GraphRAG的介绍
目前微软已经开源了GraphRAG的完整项目代码。对于某一些LLM的下游任务则可以使用GraphRAG去增强自己业务的RAG的表现。项目给出了两种使用方式：
- 在打包好的项目状态下运行，可进行尝试使用。
- 在源码基础上运行，适合为了下游任务的微调时使用。
如果需要利用Ollama部署本地大模型的可以参考我的另一篇博客
以下在通过自身的实践之后的给出对这两种方式的使用教程，如果还有什么问题在评论区交流。
一、在源码基础上运行（便于后续修改）
1. 准备环境（在终端运行）
（1）创建虚拟环境（已安装好anaconda），此处建议使用python3.11:
conda create -n GraphRAG python=3.11conda activate GraphRAG2. 下载源码并进入目录
git clone https://github.com/microsoft/graphrag.git cd graphrag3. 下载依赖并初始化项目
(1)安装poetry资源包管理工具及相关依赖：
pip install poetry poetry install（2）初始化
poetry run poe index --init --root . 正确运行后，此处会在graphrag目录下生成output、prompts、.env、settings.yaml文件
4. 下载并将待检索的文档document放入./input/目录下
mkdir ./inputcurl https://www.xxx.com/xxx.txt > ./input/book.txt #示例，可以替换为任何的txt文件5.修改相关配置文件
（1）修改.env文件（默认是隐藏的）中的api_key
vi .env #进入.env文件，并修改为自己的api_key修改后是全局配置，后续不需要再次修改了
（2）修改settings.yaml文件，修改其中的使用的llm模型和对应的api_base
提前说明，因为GraphRAG需要多次调用大模型和Embedding，默认使用的是openai的GPT-4,花费及其昂贵（
土豪当我没说，配置也不需要改），建议大家可以使用其他模型或国产大模型的api
我这里使用的是agicto提供的APIkey(主要是新用户注册可以免费获取到10块钱的调用额度，白嫖还是挺爽的)。我在这里主要就修改了API地址和调用模型的名称，修改完成后的settings文件完整内容如下：
（代码行后有标记的为需要修改的地方），如果用的是agicto则则不用修改settings.yaml
encoding_model: cl100k_baseskip_workflows: []llm: api_key: ${GRAPHRAG_API_KEY} type: openai_chat # or azure_openai_chat model: deepseek-chat #修改 model_supports_json: false # recommended if this is available for your model. api_base: https://api.agicto.cn/v1 #修改 # max_tokens: 4000 # request_timeout: 180.0 # 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 madeparallelization: stagger: 0.3 # num_threads: 50 # the number of threads to use for parallel processingasync_mode: threaded # or asyncioembeddings: ## parallelization: override the global parallelization settings for embeddings async_mode: threaded # or asyncio llm: api_key: ${GRAPHRAG_API_KEY} type: openai_embedding # or azure_openai_embedding model: text-embedding-3-small #修改 api_base: https://api.agicto.cn/v1 #修改 # api_base: https://<instance>.openai.azure.com # api_version: 2024-02-15-preview # organization: <organization_id> # deployment_name: <azure_model_deployment_name> # tokens_per_minute: 150_000 # set a leaky bucket throttle # requests_per_minute: 10_000 # set a leaky bucket throttle # max_retries: 10 # max_retry_wait: 10.0 # sleep_on_rate_limit_recommendation: true # whether to sleep when azure suggests wait-times # concurrent_requests: 25 # the number of parallel inflight requests that may be made # batch_size: 16 # the number of documents to send in a single request # batch_max_tokens: 8191 # the maximum number of tokens to send in a single request # target: required # or optional chunks: size: 300 overlap: 100 group_by_columns: [id] # by default, we don't allow chunks to cross documents input: type: file # or blob file_type: text # or csv base_dir: "input" file_encoding: utf-8 file_pattern: ".*//.txt$"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: 0summarize_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: 500claim_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: 0community_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: 8000cluster_graph: max_cluster_size: 10embed_graph: enabled: false # if true, will generate node2vec embeddings for nodes # num_walks: 10 # walk_length: 40 # window_size: 2 # iterations: 3 # random_seed: 597832umap: enabled: false # if true, will generate UMAP embeddings for nodessnapshots: graphml: false raw_entities: false top_level_nodes: falselocal_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: 12000global_search: # max_tokens: 12000 # data_max_tokens: 12000 # map_max_tokens: 1000 # reduce_max_tokens: 2000 # concurrency: 326.构建GraphRAG的索引（耗时较长，取决于document的长度）
poetry run poe index --root . 成功后如下：
⠋ GraphRAG Indexer ├── Loading Input (InputFileType.text) - 1 files loaded (0 filtered) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:00 0:00:00├── create_base_text_units├── create_base_extracted_entities├── create_summarized_entities├── create_base_entity_graph├── create_final_entities├── create_final_nodes├── create_final_communities├── join_text_units_to_entity_ids├── create_final_relationships├── join_text_units_to_relationship_ids├── create_final_community_reports├── create_final_text_units├── create_base_documents└── create_final_documents🚀 All workflows completed successfully.7.进行查询
此处GraphRAG提供了两种查询方式
1）全局查询 ：更侧重全文理解
poetry run poe query --root . --method global "本文主要讲了什么" 运行成功后可以看到输出结果
2）局部查询：更侧重细节
poetry run poe query --root . --method local "本文主要讲了什么" 运行成功后可以看到输出结果
8. 总结
上述过程为已经验证过的，如果报错可以检查是否正确配置api_key及api_base
二、在python包的基础上进行（快速尝试）
1. 环境安装
pip install graphrag2. 初始化项目
创建一个临时的文件夹graphrag，用于存在运行时数据
mkdir ./graphrag/inputcurl https://www.xxx.com/xxx.txt > ./myTest/input/book.txt // 这里是示例代码，根据实际情况放入自己要测试的txt文本即可。cd ./graphragpython -m graphrag.index --init3. 配置相关文件（可参考上述的配置文件过程）
4. 执行并构建图索引
python -m graphrag.index5.进行查询
1）全局查询
python -m graphrag.query --root ../myTest --method global "这篇文章主要讲述了什么内容?"2）局部查询
python -m graphrag.query --root ../myTest --method local "这篇文章主要讲述了什么内容?"总结
通过以上两种方式，我们已经尝试了利用源码和python资源包进行配置GraphRAG的方式。大家可以按照自己的需求尝试以上两种方法。如果还有问题，欢迎在评论区讨论！
