> ## 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 集成。

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

## 安装和设置

安装 Python SDK：

<CodeGroup>
  ```bash pip theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
  pip install langchain-pinecone
  ```

  ```bash uv theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
  uv add langchain-pinecone
  ```
</CodeGroup>

## 向量存储

存在一个围绕 Pinecone 索引的包装器，允许您将其用作向量存储，
无论是用于语义搜索还是示例选择。

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

有关 Pinecone 向量存储的详细逐步指南，请参阅 [此笔记本](/oss/python/integrations/vectorstores/pinecone)

### 稀疏向量存储

LangChain 的 `PineconeSparseVectorStore` 利用 Pinecone 的稀疏英语模型实现稀疏检索。它将文本映射为稀疏向量，并支持添加文档和相似度搜索。

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

# Initialize sparse vector store
vector_store = PineconeSparseVectorStore(
    index=my_index,
    embedding_model="pinecone-sparse-english-v0"
)
# Add documents
vector_store.add_documents(documents)
# Query
results = vector_store.similarity_search("your query", k=3)
```

有关更详细的逐步指南，请参阅 [Pinecone 稀疏向量存储笔记本](/oss/python/integrations/vectorstores/pinecone_sparse)。

### 稀疏嵌入

LangChain 的 `PineconeSparseEmbeddings` 使用 Pinecone 的 `pinecone-sparse-english-v0` 模型提供稀疏嵌入生成。

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

# Initialize sparse embeddings
sparse_embeddings = PineconeSparseEmbeddings(
    model="pinecone-sparse-english-v0"
)
# Embed a single query (returns SparseValues)
query_embedding = sparse_embeddings.embed_query("sample text")

# Embed multiple documents (returns list of SparseValues)
docs = ["Document 1 content", "Document 2 content"]
doc_embeddings = sparse_embeddings.embed_documents(docs)
```

有关更多详细用法，请参阅 [Pinecone 稀疏嵌入笔记本](/oss/python/integrations/vectorstores/pinecone_sparse)。

## 检索器

### Pinecone 混合搜索

<CodeGroup>
  ```bash pip theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
  pip install pinecone pinecone-text
  ```

  ```bash uv theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
  uv add pinecone pinecone-text
  ```
</CodeGroup>

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
from langchain_community.retrievers import (
    PineconeHybridSearchRetriever,
)
```

有关更多信息，请参阅 [此笔记本](/oss/python/integrations/retrievers/pinecone_hybrid_search)。

### 自查询检索器

Pinecone 向量存储可用作自查询的检索器。

***

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