdir -p ./ragtest/input

新建一个input文件夹，这里边是放训练用的txt文件用的。

python -m graphrag.index --init --root ./ragtest

初始化项目，创建一个ragtest文件夹，里边放进去项目所需要的文件，文件结构如下，

1. cache中为运算过程中生成的文件。

2. input为训练用的txt文件。

3. output为输出的文件和log文件。

4. prompts为提示词文件（项目本身有的，也可以自行调整）。

5. .env文件为环境配置文件，可以设定需要的api_key等。

6. settings.yaml为最重要的配置文件，设置相应的模型等，会在下方进行叙述。

接下来对settings.yaml进行介绍，内容如下：

在刚刚下载时会有不同，进行修改即可。

encoding_model: cl100k_base
skip_workflows: []
llm:
  api_key: ${GRAPHRAG_API_KEY} #此处对应相应的.env文件内容。
  type: openai_chat # or azure_openai_chat  此处不需要更改，ollama跟openai的类似，用这个即可。
  model: gemma2   #填上ollama中下载的模型
  model_supports_json: true # recommended if this is available for your model.
  # max_tokens: 4000
  # request_timeout: 180.0
  api_base: http://localhost:11434/v1  #此处为ollama确定的，可以进去ollama官网查看，下方会附图
  # api_version: 2024-02-15-preview
  # organization: <organization_id>
  # deployment_name: <azure_model_deployment_name>
  # tokens_per_minute: 150_000 # set a leaky bucket throttle
  # requests_per_minute: 10_000 # set a leaky bucket throttle
  # max_retries: 10
  # max_retry_wait: 10.0
  # sleep_on_rate_limit_recommendation: true # whether to sleep when azure suggests wait-times
  # concurrent_requests: 25 # the number of parallel inflight requests that may be made

parallelization:
  stagger: 0.3
  # num_threads: 50 # the number of threads to use for parallel processing

async_mode: threaded # or asyncio

embeddings:
  ## parallelization: override the global parallelization settings for embeddings
  async_mode: threaded # or asyncio
  llm:
    api_key: ${GRAPHRAG_API_KEY}  #.env文件中配置
    type: openai_embedding # or azure_openai_embedding  这个也是不用改，graphrag只能指定某几个
    model: nomic-ai/nomic-embed-text-v1.5-GGUF/nomic-embed-text-v1.5.Q4_K_M.gguf    #LM Studio的embedding模型名称，上文有提到
    api_base: http://localhost:1234/v1  #LM Studio内服务器的embedding模型地址，上文也有提到。
    # api_version: 2024-02-15-preview
    # organization: <organization_id>
    # deployment_name: <azure_model_deployment_name>
    # tokens_per_minute: 150_000 # set a leaky bucket throttle
    # requests_per_minute: 10_000 # set a leaky bucket throttle
    # max_retries: 10
    # max_retry_wait: 10.0
    # sleep_on_rate_limit_recommendation: true # whether to sleep when azure suggests wait-times
    # concurrent_requests: 25 # the number of parallel inflight requests that may be made
    # batch_size: 16 # the number of documents to send in a single request
    # batch_max_tokens: 8191 # the maximum number of tokens to send in a single request
    # target: required # or optional
  


chunks:
  size: 300
  overlap: 100
  group_by_columns: [id] # by default, we don't allow chunks to cross documents
    
input:
  type: file # or blob
  file_type: text # or csv
  base_dir: "input"
  file_encoding: utf-8
  file_pattern: ".*\\.txt$"

cache:
  type: file # or blob
  base_dir: "cache"
  # connection_string: <azure_blob_storage_connection_string>
  # container_name: <azure_blob_storage_container_name>

storage:
  type: file # or blob
  base_dir: "output/${timestamp}/artifacts"
  # connection_string: <azure_blob_storage_connection_string>
  # container_name: <azure_blob_storage_container_name>

reporting:
  type: file # or console, blob
  base_dir: "output/${timestamp}/reports"
  # connection_string: <azure_blob_storage_connection_string>
  # container_name: <azure_blob_storage_container_name>

entity_extraction:
  ## llm: override the global llm settings for this task
  ## parallelization: override the global parallelization settings for this task
  ## async_mode: override the global async_mode settings for this task
  prompt: "prompts/entity_extraction.txt"
  entity_types: [organization,person,geo,event]
  max_gleanings: 0

summarize_descriptions:
  ## llm: override the global llm settings for this task
  ## parallelization: override the global parallelization settings for this task
  ## async_mode: override the global async_mode settings for this task
  prompt: "prompts/summarize_descriptions.txt"
  max_length: 500

claim_extraction:
  ## llm: override the global llm settings for this task
  ## parallelization: override the global parallelization settings for this task
  ## async_mode: override the global async_mode settings for this task
  # enabled: true
  prompt: "prompts/claim_extraction.txt"
  description: "Any claims or facts that could be relevant to information discovery."
  max_gleanings: 0

community_report:
  ## llm: override the global llm settings for this task
  ## parallelization: override the global parallelization settings for this task
  ## async_mode: override the global async_mode settings for this task
  prompt: "prompts/community_report.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
  # num_walks: 10
  # walk_length: 40
  # window_size: 2
  # iterations: 3
  # random_seed: 597832

umap:
  enabled: false # if true, will generate UMAP embeddings for nodes

snapshots:
  graphml: false
  raw_entities: false
  top_level_nodes: false

local_search:
  # text_unit_prop: 0.5
  # community_prop: 0.1
  # conversation_history_max_turns: 5
  # top_k_mapped_entities: 10
  # top_k_relationships: 10
  # max_tokens: 12000

global_search:
  # max_tokens: 12000
  # data_max_tokens: 12000
  # map_max_tokens: 1000
  # reduce_max_tokens: 2000
  # concurrency: 32

其他内容会在后续实验中进行补充。

在以上环境、模型、.env、settings.yaml配置好后即可运行下方代码运行。

python -m graphrag.index --root ./ragtest

此时已经完成训练。