> ## Documentation Index
> Fetch the complete documentation index at: https://docs.upsonic.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Azure OpenAI Embeddings

> Using Azure OpenAI embedding models with Upsonic

## Overview

Azure OpenAI provides managed access to OpenAI embedding models through Azure infrastructure. Supports both API key and Managed Identity authentication with enterprise-grade security and compliance features.

**Provider Class:** `AzureOpenAIEmbedding`

**Config Class:** `AzureOpenAIEmbeddingConfig`

## Dependencies

```bash theme={null}
uv pip install openai
```

For Managed Identity support:

```bash theme={null}
uv pip install azure-identity
```

## Examples

```python theme={null}
from upsonic import Agent, Task, KnowledgeBase
from upsonic.embeddings import AzureOpenAIEmbedding, AzureOpenAIEmbeddingConfig
from upsonic.vectordb import ChromaProvider, ChromaConfig, ConnectionConfig, Mode

# Create embedding provider with API key
embedding = AzureOpenAIEmbedding(AzureOpenAIEmbeddingConfig(
    azure_endpoint="https://your-resource.openai.azure.com/",
    deployment_name="text-embedding-ada-002",
    model_name="text-embedding-ada-002"
))

# Setup KnowledgeBase
vectordb = ChromaProvider(ChromaConfig(
    collection_name="azure_docs",
    vector_size=1536,
    connection=ConnectionConfig(mode=Mode.IN_MEMORY)
))

kb = KnowledgeBase(
    sources=["document.txt"],
    embedding_provider=embedding,
    vectordb=vectordb
)

# Query with Agent
agent = Agent("anthropic/claude-sonnet-4-5")
task = Task("What is this document about?", context=[kb])
result = agent.do(task)
print(result)
```

## Parameters

| Parameter                  | Type          | Description                                                         | Default                    | Source   |
| -------------------------- | ------------- | ------------------------------------------------------------------- | -------------------------- | -------- |
| `azure_endpoint`           | `str \| None` | Azure OpenAI endpoint URL                                           | `None`                     | Specific |
| `api_key`                  | `str \| None` | Azure OpenAI API key (uses AZURE\_OPENAI\_API\_KEY env var if None) | `None`                     | Specific |
| `deployment_name`          | `str \| None` | Azure deployment name                                               | `None`                     | Specific |
| `api_version`              | `str`         | Azure OpenAI API version                                            | `"2024-02-01"`             | Specific |
| `use_managed_identity`     | `bool`        | Use Azure Managed Identity                                          | `False`                    | Specific |
| `tenant_id`                | `str \| None` | Azure tenant ID                                                     | `None`                     | Specific |
| `client_id`                | `str \| None` | Azure client ID for managed identity                                | `None`                     | Specific |
| `model_name`               | `str`         | Embedding model name                                                | `"text-embedding-ada-002"` | Specific |
| `enable_content_filtering` | `bool`        | Enable Azure content filtering                                      | `True`                     | Specific |
| `data_residency_region`    | `str \| None` | Data residency region                                               | `None`                     | Specific |
| `parallel_requests`        | `int`         | Parallel requests (Azure has lower limits)                          | `3`                        | Specific |
| `requests_per_minute`      | `int`         | Requests per minute for Azure                                       | `240`                      | Specific |
| `tokens_per_minute`        | `int`         | Tokens per minute for Azure                                         | `240000`                   | Specific |
| `batch_size`               | `int`         | Batch size for document embedding                                   | `100`                      | Base     |
| `max_retries`              | `int`         | Maximum number of retries on failure                                | `3`                        | Base     |
| `normalize_embeddings`     | `bool`        | Whether to normalize embeddings to unit length                      | `True`                     | Base     |