Neo4j 集成 - 以编程方式使用文档

>Neo4j 是一种由 Neo4j, Inc.

>数据元素 Neo4j 存储的是节点、连接节点的边以及节点和边的属性。据其开发者描述，它是一种符合 ACID 规范的事务数据库，具有原生图存储和处理功能， Neo4j 有非开源的"社区版"，使用修改后的 GNU 通用公共许可证授权，带有在线备份和高可用性扩展的版本则采用闭源商业许可证授权。Neo 还以 Neo4j 闭源商业条款授权这些扩展。

>本笔记本展示了如何使用 LLM 为图数据库提供自然语言接口，您可以使用 Cypher 查询语言进行查询。

>Cypher) 是一种声明式图查询语言，允许在属性图中进行表达性强且高效的数据查询。

设置

您需要有一个运行中的 Neo4j 实例。一种选择是在他们的 Aura 云服务中创建免费的 Neo4j 数据库实例。您也可以使用 Neo4j Desktop 应用程序在本地运行数据库，或运行 docker 容器。您可以通过执行以下脚本运行本地 docker 容器：

docker run \
    --name neo4j \
    -p 7474:7474 -p 7687:7687 \
    -d \
    -e NEO4J_AUTH=neo4j/password \
    -e NEO4J_PLUGINS=\[\"apoc\"\]  \
    neo4j:latest

如果您使用 docker 容器，需要等待几秒钟让数据库启动。

from langchain_neo4j import GraphCypherQAChain, Neo4jGraph
from langchain_openai import ChatOpenAI

graph = Neo4jGraph(url="bolt://localhost:7687", username="neo4j", password="password")

本指南默认使用 OpenAI 模型。

if "OPENAI_API_KEY" not in os.environ:
    os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")

填充数据库

假设您的数据库为空，您可以使用 Cypher 查询语言填充数据库。以下 Cypher 语句是幂等的，这意味着无论您运行一次还是多次，数据库信息都将相同。

graph.query(
    """
MERGE (m:Movie {name:"Top Gun", runtime: 120})
WITH m
UNWIND ["Tom Cruise", "Val Kilmer", "Anthony Edwards", "Meg Ryan"] AS actor
MERGE (a:Actor {name:actor})
MERGE (a)-[:ACTED_IN]->(m)
"""
)

[]

刷新图模式信息

如果数据库模式发生变化，您可以刷新生成 Cypher 语句所需的模式信息。

graph.refresh_schema()

print(graph.schema)

Node properties:
Movie {runtime: INTEGER, name: STRING}
Actor {name: STRING}
Relationship properties:

The relationships:
(:Actor)-[:ACTED_IN]->(:Movie)

增强的模式信息

选择增强的模式版本使系统能够自动扫描数据库中的示例值并计算一些分布指标。例如，如果节点属性具有少于 10 个不同的值，我们会在模式中返回所有可能的值。否则，只返回每个节点和关系属性的单个示例值。

enhanced_graph = Neo4jGraph(
    url="bolt://localhost:7687",
    username="neo4j",
    password="password",
    enhanced_schema=True,
)
print(enhanced_graph.schema)

Node properties:
- **Movie**
  - `runtime`: INTEGER Min: 120, Max: 120
  - `name`: STRING Available options: ['Top Gun']
- **Actor**
  - `name`: STRING Available options: ['Tom Cruise', 'Val Kilmer', 'Anthony Edwards', 'Meg Ryan']
Relationship properties:

The relationships:
(:Actor)-[:ACTED_IN]->(:Movie)

查询图

我们现在可以使用图 Cypher 问答链来询问图相关问题

chain = GraphCypherQAChain.from_llm(
    ChatOpenAI(temperature=0), graph=graph, verbose=True, allow_dangerous_requests=True
)

chain.invoke({"query": "Who played in Top Gun?"})

> Entering new GraphCypherQAChain chain...
Generated Cypher:
MATCH (a:Actor)-[:ACTED_IN]->(m:Movie)
WHERE m.name = 'Top Gun'
RETURN a.name
Full Context:
[{'a.name': 'Tom Cruise'}, {'a.name': 'Val Kilmer'}, {'a.name': 'Anthony Edwards'}, {'a.name': 'Meg Ryan'}]

> Finished chain.

{'query': 'Who played in Top Gun?',
 'result': 'Tom Cruise, Val Kilmer, Anthony Edwards, and Meg Ryan played in Top Gun.'}

限制结果数量

您可以使用 top_k parameter. 来限制 Cypher QA Chain 返回的结果数量。默认值为 10。

chain = GraphCypherQAChain.from_llm(
    ChatOpenAI(temperature=0),
    graph=graph,
    verbose=True,
    top_k=2,
    allow_dangerous_requests=True,
)

chain.invoke({"query": "Who played in Top Gun?"})

> Entering new GraphCypherQAChain chain...
Generated Cypher:
MATCH (a:Actor)-[:ACTED_IN]->(m:Movie)
WHERE m.name = 'Top Gun'
RETURN a.name
Full Context:
[{'a.name': 'Tom Cruise'}, {'a.name': 'Val Kilmer'}]

> Finished chain.

{'query': 'Who played in Top Gun?',
 'result': 'Tom Cruise, Val Kilmer played in Top Gun.'}

返回中间结果

您可以使用 return_intermediate_steps 参数返回 Cypher QA Chain 中的中间步骤。

chain = GraphCypherQAChain.from_llm(
    ChatOpenAI(temperature=0),
    graph=graph,
    verbose=True,
    return_intermediate_steps=True,
    allow_dangerous_requests=True,
)

result = chain.invoke({"query": "Who played in Top Gun?"})
print(f"Intermediate steps: {result['intermediate_steps']}")
print(f"Final answer: {result['result']}")

> Entering new GraphCypherQAChain chain...
Generated Cypher:
MATCH (a:Actor)-[:ACTED_IN]->(m:Movie)
WHERE m.name = 'Top Gun'
RETURN a.name
Full Context:
[{'a.name': 'Tom Cruise'}, {'a.name': 'Val Kilmer'}, {'a.name': 'Anthony Edwards'}, {'a.name': 'Meg Ryan'}]

> Finished chain.
Intermediate steps: [{'query': "MATCH (a:Actor)-[:ACTED_IN]->(m:Movie)\nWHERE m.name = 'Top Gun'\nRETURN a.name"}, {'context': [{'a.name': 'Tom Cruise'}, {'a.name': 'Val Kilmer'}, {'a.name': 'Anthony Edwards'}, {'a.name': 'Meg Ryan'}]}]
Final answer: Tom Cruise, Val Kilmer, Anthony Edwards, and Meg Ryan played in Top Gun.

返回直接结果

您可以使用 return_direct 参数从 Cypher QA Chain 返回直接结果。

chain = GraphCypherQAChain.from_llm(
    ChatOpenAI(temperature=0),
    graph=graph,
    verbose=True,
    return_direct=True,
    allow_dangerous_requests=True,
)

chain.invoke({"query": "Who played in Top Gun?"})

> Entering new GraphCypherQAChain chain...
Generated Cypher:
MATCH (a:Actor)-[:ACTED_IN]->(m:Movie)
WHERE m.name = 'Top Gun'
RETURN a.name

> Finished chain.

{'query': 'Who played in Top Gun?',
 'result': [{'a.name': 'Tom Cruise'},
  {'a.name': 'Val Kilmer'},
  {'a.name': 'Anthony Edwards'},
  {'a.name': 'Meg Ryan'}]}

在 Cypher 生成提示中添加示例

您可以为特定问题定义希望 LLM 生成的 Cypher 语句

from langchain_core.prompts.prompt import PromptTemplate

CYPHER_GENERATION_TEMPLATE = """Task:Generate Cypher statement to query a graph database.
Instructions:
Use only the provided relationship types and properties in the schema.
Do not use any other relationship types or properties that are not provided.
Schema:
{schema}
Note: Do not include any explanations or apologies in your responses.
Do not respond to any questions that might ask anything else than for you to construct a Cypher statement.
Do not include any text except the generated Cypher statement.
Examples: Here are a few examples of generated Cypher statements for particular questions:
# How many people played in Top Gun?
MATCH (m:Movie {{name:"Top Gun"}})<-[:ACTED_IN]-()
RETURN count(*) AS numberOfActors

The question is:
{question}"""

CYPHER_GENERATION_PROMPT = PromptTemplate(
    input_variables=["schema", "question"], template=CYPHER_GENERATION_TEMPLATE
)

chain = GraphCypherQAChain.from_llm(
    ChatOpenAI(temperature=0),
    graph=graph,
    verbose=True,
    cypher_prompt=CYPHER_GENERATION_PROMPT,
    allow_dangerous_requests=True,
)

chain.invoke({"query": "How many people played in Top Gun?"})

> Entering new GraphCypherQAChain chain...
Generated Cypher:
MATCH (m:Movie {name:"Top Gun"})<-[:ACTED_IN]-()
RETURN count(*) AS numberOfActors
Full Context:
[{'numberOfActors': 4}]

> Finished chain.

{'query': 'How many people played in Top Gun?',
 'result': 'There were 4 actors in Top Gun.'}

使用独立的 LLM 进行 Cypher 查询和答案生成

你可以使用 cypher_llm 和 qa_llm 参数来定义不同的 LLM

chain = GraphCypherQAChain.from_llm(
    graph=graph,
    cypher_llm=ChatOpenAI(temperature=0, model="gpt-3.5-turbo"),
    qa_llm=ChatOpenAI(temperature=0, model="gpt-3.5-turbo-16k"),
    verbose=True,
    allow_dangerous_requests=True,
)

chain.invoke({"query": "Who played in Top Gun?"})

> Entering new GraphCypherQAChain chain...
Generated Cypher:
MATCH (a:Actor)-[:ACTED_IN]->(m:Movie)
WHERE m.name = 'Top Gun'
RETURN a.name
Full Context:
[{'a.name': 'Tom Cruise'}, {'a.name': 'Val Kilmer'}, {'a.name': 'Anthony Edwards'}, {'a.name': 'Meg Ryan'}]

> Finished chain.

{'query': 'Who played in Top Gun?',
 'result': 'Tom Cruise, Val Kilmer, Anthony Edwards, and Meg Ryan played in Top Gun.'}

忽略指定的节点和关系类型

你可以使用 include_types or exclude_types 来在生成 Cypher 语句时忽略图谱的部分架构。

chain = GraphCypherQAChain.from_llm(
    graph=graph,
    cypher_llm=ChatOpenAI(temperature=0, model="gpt-3.5-turbo"),
    qa_llm=ChatOpenAI(temperature=0, model="gpt-3.5-turbo-16k"),
    verbose=True,
    exclude_types=["Movie"],
    allow_dangerous_requests=True,
)

# Inspect graph schema
print(chain.graph_schema)

Node properties are the following:
Actor {name: STRING}
Relationship properties are the following:

The relationships are the following:

验证生成的 Cypher 语句

你可以使用 validate_cypher 参数来验证并纠正生成的 Cypher 语句中的关系方向

chain = GraphCypherQAChain.from_llm(
    llm=ChatOpenAI(temperature=0, model="gpt-3.5-turbo"),
    graph=graph,
    verbose=True,
    validate_cypher=True,
    allow_dangerous_requests=True,
)

chain.invoke({"query": "Who played in Top Gun?"})

> Entering new GraphCypherQAChain chain...
Generated Cypher:
MATCH (a:Actor)-[:ACTED_IN]->(m:Movie)
WHERE m.name = 'Top Gun'
RETURN a.name
Full Context:
[{'a.name': 'Tom Cruise'}, {'a.name': 'Val Kilmer'}, {'a.name': 'Anthony Edwards'}, {'a.name': 'Meg Ryan'}]

> Finished chain.

{'query': 'Who played in Top Gun?',
 'result': 'Tom Cruise, Val Kilmer, Anthony Edwards, and Meg Ryan played in Top Gun.'}

Provide context from database results as tool/function output

你可以使用 use_function_response parameter to pass context from database results to an LLM as a tool/function output. This method improves the response accuracy and relevance of an answer as the LLM follows the provided context more closely. _使用此功能需要使用支持原生函数调用的 LLM_.

chain = GraphCypherQAChain.from_llm(
    llm=ChatOpenAI(temperature=0, model="gpt-3.5-turbo"),
    graph=graph,
    verbose=True,
    use_function_response=True,
    allow_dangerous_requests=True,
)
chain.invoke({"query": "Who played in Top Gun?"})

> Entering new GraphCypherQAChain chain...
Generated Cypher:
MATCH (a:Actor)-[:ACTED_IN]->(m:Movie)
WHERE m.name = 'Top Gun'
RETURN a.name
Full Context:
[{'a.name': 'Tom Cruise'}, {'a.name': 'Val Kilmer'}, {'a.name': 'Anthony Edwards'}, {'a.name': 'Meg Ryan'}]

> Finished chain.

{'query': 'Who played in Top Gun?',
 'result': 'The main actors in Top Gun are Tom Cruise, Val Kilmer, Anthony Edwards, and Meg Ryan.'}

使用函数响应功能时，你可以通过提供 function_response_system 来指示模型如何生成答案。

_请注意 qa_prompt 在使用以下功能时不会生效 use_function_response_

chain = GraphCypherQAChain.from_llm(
    llm=ChatOpenAI(temperature=0, model="gpt-3.5-turbo"),
    graph=graph,
    verbose=True,
    use_function_response=True,
    function_response_system="Respond as a pirate!",
    allow_dangerous_requests=True,
)
chain.invoke({"query": "Who played in Top Gun?"})

> Entering new GraphCypherQAChain chain...
Generated Cypher:
MATCH (a:Actor)-[:ACTED_IN]->(m:Movie)
WHERE m.name = 'Top Gun'
RETURN a.name
Full Context:
[{'a.name': 'Tom Cruise'}, {'a.name': 'Val Kilmer'}, {'a.name': 'Anthony Edwards'}, {'a.name': 'Meg Ryan'}]

> Finished chain.

{'query': 'Who played in Top Gun?',
 'result': "Arrr matey! In the film Top Gun, ye be seein' Tom Cruise, Val Kilmer, Anthony Edwards, and Meg Ryan sailin' the high seas of the sky! Aye, they be a fine crew of actors, they be!"}