GraphRAG——v0.3.5版本

理论部分

安装

## 创建虚拟环境
conda create -n GraphRAG_0_3_6 python=3.11# 激活虚拟环境
source activate GraphRAG_0_3_6# 安装相关依赖包
# 我安装的版本是graphrag==0.3.5
pip install graphrag==0.3.5 --default-timeout=100 -i https://pypi.tuna.tsinghua.edu.cn/simplepip install future --default-timeout=100 -i https://pypi.tuna.tsinghua.edu.cn/simple
pip install fastapi==0.112.0 --default-timeout=100 -i https://pypi.tuna.tsinghua.edu.cn/simple  uvicorn==0.30.6

知识图谱生成

# 创建文件目录
mkdir -p ./ragtest/input#下载测试txt文档
curl https://www.gutenberg.org/cache/epub/24022/pg24022.txt -o ./ragtest/input/book.txt# 设置你的工作区变量
"""
要初始化你的工作区，首先运行 graphrag init 命令。由于我们在上一步已经配置了一个名为 ./ragtest 的目录，运行以下命令：
"""
# 初始化配置（首次）
python -m graphrag.index --init --root ./ragtest

其会生成相关的文件如下所示：

修改相关的配置文件settings.yaml，内容如下：

encoding_model: cl100k_base
skip_workflows: []
llm:api_key: ${GRAPHRAG_API_KEY}type: openai_chat # or azure_openai_chatmodel: gpt-3.5-turbomodel_supports_json: true # recommended if this is available for your model.# max_tokens: 4000# request_timeout: 180.0api_base: http://192.168.41.216:8082/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 throttlemax_retries: 10# max_retry_wait: 10.0# sleep_on_rate_limit_recommendation: true # whether to sleep when azure suggests wait-timesconcurrent_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 generateparallelization: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 embeddingsasync_mode: threaded # or asynciollm:api_key: ${GRAPHRAG_API_KEY}type: openai_embedding # or azure_openai_embeddingmodel: gpt-4api_base: http://192.168.41.216:8080/v1# api_version: 2024-02-15-preview# organization: <organization_id># deployment_name: <azure_model_deployment_name># tokens_per_minute: 150_000 # set a leaky bucket throttle# requests_per_minute: 10_000 # set a leaky bucket throttlemax_retries: 10# max_retry_wait: 10.0# sleep_on_rate_limit_recommendation: true # whether to sleep when azure suggests wait-times# concurrent_requests: 25 # the number of parallel inflight requests that may be made# batch_size: 16 # the number of documents to send in a single request# batch_max_tokens: 8191 # the maximum number of tokens to send in a single request# target: required # or optionalchunks:size: 1200overlap: 100group_by_columns: [id] # by default, we don't allow chunks to cross documentsinput:type: file # or blobfile_type: text # or csvbase_dir: "input"file_encoding: utf-8file_pattern: ".*\\.txt$"cache:type: file # or blobbase_dir: "cache"# connection_string: <azure_blob_storage_connection_string># container_name: <azure_blob_storage_container_name>storage:type: file # or blobbase_dir: "output/${timestamp}/artifacts"# connection_string: <azure_blob_storage_connection_string># container_name: <azure_blob_storage_container_name>reporting:type: file # or console, blobbase_dir: "output/${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 taskprompt: "prompts/entity_extraction.txt"entity_types: [organization,person,geo,event]max_gleanings: 1summarize_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 taskprompt: "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 taskenabled: trueprompt: "prompts/claim_extraction.txt"description: "Any claims or facts that could be relevant to information discovery."max_gleanings: 1community_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 taskprompt: "prompts/community_report.txt"max_length: 2000max_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: falseraw_entities: falsetop_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# llm_temperature: 0 # temperature for sampling# llm_top_p: 1 # top-p sampling# llm_n: 1 # Number of completions to generate# max_tokens: 12000global_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

注意：

claim_extraction:enabled: true   # 一定要将其改成true，否则不会生成create_final_covariates.parquet文件

经过上述配置文件修改后
我在生成过程中，当所处理的文本较短时，可以正常生成如下所需文件

当时当文本文件较大时，create_final_community_reports.parquet文件会没有生成，解决的方案如下所示：
进入到以下目录下：

miniconda3/envs/GraphRAG_0_3_6/lib/python3.11/site-packages/graphrag/index/emit

修改parquet_table_emitter.py文件：
由：

# Copyright (c) 2024 Microsoft Corporation.
# Licensed under the MIT License"""ParquetTableEmitter module."""import logging
import tracebackimport pandas as pd
from pyarrow.lib import ArrowInvalid, ArrowTypeErrorfrom graphrag.index.storage import PipelineStorage
from graphrag.index.typing import ErrorHandlerFnfrom .table_emitter import TableEmitterlog = logging.getLogger(__name__)class ParquetTableEmitter(TableEmitter):"""ParquetTableEmitter class."""_storage: PipelineStorage_on_error: ErrorHandlerFndef __init__(self,storage: PipelineStorage,on_error: ErrorHandlerFn,):"""Create a new Parquet Table Emitter."""self._storage = storageself._on_error = on_errorasync def emit(self, name: str, data: pd.DataFrame) -> None:"""Emit a dataframe to storage."""filename = f"{name}.parquet"log.info("emitting parquet table %s", filename)try:await self._storage.set(filename, data.to_parquet())except ArrowTypeError as e:log.exception("Error while emitting parquet table")self._on_error(e,traceback.format_exc(),None,)except ArrowInvalid as e:log.exception("Error while emitting parquet table")self._on_error(e,traceback.format_exc(),None,)

改为如下：

# Copyright (c) 2024 Microsoft Corporation.
# Licensed under the MIT License"""ParquetTableEmitter module."""import logging,shutil
import tracebackimport pandas as pd
from pyarrow.lib import ArrowInvalid, ArrowTypeErrorfrom graphrag.index.storage import PipelineStorage
from graphrag.index.typing import ErrorHandlerFnfrom .table_emitter import TableEmitterlog = logging.getLogger(__name__)class ParquetTableEmitter(TableEmitter):"""ParquetTableEmitter class."""_storage: PipelineStorage_on_error: ErrorHandlerFndef __init__(self,storage: PipelineStorage,on_error: ErrorHandlerFn,):"""Create a new Parquet Table Emitter."""self._storage = storageself._on_error = on_errorasync def emit(self, name: str, data: pd.DataFrame) -> None:"""Emit a dataframe to storage."""filename = f"{name}.parquet"log.info("emitting parquet table %s", filename)try:open('./buf.csv','w+',encoding='UTF-8')data.to_csv('./buf.csv',encoding='UTF-8')data=pd.read_csv('./buf.csv',encoding='UTF-8')data['community']=data['community'].astype(str)await self._storage.set(filename, data.to_parquet())shutil.rmtree('./buf.csv')except ArrowTypeError as e:log.exception("Error while emitting parquet table")self._on_error(e,traceback.format_exc(),None,)except ArrowInvalid as e:log.exception("Error while emitting parquet table")self._on_error(e,traceback.format_exc(),None,)

上述修改完毕后，在项目根目录下执行以下语句：

# 开始索引生成
python -m graphrag.index --root ./ragtest