> ## Documentation Index > Fetch the complete documentation index at: https://langchain-zh.cn/llms.txt > Use this file to discover all available pages before exploring further. # PGVector 集成 > 使用 LangChain Python 与 PGVector 向量存储进行集成。 > 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](https://github.com/langchain-ai/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： ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} 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： ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} %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](/langsmith/home) API 密钥： ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} os.environ["LANGSMITH_API_KEY"] = getpass.getpass("Enter your LangSmith API key: ") os.environ["LANGSMITH_TRACING"] = "true" ``` ## 实例化 ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} # | output: false # | echo: false from langchain_openai import OpenAIEmbeddings embeddings = OpenAIEmbeddings(model="text-embedding-3-large") ``` ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} 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。 ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} 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]) ``` ```text theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] ``` ### 从向量存储删除项目 ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} 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： ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} results = vector_store.similarity_search( "kitty", k=10, filter={"id": {"$in": [1, 5, 2, 9]}} ) for doc in results: print(f"* {doc.page_content} [{doc.metadata}]") ``` ```text theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} * there are cats in the pond [{'id': 1, 'topic': 'animals', 'location': 'pond'}] * the library hosts a weekly story time for kids [{'id': 9, 'topic': 'reading', 'location': 'library'}] * ducks are also found in the pond [{'id': 2, 'topic': 'animals', 'location': 'pond'}] * the new art exhibit is fascinating [{'id': 5, 'topic': 'art', 'location': 'museum'}] ``` 如果您提供包含多个字段但没有操作符的字典，顶级将被解释为逻辑 **AND** 过滤器 ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} vector_store.similarity_search( "ducks", k=10, filter={"id": {"$in": [1, 5, 2, 9]}, "location": {"$in": ["pond", "market"]}}, ) ``` ```text theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} [Document(metadata={'id': 1, 'topic': 'animals', 'location': 'pond'}, page_content='there are cats in the pond'), Document(metadata={'id': 2, 'topic': 'animals', 'location': 'pond'}, page_content='ducks are also found in the pond')] ``` ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} vector_store.similarity_search( "ducks", k=10, filter={ "$and": [ {"id": {"$in": [1, 5, 2, 9]}}, {"location": {"$in": ["pond", "market"]}}, ] }, ) ``` ```text theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} [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： ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} 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}]") ``` ```text theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} * [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](https://reference.langchain.com/python/langchain-postgres/vectorstores/PGVector)。 ### 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。 ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} retriever = vector_store.as_retriever(search_type="mmr", search_kwargs={"k": 1}) retriever.invoke("kitty") ``` ```text theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} [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： * [教程](/oss/python/langchain/rag) * [方法：使用 RAG 问答](https://python.langchain.com/docs/how_to/#qa-with-rag) * [检索概念文档](https://python.langchain.com/docs/concepts/retrieval) *** ## 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](https://reference.langchain.com/python/langchain-postgres/vectorstores/PGVector) ***
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