> ## Documentation Index > Fetch the complete documentation index at: https://cndoc-langchain.site/llms.txt > Use this file to discover all available pages before exploring further. # PGVectorStore 集成 > 使用 LangChain Python 与 PGVectorStore 集成。 `PGVectorStore` 是一个使用 `postgres` 作为后端的 LangChain 向量存储实现。 本笔记本介绍如何使用 `PGVectorStore` API。 代码位于一个名为 [langchain-postgres](https://github.com/langchain-ai/langchain-postgres/) 的集成包中。 ## 设置 此包需要一个启用了 `pgvector` 扩展的 PostgreSQL 数据库。 您可以运行以下命令来启动一个启用了 `pgvector` 的 Postgres 实例容器： ```shell 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 ``` ### 安装 安装集成库 `langchain-postgres`。 ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} pip install -qU langchain-postgres # 本笔记本还需要以下依赖项 pip install -qU langchain-core langchain-cohere sqlalchemy ``` ### 设置您的 Postgres 值 设置您的 Postgres 值，以便针对 Postgres 实例测试本笔记本中的功能。 ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} # @title 设置您的值或使用默认值连接到 Docker { display-mode: "form" } POSTGRES_USER = "langchain" # @param {type: "string"} POSTGRES_PASSWORD = "langchain" # @param {type: "string"} POSTGRES_HOST = "localhost" # @param {type: "string"} POSTGRES_PORT = "6024" # @param {type: "string"} POSTGRES_DB = "langchain" # @param {type: "string"} TABLE_NAME = "vectorstore" # @param {type: "string"} VECTOR_SIZE = 1024 # @param {type: "int"} ``` ## 初始化 ### PGEngine 连接池 建立 PostgreSQL 作为向量存储的要求和参数之一是 `PGEngine` 对象。`PGEngine` 配置了与 Postgres 数据库的共享连接池。这是管理连接数量并通过缓存数据库连接减少延迟的行业最佳实践。 `PGVectorStore` 可以与 `asyncpg` 和 `psycopg3` 驱动程序一起使用。 要使用 `PGEngine.from_connection_string()` 创建 `PGEngine`，您需要提供： 1. `url`：使用 `postgresql+asyncpg` 驱动程序的连接字符串。 **注意：** 本教程演示了异步接口。所有异步方法都有对应的同步方法。 ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} # 参见上面的 docker 命令以启动启用了 pgvector 的 Postgres 实例。 CONNECTION_STRING = ( f"postgresql+asyncpg://{POSTGRES_USER}:{POSTGRES_PASSWORD}@{POSTGRES_HOST}" f":{POSTGRES_PORT}/{POSTGRES_DB}" ) # 要使用 psycopg3 驱动程序，请将连接字符串设置为 `postgresql+psycopg://` ``` ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} from langchain_postgres import PGEngine pg_engine = PGEngine.from_connection_string(url=CONNECTION_STRING) ``` 要使用 `PGEngine.from_engine()` 创建 `PGEngine`，您需要提供： 1. `engine`：一个 `AsyncEngine` 对象 ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} from sqlalchemy.ext.asyncio import create_async_engine # 创建一个 SQLAlchemy 异步引擎 engine = create_async_engine( CONNECTION_STRING, ) pg_engine = PGEngine.from_engine(engine=engine) ``` ### 初始化表 `PGVectorStore` 类需要一个数据库表。`PGEngine` 引擎有一个辅助方法 `ainit_vectorstore_table()`，可用于为您创建具有适当模式的表。 有关自定义模式，请参阅[创建自定义向量存储](#创建自定义向量存储)或[使用现有表创建向量存储](#使用现有表创建向量存储)。 ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} await pg_engine.ainit_vectorstore_table( table_name=TABLE_NAME, vector_size=VECTOR_SIZE, ) ``` #### 可选提示：💡 您还可以通过在传递 `table_name` 的任何地方传递 `schema_name` 来指定模式名称。例如： ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} SCHEMA_NAME="my_schema" await pg_engine.ainit_vectorstore_table( table_name=TABLE_NAME, vector_size=768, schema_name=SCHEMA_NAME, # 默认值："public" ) ``` ### 创建嵌入类实例 您可以使用任何 [LangChain 嵌入模型](https://python.langchain.com/docs/integrations/embeddings/)。 ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} from langchain_cohere import CohereEmbeddings embedding = CohereEmbeddings(model="embed-english-v3.0") ``` ### 初始化默认 PGVectorStore 使用默认表模式连接到向量存储。 有关自定义模式，请参阅[创建自定义向量存储](#创建自定义向量存储)或[使用现有表创建向量存储](#使用现有表创建向量存储)。 ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} from langchain_postgres import PGVectorStore store = await PGVectorStore.create( engine=pg_engine, table_name=TABLE_NAME, # schema_name=SCHEMA_NAME, embedding_service=embedding, ) ``` ## 管理向量存储 ### 添加文档 将文档添加到向量存储。元数据存储在 JSON 列中，有关存储用于过滤的元数据，请参阅“创建自定义向量存储”。 ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} import uuid from langchain_core.documents import Document docs = [ Document( id=str(uuid.uuid4()), page_content="Red Apple", metadata={"description": "red", "content": "1", "category": "fruit"}, ), Document( id=str(uuid.uuid4()), page_content="Banana Cavendish", metadata={"description": "yellow", "content": "2", "category": "fruit"}, ), Document( id=str(uuid.uuid4()), page_content="Orange Navel", metadata={"description": "orange", "content": "3", "category": "fruit"}, ), ] await store.aadd_documents(docs) ``` ### 添加文本 如果未结构化为 Document，可直接将文本添加到向量存储。 ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} import uuid all_texts = ["Apples and oranges", "Cars and airplanes", "Pineapple", "Train", "Banana"] metadatas = [{"len": len(t)} for t in all_texts] ids = [str(uuid.uuid4()) for _ in all_texts] await store.aadd_texts(all_texts, metadatas=metadatas, ids=ids) ``` ### 删除文档 可以使用 id 删除文档。 ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} await store.adelete([ids[1]]) ``` ## 查询向量存储 ### 搜索文档 使用自然语言查询搜索相似文档。 ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} query = "I'd like a fruit." docs = await store.asimilarity_search(query) print(docs) ``` ### 按向量搜索文档 使用向量嵌入搜索相似文档。 ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} query_vector = embedding.embed_query(query) docs = await store.asimilarity_search_by_vector(query_vector, k=2) print(docs) ``` ## 添加索引 通过应用向量索引加速向量搜索查询。了解更多关于[向量索引](https://cloud.google.com/blog/products/databases/faster-similarity-search-performance-with-pgvector-indexes)的信息。 如果未提供名称，索引将使用默认索引名称。要添加多个索引，需要不同的索引名称。 ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} from langchain_postgres.v2.indexes import HNSWIndex, IVFFlatIndex index = IVFFlatIndex() await store.aapply_vector_index(index) index = HNSWIndex(name="my-hnsw-index") await store.aapply_vector_index(index) ``` 设置索引参数以调整索引，在召回率和 QPS 之间实现最佳平衡。 ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} index = IVFFlatIndex(name="my-ivfflat", lists=120) await store.aapply_vector_index(index) ``` ### 重新索引 使用存储在索引表中的数据重建索引，替换旧的索引副本。某些索引类型在添加大量新数据后可能需要重新索引。 ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} await store.areindex() # 使用默认索引名称重新索引 await store.areindex("my-hnsw-index") # 使用索引名称重新索引 ``` ### 删除索引 移除向量索引。 ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} await store.adrop_vector_index() # 使用默认名称删除索引 await store.adrop_vector_index("my-hnsw-index") # 使用索引名称删除索引 ``` ### 创建自定义向量存储 使用特殊列名或自定义元数据列自定义向量存储。 `ainit_vectorstore_table` * 使用字段 `content_column`、`embedding_column`、`metadata_columns`、`metadata_json_column`、`id_column` 重命名列。 * 使用 `Column` 类创建自定义 id 或元数据列。Column 由名称和数据类型定义。可以使用任何 Postgres [数据类型](https://www.postgresql.org/docs/current/datatype.html)。 * 使用 `store_metadata` 创建 JSON 列以存储额外的元数据。 #### 可选提示：💡 要使用非 uuid id，您必须自定义 id 列： ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} await pg_engine.ainit_vectorstore_table( ..., id_column=Column(name="langchain_id", data_type="INTEGER") ) ``` `PGVectorStore` * 使用字段 `content_column`、`embedding_column`、`metadata_columns`、`metadata_json_column`、`id_column` 重命名列。 * `ignore_metadata_columns` 忽略不应用于 Document 元数据的列。当使用预先存在的表且并非所有数据列都必要时，这很有帮助。 * 使用不同的 `distance_strategy` 进行向量搜索期间的相似度计算。 * 使用 `index_query_options` 在向量搜索期间调整本地索引参数。 ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} from langchain_postgres import Column # 设置表名 TABLE_NAME = "vectorstore_custom" # SCHEMA_NAME = "my_schema" await pg_engine.ainit_vectorstore_table( table_name=TABLE_NAME, # schema_name=SCHEMA_NAME, vector_size=VECTOR_SIZE, metadata_columns=[Column("len", "INTEGER")], ) # 初始化 PGVectorStore custom_store = await PGVectorStore.create( engine=pg_engine, table_name=TABLE_NAME, # schema_name=SCHEMA_NAME, embedding_service=embedding, metadata_columns=["len"], ) ``` ### 使用元数据过滤器搜索文档 向量存储可以利用关系数据来过滤相似性搜索。向量存储支持一组可以应用于文档元数据字段的过滤器。有关如何迁移到使用元数据列的详细信息，请参阅[迁移指南](https://github.com/langchain-ai/langchain-postgres/blob/main/examples/migrate_pgvector_to_pgvectorstore.ipynb)。 `PGVectorStore` 目前支持以下运算符和所有 Postgres 数据类型。 | 运算符 | 含义/类别 | | --------- | ----------------- | | \$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 | 特殊情况 (in) | | \$nin | 特殊情况 (not in) | | \$between | 特殊情况 (between) | | \$exists | 特殊情况 (is null) | | \$like | 文本 (like) | | \$ilike | 文本 (不区分大小写的 like) | | \$and | 逻辑 (and) | | \$or | 逻辑 (or) | ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} import uuid docs = [ Document( id=str(uuid.uuid4()), page_content="Red Apple", metadata={"description": "red", "content": "1", "category": "fruit"}, ), Document( id=str(uuid.uuid4()), page_content="Banana Cavendish", metadata={"description": "yellow", "content": "2", "category": "fruit"}, ), Document( id=str(uuid.uuid4()), page_content="Orange Navel", metadata={"description": "orange", "content": "3", "category": "fruit"}, ), ] await custom_store.aadd_documents(docs) # 在搜索时使用字典过滤器 docs = await custom_store.asimilarity_search(query, filter={"content": {"$gte": 1}}) print(docs) ``` ### 使用现有表创建向量存储 可以在现有表上构建向量存储。 假设 PG 数据库中有一个预先存在的表：`products`，它存储电子商务企业的商品详情。
点击查看表模式详情 ### 创建表的 SQL 查询 ``` CREATE TABLE products ( product_id SERIAL PRIMARY KEY, name VARCHAR(255) NOT NULL, description TEXT, price_usd DECIMAL(10, 2) NOT NULL, category VARCHAR(255), quantity INT DEFAULT 0, sku VARCHAR(255) UNIQUE NOT NULL, image_url VARCHAR(255), metadata JSON, embed vector(768) DEFAULT NULL -- 向量维度取决于嵌入模型 ); ``` ### 插入记录 ``` INSERT INTO products (name, description, price_usd, category, quantity, sku, image_url, METADATA, embed) VALUES ('Laptop', 'High-performance gaming laptop', 1200.00, 'Electronics', 10, 'SKU12345', 'https://example.com/laptop.jpg', '{"category" : "Electronics", "name" : "Laptop", "description" : "High-performance gaming laptop"}', 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('Smartphone', 'Latest model with high-resolution camera', 800.00, 'Electronics', 15, 'SKU12346', 'https://example.com/smartphone.jpg', '{"category" : "Electronics", "name" : "Smartphone", "description" : "Latest model with high-resolution camera"}', 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('Coffee Maker', 'Brews coffee in under 5 minutes', 99.99, 'Kitchen Appliances', 20, 'SKU12347', 'https://example.com/coffeemaker.jpg', '{"category" : "Kitchen Appliances", "name" : "Coffee Maker", "description" : "Brews coffee in under 5 minutes"}', 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('Bluetooth Headphones', 'Noise cancelling, over the ear headphones', 250.00, 'Accessories', 5, 'SKU12348', 'https://example.com/headphones.jpg', '{"category" : "Accessories", "name" : "Bluetooth Headphones", "description" : "Noise cancelling, over the ear headphones"}', 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