> ## 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.

# Pinecone 集成

> 使用 LangChain Python 与 Pinecone 向量存储进行集成。

> [Pinecone](https://docs.pinecone.io/docs/overview) 是一个功能广泛的向量数据库。

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。

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
pip install -qU langchain langchain-pinecone langchain-openai
```

迁移说明：如果您是从 `langchain_community.vectorstores` 的 Pinecone 实现迁移过来，在安装依赖 `pinecone-client` v6 的 `langchain-pinecone` 之前，您可能需要移除您的 `pinecone-client` v2 依赖项。

### 凭据

创建一个新的 Pinecone 账户，或登录现有账户，并创建一个用于本笔记本的 API 密钥。

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
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)
```

如果您想要获得模型调用的自动追踪，也可以通过取消注释以下内容来设置您的 [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"
```

## 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。

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
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)
```

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
from langchain_openai import OpenAIEmbeddings

embeddings = OpenAIEmbeddings(model="text-embedding-3-small")
```

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
from langchain_pinecone import PineconeVectorStore

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

## 管理向量存储

一旦创建了您的向量存储，我们可以通过添加和删除不同的项目来与其交互。

### 向向量存储添加项目

我们可以使用 `add_documents` 函数将项目添加到我们的向量存储中。

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
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

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
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期间对其进行查询。

### 直接查询

执行简单的相似度搜索可以如下所示：

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
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}]")
```

#### 带分数的相似度搜索

您也可以带分数进行搜索：

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
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

本笔记本未列出更多搜索方法（例如 MMR），要查找所有方法，请务必阅读 [API 参考](https://reference.langchain.com/python/langchain-pinecone/vectorstores/PineconeVectorStore)。

### 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。

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
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"})
```

## 检索增强生成 (RAG) 的使用

关于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：

* [教程](/oss/python/langchain/rag)
* [操作指南：使用 RAG 问答](https://python.langchain.com/docs/how_to/#qa-with-rag)
* [检索概念文档](https://python.langchain.com/docs/concepts/retrieval)

***

## API 参考

有关所有功能和配置的详细文档，请前往 [API 参考](https://reference.langchain.com/python/langchain-pinecone/vectorstores/PineconeVectorStore)

***

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