> ## 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.

# OpenAI Embeddings

> Using OpenAI embedding models with Upsonic

## Overview

OpenAI provides high-quality embedding models including text-embedding-3-small, text-embedding-3-large, and text-embedding-ada-002. These models offer excellent performance for document and query embeddings with automatic batching and rate limiting.

**Provider Class:** `OpenAIEmbedding`

**Config Class:** `OpenAIEmbeddingConfig`

## Dependencies

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

## Examples

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

# Create embedding provider
embedding = OpenAIEmbedding(OpenAIEmbeddingConfig(
    model_name="text-embedding-3-small",
    batch_size=100
))

# Setup KnowledgeBase
vectordb = ChromaProvider(ChromaConfig(
    collection_name="openai_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   |
| ---------------------- | ------------- | ------------------------------------------------------ | -------------------------- | -------- |
| `model_name`           | `str`         | OpenAI embedding model name                            | `"text-embedding-3-small"` | Specific |
| `api_key`              | `str \| None` | OpenAI API key (uses OPENAI\_API\_KEY env var if None) | `None`                     | Specific |
| `organization`         | `str \| None` | OpenAI organization ID                                 | `None`                     | Specific |
| `base_url`             | `str \| None` | Custom OpenAI API base URL                             | `None`                     | Specific |
| `enable_rate_limiting` | `bool`        | Enable intelligent rate limiting                       | `True`                     | Specific |
| `requests_per_minute`  | `int`         | Max requests per minute                                | `3000`                     | Specific |
| `tokens_per_minute`    | `int`         | Max tokens per minute                                  | `1000000`                  | Specific |
| `parallel_requests`    | `int`         | Number of parallel requests                            | `5`                        | Specific |
| `request_timeout`      | `float`       | Request timeout in seconds                             | `60.0`                     | 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     |
| `show_progress`        | `bool`        | Whether to show progress during batch operations       | `True`                     | Base     |