GraphRAG本地部署使用及兼容千帆通义

🕗 发布于 2024-11-07 07:05 python ai

文章目录

前言
一、GraphRAG本地安装
二、GraphRAG兼容千帆通义等大模型
三、知识库构建与使用
- 1.知识库构建
- 2.执行查询

前言

GraphRAG是一种基于知识图谱的检索增强生成（RAG）应用，不同于传统基于向量检索的RAG应用，其允许进行更深层、更细致与上下文感知的检索，从而帮助获得更高质量的输出。

GitHub地址：https://github.com/microsoft/graphrag
文档地址：https://microsoft.github.io/graphrag/get_started/

graphrag-more地址：https://github.com/guoyao/graphrag-more

一、GraphRAG本地安装

1.创建环境并安装

#使用conda创建graphrag虚拟环境
conda create -n graphrag python=3.10
#安装graphrag
pip install graphrag

2.准备demo数据

# 创建demo目录
mkdir -p ./ragtest/input
# 下载微软官方demo数据
curl https://www.gutenberg.org/cache/epub/24022/pg24022.txt -o ./ragtest/input/book.txt

3.初始化demo目录

要初始化工作区，请先运行graphrag init命令。由于我们在上一步中已经配置了一个名为./ragtest的目录，请运行以下命令：

graphrag init --root ./ragtest

这将在./ragtest目录中创建两个文件：.env和settings.yaml。

.env包含运行GraphRAG所需的环境变量。如果您检查文件，您将看到一个定义的环境变量，GRAPHRAG_API_KEY=<API_KEY>。这是OpenAI API或Azure OpenAI端点的API密钥。您可以用您自己的API密钥替换它。如果您正在使用另一种形式的身份验证（即托管身份），请删除此文件。
settings.yaml包含GraphRAG的设置。您可以修改此文件来更改管道的设置。

二、GraphRAG兼容千帆通义等大模型

国内使用 OpenAI / AzureOpenAI 诸多不便，因此fork了官方代码创建了个新代码库：graphrag-more在其基础上做了小部分修改，支持使用百度千帆、阿里通义、Ollama。

安装步骤如下

1.安装 graphrag-more

如需二次开发或者调试的话，也可以直接使用源码的方式，步骤如下：
下载 graphrag-more 代码库
git clone https://github.com/guoyao/graphrag-more.git
安装依赖包
这里使用 poetry 来管理python虚拟环境
安装 poetry 参考：https://python-poetry.org/docs/#installation
cd graphrag-more
poetry install

conda create -n graphrag python=3.10
pip install graphrag-more
#or 如果使用了国内的镜像源需要改为官方的镜像源下载
pip install -i https://pypi.org/simple graphrag-more

2.准备Demo数据

# 创建demo目录
mkdir -p ./ragtest-more/input

# 下载微软官方demo数据
curl https://www.gutenberg.org/cache/epub/24022/pg24022.txt -o ./ragtest-more/input/book.txt

3.初始化demo目录

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

4.移动和修改 settings.yaml 文件

根据选用的模型（千帆、通义、Ollama）将 example_settings 文件夹对应模型的 settings.yaml 文件复制到 ragtest 目录，覆盖初始化过程生成的 settings.yaml 文件。

每个settings.yaml里面都设置了默认的 llm 和 embeddings 模型，根据你自己要使用的模型修改 settings.yaml 文件的 model 配置
千帆默认使用 qianfan.ERNIE-3.5-128K 和 qianfan.bge-large-zh ，注意：必须带上 qianfan. 前缀！！！
通义默认使用 tongyi.qwen-plus 和 tongyi.text-embedding-v2 ，注意：必须带上 tongyi. 前缀！！！
Ollama默认使用 ollama.mistral:latest 和 ollama.quentinz/bge-large-zh-v1.5:latest ，注意：<=0.3.0版本时，其llm模型不用带前缀，>=0.3.1版本时，其llm模型必须带上 ollama. 前缀，embeddings模型必须带 ollama. 前缀！！！

以下是Ollama、千帆、通义千问的配置文件内容

ollama


encoding_model: cl100k_base
skip_workflows: []
llm:
  api_key: ${GRAPHRAG_API_KEY}
  type: openai_chat # or azure_openai_chat
  model: ollama.mistral:latest
  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
  # 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
  # temperature: 0 # temperature for sampling
  # top_p: 1 # top-p sampling
  # n: 1 # Number of completions to generate

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
  # target: required # or all
  llm:
    api_key: ${GRAPHRAG_API_KEY}
    type: openai_embedding # or azure_openai_embedding
    model: ollama.quentinz/bge-large-zh-v1.5:latest
    # api_base: http://localhost:11434/api
    # 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" # 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" # output/${timestamp}/reports
  # connection_string: <azure_blob_storage_connection_string>
  # container_name: <azure_blob_storage_container_name>

entity_extraction:
  ## strategy: fully override the entity extraction strategy.
  ##   type: one of graph_intelligence, graph_intelligence_json and nltk
  ## 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: 1

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: 1

community_reports:
  ## 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
  # llm_temperature: 0 # temperature for sampling
  # llm_top_p: 1 # top-p sampling
  # llm_n: 1 # Number of completions to generate
  # max_tokens: 12000

global_search:
  # llm_temperature: 0 # temperature for sampling
  # llm_top_p: 1 # top-p sampling
  # llm_n: 1 # Number of completions to generate
  # max_tokens: 12000
  # data_max_tokens: 12000
  # map_max_tokens: 1000
  # reduce_max_tokens: 2000
  # concurrency: 32

千帆


encoding_model: cl100k_base
skip_workflows: []
llm:
  api_key: ${GRAPHRAG_API_KEY}
  type: openai_chat # or azure_openai_chat
  model: qianfan.ERNIE-3.5-128K
  model_supports_json: false # recommended if this is available for your model, original default is true
  # max_tokens: 4000
  # request_timeout: 180.0
  # api_base: https://<instance>.openai.azure.com
  # 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: 120 # set a leaky bucket throttle，original default is 10_000
  # max_retries: 10
  # max_retry_wait: 10.0
  # sleep_on_rate_limit_recommendation: true # whether to sleep when azure suggests wait-times
  # concurrent_requests: 2 # the number of parallel inflight requests that may be made，original default is 25
  temperature: 1e-10 # temperature for sampling, original default is 0
  # top_p: 1 # top-p sampling
  # n: 1 # Number of completions to generate

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

async_mode: asyncio # or threaded

embeddings:
  ## parallelization: override the global parallelization settings for embeddings
  async_mode: asyncio # or threaded
  # target: required # or all
  llm:
    api_key: ${GRAPHRAG_API_KEY}
    type: openai_embedding # or azure_openai_embedding
    model: qianfan.bge-large-zh
    # api_base: https://<instance>.openai.azure.com
    # 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: 1200
  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" # 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" # output/${timestamp}/reports
  # connection_string: <azure_blob_storage_connection_string>
  # container_name: <azure_blob_storage_container_name>

entity_extraction:
  ## strategy: fully override the entity extraction strategy.
  ##   type: one of graph_intelligence, graph_intelligence_json and nltk
  ## 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: 1

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: 1

community_reports:
  ## 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
  llm_temperature: 1e-10 # temperature for sampling, original default is 0
  # llm_top_p: 1 # top-p sampling
  # llm_n: 1 # Number of completions to generate
  # max_tokens: 5000 # original default is 12000

global_search:
  llm_temperature: 1e-10 # temperature for sampling, original default is 0
  # llm_top_p: 1 # top-p sampling
  # llm_n: 1 # Number of completions to generate
  # max_tokens: 5000 # original default is 12000
  # data_max_tokens: 12000
  # map_max_tokens: 1000
  # reduce_max_tokens: 2000
  # concurrency: 32

通义千问


encoding_model: cl100k_base
skip_workflows: []
llm:
  api_key: ${GRAPHRAG_API_KEY}
  type: openai_chat # or azure_openai_chat
  model: tongyi.qwen-plus
  model_supports_json: false # recommended if this is available for your model, original default is true
  # max_tokens: 4000
  # request_timeout: 180.0
  # api_base: https://<instance>.openai.azure.com
  # 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
  # temperature: 0 # temperature for sampling
  # top_p: 1 # top-p sampling
  # n: 1 # Number of completions to generate

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
  # target: required # or all
  llm:
    api_key: ${GRAPHRAG_API_KEY}
    type: openai_embedding # or azure_openai_embedding
    model: tongyi.text-embedding-v2
    # api_base: https://<instance>.openai.azure.com
    # 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: 1200
  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" # 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" # output/${timestamp}/reports
  # connection_string: <azure_blob_storage_connection_string>
  # container_name: <azure_blob_storage_container_name>

entity_extraction:
  ## strategy: fully override the entity extraction strategy.
  ##   type: one of graph_intelligence, graph_intelligence_json and nltk
  ## 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: 1

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: 1

community_reports:
  ## 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
  # llm_temperature: 0 # temperature for sampling
  # llm_top_p: 1 # top-p sampling
  # llm_n: 1 # Number of completions to generate
  # max_tokens: 12000

global_search:
  # llm_temperature: 0 # temperature for sampling
  # llm_top_p: 1 # top-p sampling
  # llm_n: 1 # Number of completions to generate
  # max_tokens: 12000
  # data_max_tokens: 12000
  # map_max_tokens: 1000
  # reduce_max_tokens: 2000
  # concurrency: 32

三、知识库构建与使用

1.知识库构建

构建过程可能会触发 rate limit （限速）导致构建失败，重复执行几次，或者尝试调小 settings.yaml 中的 requests_per_minute 和 concurrent_requests 配置，然后重试

#如果使用是graphrag
graphrag index --root ./ragtest
#如果使用的是graphrag-more
python -m graphrag.index --root ./ragtest-more

需要注意的是如果使用graphrag-more需要将秘钥配置环境变量

根据选用的模型，配置对应的环境变量，若使用Ollama需要安装并下载对应模型

1.千帆：需配置环境变量 QIANFAN_AK、QIANFAN_SK（注意是应用的AK/SK，不是安全认证的Access Key/Secret Key），如何获取请参考官方文档
2.通义：需配置环境变量 TONGYI_API_KEY（从0.3.6.1版本开始，也支持使用 DASHSCOPE_API_KEY，同时都配置的情况下 TONGYI_API_KEY 优先级高于 DASHSCOPE_API_KEY），如何获取请参考官方文档
3.Ollama：
安装：https://ollama.com/download ，安装后启动
下载模型
ollama pull mistral:latest
ollama pull quentinz/bge-large-zh-v1.5:latest

配置环境变量

linux: export QIANFAN_AK=“value”

在这里插入图片描述

2.执行查询

graphrag

# global query
graphrag query \
--root ./ragtest \
--method global \
--query "What are the top themes in this story?"

# local query
graphrag query \
--root ./ragtest \
--method local \
--query "Who is Scrooge and what are his main relationships?"

graphrag-more

# global query
python -m graphrag.query \
--root ./ragtest-more \
--method global \
"What are the top themes in this story?"

# local query
python -m graphrag.query \
--root ./ragtest-more \
--method local \
"Who is Scrooge, and what are his main relationships?"

运行结果如下
在这里插入图片描述

原文地址：https://blog.csdn.net/qq_43548590/article/details/143578223

免责声明：本站文章内容转载自网络资源，如本站内容侵犯了原著者的合法权益，可联系本站删除。更多内容请关注自学内容网（zxcms.com）！

上一篇：订单分库分表
下一篇：JavaEE-多线程初阶(4)

【原创】java+ssm+mysql收纳培训网系统设计与实现
使用java+ssm+mysql开发的收纳视频培训网，系统包含超级管理员，系统管理员、培训师、用户角色，功能如下：超级管理员：管理员管理；用户管理（培训师、用户）；课程管理（课程、课程视频、评论）；预
阅读更多2024-11-07
C# 内存管理与对象生命周期在面向对象设计中的重要性
C# 通过 CLR 提供了自动的内存管理，使用堆（Heap）和栈（Stack）两种不同的内存区域来管理程序中的内存。栈（Stack）：用于存储局部变量和方法调用相关的数据。栈是一种后进先出（LIFO）
阅读更多2024-11-07
Swift 开发教程系列 - 第2章：Swift 基础语法
在这个章节中，我们将介绍 Swift 的基础语法，包括变量与常量的定义、数据类型、类型转换和操作符的使用。掌握这些基础知识能帮助你更熟练地理解和编写 Swift 代码。
阅读更多2024-11-07
【k8s】ClusterIP能http访问，但是不能ping 的原因
ClusterIP是一个虚拟 IP 地址，无法响应ping请求。你可以使用curltelnet或nc等工具来测试ClusterIP服务的连通性。kube-proxy通过iptables或ipvs规则来
阅读更多2024-11-07
代码随想录 | Day38 | 动态规划：01背包应用目标和&&一和零
代码都不难写，这真的不能怪笔者拖更，这两道题真给我整麻了。
阅读更多2024-11-07
[vulnhub]Empire: Breakout
vulnhub - Empire: Breakout
阅读更多2024-11-07
PPO(Proximal Policy Optimization)算法介绍
PPO通过引入剪辑损失函数，有效地限制了策略更新的幅度，兼顾了学习效率和更新稳定性。在RLHF中，PPO作为微调预训练语言模型的关键算法，发挥了重要作用，使得模型能够从人类反馈中高效学习，生成更符合人
阅读更多2024-11-07
Ionic Range: Building Dynamic Range Sliders for Mobile Apps
ion-range。
阅读更多2024-11-07
Flink滑动窗口（Sliding）中window和windowAll的区别
#滑动窗口的window和windowall的区别
阅读更多2024-11-07
jmeter结合ansible分布式压测--3压测执行
1、执行命令： ./jmeter-5.4.1/bin/jmeter.sh -n -t streamgwInfer/streamgw_pushUrls.jmx -R 11.xxx.xxx.48,11.x
阅读更多2024-11-07

GraphRAG本地部署使用及兼容千帆通义

文章目录

前言

一、GraphRAG本地安装

1.创建环境并安装

2.准备demo数据

3.初始化demo目录

二、GraphRAG兼容千帆通义等大模型

1.安装 graphrag-more

2.准备Demo数据

3.初始化demo目录

4.移动和修改 settings.yaml 文件

三、知识库构建与使用

1.知识库构建

2.执行查询

相关文章