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

# Create Embedding

> Create an embedding vector representing the input text

## Method Signature

```python theme={null}
client.embeddings.create(
    input: Union[str, List[str], List[int], List[List[int]]],
    model: Union[str, EmbeddingModel],
    dimensions: Optional[int] = None,
    encoding_format: Literal["float", "base64"] = "base64",
    user: Optional[str] = None
) -> CreateEmbeddingResponse
```

## Parameters

<ParamField path="input" type="Union[str, List[str], List[int], List[List[int]]]" required>
  Input text to embed, encoded as a string or array of tokens. To embed multiple inputs in a single request, pass an array of strings or array of token arrays.

  **Constraints:**

  * Must not exceed the max input tokens for the model (8192 tokens for all embedding models)
  * Cannot be an empty string
  * Any array must be 2048 dimensions or less
  * Maximum of 300,000 tokens summed across all inputs in a single request
</ParamField>

<ParamField path="model" type="Union[str, EmbeddingModel]" required>
  ID of the model to use. You can use the [List models](/api/models/list) API to see all available models, or see the [Model overview](https://platform.openai.com/docs/models) for descriptions.

  **Popular models:**

  * `text-embedding-3-small`
  * `text-embedding-3-large`
  * `text-embedding-ada-002`
</ParamField>

<ParamField path="dimensions" type="int">
  The number of dimensions the resulting output embeddings should have. Only supported in `text-embedding-3` and later models.
</ParamField>

<ParamField path="encoding_format" type="Literal['float', 'base64']" default="base64">
  The format to return the embeddings in. Can be either `float` or `base64`.

  **Note:** The SDK automatically decodes base64 embeddings to float arrays for convenience.
</ParamField>

<ParamField path="user" type="str">
  A unique identifier representing your end-user, which can help OpenAI monitor and detect abuse. [Learn more](https://platform.openai.com/docs/guides/safety-best-practices#end-user-ids).
</ParamField>

## Response

Returns a `CreateEmbeddingResponse` object:

```python theme={null}
class CreateEmbeddingResponse(BaseModel):
    data: List[Embedding]  # The list of embeddings generated by the model
    model: str  # The name of the model used to generate the embedding
    object: Literal["list"]  # Always "list"
    usage: Usage  # Token usage information

class Embedding(BaseModel):
    embedding: List[float]  # The embedding vector
    index: int  # The index of the embedding in the list
    object: Literal["embedding"]  # Always "embedding"

class Usage(BaseModel):
    prompt_tokens: int  # The number of tokens used by the prompt
    total_tokens: int  # The total number of tokens used
```

## Examples

### Single Text Embedding

```python theme={null}
from openai import OpenAI

client = OpenAI()

response = client.embeddings.create(
    input="The quick brown fox jumps over the lazy dog",
    model="text-embedding-3-small"
)

print(response.data[0].embedding)
print(f"Tokens used: {response.usage.total_tokens}")
```

### Multiple Texts in One Request

```python theme={null}
response = client.embeddings.create(
    input=[
        "First document to embed",
        "Second document to embed",
        "Third document to embed"
    ],
    model="text-embedding-3-small"
)

for i, embedding_obj in enumerate(response.data):
    print(f"Embedding {i}: {len(embedding_obj.embedding)} dimensions")
```

### Using Custom Dimensions

```python theme={null}
response = client.embeddings.create(
    input="Text to embed",
    model="text-embedding-3-large",
    dimensions=256  # Reduce from default 3072 to 256
)

print(f"Embedding dimensions: {len(response.data[0].embedding)}")
```

### Embedding Tokens Directly

```python theme={null}
import tiktoken

encoding = tiktoken.get_encoding("cl100k_base")
tokens = encoding.encode("Text to embed")

response = client.embeddings.create(
    input=tokens,
    model="text-embedding-3-small"
)
```

## Async Usage

```python theme={null}
from openai import AsyncOpenAI

client = AsyncOpenAI()

response = await client.embeddings.create(
    input="Text to embed",
    model="text-embedding-3-small"
)
```

## Notes

* The Python SDK automatically optimizes embedding encoding by using base64 format by default and decoding it to floats
* If NumPy is installed, the SDK uses it for faster base64 decoding
* See the [Embeddings Guide](https://platform.openai.com/docs/guides/embeddings) for best practices and use cases
* Use [tiktoken](https://cookbook.openai.com/examples/how_to_count_tokens_with_tiktoken) to count tokens before sending requests