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Memgraph 是一个开源图数据库，针对动态分析环境进行了优化，并与 Neo4j 兼容。Memgraph 使用 Cypher 进行数据库查询——Cypher 是属性图数据库中应用最广泛、完全指定且开放的查询语言。 本笔记本将向您展示如何使用自然语言查询 Memgraph 以及如何从非结构化数据构建知识图谱。 但首先，请确保完成所有设置。

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设置

要完成本指南，您需要安装 Docker 和 Python 3.x。 首次快速运行 Memgraph Platform（Memgraph 数据库 + MAGE 库 + Memgraph Lab），请执行以下操作： 在 Linux/MacOS 上：
curl https://install.memgraph.com | sh
在 Windows 上 
iwr https://windows.memgraph.com | iex
这两个命令都运行一个脚本，该脚本将 Docker Compose 文件下载到您的系统，并在两个独立的容器中构建并启动 `memgraph-mage` 和 `memgraph-lab` Docker 服务。现在 Memgraph 已启动并运行！在 Memgraph 文档上阅读有关安装过程的更多信息。 要使用 LangChain，请安装并导入所有必要的包。我们将使用包管理器 pip，并带有 `--user` 标志，以确保正确的权限。如果您安装了 Python 3.4 或更高版本，`pip` 默认包含在内。您可以使用以下命令安装所有必需的包：
pip install langchain langchain-openai langchain-memgraph --user
您可以运行本笔记本中提供的代码块，也可以使用单独的 Python 文件来试验 Memgraph 和 LangChain。

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自然语言查询

Memgraph 与 LangChain 的集成包括自然语言查询。要使用它，首先要进行所有必要的导入。我们将根据它们在代码中出现的顺序进行讨论。 首先，实例化 `MemgraphGraph`。此对象保存与正在运行的 Memgraph 实例的连接。确保正确设置所有环境变量。
import os

from langchain_core.prompts import PromptTemplate
from langchain_memgraph.chains.graph_qa import MemgraphQAChain
from langchain_memgraph.graphs.memgraph import MemgraphLangChain
from langchain_openai import ChatOpenAI

url = os.environ.get("MEMGRAPH_URI", "bolt://:7687")
username = os.environ.get("MEMGRAPH_USERNAME", "")
password = os.environ.get("MEMGRAPH_PASSWORD", "")

graph = MemgraphLangChain(
    url=url, username=username, password=password, refresh_schema=False
)
`refresh_schema` 最初设置为 `False`，因为数据库中尚无数据，我们希望避免不必要的数据库调用。

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填充数据库

要填充数据库，首先要确保它是空的。最有效的方法是切换到内存分析存储模式，删除图并返回到内存事务模式。了解更多关于 Memgraph 的存储模式。 我们将添加到数据库的数据是关于各种平台上提供的不同类型视频游戏以及与发布商相关的信息。
# Drop graph
graph.query("STORAGE MODE IN_MEMORY_ANALYTICAL")
graph.query("DROP GRAPH")
graph.query("STORAGE MODE IN_MEMORY_TRANSACTIONAL")

# Creating and executing the seeding query
query = """
    MERGE (g:Game {name: "Baldur's Gate 3"})
    WITH g, ["PlayStation 5", "Mac OS", "Windows", "Xbox Series X/S"] AS platforms,
            ["Adventure", "Role-Playing Game", "Strategy"] AS genres
    FOREACH (platform IN platforms |
        MERGE (p:Platform {name: platform})
        MERGE (g)-[:AVAILABLE_ON]->(p)
    )
    FOREACH (genre IN genres |
        MERGE (gn:Genre {name: genre})
        MERGE (g)-[:HAS_GENRE]->(gn)
    )
    MERGE (p:Publisher {name: "Larian Studios"})
    MERGE (g)-[:PUBLISHED_BY]->(p);
"""

graph.query(query)

[]
请注意 `graph` 对象如何拥有 `query` 方法。该方法在 Memgraph 中执行查询，并且也由 `MemgraphQAChain` 用于查询数据库。

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刷新图模式

由于新的数据是在 Memgraph 中创建的，因此有必要刷新模式。生成的模式将由 `MemgraphQAChain` 使用，以指导 LLM 更好地生成 Cypher 查询。 
graph.refresh_schema()
要熟悉数据并验证更新后的图模式，可以使用以下语句打印它 
print(graph.get_schema)

Node labels and properties (name and type) are:
- labels: (:Platform)
  properties:
    - name: string
- labels: (:Genre)
  properties:
    - name: string
- labels: (:Game)
  properties:
    - name: string
- labels: (:Publisher)
  properties:
    - name: string

Nodes are connected with the following relationships:
(:Game)-[:HAS_GENRE]->(:Genre)
(:Game)-[:PUBLISHED_BY]->(:Publisher)
(:Game)-[:AVAILABLE_ON]->(:Platform)

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查询数据库

要与 OpenAI API 交互，您必须将您的 API 密钥配置为环境变量。这确保了您的请求的正确授权。您可以在此处找到获取 API 密钥的更多信息。要配置 API 密钥，您可以使用 Python os 包 
os.environ["OPENAI_API_KEY"] = "your-key-here"
如果您正在 Jupyter notebook 中运行代码，请运行上述代码片段。 接下来，创建 `MemgraphQAChain`，它将用于基于您的图数据进行问答。`temperature` 参数设置为零，以确保可预测且一致的答案。您可以将 `verbose` 参数设置为 `True`，以接收有关查询生成的更详细消息。
chain = MemgraphQAChain.from_llm(
    ChatOpenAI(temperature=0),
    graph=graph,
    model_name="gpt-4-turbo",
    allow_dangerous_requests=True,
)
现在你可以开始提问了！ 
response = chain.invoke("Which platforms is Baldur's Gate 3 available on?")
print(response["result"])

MATCH (:Game{name: "Baldur's Gate 3"})-[:AVAILABLE_ON]->(platform:Platform)
RETURN platform.name
Baldur's Gate 3 is available on PlayStation 5, Mac OS, Windows, and Xbox Series X/S.

response = chain.invoke("Is Baldur's Gate 3 available on Windows?")
print(response["result"])

MATCH (:Game{name: "Baldur's Gate 3"})-[:AVAILABLE_ON]->(:Platform{name: "Windows"})
RETURN "Yes"
Yes, Baldur's Gate 3 is available on Windows.

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链修改器

要修改链的行为并获取更多上下文或附加信息，您可以修改链的参数。

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返回直接查询结果

`return_direct` 修饰符指定是返回已执行 Cypher 查询的直接结果还是经过处理的自然语言响应。 
# Return the result of querying the graph directly
chain = MemgraphQAChain.from_llm(
    ChatOpenAI(temperature=0),
    graph=graph,
    return_direct=True,
    allow_dangerous_requests=True,
    model_name="gpt-4-turbo",
)

response = chain.invoke("Which studio published Baldur's Gate 3?")
print(response["result"])

MATCH (g:Game {name: "Baldur's Gate 3"})-[:PUBLISHED_BY]->(p:Publisher)
RETURN p.name
[{'p.name': 'Larian Studios'}]

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返回查询中间步骤

`return_intermediate_steps` 链修改器通过在初始查询结果之外包含查询的中间步骤来增强返回的响应。 
# Return all the intermediate steps of query execution
chain = MemgraphQAChain.from_llm(
    ChatOpenAI(temperature=0),
    graph=graph,
    allow_dangerous_requests=True,
    return_intermediate_steps=True,
    model_name="gpt-4-turbo",
)

response = chain.invoke("Is Baldur's Gate 3 an Adventure game?")
print(f"Intermediate steps: {response['intermediate_steps']}")
print(f"Final response: {response['result']}")

MATCH (:Game {name: "Baldur's Gate 3"})-[:HAS_GENRE]->(:Genre {name: "Adventure"})
RETURN "Yes"
Intermediate steps: [{'query': 'MATCH (:Game {name: "Baldur\'s Gate 3"})-[:HAS_GENRE]->(:Genre {name: "Adventure"})\nRETURN "Yes"'}, {'context': [{'"Yes"': 'Yes'}]}]
Final response: Yes.

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限制查询结果数量

当您想要限制查询结果的最大数量时，可以使用 `top_k` 修饰符。 
# Limit the maximum number of results returned by query
chain = MemgraphQAChain.from_llm(
    ChatOpenAI(temperature=0),
    graph=graph,
    top_k=2,
    allow_dangerous_requests=True,
    model_name="gpt-4-turbo",
)

response = chain.invoke("What genres are associated with Baldur's Gate 3?")
print(response["result"])

MATCH (:Game {name: "Baldur's Gate 3"})-[:HAS_GENRE]->(g:Genre)
RETURN g.name;
Adventure, Role-Playing Game

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高级查询

随着解决方案复杂性的增加，您可能会遇到需要谨慎处理的不同用例。确保应用程序的可扩展性对于保持流畅的用户流程至关重要。 让我们再次实例化我们的链，并尝试提出一些用户可能会问的问题。
chain = MemgraphQAChain.from_llm(
    ChatOpenAI(temperature=0),
    graph=graph,
    model_name="gpt-4-turbo",
    allow_dangerous_requests=True,
)

response = chain.invoke("Is Baldur's Gate 3 available on PS5?")
print(response["result"])

MATCH (:Game{name: "Baldur's Gate 3"})-[:AVAILABLE_ON]->(:Platform{name: "PS5"})
RETURN "Yes"
I don't know the answer.
生成的 Cypher 查询看起来不错，但我们没有收到任何响应信息。这说明了在使用 LLM 时遇到的一个常见挑战——用户提问方式与数据存储方式之间存在不匹配。在这种情况下，用户感知与实际数据存储之间的差异可能导致不匹配。提示词优化是一种有效的解决方案，可以解决这个问题，它通过磨练模型的提示词以更好地理解这些区别。通过提示词优化，模型在生成精确和相关查询方面的能力得到提高，从而成功检索所需的数据。

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提示词优化

为了解决这个问题，我们可以调整 QA 链的初始 Cypher 提示词。这包括向 LLM 提供关于用户如何引用特定平台（例如本例中的 PS5）的指导。我们使用 LangChain 的 `PromptTemplate` 来实现这一点，创建一个修改后的初始提示词。然后将这个修改后的提示词作为参数提供给我们的优化后的 `MemgraphQAChain` 实例。 
MEMGRAPH_GENERATION_TEMPLATE = """Your task is to directly translate natural language inquiry into precise and executable Cypher query for Memgraph database.
You will utilize a provided database schema to understand the structure, nodes and relationships within the Memgraph database.
Instructions:
- Use provided node and relationship labels and property names from the
schema which describes the database's structure. Upon receiving a user
question, synthesize the schema to craft a precise Cypher query that
directly corresponds to the user's intent.
- Generate valid executable Cypher queries on top of Memgraph database.
Any explanation, context, or additional information that is not a part
of the Cypher query syntax should be omitted entirely.
- Use Memgraph MAGE procedures instead of Neo4j APOC procedures.
- Do not include any explanations or apologies in your responses.
- Do not include any text except the generated Cypher statement.
- For queries that ask for information or functionalities outside the direct
generation of Cypher queries, use the Cypher query format to communicate
limitations or capabilities. For example: RETURN "I am designed to generate
Cypher queries based on the provided schema only."
Schema:
{schema}

With all the above information and instructions, generate Cypher query for the
user question.
If the user asks about PS5, Play Station 5 or PS 5, that is the platform called PlayStation 5.

The question is:
{question}"""

MEMGRAPH_GENERATION_PROMPT = PromptTemplate(
    input_variables=["schema", "question"], template=MEMGRAPH_GENERATION_TEMPLATE
)

chain = MemgraphQAChain.from_llm(
    ChatOpenAI(temperature=0),
    cypher_prompt=MEMGRAPH_GENERATION_PROMPT,
    graph=graph,
    model_name="gpt-4-turbo",
    allow_dangerous_requests=True,
)

response = chain.invoke("Is Baldur's Gate 3 available on PS5?")
print(response["result"])

MATCH (:Game{name: "Baldur's Gate 3"})-[:AVAILABLE_ON]->(:Platform{name: "PlayStation 5"})
RETURN "Yes"
Yes, Baldur's Gate 3 is available on PS5.
现在，通过修订后的初始 Cypher 提示，其中包含平台命名指南，我们正在获得准确且相关的结果，这些结果与用户查询更加吻合。 这种方法允许进一步改进您的 QA 链。您可以轻松地将额外的提示优化数据集成到您的链中，从而增强您的应用程序的整体用户体验。

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构建知识图谱

将非结构化数据转换为结构化数据并非易事。本指南将展示如何利用 LLM 帮助我们完成此任务，以及如何在 Memgraph 中构建知识图谱。知识图谱创建完成后，您可以将其用于您的 GraphRAG 应用程序。 从文本构建知识图谱的步骤如下：

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从文本中提取结构化信息

除了设置部分中的所有导入外，还导入 `LLMGraphTransformer` 和 `Document`，它们将用于从文本中提取结构化信息。 
from langchain_core.documents import Document
from langchain_experimental.graph_transformers import LLMGraphTransformer
下面是关于查尔斯·达尔文（来源）的一段示例文本，将从中构建知识图谱。 
text = """
    Charles Robert Darwin was an English naturalist, geologist, and biologist,
    widely known for his contributions to evolutionary biology. His proposition that
    all species of life have descended from a common ancestor is now generally
    accepted and considered a fundamental scientific concept. In a joint
    publication with Alfred Russel Wallace, he introduced his scientific theory that
    this branching pattern of evolution resulted from a process he called natural
    selection, in which the struggle for existence has a similar effect to the
    artificial selection involved in selective breeding. Darwin has been
    described as one of the most influential figures in human history and was
    honoured by burial in Westminster Abbey.
"""
下一步是从所需的 LLM 初始化 `LLMGraphTransformer` 并将文档转换为图结构。 
llm = ChatOpenAI(temperature=0, model_name="gpt-4-turbo")
llm_transformer = LLMGraphTransformer(llm=llm)
documents = [Document(page_content=text)]
graph_documents = llm_transformer.convert_to_graph_documents(documents)
在底层，LLM 从文本中提取重要实体，并将其作为节点和关系列表返回。它看起来像这样 
print(graph_documents)

[GraphDocument(nodes=[Node(id='Charles Robert Darwin', type='Person', properties={}), Node(id='English', type='Nationality', properties={}), Node(id='Naturalist', type='Profession', properties={}), Node(id='Geologist', type='Profession', properties={}), Node(id='Biologist', type='Profession', properties={}), Node(id='Evolutionary Biology', type='Field', properties={}), Node(id='Common Ancestor', type='Concept', properties={}), Node(id='Scientific Concept', type='Concept', properties={}), Node(id='Alfred Russel Wallace', type='Person', properties={}), Node(id='Natural Selection', type='Concept', properties={}), Node(id='Selective Breeding', type='Concept', properties={}), Node(id='Westminster Abbey', type='Location', properties={})], relationships=[Relationship(source=Node(id='Charles Robert Darwin', type='Person', properties={}), target=Node(id='English', type='Nationality', properties={}), type='NATIONALITY', properties={}), Relationship(source=Node(id='Charles Robert Darwin', type='Person', properties={}), target=Node(id='Naturalist', type='Profession', properties={}), type='PROFESSION', properties={}), Relationship(source=Node(id='Charles Robert Darwin', type='Person', properties={}), target=Node(id='Geologist', type='Profession', properties={}), type='PROFESSION', properties={}), Relationship(source=Node(id='Charles Robert Darwin', type='Person', properties={}), target=Node(id='Biologist', type='Profession', properties={}), type='PROFESSION', properties={}), Relationship(source=Node(id='Charles Robert Darwin', type='Person', properties={}), target=Node(id='Evolutionary Biology', type='Field', properties={}), type='CONTRIBUTION', properties={}), Relationship(source=Node(id='Common Ancestor', type='Concept', properties={}), target=Node(id='Scientific Concept', type='Concept', properties={}), type='BASIS', properties={}), Relationship(source=Node(id='Charles Robert Darwin', type='Person', properties={}), target=Node(id='Alfred Russel Wallace', type='Person', properties={}), type='COLLABORATION', properties={}), Relationship(source=Node(id='Natural Selection', type='Concept', properties={}), target=Node(id='Selective Breeding', type='Concept', properties={}), type='COMPARISON', properties={}), Relationship(source=Node(id='Charles Robert Darwin', type='Person', properties={}), target=Node(id='Westminster Abbey', type='Location', properties={}), type='BURIAL', properties={})], source=Document(metadata={}, page_content='\n    Charles Robert Darwin was an English naturalist, geologist, and biologist,\n    widely known for his contributions to evolutionary biology. His proposition that\n    all species of life have descended from a common ancestor is now generally\n    accepted and considered a fundamental scientific concept. In a joint\n    publication with Alfred Russel Wallace, he introduced his scientific theory that\n    this branching pattern of evolution resulted from a process he called natural\n    selection, in which the struggle for existence has a similar effect to the\n    artificial selection involved in selective breeding. Darwin has been\n    described as one of the most influential figures in human history and was\n    honoured by burial in Westminster Abbey.\n'))]

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存储到 Memgraph

一旦您以 `GraphDocument` 格式（即节点和关系）准备好数据，您就可以使用 `add_graph_documents` 方法将其导入 Memgraph。该方法将 `graph_documents` 列表转换为需要在 Memgraph 中执行的适当 Cypher 查询。完成此操作后，知识图谱将存储在 Memgraph 中。 
# Empty the database
graph.query("STORAGE MODE IN_MEMORY_ANALYTICAL")
graph.query("DROP GRAPH")
graph.query("STORAGE MODE IN_MEMORY_TRANSACTIONAL")

# Create KG
graph.add_graph_documents(graph_documents)
图构建过程是非确定性的，因为用于从非结构化数据生成节点和关系的 LLM 是非确定性的。

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附加选项

此外，您可以灵活地根据自己的要求定义要提取的特定类型的节点和关系。 
llm_transformer_filtered = LLMGraphTransformer(
    llm=llm,
    allowed_nodes=["Person", "Nationality", "Concept"],
    allowed_relationships=["NATIONALITY", "INVOLVED_IN", "COLLABORATES_WITH"],
)
graph_documents_filtered = llm_transformer_filtered.convert_to_graph_documents(
    documents
)

print(f"Nodes:{graph_documents_filtered[0].nodes}")
print(f"Relationships:{graph_documents_filtered[0].relationships}")

Nodes:[Node(id='Charles Robert Darwin', type='Person', properties={}), Node(id='English', type='Nationality', properties={}), Node(id='Evolutionary Biology', type='Concept', properties={}), Node(id='Natural Selection', type='Concept', properties={}), Node(id='Alfred Russel Wallace', type='Person', properties={})]
Relationships:[Relationship(source=Node(id='Charles Robert Darwin', type='Person', properties={}), target=Node(id='English', type='Nationality', properties={}), type='NATIONALITY', properties={}), Relationship(source=Node(id='Charles Robert Darwin', type='Person', properties={}), target=Node(id='Evolutionary Biology', type='Concept', properties={}), type='INVOLVED_IN', properties={}), Relationship(source=Node(id='Charles Robert Darwin', type='Person', properties={}), target=Node(id='Natural Selection', type='Concept', properties={}), type='INVOLVED_IN', properties={}), Relationship(source=Node(id='Charles Robert Darwin', type='Person', properties={}), target=Node(id='Alfred Russel Wallace', type='Person', properties={}), type='COLLABORATES_WITH', properties={})]
您的图还可以将 `__Entity__` 标签应用于所有节点，这些节点将被索引以实现更快检索。 
# Drop graph
graph.query("STORAGE MODE IN_MEMORY_ANALYTICAL")
graph.query("DROP GRAPH")
graph.query("STORAGE MODE IN_MEMORY_TRANSACTIONAL")

# Store to Memgraph with Entity label
graph.add_graph_documents(graph_documents, baseEntityLabel=True)
还有一个选项是包含图中获取的信息来源。为此，请将 `include_source` 设置为 `True`，然后源文档将被存储，并通过 `MENTIONS` 关系链接到图中的节点。 
# Drop graph
graph.query("STORAGE MODE IN_MEMORY_ANALYTICAL")
graph.query("DROP GRAPH")
graph.query("STORAGE MODE IN_MEMORY_TRANSACTIONAL")

# Store to Memgraph with source included
graph.add_graph_documents(graph_documents, include_source=True)
请注意，由于文档没有任何 `id`，因此内容的存储方式和 `id` 属性的生成方式。您可以同时拥有 `__Entity__` 标签和文档源。但是，请注意两者都会占用内存，尤其是源由于内容字符串较长而占用更多内存。 最后，您可以查询知识图谱，如前一节所述：
chain = MemgraphQAChain.from_llm(
    ChatOpenAI(temperature=0),
    graph=graph,
    model_name="gpt-4-turbo",
    allow_dangerous_requests=True,
)
print(chain.invoke("Who Charles Robert Darwin collaborated with?")["result"])

MATCH (:Person {id: "Charles Robert Darwin"})-[:COLLABORATION]->(collaborator)
RETURN collaborator;
Alfred Russel Wallace
在 GitHub 上编辑此页面源文件。
以编程方式连接这些文档到 Claude、VSCode 等，通过 MCP 获取实时答案。
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