AzureOpenAIEmbeddings 集成

使用 LangChain Python 集成 AzureOpenAIEmbeddings 向量嵌入模型。

本指南将帮助您开始使用 AzureOpenAI 向量嵌入模型 using LangChain. For detailed documentation on AzureOpenAIEmbeddings 功能和配置选项的详细文档，请参阅 API reference.

概述

集成详情

设置

要访问 AzureOpenAI embedding 模型，您需要创建一个 Azure 账户，获取 API 密钥，并安装 langchain-openai 集成包。

凭证

You’ll need to have an Azure OpenAI instance deployed. You can deploy a version on Azure Portal following this guide.

Once you have your instance running, make sure you have the name of your instance and key. You can find the key in the Azure Portal, under the “Keys and Endpoint” section of your instance.

AZURE_OPENAI_ENDPOINT=<YOUR API ENDPOINT>
AZURE_OPENAI_API_KEY=<YOUR_KEY>
AZURE_OPENAI_API_VERSION="2024-02-01"

import getpass
import os

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

要启用模型调用的自动追踪，请设置您的 LangSmith API key:

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 AzureOpenAI 集成位于 langchain-openai 包中：

pip install -qU langchain-openai

实例化

现在我们可以实例化模型对象并生成聊天补全：

from langchain_openai import AzureOpenAIEmbeddings

embeddings = AzureOpenAIEmbeddings(
    model="text-embedding-3-large",
    # dimensions: Optional[int] = None, # Can specify dimensions with new text-embedding-3 models
    # azure_endpoint="https://<your-endpoint>.openai.azure.com/", If not provided, will read env variable AZURE_OPENAI_ENDPOINT
    # api_key=... # Can provide an API key directly. If missing read env variable AZURE_OPENAI_API_KEY
    # openai_api_version=..., # If not provided, will read env variable AZURE_OPENAI_API_VERSION
)

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.

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.

# 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

'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:

single_vector = embeddings.embed_query(text)
print(str(single_vector)[:100])  # Show the first 100 characters of the vector

[-0.0011676070280373096, 0.007125577889382839, -0.014674457721412182, -0.034061674028635025, 0.01128

Embed multiple texts

You can embed multiple texts with embed_documents:

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

[-0.0011966148158535361, 0.007160289213061333, -0.014659193344414234, -0.03403077274560928, 0.011280
[-0.005595256108790636, 0.016757294535636902, -0.011055258102715015, -0.031094247475266457, -0.00363

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API 参考

For detailed documentation on AzureOpenAIEmbeddings 功能和配置选项的详细文档，请参阅 API reference.

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