PGVector
使用
postgres作为后端，并利用pgvector外延。
代码位于名为 langchain_postgres 的集成包中。
地位
此代码已从langchain_community导入到一个名为langchain-postgres.已进行以下更改：
- langchain_postgres仅适用于 psycopg3。请从
postgresql+psycopg2://...自postgresql+psycopg://langchain:langchain@...（是的，司机的名字是psycopg不psycopg3，但它将使用psycopg3. - 嵌入存储和集合的架构已更改，以使add_documents能够正确使用用户指定的 ID。
- 现在必须传递一个显式 connection 对象。
目前，没有支持在 Schema 更改时轻松迁移数据的机制。因此，vectorstore 中的任何架构更改都需要用户重新创建表并重新添加文档。 如果这是一个问题，请使用其他 vectorstore。如果没有，此实现应该适合您的用例。
设置
首先 donwload 合作伙伴包：
pip install -qU langchain_postgres
您可以运行以下命令，使用pgvector外延：
%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任何凭证，只需确保您下载了langchain_postgres包并正确启动 postgres 容器。
如果您想获得一流的模型调用自动跟踪，您还可以通过取消下面的注释来设置 LangSmith API 密钥：
# os.environ["LANGSMITH_API_KEY"] = getpass.getpass("Enter your LangSmith API key: ")
# os.environ["LANGSMITH_TRACING"] = "true"
实例
选择嵌入模型：
pip install -qU langchain-openai
import getpass
import os
if not os.environ.get("OPENAI_API_KEY"):
os.environ["OPENAI_API_KEY"] = getpass.getpass("Enter API key for OpenAI: ")
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,
)
管理矢量存储
将项目添加到向量存储
请注意，按 ID 添加文档将覆盖与该 ID 匹配的任何现有文档。
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])
API 参考：文档
[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
从 vector store 中删除项目
vector_store.delete(ids=["3"])
查询向量存储
创建矢量存储并添加相关文档后，您很可能希望在链或代理运行期间对其进行查询。
筛选支持
vectorstore 支持一组可应用于文档的元数据字段的过滤器。
| 算子 | 含义/类别 |
|---|---|
| $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) |
| $or | Logical (or) |
直接查询
可以按如下方式执行简单的相似性搜索：
results = vector_store.similarity_search(
"kitty", k=10, filter={"id": {"$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'}]
如果您提供具有多个字段但没有运算符的 dict，则顶层将被解释为逻辑 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')]
如果要执行相似性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'}]
有关可对PGVectorvector store 中，请参考 API 参考。
通过转换为 retriever 进行查询
您还可以将 vector store 转换为检索器，以便在您的链中更轻松地使用。
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
有关如何使用此向量存储进行检索增强生成 （RAG） 的指南，请参阅以下部分：
API 参考
有关所有__ModuleName__VectorStore功能和配置的详细文档，请访问 API 参考：https://python.langchain.com/api_reference/postgres/vectorstores/langchain_postgres.vectorstores.PGVector.html