---
id: llama-index-embeddings-azure-openai
version: "0.5.2"
license: MIT
license_treatment: permissive
maintenance: active
---

# llama-index-embeddings-azure-openai — llama-index embeddings azure openai integration

License: permissive · Maintenance: active · Downloads: 508.6K/mo

## What it is and what it does

This package bridges Azure OpenAI's embedding API with LlamaIndex, a framework for building retrieval-augmented generation (RAG) and semantic search applications. It translates text into dense vector embeddings using Azure-hosted OpenAI models, enabling similarity-based document retrieval and semantic matching within LlamaIndex pipelines.

The integration handles authentication, API communication, and embedding format conversion so that developers can use Azure OpenAI embeddings as a drop-in component in LlamaIndex workflows. It sits alongside llama-index-embeddings-openai and llama-index-llms-azure-openai within the broader LlamaIndex ecosystem, allowing teams already committed to Azure to avoid vendor lock-in to OpenAI's public API.

Use it for:

- Build semantic search over document collections using Azure OpenAI embeddings within a LlamaIndex retrieval pipeline.
- Implement retrieval-augmented generation (RAG) workflows that fetch relevant context from Azure-indexed documents before prompting an LLM.
- Migrate existing LlamaIndex applications from public OpenAI to Azure-hosted models for compliance or cost reasons.
- Combine Azure OpenAI embeddings with LlamaIndex's vector store integrations to power enterprise knowledge bases.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

Integrates Azure OpenAI's embedding models with LlamaIndex, enabling vector representations of text for semantic search and retrieval-augmented generation workflows.

Yes, if you are already using LlamaIndex and need to use Azure OpenAI for embeddings. The package has low install friction, active maintenance, MIT licensing, no known vulnerabilities, and fills a clear role in the LlamaIndex ecosystem. Not necessary if you are using public OpenAI embeddings or a different embedding provider.

## Install

pip install llama-index-embeddings-azure-openai
uv add llama-index-embeddings-azure-openai
poetry add llama-index-embeddings-azure-openai

## Installing llama-index-embeddings-azure-openai

Before you install: Low install friction with a pure-Python wheel. Actively maintained as of March 2026. Depends on llama-index-core, llama-index-embeddings-openai, and llama-index-llms-azure-openai, all within the same ecosystem.

License in practice: MIT license permits commercial and private use with minimal restrictions, making it suitable for most production deployments.

Quickstart:

pip install llama-index-embeddings-azure-openai

from llama_index.embeddings.azure_openai import AzureOpenAIEmbedding

embedding = AzureOpenAIEmbedding()
vectors = embedding.get_text_embedding("sample text")

Requires Python 3.10 or later and valid Azure OpenAI credentials (API key, endpoint, deployment name).

Verify before relying:

- Whether this package requires additional Azure SDK dependencies beyond what llama-index-llms-azure-openai already provides.
- Support for specific Azure OpenAI embedding model versions or deprecation timelines.

## Package facts

- License: MIT (permissive)
- Python support: supports_current
- Install friction: low
- Maintenance: active
- Downloads: 508.6K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

azure openai embeddings, llama index azure integration, vector embeddings azure, semantic search azure openai, rag embeddings azure, text to vector azure, llama index azure openai, embeddings, rag, azure-integration

[View on SkillFed](https://skillfed.io/packages/llama-index-embeddings-azure-openai) · [View on PyPI](https://pypi.org/project/llama-index-embeddings-azure-openai/)