GraphRAG：LLM之使用neo4j可视化GraphRAG运行结果

本文主要是介绍GraphRAG：LLM之使用neo4j可视化GraphRAG运行结果，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！

前言

微软开源的GraphRAG是真的不好用，起码现在是，太多吐槽点了

如果你没有安装好GraphRAG，请看我的这篇文章：

GraphRAG：LLM之本地部署GraphRAG（GLM-4+Xinference的embedding模型）（附带ollma部署方式

然后你需要安装docker：

Docker之基于Ubuntu安装

Neo4j

还是不说简介，有空再补

Neo4j Dcocker安装

docker run \-p 7474:7474 -p 7687:7687 \--name neo4j-apoc \-e NEO4J_apoc_export_file_enabled=true \-e NEO4J_apoc_import_file_enabled=true \-e NEO4J_apoc_import_file_use__neo4j__config=true \-e NEO4J_PLUGINS=\[\"apoc\"\] \neo4j:5.21.2

然后运行到命令行出现started

点击链接进入界面

会让你输入账户和密码，默认都是neo4j

点击红框位置，可以查看版本信息和生成的标签等

下载关于neo4j的python包

下载之后就可以通过python去控制neo4j了

import pandas as pd
from neo4j import GraphDatabase
import timeNEO4J_URI = "neo4j://localhost"  # or neo4j+s://xxxx.databases.neo4j.io
NEO4J_USERNAME = "neo4j"
NEO4J_PASSWORD = "你自己的密码" 
NEO4J_DATABASE = "neo4j"# Create a Neo4j driver
driver = GraphDatabase.driver(NEO4J_URI, auth=(NEO4J_USERNAME, NEO4J_PASSWORD))GRAPHRAG_FOLDER = "/home/nlp/graphrag/result/output/20240814-151056/artifacts" #输入你的路径statements = """
create constraint chunk_id if not exists for (c:__Chunk__) require c.id is unique;
create constraint document_id if not exists for (d:__Document__) require d.id is unique;
create constraint entity_id if not exists for (c:__Community__) require c.community is unique;
create constraint entity_id if not exists for (e:__Entity__) require e.id is unique;
create constraint entity_title if not exists for (e:__Entity__) require e.name is unique;
create constraint entity_title if not exists for (e:__Covariate__) require e.title is unique;
create constraint related_id if not exists for ()-[rel:RELATED]->() require rel.id is unique;
""".split(";")for statement in statements:if len((statement or "").strip()) > 0:print(statement)driver.execute_query(statement)def batched_import(statement, df, batch_size=1000):"""Import a dataframe into Neo4j using a batched approach.Parameters: statement is the Cypher query to execute, df is the dataframe to import, and batch_size is the number of rows to import in each batch."""total = len(df)start_s = time.time()for start in range(0,total, batch_size):batch = df.iloc[start: min(start+batch_size,total)]result = driver.execute_query("UNWIND $rows AS value " + statement,rows=batch.to_dict('records'),database_=NEO4J_DATABASE)print(result.summary.counters)print(f'{total} rows in { time.time() - start_s} s.')return total# 导入文档 create_final_documents
doc_df = pd.read_parquet(f'{GRAPHRAG_FOLDER}/create_final_documents.parquet', columns=["id", "title"])
doc_df.head(2)
# import documents
#
statement = """
MERGE (d:__Document__ {id:value.id})
SET d += value {.title}
"""
batched_import(statement, doc_df)# 导入 文本联系
text_df = pd.read_parquet(f'{GRAPHRAG_FOLDER}/create_final_text_units.parquet',columns=["id","text","n_tokens","document_ids"])
text_df.head(2)
statement = """
MERGE (c:__Chunk__ {id:value.id})
SET c += value {.text, .n_tokens}
WITH c, value
UNWIND value.document_ids AS document
MATCH (d:__Document__ {id:document})
MERGE (c)-[:PART_OF]->(d)
"""
batched_import(statement, text_df)# 导入 抽取的实体
entity_df = pd.read_parquet(f'{GRAPHRAG_FOLDER}/create_final_entities.parquet',columns=["name", "type", "description", "human_readable_id", "id", "description_embedding","text_unit_ids"])
entity_df.head(2)
#
entity_statement = """
MERGE (e:__Entity__ {id:value.id})
SET e += value {.human_readable_id, .description, name:replace(value.name,'"','')}
WITH e, value
CALL db.create.setNodeVectorProperty(e, "description_embedding", value.description_embedding)
CALL apoc.create.addLabels(e, case when coalesce(value.type,"") = "" then [] else [apoc.text.upperCamelCase(replace(value.type,'"',''))] end) yield node
UNWIND value.text_unit_ids AS text_unit
MATCH (c:__Chunk__ {id:text_unit})
MERGE (c)-[:HAS_ENTITY]->(e)
"""
batched_import(entity_statement, entity_df)# 导入实体关系
rel_df = pd.read_parquet(f'{GRAPHRAG_FOLDER}/create_final_relationships.parquet',columns=["source", "target", "id", "rank", "weight", "human_readable_id", "description","text_unit_ids"])
rel_df.head(2)
rel_statement = """MATCH (source:__Entity__ {name:replace(value.source,'"','')})MATCH (target:__Entity__ {name:replace(value.target,'"','')})// not necessary to merge on id as there is only one relationship per pairMERGE (source)-[rel:RELATED {id: value.id}]->(target)SET rel += value {.rank, .weight, .human_readable_id, .description, .text_unit_ids}RETURN count(*) as createdRels
"""
batched_import(rel_statement, rel_df)# 导入 社区
community_df = pd.read_parquet(f'{GRAPHRAG_FOLDER}/create_final_communities.parquet',columns=["id", "level", "title", "text_unit_ids", "relationship_ids"])community_df.head(2)statement = """
MERGE (c:__Community__ {community:value.id})
SET c += value {.level, .title}
/*
UNWIND value.text_unit_ids as text_unit_id
MATCH (t:__Chunk__ {id:text_unit_id})
MERGE (c)-[:HAS_CHUNK]->(t)
WITH distinct c, value
*/
WITH *
UNWIND value.relationship_ids as rel_id
MATCH (start:__Entity__)-[:RELATED {id:rel_id}]->(end:__Entity__)
MERGE (start)-[:IN_COMMUNITY]->(c)
MERGE (end)-[:IN_COMMUNITY]->(c)
RETURn count(distinct c) as createdCommunities
"""batched_import(statement, community_df)# 导入社区报告
community_report_df = pd.read_parquet(f'{GRAPHRAG_FOLDER}/create_final_community_reports.parquet',columns=["id", "community", "level", "title", "summary", "findings", "rank","rank_explanation", "full_content"])
community_report_df.head(2)
# import communities
#
community_statement = """MATCH (c:__Community__ {community: value.community})
SET c += value {.level, .title, .rank, .rank_explanation, .full_content, .summary}
WITH c, value
UNWIND range(0, size(value.findings)-1) AS finding_idx
WITH c, value, finding_idx, value.findings[finding_idx] as finding
MERGE (c)-[:HAS_FINDING]->(f:Finding {id: finding_idx})
SET f += finding"""
batched_import(community_statement, community_report_df)

在运行的时候可能会出现apoc相关的错误，这是neo4j的一个插件，可能网络问题？你没安装好

看看官方安装说明：官方apoc安装说明

网上也有人说从github上下载再放到目录上的：github地址

确定自己有没有安装成功输入：return apoc.version() 成功会显示版本信息