> ## Documentation Index
> Fetch the complete documentation index at: https://nvd-54.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.
# PerplexityEmbeddings 集成
> 使用 LangChain Python 集成 Perplexity's 向量嵌入模型 。
本指南将帮助您开始使用 Perplexity 向量嵌入模型 using LangChain. For detailed documentation on `PerplexityEmbeddings` 功能和配置选项的详细文档，请参阅 [API reference](https://reference.langchain.com/python/langchain-perplexity/embeddings/PerplexityEmbeddings).
## 概述
### 集成详情
| 类 | 包 | 本地 | Py support | 包 downloads | 包 latest |
| :-------------------------------------------------------------------------------------------------------------------- | :------------------------------------------------------------------------------------------------------- | :-: | :--------: | :---------------------------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------------: |
| [`PerplexityEmbeddings`](https://reference.langchain.com/python/langchain-perplexity/embeddings/PerplexityEmbeddings) | [`langchain-perplexity`](https://github.com/langchain-ai/langchain/tree/master/libs/partners/perplexity) | ❌ | ✅ |  |  |
## 设置
要访问 Perplexity embedding 模型，您需要创建一个 Perplexity 账户，获取 API 密钥，并安装 `langchain-perplexity` 集成包。
### 凭证
前往 [https://www.perplexity.ai/account/api/keys](https://www.perplexity.ai/account/api/keys) 注册 the Perplexity API 并生成 API 密钥。 完成后，设置 `PPLX_API_KEY` (or `PERPLEXITY_API_KEY`) 环境变量：
```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
import getpass
import os
if not os.getenv("PPLX_API_KEY"):
os.environ["PPLX_API_KEY"] = getpass.getpass("Enter your Perplexity API key: ")
```
要启用模型调用的自动追踪，请设置您的 [LangSmith](/langsmith/home) API key:
```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
os.environ["LANGSMITH_TRACING"] = "true"
os.environ["LANGSMITH_API_KEY"] = getpass.getpass("请输入您的 LangSmith API 密钥: ")
```
### 安装
LangChain 的 Perplexity 集成位于 `langchain-perplexity` 包中：
```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
pip install -qU langchain-perplexity
```
## 实例化
Now we can instantiate our embedding model object and generate embeddings:
```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
from langchain_perplexity import PerplexityEmbeddings
embeddings = PerplexityEmbeddings(
model="pplx-embed-v1-4b",
# api_key="...", # if you prefer to pass the key explicitly
# request_timeout=60,
# max_retries=6,
)
```
Available models include `pplx-embed-v1-4b` (default) and `pplx-embed-v1-0.6b`. 请参阅 [Perplexity Embeddings API reference](https://docs.perplexity.ai/api-reference/embeddings-post) for the current list and dimensions.
## 索引与检索
向量嵌入模型常用于检索增强生成 (RAG) 流程中, 既用于索引数据，也用于后续检索数据。 更详细的说明请参阅我们的 [RAG tutorials](/oss/python/langchain/rag).
下面展示如何使用 `embeddings` 对象来索引和检索数据。 在此示例中，我们将在 `InMemoryVectorStore`.
```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
# 使用示例文本创建向量存储
from langchain_core.vectorstores import InMemoryVectorStore
text = "LangChain is the framework for building context-aware reasoning applications"
vectorstore = InMemoryVectorStore.from_texts(
[text],
embedding=embeddings,
)
# Use the vectorstore as a retriever
retriever = vectorstore.as_retriever()
# Retrieve the most similar text
retrieved_documents = retriever.invoke("What is LangChain?")
# show the retrieved document's content
retrieved_documents[0].page_content
```
```text theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
'LangChain is the framework for building context-aware reasoning applications'
```
## 直接使用
Under the hood, the vectorstore and retriever implementations are calling `embeddings.embed_documents(...)` and `embeddings.embed_query(...)` to create embeddings for the text(s) used in `from_texts` and retrieval `invoke` operations, respectively.
You can directly call these methods to get embeddings for your own use cases.
### Embed single texts
You can embed single texts or documents with `embed_query`:
```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
single_vector = embeddings.embed_query(text)
print(str(single_vector)[:100]) # Show the first 100 characters of the vector
```
### Embed multiple texts
You can embed multiple texts with `embed_documents`:
```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
text2 = (
"LangGraph is a library for building stateful, multi-actor applications with LLMs"
)
two_vectors = embeddings.embed_documents([text, text2])
for vector in two_vectors:
print(str(vector)[:100]) # Show the first 100 characters of the vector
```
### Async usage
`PerplexityEmbeddings` also exposes async methods:
```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
single_vector = await embeddings.aembed_query(text)
two_vectors = await embeddings.aembed_documents([text, text2])
```
Perplexity returns base64-encoded signed int8 embeddings. `PerplexityEmbeddings` decodes these into `list[float]` values in the range `[-128, 127]`. The magnitude is preserved from the API's quantized output; cosine similarity is unaffected by the lack of unit-length normalization.
***
## API 参考
For detailed documentation on `PerplexityEmbeddings` 功能和配置选项的详细文档，请参阅 [API reference](https://reference.langchain.com/python/langchain-perplexity/embeddings/PerplexityEmbeddings) and the [Perplexity Embeddings API documentation](https://docs.perplexity.ai/api-reference/embeddings-post).
***
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