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Pinecone

Pinecone 是一个功能广泛的向量数据库。

此笔记本展示了如何使用与Pinecone向量数据库相关功能。

设置​

要使用 PineconeVectorStore，您首先需要安装配套软件包，以及本笔记本中使用的其他软件包。

pip install -qU langchain langchain-pinecone langchain-openai

迁移说明：如果您从 langchain_community.vectorstores 版本的 Pinecone 迁移，可能需要在安装依赖于 pinecone-client v6 的 langchain-pinecone 之前，移除您的 pinecone-client v2 依赖。

Credentials​

创建一个新的 Pinecone 账户，或登录您现有的账户，并创建一个 API 密钥以在本笔记本中使用。

import getpass
import os

from pinecone import Pinecone

if not os.getenv("PINECONE_API_KEY"):
    os.environ["PINECONE_API_KEY"] = getpass.getpass("Enter your Pinecone API key: ")

pinecone_api_key = os.environ.get("PINECONE_API_KEY")

pc = Pinecone(api_key=pinecone_api_key)

如果您希望自动跟踪模型调用，也可以通过取消注释下方的代码来设置您的LangSmith API密钥：

# os.environ["LANGSMITH_API_KEY"] = getpass.getpass("Enter your LangSmith API key: ")
# os.environ["LANGSMITH_TRACING"] = "true"

初始化​

在初始化我们的向量存储之前，让我们先连接到一个 Pinecone 索引。如果不存在名为 index_name 的索引，则将创建一个。

from pinecone import ServerlessSpec

index_name = "langchain-test-index"  # change if desired

if not pc.has_index(index_name):
    pc.create_index(
        name=index_name,
        dimension=1536,
        metric="cosine",
        spec=ServerlessSpec(cloud="aws", region="us-east-1"),
    )

index = pc.Index(index_name)
from langchain_openai import OpenAIEmbeddings

embeddings = OpenAIEmbeddings(model="text-embedding-3-small")
API 参考:OpenAI 嵌入
from langchain_pinecone import PineconeVectorStore

vector_store = PineconeVectorStore(index=index, embedding=embeddings)

管理向量存储​

创建向量存储后，我们可以对其进行交互，通过添加和删除不同的项。

添加项到向量存储​

我们可以通过使用add_documents函数来向我们的向量存储中添加项目。

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)
API 参考:文档

删除向量存储中的项​

vector_store.delete(ids=[uuids[-1]])

查询向量存储 ​

一旦您的向量存储已经创建并添加了相关文档，您很可能在运行链或代理的过程中希望对其进行查询。

查询直接​

简单进行相似性搜索可以按照以下方式进行：

results = vector_store.similarity_search(
    "LangChain provides abstractions to make working with LLMs easy",
    k=2,
    filter={"source": "tweet"},
)
for res in results:
    print(f"* {res.page_content} [{res.metadata}]")

相似性搜索（带分数）​

也可以按分数搜索:

results = vector_store.similarity_search_with_score(
    "Will it be hot tomorrow?", k=1, filter={"source": "news"}
)
for res, score in results:
    print(f"* [SIM={score:3f}] {res.page_content} [{res.metadata}]")

其他搜索方法​

有更多未在此笔记本中列出的搜索方法（例如 MMR），要查找所有方法，请务必阅读API 参考文档。

查询通过转换为检索器​

您也可以将向量存储转换为检索器，以便在链条中更方便地使用。

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", filter={"source": "news"})

使用检索增强生成​

对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：

API 参考​

所有功能和配置的详细文档请参阅API参考：https://python.langchain.com/api_reference/pinecone/vectorstores/langchain_pinecone.vectorstores.PineconeVectorStore.html