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Pinecone

Pinecone 是一个具有广泛功能的向量数据库。

本笔记本展示了如何使用与Pinecone向量数据库相关的功能。

设置​

要使用PineconeVectorStore，您首先需要安装合作伙伴包，以及本笔记本中使用的其他包。

%pip install -qU langchain-pinecone pinecone-notebooks

迁移说明：如果您正在从langchain_community.vectorstores实现的Pinecone迁移，您可能需要在安装langchain-pinecone之前移除您的pinecone-client v2依赖项，因为langchain-pinecone依赖于pinecone-client v3。

凭证​

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

import getpass
import os
import time

from pinecone import Pinecone, ServerlessSpec

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)

如果你想获取模型调用的自动追踪，你也可以通过取消注释以下内容来设置你的 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，让我们连接到一个Pinecone索引。如果名为index_name的索引不存在，它将被创建。

import time

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

existing_indexes = [index_info["name"] for index_info in pc.list_indexes()]

if index_name not in existing_indexes:
    pc.create_index(
        name=index_name,
        dimension=3072,
        metric="cosine",
        spec=ServerlessSpec(cloud="aws", region="us-east-1"),
    )
    while not pc.describe_index(index_name).status["ready"]:
        time.sleep(1)

index = pc.Index(index_name)

现在我们的Pinecone索引已经设置好了，我们可以初始化我们的向量存储。

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_pinecone import PineconeVectorStore

vector_store = PineconeVectorStore(index=index, embedding=embeddings)
API Reference:PineconeVectorStore

管理向量存储​

一旦你创建了你的向量存储，我们可以通过添加和删除不同的项目来与之交互。

添加项目到向量存储​

我们可以使用add_documents函数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 chocalate 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 Reference:Document
['167b8681-5974-467f-adcb-6e987a18df01',
 'd16010fd-41f8-4d49-9c22-c66d5555a3fe',
 'ffcacfb3-2bc2-44c3-a039-c2256a905c0e',
 'cf3bfc9f-5dc7-4f5e-bb41-edb957394126',
 'e99b07eb-fdff-4cb9-baa8-619fd8efeed3',
 '68c93033-a24f-40bd-8492-92fa26b631a4',
 'b27a4ecb-b505-4c5d-89ff-526e3d103558',
 '4868a9e6-e6fb-4079-b400-4a1dfbf0d4c4',
 '921c0e9c-0550-4eb5-9a6c-ed44410788b2',
 'c446fc23-64e8-47e7-8c19-ecf985e9411e']

从向量存储中删除项目​

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

查询向量存储​

一旦您的向量存储已创建并且相关文档已添加，您很可能希望在链或代理运行期间查询它。

直接查询​

执行简单的相似性搜索可以按如下方式进行：

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}]")
* Building an exciting new project with LangChain - come check it out! [{'source': 'tweet'}]
* LangGraph is the best framework for building stateful, agentic applications! [{'source': 'tweet'}]

带分数的相似性搜索​

你也可以使用分数进行搜索：

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}]")
* [SIM=0.553187] The weather forecast for tomorrow is cloudy and overcast, with a high of 62 degrees. [{'source': 'news'}]

其他搜索方法​

本笔记本中未列出更多的搜索方法（如MMR），要找到所有方法，请务必阅读API参考。

通过转换为检索器进行查询​

你也可以将向量存储转换为检索器，以便在你的链中更轻松地使用。

retriever = vector_store.as_retriever(
    search_type="similarity_score_threshold",
    search_kwargs={"k": 1, "score_threshold": 0.5},
)
retriever.invoke("Stealing from the bank is a crime", filter={"source": "news"})
[Document(metadata={'source': 'news'}, page_content='Robbers broke into the city bank and stole $1 million in cash.')]

检索增强生成的使用​

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/pinecone/vectorstores/langchain_pinecone.vectorstores.PineconeVectorStore.html

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