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.
turbopuffer 是一款快速、高性价比的用于搜索和检索的向量数据库。
本指南展示了如何在 LangChain 中使用 TurbopufferVectorStore。
要使用 turbopuffer 向量存储，您需要安装 langchain-turbopuffer 集成包。
pip install -qU langchain-turbopuffer
import getpass
import os
if not os.getenv("TURBOPUFFER_API_KEY"):
os.environ["TURBOPUFFER_API_KEY"] = getpass.getpass("Enter your turbopuffer API key: ")
os.environ["LANGSMITH_API_KEY"] = getpass.getpass("Enter your LangSmith API key: ")
os.environ["LANGSMITH_TRACING"] = "true"
初始化
创建 turbopuffer 客户端和命名空间，然后初始化向量存储：
from langchain_openai import OpenAIEmbeddings
from turbopuffer import Turbopuffer
tpuf = Turbopuffer(region="gcp-us-central1")
ns = tpuf.namespace("langchain-test")
embeddings = OpenAIEmbeddings(model="text-embedding-3-small")
from langchain_turbopuffer import TurbopufferVectorStore
vector_store = TurbopufferVectorStore(embedding=embeddings, namespace=ns)
管理向量存储
创建向量存储后，您可以通过添加和删除项目进行交互。
向向量存储添加项目
from uuid import uuid4
from langchain_core.documents import Document
document_1 = Document(
page_content="I had chocolate chip pancakes and scrambled eggs for breakfast this morning.",
metadata={"source": "tweet"},
)
document_2 = Document(
page_content="The weather forecast for tomorrow is cloudy and overcast, with a high of 62 degrees.",
metadata={"source": "news"},
)
document_3 = Document(
page_content="Building an exciting new project with LangChain - come check it out!",
metadata={"source": "tweet"},
)
document_4 = Document(
page_content="Robbers broke into the city bank and stole $1 million in cash.",
metadata={"source": "news"},
)
document_5 = Document(
page_content="Wow! That was an amazing movie. I can't wait to see it again.",
metadata={"source": "tweet"},
)
document_6 = Document(
page_content="Is the new iPhone worth the price? Read this review to find out.",
metadata={"source": "website"},
)
document_7 = Document(
page_content="The top 10 soccer players in the world right now.",
metadata={"source": "website"},
)
document_8 = Document(
page_content="LangGraph is the best framework for building stateful, agentic applications!",
metadata={"source": "tweet"},
)
document_9 = Document(
page_content="The stock market is down 500 points today due to fears of a recession.",
metadata={"source": "news"},
)
document_10 = Document(
page_content="I have a bad feeling I am going to get deleted :(",
metadata={"source": "tweet"},
)
documents = [
document_1,
document_2,
document_3,
document_4,
document_5,
document_6,
document_7,
document_8,
document_9,
document_10,
]
uuids = [str(uuid4()) for _ in range(len(documents))]
vector_store.add_documents(documents=documents, ids=uuids)
从向量存储删除项目
vector_store.delete(ids=[uuids[-1]])
查询向量存储
一旦您的向量存储已创建并添加了相关文档，您很可能希望在运行链或代理时对其进行查询。
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(
"LangChain provides abstractions to make working with LLMs easy",
k=2,
filters=("source", "Eq", "tweet"),
)
for res in results:
print(f"* {res.page_content} [{res.metadata}]")
带分数的相似度搜索
您也可以使用分数进行搜索。距离越低意味着越相似：
results = vector_store.similarity_search_with_score(
"Will it be hot tomorrow?", k=1, filters=("source", "Eq", "news")
)
for res, score in results:
print(f"* [SIM={score:3f}] {res.page_content} [{res.metadata}]")
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，以便在链中更轻松地使用。
retriever = vector_store.as_retriever(
search_type="similarity_score_threshold",
search_kwargs={"k": 1, "score_threshold": 0.4},
)
retriever.invoke("Stealing from the bank is a crime")
results = vector_store.similarity_search(
"interesting articles",
k=2,
filters=("source", "Eq", "website"),
)
