Neo4j 向量索引
Neo4j 是一个开源的图数据库，集成了对向量相似性搜索的支持
它支持：
- 近似最近邻搜索
- 欧几里得相似度和余弦相似度
- 结合向量和关键字搜索的混合搜索
本笔记本展示了如何使用Neo4j向量索引（Neo4jVector）。
请参阅安装指南。
# Pip install necessary package
%pip install --upgrade --quiet neo4j
%pip install --upgrade --quiet langchain-openai langchain-neo4j
%pip install --upgrade --quiet tiktoken
我们想要使用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
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.
--------------------------------------------------------------------------------
使用向量存储
上面，我们从零开始创建了一个向量存储。然而，很多时候我们希望使用现有的向量存储。为了做到这一点，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,
)
我们也可以使用from_existing_graph方法从现有图中初始化一个向量存储。该方法从数据库中提取相关的文本信息，并计算并将文本嵌入存储回数据库。
# 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，其中嵌入作为关系属性存储并索引。关系向量索引无法通过 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')]
元数据过滤
Neo4j 向量存储还支持通过结合并行运行时和精确最近邻搜索来进行元数据过滤。 需要 Neo4j 5.18 或更高版本。
等式过滤具有以下语法。
existing_graph.similarity_search(
"Slovenia",
filter={"hobby": "Bicycle", "name": "Tomaz"},
)
[Document(page_content='\nname: Tomaz\nlocation: Slovenia', metadata={'age': 33, 'hobby': 'Bicycle'})]
元数据过滤还支持以下操作符：
$eq: 等于$ne: 不等于$lt: 小于$lte: 小于或等于$gt: 大于$gte: 大于或等于$in: 在一个值列表中$nin: 不在值列表中$between: 在两个值之间$like: 文本包含值$ilike: 小写文本包含值
existing_graph.similarity_search(
"Slovenia",
filter={"hobby": {"$eq": "Bicycle"}, "age": {"$gt": 15}},
)
[Document(page_content='\nname: Tomaz\nlocation: Slovenia', metadata={'age': 33, 'hobby': 'Bicycle'})]
你也可以在过滤器之间使用OR运算符
existing_graph.similarity_search(
"Slovenia",
filter={"$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向量存储中。
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
您还可以通过使用自定义的Cypher片段来定制响应，该片段可以从图中获取其他信息。 在底层，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] = 用于填充文档的page_content的值score: Float = 相似度分数metadata: Dict = 文档的附加元数据
了解更多信息，请阅读这篇博客文章。
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'})]
这里是一个示例，将除了embedding之外的所有节点属性作为字典传递给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'})]
你也可以将Cypher参数传递给检索查询。 参数可以用于额外的过滤、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 集成了向量和关键字索引，这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，您必须提供向量和关键字索引
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
本节展示了如何使用Neo4jVector作为检索器。
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'})
带来源的问题回答
本节介绍如何通过索引进行带来源的问答。这是通过使用RetrievalQAWithSourcesChain来实现的，该链从索引中查找文档。
from langchain.chains import RetrievalQAWithSourcesChain
from langchain_openai import ChatOpenAI
API Reference:RetrievalQAWithSourcesChain | ChatOpenAI
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'}