Neo4j 向量索引

Neo4j 是一个开源的图形数据库，集成了对向量相似性搜索的支持

它支持：

• 近似最近邻搜索
• 欧几里得相似性和余弦相似性
• 结合向量搜索和关键字搜索的混合搜索

此笔记本演示如何使用 Neo4j 向量索引 （Neo4jVector).

请参阅安装说明。

# Pip install necessary package
%pip install --upgrade --quiet  neo4j
%pip install --upgrade --quiet  langchain-openai langchain-neo4j
%pip install --upgrade --quiet  tiktoken

我们想使用OpenAIEmbeddings所以我们必须获取 OpenAI API 密钥。

import getpass
import os

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

OpenAI API Key: ········

from langchain_community.document_loaders import TextLoader
from langchain_core.documents import Document
from langchain_neo4j import Neo4jVector
from langchain_openai import OpenAIEmbeddings
from langchain_text_splitters import CharacterTextSplitter

API 参考：TextLoader | 文档 | Neo4j向量 | OpenAI 嵌入 | CharacterTextSplitter（字符文本拆分器）

loader = TextLoader("../../how_to/state_of_the_union.txt")

documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
docs = text_splitter.split_documents(documents)

embeddings = OpenAIEmbeddings()

# Neo4jVector requires the Neo4j database credentials

url = "bolt://localhost:7687"
username = "neo4j"
password = "password"

# You can also use environment variables instead of directly passing named parameters
# os.environ["NEO4J_URI"] = "bolt://localhost:7687"
# os.environ["NEO4J_USERNAME"] = "neo4j"
# os.environ["NEO4J_PASSWORD"] = "pleaseletmein"

使用余弦距离进行相似性搜索（默认）

# The Neo4jVector Module will connect to Neo4j and create a vector index if needed.

db = Neo4jVector.from_documents(
    docs, OpenAIEmbeddings(), url=url, username=username, password=password
)

query = "What did the president say about Ketanji Brown Jackson"
docs_with_score = db.similarity_search_with_score(query, k=2)

for doc, score in docs_with_score:
    print("-" * 80)
    print("Score: ", score)
    print(doc.page_content)
    print("-" * 80)

--------------------------------------------------------------------------------
Score:  0.9076391458511353
Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. 

Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. 

One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. 

And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.
--------------------------------------------------------------------------------
--------------------------------------------------------------------------------
Score:  0.8912242650985718
A former top litigator in private practice. A former federal public defender. And from a family of public school educators and police officers. A consensus builder. Since she’s been nominated, she’s received a broad range of support—from the Fraternal Order of Police to former judges appointed by Democrats and Republicans. 

And if we are to advance liberty and justice, we need to secure the Border and fix the immigration system. 

We can do both. At our border, we’ve installed new technology like cutting-edge scanners to better detect drug smuggling.  

We’ve set up joint patrols with Mexico and Guatemala to catch more human traffickers.  

We’re putting in place dedicated immigration judges so families fleeing persecution and violence can have their cases heard faster. 

We’re securing commitments and supporting partners in South and Central America to host more refugees and secure their own borders.
--------------------------------------------------------------------------------

使用 vectorstore

上面，我们从头开始创建了一个 vectorstore。但是，很多时候我们希望使用现有的 vectorstore。 为此，我们可以直接初始化它。

index_name = "vector"  # default index name

store = Neo4jVector.from_existing_index(
    OpenAIEmbeddings(),
    url=url,
    username=username,
    password=password,
    index_name=index_name,
)

我们还可以使用from_existing_graph方法。此方法从数据库中提取相关的文本信息，并计算文本嵌入并将其存储回数据库。

# First we create sample data in graph
store.query(
    "CREATE (p:Person {name: 'Tomaz', location:'Slovenia', hobby:'Bicycle', age: 33})"
)

[]

# Now we initialize from existing graph
existing_graph = Neo4jVector.from_existing_graph(
    embedding=OpenAIEmbeddings(),
    url=url,
    username=username,
    password=password,
    index_name="person_index",
    node_label="Person",
    text_node_properties=["name", "location"],
    embedding_node_property="embedding",
)
result = existing_graph.similarity_search("Slovenia", k=1)

result[0]

Document(page_content='\nname: Tomaz\nlocation: Slovenia', metadata={'age': 33, 'hobby': 'Bicycle'})

Neo4j 还支持关系向量索引，其中嵌入向量存储为关系属性并编制索引。关系向量索引不能通过 LangChain 填充，但您可以将其连接到现有的关系向量索引。

# First we create sample data and index in graph
store.query(
    "MERGE (p:Person {name: 'Tomaz'}) "
    "MERGE (p1:Person {name:'Leann'}) "
    "MERGE (p1)-[:FRIEND {text:'example text', embedding:$embedding}]->(p2)",
    params={"embedding": OpenAIEmbeddings().embed_query("example text")},
)
# Create a vector index
relationship_index = "relationship_vector"
store.query(
    """
CREATE VECTOR INDEX $relationship_index
IF NOT EXISTS
FOR ()-[r:FRIEND]-() ON (r.embedding)
OPTIONS {indexConfig: {
 `vector.dimensions`: 1536,
 `vector.similarity_function`: 'cosine'
}}
""",
    params={"relationship_index": relationship_index},
)

[]

relationship_vector = Neo4jVector.from_existing_relationship_index(
    OpenAIEmbeddings(),
    url=url,
    username=username,
    password=password,
    index_name=relationship_index,
    text_node_property="text",
)
relationship_vector.similarity_search("Example")

[Document(page_content='example text')]

元数据筛选

Neo4j 矢量存储还通过结合并行运行时和精确最近邻搜索来支持元数据筛选。需要 Neo4j 5.18 或更高版本。

相等筛选具有以下语法。

existing_graph.similarity_search(
    "Slovenia",
    filter={"hobby": "Bicycle", "name": "Tomaz"},
)

[Document(page_content='\nname: Tomaz\nlocation: Slovenia', metadata={'age': 33, 'hobby': 'Bicycle'})]

元数据筛选还支持以下运算符：

• $eq: Equal
• $ne: Not Equal
• $lt: Less than
• $lte: Less than or equal
• $gt: Greater than
• $gte: Greater than or equal
• $in: In a list of values
• $nin: Not in a list of values
• $between: Between two values
• $like: Text contains value
• $ilike: lowered text contains value

existing_graph.similarity_search(
    "Slovenia",
    filter={"hobby": {"$eq": "Bicycle"}, "age": {"$gt": 15}},
)

[Document(page_content='\nname: Tomaz\nlocation: Slovenia', metadata={'age': 33, 'hobby': 'Bicycle'})]

您还可以使用OR运算符

existing_graph.similarity_search(
    "Slovenia",
    filter={"$or": [{"hobby": {"$eq": "Bicycle"}}, {"age": {"$gt": 15}}]},
)

[Document(page_content='\nname: Tomaz\nlocation: Slovenia', metadata={'age': 33, 'hobby': 'Bicycle'})]

添加文档

我们可以将文档添加到现有的 vectorstore 中。

store.add_documents([Document(page_content="foo")])

['acbd18db4cc2f85cedef654fccc4a4d8']

docs_with_score = store.similarity_search_with_score("foo")

docs_with_score[0]

(Document(page_content='foo'), 0.9999997615814209)

使用检索查询自定义响应

您还可以使用自定义 Cypher 代码段来自定义响应，该代码段可以从图表中获取其他信息。 在后台，最终的 Cypher 语句是这样构建的：

read_query = (
  "CALL db.index.vector.queryNodes($index, $k, $embedding) "
  "YIELD node, score "
) + retrieval_query

检索查询必须返回以下三列：

• text： Union[str， Dict] = 用于填充的值page_content文档的
• score： 浮点数 = 相似度分数
• metadata： Dict = 文档的其他元数据

在此博客文章中了解更多信息。

retrieval_query = """
RETURN "Name:" + node.name AS text, score, {foo:"bar"} AS metadata
"""
retrieval_example = Neo4jVector.from_existing_index(
    OpenAIEmbeddings(),
    url=url,
    username=username,
    password=password,
    index_name="person_index",
    retrieval_query=retrieval_query,
)
retrieval_example.similarity_search("Foo", k=1)

[Document(page_content='Name:Tomaz', metadata={'foo': 'bar'})]

下面是一个传递除embedding作为字典更改为text列

retrieval_query = """
RETURN node {.name, .age, .hobby} AS text, score, {foo:"bar"} AS metadata
"""
retrieval_example = Neo4jVector.from_existing_index(
    OpenAIEmbeddings(),
    url=url,
    username=username,
    password=password,
    index_name="person_index",
    retrieval_query=retrieval_query,
)
retrieval_example.similarity_search("Foo", k=1)

[Document(page_content='name: Tomaz\nage: 33\nhobby: Bicycle\n', metadata={'foo': 'bar'})]

您还可以将 Cypher 参数传递给检索查询。 参数可用于其他过滤、遍历等。

retrieval_query = """
RETURN node {.*, embedding:Null, extra: $extra} AS text, score, {foo:"bar"} AS metadata
"""
retrieval_example = Neo4jVector.from_existing_index(
    OpenAIEmbeddings(),
    url=url,
    username=username,
    password=password,
    index_name="person_index",
    retrieval_query=retrieval_query,
)
retrieval_example.similarity_search("Foo", k=1, params={"extra": "ParamInfo"})

[Document(page_content='location: Slovenia\nextra: ParamInfo\nname: Tomaz\nage: 33\nhobby: Bicycle\nembedding: None\n', metadata={'foo': 'bar'})]

混合搜索（向量 + 关键词）

Neo4j 集成了向量索引和关键字索引，允许您使用混合搜索方法

# The Neo4jVector Module will connect to Neo4j and create a vector and keyword indices if needed.
hybrid_db = Neo4jVector.from_documents(
    docs,
    OpenAIEmbeddings(),
    url=url,
    username=username,
    password=password,
    search_type="hybrid",
)

要从现有索引加载混合搜索，您必须同时提供 vector 和 keyword 索引

index_name = "vector"  # default index name
keyword_index_name = "keyword"  # default keyword index name

store = Neo4jVector.from_existing_index(
    OpenAIEmbeddings(),
    url=url,
    username=username,
    password=password,
    index_name=index_name,
    keyword_index_name=keyword_index_name,
    search_type="hybrid",
)

检索器选项

本节介绍如何使用Neo4jVector作为Retriever。

retriever = store.as_retriever()
retriever.invoke(query)[0]

Document(page_content='Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \n\nTonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \n\nOne of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n\nAnd I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.', metadata={'source': '../../how_to/state_of_the_union.txt'})

使用源进行问答

本节介绍如何对 Index 上的 sources 进行问答。它通过使用RetrievalQAWithSourcesChain，它执行 Index 中的文档查找。

from langchain.chains import RetrievalQAWithSourcesChain
from langchain_openai import ChatOpenAI

API 参考：RetrievalQAWithSourcesChain | 聊天OpenAI

chain = RetrievalQAWithSourcesChain.from_chain_type(
    ChatOpenAI(temperature=0), chain_type="stuff", retriever=retriever
)

chain.invoke(
    {"question": "What did the president say about Justice Breyer"},
    return_only_outputs=True,
)

{'answer': 'The president honored Justice Stephen Breyer for his service to the country and mentioned his retirement from the United States Supreme Court.\n',
 'sources': '../../how_to/state_of_the_union.txt'}

相关

• 矢量存储概念指南
• Vector store 操作指南