GraphRAG本地部署（Xinference本地模型）+ neo4j可视化_graphrag支持xinference

本文配置：

模型供应商可以选择ollama，Xinference等供应商，也可以用我们本地api封装的大模型

一、拉取graphrag框架

graphrag我们可快速开始，无需下载官方项目

pip install graphrag

直接运行即可

然后我们只需要在本地创建一个文件夹

mkdir my_graphrag
cd my_graphrag
python -m graphrag.index --init --root ./ragtest

运行成功后目录如下：

接下来我们可以看到有一个yaml文件，打开他可以直接修改配置，注意我们用的是Xinference，所以切记首字母大写，很多博主都是小写，这或许是个坑，这里谨慎一点。

 我们只修改这俩部分即可，其他的按个人需求定制即可

接下来我们将一篇txt文件放入input中即可：（我们不用官方的，直接创建文件夹即可）

mkdir input

接下来我们只需要索引即可：

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

拉取实体可能会慢一些，这个跟网络有关系，所以需要等一会直到看见以下照片，运行正常后：

如果出现报错 create_base_entity_graph 需要修改源码文件

①anaconda->envs->bzp_graphrag（你自己取的环境名字）

②/lib/python3.11/site-packages/graphrag/llm/openai/openai_chat_llm.py

 我们寻找到class OpenAIChatLLM这个类下面的_invoke_json（）函数，大概在第60行左右。

然后再定位到是这个出问题。这个代码表达的是如果参数kwargs.get("is_response_valid")可以找到到就采用它，找不到就默认用(lambda _x: True)。我们不使用openai就会导致，每次既能get到kwargs.get("is_response_valid")，又得到kwargs.get("is_response_valid")值是false的。

is_response_valid = kwargs.get("is_response_valid") or (lambda _x: True)

       因此我们就索性，删掉kwargs['is_response_valid']这个选项。我们就在上面这个代码加上如下改动即可，这样每次就会返回lambad等于True

if 'is_response_valid' in kwargs:
    del kwargs['is_response_valid']
is_response_valid = kwargs.get("is_response_valid") or (lambda _x: True)

 修改完后我们重新拉起索引即可，成功后我们就可以和模型对话了

执行global输入

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

执行local输入

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

这里一般都会正常运行对话就不做演示了。

二、Neo4j导入工作：

我们准备一个脚本把output/xxxx/artifacts/中所有.parquet的文件转换为csv

import os
import pandas as pd
import csv
 
parquet_dir = 'graphrag_cs_glm/ragtest/output/20240729-141251/artifacts'
csv_dir = 'neo4j-community-4.3.5/import'
 
def clean_quotes(value):
    if isinstance(value,str):
        value = value.strip().replace('""','"').replace('"','')
        if ',' in value or '"' in value:
            value = f'"{value}"'
    return value
 
 
for file_name in os.listdir(parquet_dir):
    if file_name.endswith('.parquet'):
        parquet_file = os.path.join(parquet_dir,file_name)
        csv_file = os.path.join(csv_dir,file_name.replace('.parquet','.csv'))
 
        df = pd.read_parquet(parquet_file)
 
        for column in df.select_dtypes(include=['object']).columns:
            df[column] = df[column].apply(clean_quotes)
 
        df.to_csv(csv_file,index=False,quoting=csv.QUOTE_NONNUMERIC)
        print(f'数据{parquet_file} to {csv_file} successfull')
print('All parquet files have been converted to CSV.')

按照需求替换到自己neo4j文件下的路径即可

接下来就是neo4j的工作

我们需要把配置conf文件中的neo4j.conf的input注释去掉

dbms.directories.import=import

接下来我们只需要打开neo4j运行一下命令即可：

// 1. Import Documents
LOAD CSV WITH HEADERS FROM 'file:///create_final_documents.csv' AS row
CREATE (d:Document {
    id: row.id,
    title: row.title,
    raw_content: row.raw_content,
    text_unit_ids: row.text_unit_ids
});
 
// 2. Import Text Units
LOAD CSV WITH HEADERS FROM 'file:///create_final_text_units.csv' AS row
CREATE (t:TextUnit {
    id: row.id,
    text: row.text,
    n_tokens: toFloat(row.n_tokens),
    document_ids: row.document_ids,
    entity_ids: row.entity_ids,
    relationship_ids: row.relationship_ids
});
 
// 3. Import Entities
LOAD CSV WITH HEADERS FROM 'file:///create_final_entities.csv' AS row
CREATE (e:Entity {
    id: row.id,
    name: row.name,
    type: row.type,
    description: row.description,
    human_readable_id: toInteger(row.human_readable_id),
    text_unit_ids: row.text_unit_ids
});
 
// 4. Import Relationships
LOAD CSV WITH HEADERS FROM 'file:///create_final_relationships.csv' AS row
CREATE (r:Relationship {
    source: row.source,
    target: row.target,
    weight: toFloat(row.weight),
    description: row.description,
    id: row.id,
    human_readable_id: row.human_readable_id,
    source_degree: toInteger(row.source_degree),
    target_degree: toInteger(row.target_degree),
    rank: toInteger(row.rank),
    text_unit_ids: row.text_unit_ids
});
 
// 5. Import Nodes
LOAD CSV WITH HEADERS FROM 'file:///create_final_nodes.csv' AS row
CREATE (n:Node {
    id: row.id,
    level: toInteger(row.level),
    title: row.title,
    type: row.type,
    description: row.description,
    source_id: row.source_id,
    community: row.community,
    degree: toInteger(row.degree),
    human_readable_id: toInteger(row.human_readable_id),
    size: toInteger(row.size),
    entity_type: row.entity_type,
    top_level_node_id: row.top_level_node_id,
    x: toInteger(row.x),
    y: toInteger(row.y)
});
 
// 6. Import Communities
LOAD CSV WITH HEADERS FROM 'file:///create_final_communities.csv' AS row
CREATE (c:Community {
    id: row.id,
    title: row.title,
    level: toInteger(row.level),
    raw_community: row.raw_community,
    relationship_ids: row.relationship_ids,
    text_unit_ids: row.text_unit_ids
});
 
// 7. Import Community Reports
LOAD CSV WITH HEADERS FROM 'file:///create_final_community_reports.csv' AS row
CREATE (cr:CommunityReport {
    id: row.id,
    community: row.community,
    full_content: row.full_content,
    level: toInteger(row.level),
    rank: toFloat(row.rank),
    title: row.title,
    rank_explanation: row.rank_explanation,
    summary: row.summary,
    findings: row.findings,
    full_content_json: row.full_content_json
});
 
// 8. Create indexes for better performance
CREATE INDEX FOR (d:Document) ON (d.id);
CREATE INDEX FOR (t:TextUnit) ON (t.id);
CREATE INDEX FOR (e:Entity) ON (e.id);
CREATE INDEX FOR (r:Relationship) ON (r.id);
CREATE INDEX FOR (n:Node) ON (n.id);
CREATE INDEX FOR (c:Community) ON (c.id);
CREATE INDEX FOR (cr:CommunityReport) ON (cr.id);
 
// 9. Create relationships after all nodes are imported
MATCH (d:Document)
UNWIND split(d.text_unit_ids, ',') AS textUnitId
MATCH (t:TextUnit {id: trim(textUnitId)})
CREATE (d)-[:HAS_TEXT_UNIT]->(t);
 
MATCH (t:TextUnit)
UNWIND split(t.entity_ids, ',') AS entityId
MATCH (e:Entity {id: trim(entityId)})
CREATE (t)-[:HAS_ENTITY]->(e);
 
MATCH (t:TextUnit)
UNWIND split(t.relationship_ids, ',') AS relId
MATCH (r:Relationship {id: trim(relId)})
CREATE (t)-[:HAS_RELATIONSHIP]->(r);
 
MATCH (e:Entity)
UNWIND split(e.text_unit_ids, ',') AS textUnitId
MATCH (t:TextUnit {id: trim(textUnitId)})
CREATE (e)-[:MENTIONED_IN]->(t);
 
MATCH (r:Relationship)
MATCH (source:Entity {name: r.source})
MATCH (target:Entity {name: r.target})
CREATE (source)-[:RELATES_TO]->(target);
 
MATCH (r:Relationship)
UNWIND split(r.text_unit_ids, ',') AS textUnitId
MATCH (t:TextUnit {id: trim(textUnitId)})
CREATE (r)-[:MENTIONED_IN]->(t);
 
MATCH (c:Community)
UNWIND split(c.relationship_ids, ',') AS relId
MATCH (r:Relationship {id: trim(relId)})
CREATE (c)-[:HAS_RELATIONSHIP]->(r);
 
MATCH (c:Community)
UNWIND split(c.text_unit_ids, ',') AS textUnitId
MATCH (t:TextUnit {id: trim(textUnitId)})
CREATE (c)-[:HAS_TEXT_UNIT]->(t);
 
MATCH (cr:CommunityReport)
MATCH (c:Community {id: cr.community})
CREATE (cr)-[:REPORTS_ON]->(c);

也可以让大模型帮你生成一个，将这段代码放入neo4j的页面中运行即可：

 

这里我已经运行过了就不重复运行了，如果出现id错误，可以删除第八个字段重新运行即可

 

到这里就结束了，近期也在尝试修改prompt的调整，欢迎大家一起讨论。