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微软在今年4月份的时候提出了GraphRAG的概念，然后在上周开源了GraphRAG,Github链接见https://github.com/microsoft/graphrag,截止当前，已有6900+Star。
安装教程
官方推荐使用Python3.10-3.12版本，我使用Python3.10版本安装时，在初始化项目过程中会报错，切换到Python3.11版本后运行正常，推测是Python3.10与微软的一些最新的SDK不兼容。所以建议使用Python3.11的环境，安装GraphRAG比较简单，直接下面一行代码即可安装成功。
pip install graphrag 使用教程
在这个教程中，我们使用马伯庸的《太白金星有点烦》这个短篇小说为例，测试下使用微软开源的GraphRAG的处理效果。
注意，GraphRAG是使用LLM来提取文本片段中的实体关系，因此耗费Token数较多，如果是个人调研使用，不建议使用GPT4级别的模型（费用太高，不差钱的大佬请忽略此条建议）。综合成本和效果，我这里使用的是DeepSeek-Chat模型。
初始化项目
我这边先创建了一个临时测试目录myTest，然后按照官方教程，在myTest目录下创建了input目录，并把《太白金星有点烦》这本书的txt版本重命名为book.txt后放到input目录下。然后调用python -m graphrag.index --init 进行初始化工作，生成一些配置文件。
mkdir ./myTest/input curl https://www.xxx.com/太白金星有点烦.txt > ./myTest/input/book.txt // 这里是示例代码，大家在测试时根据实际情况放入自己要测试的txt文本即可。 cd ./myTest python -m graphrag.index --init 执行完成后，会在当前目录（即MyTest）目录下生成几个新的文件夹：output-后续执行生成的中间结果会保存到这个目录中；prompts-处理过程中用到的一些Prompt内容；.env-大模型API配置文件，里面默认就一个GRAPHRAG_API_KEY 用于配置大模型的apiKey；settings.yaml-该文件是整体的配置信息，如果我们使用的非OPENAI的官方模型和官方API，我们需要修改此配置文件来让GraphRAG按照我们指定的配置文件执行。
配置相关文件
先在.env文件中配置大模型API的Key，这个配置是全局生效的。我们在.env文件中配置完成后，不需要在settings.yaml文件中重复配置。settings.yaml中使用的默认模型为gpt-4-turbo-preview ，如果不需要修改模型以及调用的API地址，那现在就已经配置完成了，后续的配置内容可以执行忽略并直接到执行阶段。
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：
encoding_model: cl100k_base skip_workflows: [] llm: api_key: ${GRAPHRAG_API_KEY} type: openai_chat # or azure_openai_chat model: deepseek-chat model_supports_json: false # recommended if this is available for your model. api_base: https://api.agicto.cn/v1 # 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 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: ${GRAPHRAG_API_KEY} type: openai_embedding # or azure_openai_embedding model: text-embedding-3-small api_base: https://api.agicto.cn/v1 # api_base: https://<instance>.openai.azure.com # api_version: 2024-02-15-preview # organization: <organization_id> # deployment_name: <azure_model_deployment_name> # tokens_per_minute: 150_000 # set a leaky bucket throttle # requests_per_minute: 10_000 # set a leaky bucket throttle # max_retries: 10 # max_retry_wait: 10.0 # sleep_on_rate_limit_recommendation: true # whether to sleep when azure suggests wait-times # concurrent_requests: 25 # the number of parallel inflight requests that may be made # batch_size: 16 # the number of documents to send in a single request # batch_max_tokens: 8191 # the maximum number of tokens to send in a single request # target: required # or optional chunks: size: 300 overlap: 100 group_by_columns: [id] # by default, we don't allow chunks to cross documents input: type: file # or blob file_type: text # or csv base_dir: "input" file_encoding: utf-8 file_pattern: ".*\\.txt$" 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: 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 执行并构建图索引
此流程是GraphRAG的核心流程，即构建基于图的知识库用于后续的问答环节，通过以下代码即可触发执行。
python -m graphrag.index 基于微软在论文中提到的实现思路，执行过程GraphRAG主要实现了如下功能：
- Source Documents → Text Chunks：将源文档分割成文本块。
- Text Chunks → Element Instances：从每个文本块中提取图节点和边的实例。
- Element Instances → Element Summaries：为每个图元素生成摘要。
- Element Summaries → Graph Communities：使用社区检测算法将图划分为社区。
- Graph Communities → Community Summaries：为每个社区生成摘要。
- Community Summaries → Community Answers → Global Answer：使用社区摘要生成局部答案，然后汇总这些局部答案以生成全局答案。
整体执行耗时与具体的文本大小有关。我这个例子整体耗时大概20分钟，耗费人民币大约4块钱。执行过程中的输出如下：
🚀 Reading settings from settings.yaml /home/xinfeng/miniconda3/envs/graphrag-new/lib/python3.11/site-packages/numpy/core/fromnumeric.py:59: FutureWarning: 'DataFrame.swapaxes' is deprecated and will be removed in a future version. Please use 'DataFrame.transpose' instead. return bound(*args, **kwds) 🚀 create_base_text_units id chunk ... document_ids n_tokens 0 5fe95645e8592dc5146ae4e6e2343ad4 \n附：每天更新最新最全的小说：飞马书屋(FEIＭASＷ.COM)\n\n《太白金星有点烦》... ... [764c0e80c3fc53191ccd9e87ad9e4803] 300 1 e91ee08e3684833d1dd3cb26679a8e6a 歪斜斜落在殿旁台阶上。\n李长庚从鹤背上跳下来，猫腰检查了一下。台阶倒没什么事，只是仙鹤的右... ... [764c0e80c3fc53191ccd9e87ad9e4803] 300 2 7eea0da373e721b9f87ad6c7c05565de 同期飞升的神仙早换成了更威风的神兽坐骑，只有李长庚念旧，一直骑着这头老鹤四处奔波。\n李长庚... ... [764c0e80c3fc53191ccd9e87ad9e4803] 300 3 d0fbd3139f977d98891f5aeae2ac9180 形了。\n“您回来啦？” 织女头也没抬，专心看着宝鉴。\n“嗯！回来了。”\n李长庚端起童子... ... [764c0e80c3fc53191ccd9e87ad9e4803] 300 4 ab349a2200a3878ba2a340c71ba1641f 来泡，平白被自己的牛饮糟蹋了。\n李长庚嘬了嘬牙花子，悻悻坐下，把一沓玉简文书从怀里取出来。... ... [764c0e80c3fc53191ccd9e87ad9e4803] 300 .. ... ... ... ... ... 214 7f8d6ded30cb1488837df6102c77cab4 旅游。编辑说，买ps5也不能报哦。我说鹓雏非梧桐不止，非练实不食，非醴泉不饮，会看得上你这点... ... [764c0e80c3fc53191ccd9e87ad9e4803] 300 215 73b2cf432f11036b715a7ced295a6091 《两京十五日》之后，我也是写了个短篇《长安的荔枝》休息，权当运动之后的拉伸。\n最初我并没打... ... [764c0e80c3fc53191ccd9e87ad9e4803] 300 216 1a10c703e1637de884a1fad7f109a50b 头一看，好嘛，居然有十万字。\n也好，尽兴了，疲惫一扫而空，这波不亏。\n有朋友问我，你是不... ... [764c0e80c3fc53191ccd9e87ad9e4803] 300 217 239fe13a155eb285cebc6938559cf0e9 味，也不合心意。\n当然，这种乘兴而写的东西，神在意前，一气呵成，固然写得舒畅，细节不免粗糙... ... [764c0e80c3fc53191ccd9e87ad9e4803] 214 218 b9fb2d6193b2840cdce5a3cf25542ca7 凑个整，不然心里难受。\n\n ... [764c0e80c3fc53191ccd9e87ad9e4803] 14 [877 rows x 5 columns] 🚀 create_base_extracted_entities entity_graph 0 <graphml xmlns="http://graphml.graphdrawing.or... 🚀 create_summarized_entities entity_graph 0 <graphml xmlns="http://graphml.graphdrawing.or... 🚀 create_base_entity_graph level clustered_graph 0 0 <graphml xmlns="http://graphml.graphdrawing.or... 1 1 <graphml xmlns="http://graphml.graphdrawing.or... 2 2 <graphml xmlns="http://graphml.graphdrawing.or... 3 3 <graphml xmlns="http://graphml.graphdrawing.or... /home/xinfeng/miniconda3/envs/graphrag-new/lib/python3.11/site-packages/numpy/core/fromnumeric.py:59: FutureWarning: 'DataFrame.swapaxes' is deprecated and will be removed in a future version. Please use 'DataFrame.transpose' instead. return bound(*args, **kwds) /home/xinfeng/miniconda3/envs/graphrag-new/lib/python3.11/site-packages/numpy/core/fromnumeric.py:59: FutureWarning: 'DataFrame.swapaxes' is deprecated and will be removed in a future version. Please use 'DataFrame.transpose' instead. return bound(*args, **kwds) 🚀 create_final_entities id name ... text_unit_ids description_embedding 0 b45241d70f0e43fca764df95b2b81f77 "飞马书屋" ... [159e9102707eeaef1f9188407e428111, 45e28cf587e... [0.008881675079464912, 0.012866131030023098, -... 1 4119fd06010c494caa07f439b333f4c5 "马伯庸" ... [5fe95645e8592dc5146ae4e6e2343ad4] [0.03241756930947304, 0.03757039085030556, -0.... 2 d3835bf3dda84ead99deadbeac5d0d7d "太白金星李长庚" ... [5fe95645e8592dc5146ae4e6e2343ad4] [0.002768812933936715, 0.020227784290909767, -... 3 077d2820ae1845bcbb1803379a3d1eae "启明殿" ... [02c57ca370b4c0316a20148d00723bac, 046ed708031... [0.01269223727285862, 0.026068691164255142, 0.... 4 3671ea0dd4e84c1a9b02c5ab2c8f4bac "《太白金星有点烦》" ... [5fe95645e8592dc5146ae4e6e2343ad4, 7f8d6ded30c... [0.003794945077970624, 0.016000036150217056, -... .. ... ... ... ... ... 207 7ea0bc1467e84184842de2d5e5bdd78e "《长安的荔枝》" ... [7f8d6ded30cb1488837df6102c77cab4] [0.012446477077901363, 0.005391148384660482, 0... 208 056f23eb710f471393ae5dc417d83fd9 "两京十五日" ... [73b2cf432f11036b715a7ced295a6091] [0.021373916417360306, -0.0032437569461762905,... 209 e1ae27016d63447a8dfa021370cba0fa "长安的荔枝" ... [73b2cf432f11036b715a7ced295a6091] [0.022816641256213188, -0.0042687226086854935,... 210 f8c10f61a8f344cea7bdafa2d8af14b8 "新书" ... [239fe13a155eb285cebc6938559cf0e9] [0.05925222113728523, 0.02118016593158245, -0.... 211 aa7d003f25624e19bc88d3951d4dc943 "读者" ... [239fe13a155eb285cebc6938559cf0e9] [0.0453583225607872, 0.020338334143161774, -0.... [851 rows x 8 columns] /home/xinfeng/miniconda3/envs/graphrag-new/lib/python3.11/site-packages/numpy/core/fromnumeric.py:59: FutureWarning: 'DataFrame.swapaxes' is deprecated and will be removed in a future version. Please use 'DataFrame.transpose' instead. return bound(*args, **kwds) /home/xinfeng/miniconda3/envs/graphrag-new/lib/python3.11/site-packages/datashaper/engine/verbs/convert.py:72: FutureWarning: errors='ignore' is deprecated and will raise in a future version. Use to_datetime without passing `errors` and catch exceptions explicitly instead datetime_column = pd.to_datetime(column, errors="ignore") /home/xinfeng/miniconda3/envs/graphrag-new/lib/python3.11/site-packages/datashaper/engine/verbs/convert.py:72: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. datetime_column = pd.to_datetime(column, errors="ignore") 🚀 create_final_nodes level title type description ... graph_embedding top_level_node_id x y 0 0 "飞马书屋" "ORGANIZATION" 飞马书屋是一个多功能的在线阅读平台，其域名为FEIMASW.COM。作为一个组织，飞马书屋不... ... None b45241d70f0e43fca764df95b2b81f77 0 0 1 0 "马伯庸" "PERSON" "马伯庸是一位小说作者，著有《太白金星有点烦》。" ... None 4119fd06010c494caa07f439b333f4c5 0 0 2 0 "太白金星李长庚" "PERSON" "太白金星李长庚是小说《太白金星有点烦》中的主要角色，最近感到烦恼。" ... None d3835bf3dda84ead99deadbeac5d0d7d 0 0 3 0 "启明殿" "GEO" 启明府是位于仙界的一个重要组织，与三官府、二十八星宿相当，显示了其在仙界中的地位。李长庚在此... ... None 077d2820ae1845bcbb1803379a3d1eae 0 0 4 0 "《太白金星有点烦》" "EVENT" 《太白金星有点烦》是由马伯庸所著的一部小说，讲述了太白金星李长庚的故事。这部作品是作者创作的... ... None 3671ea0dd4e84c1a9b02c5ab2c8f4bac 0 0 ... ... ... ... ... ... ... ... .. .. 3399 3 "《长安的荔枝》" "EVENT" "作者在完成《两京十五日》后创作的短篇作品，作为休息和拉伸。" ... None 7ea0bc1467e84184842de2d5e5bdd78e 0 0 3400 3 "两京十五日" "EVENT" "《两京十五日》是一个文学作品，作者在此之后创作了另一个短篇《长安的荔枝》。" ... None 056f23eb710f471393ae5dc417d83fd9 0 0 3401 3 "长安的荔枝" "EVENT" "《长安的荔枝》是作者在创作《两京十五日》后写的一个短篇，作为休息和创作的延续。" ... None e1ae27016d63447a8dfa021370cba0fa 0 0 3402 3 "新书" "EVENT" "新书发布是一个即将发生的事件，作者希望得到读者的支持和关注。" ... None f8c10f61a8f344cea7bdafa2d8af14b8 0 0 3403 3 "读者" ... None aa7d003f25624e19bc88d3951d4dc943 0 0 [3404 rows x 14 columns] /home/xinfeng/miniconda3/envs/graphrag-new/lib/python3.11/site-packages/numpy/core/fromnumeric.py:59: FutureWarning: 'DataFrame.swapaxes' is deprecated and will be removed in a future version. Please use 'DataFrame.transpose' instead. return bound(*args, **kwds) /home/xinfeng/miniconda3/envs/graphrag-new/lib/python3.11/site-packages/numpy/core/fromnumeric.py:59: FutureWarning: 'DataFrame.swapaxes' is deprecated and will be removed in a future version. Please use 'DataFrame.transpose' instead. return bound(*args, **kwds) 🚀 create_final_communities id title level raw_community relationship_ids text_unit_ids 0 0 Community 0 0 0 [1c97184ce5ea4049be417a3fd125357b, 13a044c4043... [159e9102707eeaef1f9188407e428111,45e28cf587e6... 1 2 Community 2 0 2 [8d9ded5fc9cf4c4faba8c6c8cd50e2f4, 595a841aa60... [02c57ca370b4c0316a20148d00723bac,046ed708031c... 2 4 Community 4 0 4 [5a224002ecbc4725abeb5a424aaca6a6, 8826a17bbda... [d0fbd3139f977d98891f5aeae2ac9180, 27248272776... 3 3 Community 3 0 3 [ea465e5cd92247829f52ff0c8591d1bb, 2dbac25b512... [003906d4aeb4b30451d6b15477f474cf,00aa40cc8961... 4 6 Community 6 0 6 [40c2425cb1c34c1591f7cb89f9f5e0bf, 7cf59650687... [0c08b05560ec3763c4eef3215d9de406,1bf7f3f6d2d8... .. ... ... ... ... ... ... 167 171 Community 171 3 171 [cc08fc303cdc4177ad77e6e7d3d15cfd, 318a9d64ba7... [0110b1a44d2939f061fabdca3c0c822a,050f809899ba... 168 169 Community 169 3 169 [22dc64e73efe47c1be1be0552c3e935a, 0a983d6c050... [13318cc421ba835d8ee409100f7e3c43,4c0646412c3c... 169 166 Community 166 3 166 [2edf3e83c1c64da393d5206ce5b352a3, 58ff8f61ba2... [1a10c703e1637de884a1fad7f109a50b,d25d9589f7d4... 170 168 Community 168 3 168 [6104e6eabe444d6195ec6efc79a2d618, f7bdce302b5... [06047f1634e84ec122354736d0da0512,2cd2d62cc35c... 171 170 Community 170 3 170 [1268f164ec404b48a520fe672bca0f16, 2456d7a68d0... [4502bb159a6b1ae4429141760179b1f3,4a14da17885b... [172 rows x 6 columns] 🚀 join_text_units_to_entity_ids text_unit_ids entity_ids id 0 159e9102707eeaef1f9188407e428111 [b45241d70f0e43fca764df95b2b81f77, 19a7f254a5d... 159e9102707eeaef1f9188407e428111 1 45e28cf587e6d50704fd6ed866278782 [b45241d70f0e43fca764df95b2b81f77, 077d2820ae1... 45e28cf587e6d50704fd6ed866278782 2 4b8b97e111eb9dc6d262c5ec7eb60801 [b45241d70f0e43fca764df95b2b81f77, 19a7f254a5d... 4b8b97e111eb9dc6d262c5ec7eb60801 3 5fe95645e8592dc5146ae4e6e2343ad4 [b45241d70f0e43fca764df95b2b81f77, 4119fd06010... 5fe95645e8592dc5146ae4e6e2343ad4 4 6fe888799b2e26cd911859f9c31f85d6 [b45241d70f0e43fca764df95b2b81f77, 19a7f254a5d... 6fe888799b2e26cd911859f9c31f85d6 .. ... ... ... 871 73b2cf432f11036b715a7ced295a6091 [47f6d6573cf34e1096c95e36251dd60c, 056f23eb710... 73b2cf432f11036b715a7ced295a6091 872 da06f0769e85e52a06407bdf7dec4c2c [3f3a2d7aa1294116814f0b4d89baa23d, bbdd53a15e9... da06f0769e85e52a06407bdf7dec4c2c 873 239fe13a155eb285cebc6938559cf0e9 [5d398b88ee4242a59c32feb188683ec3, f8c10f61a8f... 239fe13a155eb285cebc6938559cf0e9 874 7837d3a4069066d3a313a050c5401a77 [bbdd53a15e99452a9deff05d1de2d965, d2ed972353a... 7837d3a4069066d3a313a050c5401a77 875 27b95fa0e9192d3c4088bbdd1d820b5c [9532cf83e9324ea0a46e5ac89bac407d, 8919fa72a9e... 27b95fa0e9192d3c4088bbdd1d820b5c [876 rows x 3 columns] /home/xinfeng/miniconda3/envs/graphrag-new/lib/python3.11/site-packages/numpy/core/fromnumeric.py:59: FutureWarning: 'DataFrame.swapaxes' is deprecated and will be removed in a future version. Please use 'DataFrame.transpose' instead. return bound(*args, **kwds) /home/xinfeng/miniconda3/envs/graphrag-new/lib/python3.11/site-packages/numpy/core/fromnumeric.py:59: FutureWarning: 'DataFrame.swapaxes' is deprecated and will be removed in a future version. Please use 'DataFrame.transpose' instead. return bound(*args, **kwds) /home/xinfeng/miniconda3/envs/graphrag-new/lib/python3.11/site-packages/datashaper/engine/verbs/convert.py:65: FutureWarning: errors='ignore' is deprecated and will raise in a future version. Use to_numeric without passing `errors` and catch exceptions explicitly instead column_numeric = cast(pd.Series, pd.to_numeric(column, errors="ignore")) 🚀 create_final_relationships source target weight description ... human_readable_id source_degree target_degree rank 0 "飞马书屋" "《太白金星有点烦》" 1.0 "飞马书屋提供《太白金星有点烦》这部小说的最新最全版本。" ... 0 4 4 8 1 "飞马书屋" "李长庚" 4.0 李长庚是飞马书屋小说中的角色，显示他与这个组织有文学上的联系。李长庚的对话内容被记录在飞马书... ... 1 4 323 327 2 "飞马书屋" "小说更新" 1.0 "飞马书屋提供每天最新最全的小说更新服务。" ... 2 4 1 5 3 "飞马书屋" "最好看的小说" 1.0 "飞马书屋提供最好看的小说，满足读者的阅读需求。" ... 3 4 1 5 4 "马伯庸" "《太白金星有点烦》" 1.0 "马伯庸是《太白金星有点烦》这部小说的作者。" ... 4 1 4 5 ... ... ... ... ... ... ... ... ... ... 1891 "编辑" "出版社" 1.0 "编辑在出版社工作，负责处理作者的稿件。" ... 1891 3 1 4 1892 "编辑" "我" 1.0 "作者与编辑之间存在关于创作内容和休息方式的交流和分歧。" ... 1892 3 3 6 1893 "我" "《长安的荔枝》" 1.0 "作者在完成《两京十五日》后创作了《长安的荔枝》作为休息。" ... 1893 3 2 5 1894 "《两京十五日》" "《长安的荔枝》" 1.0 "《长安的荔枝》是作者在《两京十五日》之后创作的短篇作品，作为休息。" ... 1894 1 2 3 1895 "新书" "读者" 1.0 "新书发布时，作者希望得到读者的支持和捧场，这是一种期待和互动的关系。" ... 1895 2 1 3 [1896 rows x 10 columns] 🚀 join_text_units_to_relationship_ids id relationship_ids 0 5fe95645e8592dc5146ae4e6e2343ad4 [1c97184ce5ea4049be417a3fd125357b, ae0d3104647... 1 45e28cf587e6d50704fd6ed866278782 [13a044c404394c34af1e9b07c48aa985, 8d9ded5fc9c... 2 4b8b97e111eb9dc6d262c5ec7eb60801 [13a044c404394c34af1e9b07c48aa985, a9b900821b8... 3 a55a87d948656692651bffe4d3aa5f82 [13a044c404394c34af1e9b07c48aa985, 8d9ded5fc9c... 4 e080b0c08ed32f44c6adc344b9771781 [13a044c404394c34af1e9b07c48aa985, f8402b10349... .. ... ... 871 613f893eee700fad17498654df3182c0 [58126221b0894f01bae564e2608b754d, 69b67d3b170... 872 239fe13a155eb285cebc6938559cf0e9 [fc757d03e1814784a3a213d87ea36e23, 21bd7045ca9... 873 b0c5905978e8e25106a43ca347427229 [9636a7d02e614d00ac8602bd65da987b, 1a315dfbb60... 874 7837d3a4069066d3a313a050c5401a77 [3fa936635320477cbb990905f5db11d6, 616436c3a00... 875 27b95fa0e9192d3c4088bbdd1d820b5c [c86a30f7f1fe4a01807dd66719394ec3, 392721fc26e... [876 rows x 2 columns] /home/xinfeng/miniconda3/envs/graphrag-new/lib/python3.11/site-packages/graphrag/index/graph/extractors/community_reports/prep_community_report_context.py:57: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy invalid_context_df[schemas.CONTEXT_STRING] = _sort_and_trim_context( /home/xinfeng/miniconda3/envs/graphrag-new/lib/python3.11/site-packages/graphrag/index/graph/extractors/community_reports/utils.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy df[schemas.CONTEXT_SIZE] = df[schemas.CONTEXT_STRING].apply(lambda x: num_tokens(x)) /home/xinfeng/miniconda3/envs/graphrag-new/lib/python3.11/site-packages/graphrag/index/graph/extractors/community_reports/prep_community_report_context.py:61: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy invalid_context_df[schemas.CONTEXT_EXCEED_FLAG] = 0 🚀 create_final_community_reports community full_content ... full_content_json id 0 164 # 天廷与神话组织\n\n天廷是一个神话中的组织，负责管理仙界事务和财务，与多个神话人物和地... ... {\n "title": "\u5929\u5ef7\u4e0e\u795e\u8bd... 7d7397c8-e65a-40ca-8f5e-c8ee95ec9bb0 1 166 # 宝象国与八十一难\n\n宝象国是一个地理位置，涉及多个重要事件和人物，包括玄奘、李长庚、... ... {\n "title": "\u5b9d\u8c61\u56fd\u4e0e\u516... d9067bdd-b669-4ce7-b2e8-de33e6487bcf 2 168 # 阿傩与黄风怪的复杂关系\n\n该社区围绕阿傩和黄风怪展开，涉及多个角色和组织，如正途弟子... ... {\n "title": "\u963f\u50a9\u4e0e\u9ec4\u98c... 7288eb84-717e-46aa-8ccf-90432682a374 3 169 # 三星洞与石猴社区\n\n该社区以三星洞为核心组织，涉及多个关键实体如石猴、六耳、冒名顶替... ... {\n "title": "\u4e09\u661f\u6d1e\u4e0e\u77f... 4db3be55-ad2a-4e7e-a311-29d4743a71be 4 170 # 通臂与三星洞社区\n\n该社区围绕通臂展开，涉及多个关键实体如三星洞都管、孙悟空、六耳等... ... {\n "title": "\u901a\u81c2\u4e0e\u4e09\u661... d993b5ec-6ec2-4d66-859d-512ca93bf01c .. ... ... ... ... ... 148 3 # 两界山与取经之旅\n\n该社区以两界山和取经之旅为核心，涉及多个关键实体如玄奘、阿傩长老... ... {\n "title": "\u4e24\u754c\u5c71\u4e0e\u53d... d78001e2-5247-4bf9-b2af-fd2cbb109bf4 149 4 # 天庭与神话组织社区\n\n该社区以天庭为核心，涉及多个神话组织和人物，如释门、广目天王、... ... {\n "title": "\u5929\u5ead\u4e0e\u795e\u8bd... 085b720d-4f87-43a4-8a29-4a9da687789e 150 6 # 西王母与天庭关系网络\n\n该社区以西王母为核心，涉及天庭、卷帘大将、李长庚等多个关键实... ... {\n "title": "\u897f\u738b\u6bcd\u4e0e\u592... 72ae5d47-5b05-43d1-a60a-de2f92557b56 151 8 # 文殊与普贤的佛教神祇社区\n\n该社区以文殊和普贤两位佛教菩萨为核心，围绕他们的活动和互... ... {\n "title": "\u6587\u6b8a\u4e0e\u666e\u8d2... cb782e19-0886-4bf9-93b7-bf864adfa2f3 152 9 # 护法渡劫与师徒四人\n\n该社区围绕‘护法渡劫’事件展开，涉及‘师徒四人’、‘菩萨’等关... ... {\n "title": "\u62a4\u6cd5\u6e21\u52ab\u4e0... 52ddd79d-cfa7-43d1-a070-5598db14461d [153 rows x 10 columns] 🚀 create_final_text_units id ... relationship_ids 0 5fe95645e8592dc5146ae4e6e2343ad4 ... [1c97184ce5ea4049be417a3fd125357b, ae0d3104647... 1 e91ee08e3684833d1dd3cb26679a8e6a ... [26c926c6016d4639b05427f01ba629f5, 8f6872eeb81... 2 7eea0da373e721b9f87ad6c7c05565de ... [8d9ded5fc9cf4c4faba8c6c8cd50e2f4, 595a841aa60... 3 d0fbd3139f977d98891f5aeae2ac9180 ... [ac80a99fda2b488285d29596dd4d1471, 67d6a3481e4... 4 ab349a2200a3878ba2a340c71ba1641f ... [904cd052ec194654bb72f4027e43daa3, 7e88fd2e835... .. ... ... ... 872 7f8d6ded30cb1488837df6102c77cab4 ... [6bb9bed2e39c4e31a81f12479af3d16c, 7dbca0fef7d... 873 73b2cf432f11036b715a7ced295a6091 ... [2f13e93b77b84d5994605e27c17c3244, 20574c1c47c... 874 1a10c703e1637de884a1fad7f109a50b ... [e65667ec99e145fea2055d6b583cb05b, 2edf3e83c1c... 875 239fe13a155eb285cebc6938559cf0e9 ... [fc757d03e1814784a3a213d87ea36e23, 21bd7045ca9... 876 b9fb2d6193b2840cdce5a3cf25542ca7 ... None [877 rows x 6 columns] /home/xinfeng/miniconda3/envs/graphrag-new/lib/python3.11/site-packages/datashaper/engine/verbs/convert.py:72: FutureWarning: errors='ignore' is deprecated and will raise in a future version. Use to_datetime without passing `errors` and catch exceptions explicitly instead datetime_column = pd.to_datetime(column, errors="ignore") 🚀 create_base_documents id text_units raw_content title 0 764c0e80c3fc53191ccd9e87ad9e4803 [5fe95645e8592dc5146ae4e6e2343ad4, e91ee08e368... \n附：每天更新最新最全的小说：飞马书屋(FEIＭASＷ.COM)\n\n《太白金星有点烦》... book.txt 🚀 create_final_documents id text_unit_ids raw_content title 0 764c0e80c3fc53191ccd9e87ad9e4803 [5fe95645e8592dc5146ae4e6e2343ad4, e91ee08e368... \n附：每天更新最新最全的小说：飞马书屋(FEIＭASＷ.COM)\n\n《太白金星有点烦》... book.txt ⠋ 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. 基于构建的知识库进行提问
GraphRAG支持两种提问方式，“global search"和"local search”。"global search"指的是那些需要理解整个文本语料库的问题，例如“数据集的主要主题是什么？”这类问题需要一种全局性的理解和摘要，而不是仅从文本的局部区域中检索信息。相反，"local search"在论文中通常指的是文本的局部区域或文本块，这些局部区域是RAG方法检索的单元。
让GraphRAG帮我介绍下这篇文章都讲述了什么内容，执行代码如下：
python -m graphrag.query --root ../myTest --method global "这篇文章主要讲述 了什么内容?" 输出内容为：
INFO: Reading settings from ../myTest/settings.yaml /home/xinfeng/miniconda3/envs/graphrag-new/lib/python3.11/site-packages/graphrag/query/indexer_adapters.py:71: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy entity_df["community"] = entity_df["community"].fillna(-1) /home/xinfeng/miniconda3/envs/graphrag-new/lib/python3.11/site-packages/graphrag/query/indexer_adapters.py:72: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy entity_df["community"] = entity_df["community"].astype(int) creating llm client with {'api_key': 'REDACTED,len=51', 'type': "openai_chat", 'model': 'deepseek-chat', 'max_tokens': 4000, 'request_timeout': 180.0, 'api_base': 'https://api.agicto.cn/v1', 'api_version': None, 'organization': None, 'proxy': None, 'cognitive_services_endpoint': None, 'deployment_name': None, 'model_supports_json': False, 'tokens_per_minute': 0, 'requests_per_minute': 0, 'max_retries': 10, 'max_retry_wait': 10.0, 'sleep_on_rate_limit_recommendation': True, 'concurrent_requests': 25} SUCCESS: Global Search Response: 这篇文章主要讲述了多个神话社区的故事，每个社区围绕特定的核心人物或事件展开，涉及复杂的互动关系和动态。这些社区包括玉帝与天庭神祇社orts (138, 119, 136, 93, 58, +more)] 此外，文章还涉及了多个社区和事件的复杂关系和动态，涉及不同的实体如悟空、李长庚、观音等，以及他们之间的互动和影响。这些内容涵盖了从宗教到政治的多个层面，展示了每个社区的核心角色和重要事件。[Data: Reports (125, 115, 143, 71, 92, +more)] 文章还详细描述了李长庚与天庭仙界的关系，他在天庭中的核心角色、与观音、孙悟空、玄奘及取经队伍的复杂关系，以及他在天庭中的多项关键职责和影响力。[Data: Reports (82)] 另外，文章还围绕天庭社区的织女和瑶池展开，涉及多个神话人物和事件，包括织女在天庭的角色和影响力、瑶池在天庭社区中的地位、织女与牛郎的家庭关系、织女对玄奘取经的兴趣以及织女与李长庚的工作关系。[Data: Reports (95)] 最后，文章还涉及了文殊与普贤的佛教神祇社区，围绕他们的活动和互动展开，包括取经队伍的选拔、试禅心活动以及与李长庚的复杂互动。[Data: Reports (60)] 同样的问题，我们使用"local search"的方式再问一下，执行代码
python -m graphrag.query --root ../myTest --method local "这篇文章主要讲述了什么内容?" 输出内容为：
INFO: Reading settings from ../myTest/settings.yaml [2024-07-07T13:58:58Z WARN lance::dataset] No existing dataset at /home/xinfeng/PycharmProjects/graphrag/myTest/lancedb/description_embedding.lance, it will be created /home/xinfeng/miniconda3/envs/graphrag-new/lib/python3.11/site-packages/graphrag/query/indexer_adapters.py:71: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy entity_df["community"] = entity_df["community"].fillna(-1) /home/xinfeng/miniconda3/envs/graphrag-new/lib/python3.11/site-packages/graphrag/query/indexer_adapters.py:72: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy entity_df["community"] = entity_df["community"].astype(int) creating llm client with {'api_key': 'REDACTED,len=51', 'type': "openai_chat", 'model': 'deepseek-chat', 'max_tokens': 4000, 'request_timeout': 180.0, 'api_base': 'https://api.agicto.cn/v1', 'api_version': None, 'organization': None, 'proxy': None, 'cognitive_services_endpoint': None, 'deployment_name': None, 'model_supports_json': False, 'tokens_per_minute': 0, 'requests_per_minute': 0, 'max_retries': 10, 'max_retry_wait': 10.0, 'sleep_on_rate_limit_recommendation': True, 'concurrent_requests': 25} creating embedding llm client with {'api_key': 'REDACTED,len=51', 'type': "openai_embedding", 'model': 'text-embedding-3-small', 'max_tokens': 4000, 'request_timeout': 180.0, 'api_base': 'https://api.agicto.cn/v1', 'api_version': None, 'organization': None, 'proxy': None, 'cognitive_services_endpoint': None, 'deployment_name': None, 'model_supports_json': None, 'tokens_per_minute': 0, 'requests_per_minute': 0, 'max_retries': 10, 'max_retry_wait': 10.0, 'sleep_on_rate_limit_recommendation': True, 'concurrent_requests': 25} SUCCESS: Local Search Response: 这篇文章主要讲述了李长庚在天庭仙界中的核心角色及其与多个关键人物的复杂关系，以及他在取经任务中的重要作用。以下是详细的概述： ### 李长庚在天庭中的核心角色 李长庚在天庭中担任多项关键职责，包括启明司的主持和护法锦囊设计等。他的行为和决策直接影响天庭的稳定和取经任务的进展。李长庚的复杂角色和多重职责使得他在天庭中的影响力极大，同时也带来了较高的潜在风险。[Data: Entities (5), Relationships (49, 82, 39, 58, 83, 138, 155, 74, 46)] ### 李长庚与观音的复杂关系 李长庚与观音之间的关系复杂且多层次，涉及合作、争论和策略性互动。他们共同经历了多次困难，彼此之间有着默契的默契。李长庚通过无形的影响来应对观音的威胁，而观音对李长庚的安排感到不满。这种复杂的关系对取经任务的进展有着直接的影响。[Data: Relationships (49)] ### 李长庚与孙悟空的密切关系 李长庚与孙悟空之间的关系复杂且密切，涉及指导、关心和策略性互动。孙悟空对李长庚的修行状态和关心的事情表示理解，而李长庚则提醒孙悟空注意因果。两人之间的直接交流和合作对取经任务的进展至关重要。[Data: Relationships (82)] ### 李长庚与玄奘的互动 李长庚与玄奘之间存在着一系列复杂的关系和互动。李长庚正在策划一个与玄奘旅程相关的事件，这表明他对玄奘的经历和成就有着浓厚的兴趣。两人之间的争议和合作对取经任务的进展有着重要影响。[Data: Relationships (39)] ### 李长庚与取经队伍的关系 李长庚与取经队伍的关系复杂且充满关怀。尽管他本人并未直接参与取经队伍的活动，但他的讨论发生在取经队伍活动的背景下。李长庚为取经队伍护法渡劫，显示出他对该组织的忠诚和支持。[Data: Relationships (58)] 这篇文章通过详细描述李长庚在天庭中的角色及其与观音、孙悟空、玄奘和取经队伍的关系，展现了他在天庭和取经任务中的核心地位和重要作用。 对比之下可以看出，"local search"的查询方式确实会透出更多细节信息。
以上就是这篇文章的主要内容，第二篇文章我会找一个典型的文章，对比下GraphRAG和常规RAG在实际场景中的使用效果，第三篇文章会介绍下GraphRAG的主要实现原理（工作日通常会加班，下班较晚，回家后需要陪陪家人，因此更新可能会慢点，预计下周末出第二篇）。如果想看更多的理论细节，推荐阅读https://arxiv.org/pdf/2404.16130。
感谢大家能看到最后，欢迎大家有时间也来我的个人博客看看，更新的内容会更多些。不来也没关系，我觉得有价值的内容也会继续在CSDN上更新。
