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Memgraph

Memgraph 是一个开源图数据库，专为动态分析环境而调整，并与 Neo4j 兼容。为了查询数据库，Memgraph 使用 Cypher - 最广泛采用、完全规范且开放的属性图数据库查询语言。

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中构建知识图谱。

但首先，请确保设置好一切。

设置​

要学习本指南，您需要安装 Docker 和 Python 3.x。

要首次快速运行 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

这两个命令都会运行一个脚本，该脚本会将 Docker Compose 文件下载到您的系统，并在两个单独的容器中构建并启动 memgraph-mage 和 memgraph-lab Docker 服务。现在您已经启动并运行了 Memgraph！请在 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软件包。我们将使用软件包管理器 pip，以及 --user 标志，以确保适当的权限。如果您已安装 Python 3.4 或更高版本，则默认包含 pip。您可以使用以下命令安装所有必需的软件包

pip install langchain langchain-openai neo4j --user

您可以在此笔记本中运行提供的代码块，也可以使用单独的 Python 文件来试验 Memgraph 和 LangChain。

自然语言查询​

Memgraph 与 LangChain 的集成包括自然语言查询。要使用它，首先进行所有必要的导入。我们将在代码中出现时讨论它们。

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_community.chains.graph_qa.memgraph import MemgraphQAChain
from langchain_community.graphs import MemgraphGraph
from langchain_core.prompts import PromptTemplate
from langchain_openai import ChatOpenAI

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

graph = MemgraphGraph(
    url=url, username=username, password=password, refresh_schema=False
)

refresh_schema 最初设置为 False，因为数据库中仍然没有数据，我们希望避免不必要的数据库调用。

填充数据库​

要填充数据库，首先确保它是空的。最有效的方法是切换到内存分析存储模式，删除图表，然后返回到内存事务模式。了解有关 Memgraph 存储模式的更多信息。

我们将添加到数据库的数据是关于不同类型视频游戏的信息，这些游戏可在各种平台上使用，并且与发行商相关。

# 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 对象如何保存 query 方法。该方法在 Memgraph 中执行查询，并且 MemgraphQAChain 也使用它来查询数据库。

刷新图表模式​

由于新数据是在 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()

为了熟悉数据并验证更新后的图表模式，您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)

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。这确保了对您的请求进行适当的授权。您可以在此处找到有关获取 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"

如果您在 Jupyter 笔记本中运行代码，请运行上面的代码片段。

接下来，创建 MemgraphQAChain，它将在基于您的图数据的问答过程中使用。temperature parameter 设置为零以确保可预测和一致的答案。您可以将 verbose 参数设置为 True 以接收有关查询生成的更详细的消息。

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

现在您可以开始提问了！

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.

链修饰符​

要修改您的链的行为并获取更多上下文或其他信息，您可以修改链的参数。

返回直接查询结果​

return_direct 修饰符指定是返回执行的 Cypher 查询的直接结果，还是返回处理后的自然语言响应。

# 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'}]

返回查询中间步骤​

return_intermediate_steps 链修饰符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.

限制查询结果的数量​

当您想要限制最大查询结果数时，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

高级查询​

随着解决方案复杂性的增加，您可能会遇到需要仔细处理的不同用例。确保应用程序的可扩展性对于保持流畅的用户流程而不会出现任何故障至关重要。

让我们再次实例化我们的链，并尝试提出一些用户可能提出的问题。

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 时的一个常见挑战 - 用户措辞查询的方式与数据存储方式之间的不一致。在这种情况下，用户感知与实际数据存储之间的差异可能会导致不匹配。提示改进，即磨练模型的提示以更好地掌握这些区别的过程，是一种有效的解决方案，可以解决此问题。通过提示改进，模型在生成精确且相关的查询方面获得了更高的熟练度，从而成功检索到所需的数据。

提示改进​

为了解决这个问题，我们可以调整 QA 链的初始 Cypher 提示。这涉及到向 LLM 添加有关用户如何引用特定平台（例如我们案例中的 PS5）的指导。我们使用 LangChain PromptTemplate 来实现这一点，创建一个修改后的初始提示。然后，将此修改后的提示作为参数提供给我们改进后的 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，其中包含有关平台命名的指导，我们获得了准确且相关的结果，这些结果更符合用户查询。

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改进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。

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或直接。本指南将展示如何利用 LLM 来帮助我们完成此任务，以及如何在 Memgraph 中构建知识图谱。创建知识图谱后，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是

从文本中提取结构化信息​

除了设置部分中的所有导入之外，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

以下是关于查尔斯·达尔文的示例文本（来源），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 从文本中提取重要的实体，并将它们作为节点和关系的列表返回。下面是它的样子

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'))]

存储到 Memgraph 中​

一旦您以 GraphDocument 格式准备好数据（即节点和关系），您可以使用 add_graph_documents 方法将其导入到 Memgraph 中。该方法将 graph_documents 列表转换为需要在 Memgraph 中执行的适当的 Cypher 查询。完成之后，知识图谱就存储在 Memgraph 中了。

# 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)

以下是图谱在 Memgraph Lab 中的样子（请在 localhost:3000 上查看）

memgraph-kg

如果您尝试过并获得了不同的图谱，这是预期行为。图谱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。

其他选项​

此外，您可以灵活地定义特定类型的节点和关系，以便根据您的需求进行提取。

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={})]

以下是这种情况下的图谱的样子

memgraph-kg-2

您的图谱还可以在所有节点上都有 __Entity__ 标签，这些标签将被索引以加快检索速度。

# 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)

以下是图谱的样子

memgraph-kg-3

还有一个选项可以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，并使用 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)

构建的图谱将如下所示

memgraph-kg-4

请注意，源的内容是如何存储的，以及 id 属性是如何生成的，因为文档没有任何 id。您可以结合使用 __Entity__ 标签和文档来源。但是，请注意，两者都会占用内存，尤其是包含来源时，因为内容是长字符串。

最后，您可以查询知识图谱，如之前的章节所述

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

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