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

# embed_many

> Embed multiple text values efficiently with automatic batching and retry logic.

## Overview

`embed_many` is the primary embedding function for processing multiple text values efficiently. It provides automatic batching respecting the provider's `max_batch_size` limit, retry logic with exponential back-off, and unified return objects.

## Basic usage

```python embed_many.py theme={null}
from ai_sdk import embed_many, openai

model = openai.embedding("text-embedding-3-small")

sentences = [
    "The cat sat on the mat.",
    "A dog was lying on the rug.",
    "Paris is the capital of France.",
]

res = embed_many(model=model, values=sentences)
print(len(res.embeddings))  # 3
print(res.usage)  # Token usage statistics
```

## Parameters

| Name          | Type              | Required | Description                                             |
| ------------- | ----------------- | -------- | ------------------------------------------------------- |
| `model`       | `EmbeddingModel`  | ✓        | Provider instance created via e.g. `openai.embedding()` |
| `values`      | `List[str]`       | ✓        | List of texts to embed                                  |
| `max_retries` | `int`             | 3        | Maximum number of retry attempts                        |
| `**kwargs`    | provider-specific | –        | Forwarded verbatim to the underlying SDK                |

## Return value

`EmbedManyResult` exposes:

* `embeddings`: List of embedding vectors (list of lists of floats)
* `values`: The original input texts
* `usage`: Token usage statistics (if available)
* `provider_metadata`: Provider-specific metadata

## Examples

### Basic batch embedding

```python theme={null}
from ai_sdk import embed_many, openai

model = openai.embedding("text-embedding-3-small")

texts = [
    "Machine learning is a subset of artificial intelligence.",
    "Deep learning uses neural networks with multiple layers.",
    "Natural language processing helps computers understand text.",
    "Computer vision enables machines to interpret visual information."
]

result = embed_many(model=model, values=texts)
print(f"Generated {len(result.embeddings)} embeddings")
print(f"Each embedding has {len(result.embeddings[0])} dimensions")
```

### With custom retry settings

```python theme={null}
from ai_sdk import embed_many, openai

model = openai.embedding("text-embedding-3-small")

texts = [
    "The weather is sunny today.",
    "I love programming in Python.",
    "Data science involves statistics and machine learning."
]

result = embed_many(
    model=model,
    values=texts,
    max_retries=5  # More retries for reliability
)
print(f"Successfully embedded {len(result.embeddings)} texts")
```

### Large batch processing

```python theme={null}
from ai_sdk import embed_many, openai

model = openai.embedding("text-embedding-3-small")

# Large list of documents
documents = [
    f"Document {i}: This is sample content for document number {i}."
    for i in range(100)
]

result = embed_many(model=model, values=documents)
print(f"Processed {len(result.embeddings)} documents")
print(f"Total tokens used: {result.usage.total_tokens}")
```

### Error handling

```python theme={null}
from ai_sdk import embed_many, openai

model = openai.embedding("text-embedding-3-small")

texts = [
    "Valid text here.",
    "",  # Empty text might cause issues
    "Another valid text."
]

try:
    result = embed_many(model=model, values=texts)
    print(f"Successfully embedded {len(result.embeddings)} texts")
except Exception as e:
    print(f"Embedding failed: {e}")
```

### With custom parameters

```python theme={null}
from ai_sdk import embed_many, openai

model = openai.embedding("text-embedding-3-small")

texts = [
    "Technical documentation about APIs.",
    "User manual for software installation.",
    "Tutorial on web development."
]

result = embed_many(
    model=model,
    values=texts,
    encoding_format="float",  # OpenAI-specific parameter
    dimensions=1536  # Specify embedding dimensions
)
print(f"Embeddings: {len(result.embeddings)}")
```

## Custom providers

Implement the `EmbeddingModel` ABC to bring your own model:

```python theme={null}
from ai_sdk.providers.embedding_model import EmbeddingModel

class MyFastAPIBackend(EmbeddingModel):
    max_batch_size = 128

    def embed_many(self, values, **kwargs):
        # HTTP POST → return dict with "embeddings" key
        # Implementation here
        pass

# Now use it with embed_many
model = MyFastAPIBackend()
texts = ["Hello", "World", "Test"]
result = embed_many(model=model, values=texts)
print(f"Generated {len(result.embeddings)} embeddings")
```

## Performance considerations

* **Batching**: `embed_many` automatically batches requests based on the provider's `max_batch_size`
* **Retries**: Built-in exponential backoff retry logic for reliability
* **Memory**: For very large datasets, consider processing in chunks
* **Rate limits**: Respects provider rate limits automatically

***

<Tip>
  Use <code>cosine\_similarity(vec\_a, vec\_b)</code> for quick similarity checks between embeddings.
</Tip>

{" "}