> ## Documentation Index > Fetch the complete documentation index at: https://nvd-54.mintlify.site/llms.txt > Use this file to discover all available pages before exploring further. # PGVector 集成 > 使用 LangChain Python 集成 PGVector 向量存储。 > An implementation of LangChain vectorstore abstraction using `postgres` as the backend and utilizing the `pgvector` extension. The code lives in an integration package called: [langchain-postgres](https://github.com/langchain-ai/langchain-postgres/). ## Status This code has been ported over from `langchain-community` into a dedicated package called `langchain-postgres`. The following changes have been made: * `langchain-postgres` works only with psycopg3. Please update your connnecion strings from `postgresql+psycopg2://...` to `postgresql+psycopg://langchain:langchain@...` (yes, it's the driver name is `psycopg` not `psycopg3`, but it'll use `psycopg3`. * The schema of the embedding store and collection have been changed to make add\_documents work correctly with user specified ids. * One has to pass an explicit connection object now. Currently, there is **no mechanism** that supports easy data migration on schema changes. Any schema changes in the vectorstore will require the user to recreate the tables and re-add the documents. If this is a concern, please use a different vectorstore. If not, this implementation should be fine for your use case. ## 设置 First download the partner package: ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} pip install -qU langchain-postgres ``` You can run the following command to spin up a postgres container with the `pgvector` extension: ```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 ``` ### 凭证 There are no credentials needed to run this notebook, just make sure you downloaded the `langchain-postgres` package and correctly started the postgres container. 如果您希望获得一流的模型调用自动追踪功能，还可以通过取消注释以下代码来设置 [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 Note that adding documents by ID will over-write any existing documents that match that ID. ```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，您很可能希望在链或智能体运行期间对其进行查询。 ### Filtering support The vectorstore supports a set of filters that can be applied against the metadata fields of the documents. | Operator | Meaning/Category | | --------- | ---------------------------- | | \$eq | Equality (==) | | \$ne | Inequality (!=) | | \$lt | Less than (\<) | | \$lte | Less than or equal (\<=) | | \$gt | Greater than (>) | | \$gte | Greater than or equal (>=) | | \$in | Special Cased (in) | | \$nin | Special Cased (not in) | | \$between | Special Cased (between) | | \$like | Text (like) | | \$ilike | Text (case-insensitive like) | | \$and | Logical (and) | | \$or | Logical (or) | ### 直接查询 可以按以下方式执行简单的相似度搜索： ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} results = vector_store.similarity_search( "kitty", k=10, filter={"id": {"$in": [1, 5, 2, 9]}} ) for doc in results: print(f"* {doc.page_content} [{doc.metadata}]") ``` ```text theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} * there are cats in the pond [{'id': 1, 'topic': 'animals', 'location': 'pond'}] * the library hosts a weekly story time for kids [{'id': 9, 'topic': 'reading', 'location': 'library'}] * ducks are also found in the pond [{'id': 2, 'topic': 'animals', 'location': 'pond'}] * the new art exhibit is fascinating [{'id': 5, 'topic': 'art', 'location': 'museum'}] ``` If you provide a dict with multiple fields, but no operators, the top level will be interpreted as a logical **AND** filter ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} vector_store.similarity_search( "ducks", k=10, filter={"id": {"$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获取对应分数，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'}] ``` For a full list of the different searches you can execute on a `PGVector` vector store, please refer to the [API reference](https://reference.langchain.com/python/langchain-postgres/vectorstores/PGVector). ### 转换为检索器进行查询 您还可以将向量存储转换为检索器，以便在链中更方便地使用。 ```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 有关如何将此向量存储用于检索增强生成 (RAG) 的指南，请参阅以下部分： * [Tutorials](/oss/python/langchain/rag) * [How-to: Question and answer with RAG](https://python.langchain.com/docs/how_to/#qa-with-rag) * [Retrieval conceptual docs](https://python.langchain.com/docs/concepts/retrieval) *** ## API 参考 For detailed documentation of all PGVector VectorStore features and configurations head to the [API reference](https://reference.langchain.com/python/langchain-postgres/vectorstores/PGVector) ***
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