Skip to main content
一种使用 postgres 作为后端并利用 pgvector 扩展的 LangChain 向量存储抽象实现。
代码位于一个名为：langchain-postgres 的集成包中。

​
状态

此代码已从 langchain-community 移植到一个名为 langchain-postgres 的专用包中。已进行以下更改：
  • langchain-postgres 仅支持 psycopg3。请将您的连接字符串从 postgresql+psycopg2://... 更新为 postgresql+psycopg://langchain:langchain@...（是的，驱动名称是 psycopg 而不是 psycopg3，但它会使用 psycopg3）。
  • 嵌入存储和集合的模式已更改，以便 add_documents 能正确使用用户指定的 ID。
  • 现在必须传递显式的连接对象。
目前，没有机制支持在模式更改时轻松迁移数据。向量存储中的任何模式更改都需要用户重新创建表并重新添加文档。 如果这是您关心的问题，请使用不同的向量存储。如果不是，此实现应该能满足您的需求。

​
设置

首先下载配套包： 
pip install -qU langchain-postgres
您可以运行以下命令来启动带有 pgvector 扩展的 postgres 容器： 
%docker run --name pgvector-container -e POSTGRES_USER=langchain -e POSTGRES_PASSWORD=langchain -e POSTGRES_DB=langchain -p 6024:5432 -d pgvector/pgvector:pg16

​
凭据

运行此笔记本不需要凭据，只需确保您已下载 langchain-postgres 包并正确启动了 postgres 容器。 如果您希望获得最佳级别的模型调用自动跟踪，也可以通过取消注释以下内容来设置您的 LangSmith API 密钥： 
os.environ["LANGSMITH_API_KEY"] = getpass.getpass("Enter your LangSmith API key: ")
os.environ["LANGSMITH_TRACING"] = "true"

​
实例化

# | output: false
# | echo: false
from langchain_openai import OpenAIEmbeddings

embeddings = OpenAIEmbeddings(model="text-embedding-3-large")

from langchain_postgres import PGVector

# See docker command above to launch a postgres instance with pgvector enabled.
connection = "postgresql+psycopg://langchain:langchain@localhost:6024/langchain"  # Uses psycopg3!
collection_name = "my_docs"

vector_store = PGVector(
    embeddings=embeddings,
    collection_name=collection_name,
    connection=connection,
    use_jsonb=True,
)

​
管理向量存储

​
向向量存储添加项目

注意，通过 ID 添加文档将覆盖任何匹配该 ID 的现有文档。 
from langchain_core.documents import Document

docs = [
    Document(
        page_content="there are cats in the pond",
        metadata={"id": 1, "location": "pond", "topic": "animals"},
    ),
    Document(
        page_content="ducks are also found in the pond",
        metadata={"id": 2, "location": "pond", "topic": "animals"},
    ),
    Document(
        page_content="fresh apples are available at the market",
        metadata={"id": 3, "location": "market", "topic": "food"},
    ),
    Document(
        page_content="the market also sells fresh oranges",
        metadata={"id": 4, "location": "market", "topic": "food"},
    ),
    Document(
        page_content="the new art exhibit is fascinating",
        metadata={"id": 5, "location": "museum", "topic": "art"},
    ),
    Document(
        page_content="a sculpture exhibit is also at the museum",
        metadata={"id": 6, "location": "museum", "topic": "art"},
    ),
    Document(
        page_content="a new coffee shop opened on Main Street",
        metadata={"id": 7, "location": "Main Street", "topic": "food"},
    ),
    Document(
        page_content="the book club meets at the library",
        metadata={"id": 8, "location": "library", "topic": "reading"},
    ),
    Document(
        page_content="the library hosts a weekly story time for kids",
        metadata={"id": 9, "location": "library", "topic": "reading"},
    ),
    Document(
        page_content="a cooking class for beginners is offered at the community center",
        metadata={"id": 10, "location": "community center", "topic": "classes"},
    ),
]

vector_store.add_documents(docs, ids=[doc.metadata["id"] for doc in docs])

[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]

​
从向量存储删除项目

vector_store.delete(ids=["3"])

​
查询向量存储

一旦创建了向量存储并添加了相关文档，您很可能希望在运行链或代理期间对其进行查询。

​
过滤支持

向量存储支持一组可应用于文档元数据字段的过滤器。
操作符含义/类别
$eq相等 (==)
$ne不等 (!=)
$lt小于 (<)
$lte小于或等于 (<=)
$gt大于 (>)
$gte大于或等于 (>=)
$in特殊处理 (in)
$nin特殊处理 (not in)
$between特殊处理 (between)
$like文本 (like)
$ilike文本 (不区分大小写的 like)
$and逻辑 (and)
$or逻辑 (or)

​
直接查询

执行简单的相似度搜索可以如下所示： 
results = vector_store.similarity_search(
    "kitty", k=10, filter={"id": {"$in": [1, 5, 2, 9]}}
)
for doc in results:
    print(f"* {doc.page_content} [{doc.metadata}]")

* there are cats in the pond [{'id': 1, 'topic': 'animals', 'location': 'pond'}]
* the library hosts a weekly story time for kids [{'id': 9, 'topic': 'reading', 'location': 'library'}]
* ducks are also found in the pond [{'id': 2, 'topic': 'animals', 'location': 'pond'}]
* the new art exhibit is fascinating [{'id': 5, 'topic': 'art', 'location': 'museum'}]
如果您提供包含多个字段但没有操作符的字典，顶级将被解释为逻辑 AND 过滤器 
vector_store.similarity_search(
    "ducks",
    k=10,
    filter={"id": {"$in": [1, 5, 2, 9]}, "location": {"$in": ["pond", "market"]}},
)

[Document(metadata={'id': 1, 'topic': 'animals', 'location': 'pond'}, page_content='there are cats in the pond'),
 Document(metadata={'id': 2, 'topic': 'animals', 'location': 'pond'}, page_content='ducks are also found in the pond')]

vector_store.similarity_search(
    "ducks",
    k=10,
    filter={
        "$and": [
            {"id": {"$in": [1, 5, 2, 9]}},
            {"location": {"$in": ["pond", "market"]}},
        ]
    },
)

[Document(metadata={'id': 1, 'topic': 'animals', 'location': 'pond'}, page_content='there are cats in the pond'),
 Document(metadata={'id': 2, 'topic': 'animals', 'location': 'pond'}, page_content='ducks are also found in the pond')]
如果您想执行相似度搜索并接收相应的分数，可以运行： 
results = vector_store.similarity_search_with_score(query="cats", k=1)
for doc, score in results:
    print(f"* [SIM={score:3f}] {doc.page_content} [{doc.metadata}]")

* [SIM=0.763449] there are cats in the pond [{'id': 1, 'topic': 'animals', 'location': 'pond'}]
有关可在 PGVector 向量存储上执行的不同搜索的完整列表，请参阅 API 参考。

​
转换为检索器进行查询

您也可以将向量存储转换为检索器，以便在链中更轻松地使用。 
retriever = vector_store.as_retriever(search_type="mmr", search_kwargs={"k": 1})
retriever.invoke("kitty")

[Document(metadata={'id': 1, 'topic': 'animals', 'location': 'pond'}, page_content='there are cats in the pond')]

​
用于检索增强生成的用法

关于如何使用此向量存储进行检索增强生成 (RAG) 的指南，请参见以下部分：

​
API 参考

有关所有 PGVector VectorStore 功能和配置的详细文档，请前往 API 参考
Edit this page on GitHub or file an issue.
Connect these docs to Claude, VSCode, and more via MCP for real-time answers.