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pydantic_ai.embeddings

EmbeddingModel

Bases: ABC

Abstract base class for embedding models.

Implement this class to create a custom embedding model. For most use cases, use one of the built-in implementations:

Source code in pydantic_ai_slim/pydantic_ai/embeddings/base.py 
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class EmbeddingModel(ABC):
    """Abstract base class for embedding models.

    Implement this class to create a custom embedding model. For most use cases,
    use one of the built-in implementations:

    - [`OpenAIEmbeddingModel`][pydantic_ai.embeddings.openai.OpenAIEmbeddingModel]
    - [`CohereEmbeddingModel`][pydantic_ai.embeddings.cohere.CohereEmbeddingModel]
    - [`GoogleEmbeddingModel`][pydantic_ai.embeddings.google.GoogleEmbeddingModel]
    - [`BedrockEmbeddingModel`][pydantic_ai.embeddings.bedrock.BedrockEmbeddingModel]
    - [`SentenceTransformerEmbeddingModel`][pydantic_ai.embeddings.sentence_transformers.SentenceTransformerEmbeddingModel]
    """

    _settings: EmbeddingSettings | None = None

    def __init__(
        self,
        *,
        settings: EmbeddingSettings | None = None,
    ) -> None:
        """Initialize the model with optional settings.

        Args:
            settings: Model-specific settings that will be used as defaults for this model.
        """
        self._settings = settings

    @property
    def settings(self) -> EmbeddingSettings | None:
        """Get the default settings for this model."""
        return self._settings

    @property
    def base_url(self) -> str | None:
        """The base URL for the provider API, if available."""
        return None

    @property
    @abstractmethod
    def model_name(self) -> str:
        """The name of the embedding model."""
        raise NotImplementedError()

    @property
    @abstractmethod
    def system(self) -> str:
        """The embedding model provider/system identifier (e.g., 'openai', 'cohere')."""
        raise NotImplementedError()

    @abstractmethod
    async def embed(
        self, inputs: str | Sequence[str], *, input_type: EmbedInputType, settings: EmbeddingSettings | None = None
    ) -> EmbeddingResult:
        """Generate embeddings for the given inputs.

        Args:
            inputs: A single string or sequence of strings to embed.
            input_type: Whether the inputs are queries or documents.
            settings: Optional settings to override the model's defaults.

        Returns:
            An [`EmbeddingResult`][pydantic_ai.embeddings.EmbeddingResult] containing
            the embeddings and metadata.
        """
        raise NotImplementedError

    def prepare_embed(
        self, inputs: str | Sequence[str], settings: EmbeddingSettings | None = None
    ) -> tuple[list[str], EmbeddingSettings]:
        """Prepare the inputs and settings for embedding.

        This method normalizes inputs to a list and merges settings.
        Subclasses should call this at the start of their `embed()` implementation.

        Args:
            inputs: A single string or sequence of strings.
            settings: Optional settings to merge with defaults.

        Returns:
            A tuple of (normalized inputs list, merged settings).
        """
        inputs = [inputs] if isinstance(inputs, str) else list(inputs)

        settings = merge_embedding_settings(self._settings, settings) or {}

        return inputs, settings

    async def max_input_tokens(self) -> int | None:
        """Get the maximum number of tokens that can be input to the model.

        Returns:
            The maximum token count, or `None` if unknown.
        """
        return None  # pragma: no cover

    async def count_tokens(self, text: str) -> int:
        """Count the number of tokens in the given text.

        Args:
            text: The text to tokenize and count.

        Returns:
            The number of tokens.

        Raises:
            NotImplementedError: If the model doesn't support token counting.
            UserError: If the model or tokenizer is not supported.
        """
        raise NotImplementedError

__init__ 

__init__(
    *, settings: EmbeddingSettings | None = None
) -> None

Initialize the model with optional settings.

Parameters:

Name Type Description Default
settings EmbeddingSettings | None

Model-specific settings that will be used as defaults for this model.

 None
Source code in pydantic_ai_slim/pydantic_ai/embeddings/base.py 
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def __init__(
    self,
    *,
    settings: EmbeddingSettings | None = None,
) -> None:
    """Initialize the model with optional settings.

    Args:
        settings: Model-specific settings that will be used as defaults for this model.
    """
    self._settings = settings

settings property 

settings: EmbeddingSettings | None

Get the default settings for this model.

base_url property 

base_url: str | None

The base URL for the provider API, if available.

model_name abstractmethod property 

model_name: str

The name of the embedding model.

system abstractmethod property 

system: str

The embedding model provider/system identifier (e.g., 'openai', 'cohere').

embed abstractmethod async 

embed(
    inputs: str | Sequence[str],
    *,
    input_type: EmbedInputType,
    settings: EmbeddingSettings | None = None
) -> EmbeddingResult

Generate embeddings for the given inputs.

Parameters:

Name Type Description Default
inputs str | Sequence[str]

A single string or sequence of strings to embed.

 required
input_type EmbedInputType

Whether the inputs are queries or documents.

 required
settings EmbeddingSettings | None

Optional settings to override the model's defaults.

 None

Returns:

Type Description
EmbeddingResult

An EmbeddingResult containing
EmbeddingResult

the embeddings and metadata.
Source code in pydantic_ai_slim/pydantic_ai/embeddings/base.py 
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@abstractmethod
async def embed(
    self, inputs: str | Sequence[str], *, input_type: EmbedInputType, settings: EmbeddingSettings | None = None
) -> EmbeddingResult:
    """Generate embeddings for the given inputs.

    Args:
        inputs: A single string or sequence of strings to embed.
        input_type: Whether the inputs are queries or documents.
        settings: Optional settings to override the model's defaults.

    Returns:
        An [`EmbeddingResult`][pydantic_ai.embeddings.EmbeddingResult] containing
        the embeddings and metadata.
    """
    raise NotImplementedError

prepare_embed 

prepare_embed(
    inputs: str | Sequence[str],
    settings: EmbeddingSettings | None = None,
) -> tuple[list[str], EmbeddingSettings]

Prepare the inputs and settings for embedding.

This method normalizes inputs to a list and merges settings. Subclasses should call this at the start of their embed() implementation.

Parameters:

Name Type Description Default
inputs str | Sequence[str]

A single string or sequence of strings.

 required
settings EmbeddingSettings | None

Optional settings to merge with defaults.

 None

Returns:

Type Description
tuple[list[str], EmbeddingSettings]

A tuple of (normalized inputs list, merged settings).
Source code in pydantic_ai_slim/pydantic_ai/embeddings/base.py 
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def prepare_embed(
    self, inputs: str | Sequence[str], settings: EmbeddingSettings | None = None
) -> tuple[list[str], EmbeddingSettings]:
    """Prepare the inputs and settings for embedding.

    This method normalizes inputs to a list and merges settings.
    Subclasses should call this at the start of their `embed()` implementation.

    Args:
        inputs: A single string or sequence of strings.
        settings: Optional settings to merge with defaults.

    Returns:
        A tuple of (normalized inputs list, merged settings).
    """
    inputs = [inputs] if isinstance(inputs, str) else list(inputs)

    settings = merge_embedding_settings(self._settings, settings) or {}

    return inputs, settings

max_input_tokens async 

max_input_tokens() -> int | None

Get the maximum number of tokens that can be input to the model.

Returns:

Type Description
int | None

The maximum token count, or None if unknown.
Source code in pydantic_ai_slim/pydantic_ai/embeddings/base.py 
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async def max_input_tokens(self) -> int | None:
    """Get the maximum number of tokens that can be input to the model.

    Returns:
        The maximum token count, or `None` if unknown.
    """
    return None  # pragma: no cover

count_tokens async 

count_tokens(text: str) -> int

Count the number of tokens in the given text.

Parameters:

Name Type Description Default
text str

The text to tokenize and count.

 required

Returns:

Type Description
int

The number of tokens.

Raises:

Type Description
NotImplementedError

If the model doesn't support token counting.
UserError

If the model or tokenizer is not supported.
Source code in pydantic_ai_slim/pydantic_ai/embeddings/base.py 
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async def count_tokens(self, text: str) -> int:
    """Count the number of tokens in the given text.

    Args:
        text: The text to tokenize and count.

    Returns:
        The number of tokens.

    Raises:
        NotImplementedError: If the model doesn't support token counting.
        UserError: If the model or tokenizer is not supported.
    """
    raise NotImplementedError

InstrumentedEmbeddingModel dataclass

Bases: WrapperEmbeddingModel

Embedding model which wraps another model so that requests are instrumented with OpenTelemetry.

See the Debugging and Monitoring guide for more info.

Source code in pydantic_ai_slim/pydantic_ai/embeddings/instrumented.py 
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@dataclass(init=False)
class InstrumentedEmbeddingModel(WrapperEmbeddingModel):
    """Embedding model which wraps another model so that requests are instrumented with OpenTelemetry.

    See the [Debugging and Monitoring guide](https://ai.pydantic.dev/logfire/) for more info.
    """

    instrumentation_settings: InstrumentationSettings
    """Instrumentation settings for this model."""

    def __init__(
        self,
        wrapped: EmbeddingModel | str,
        options: InstrumentationSettings | None = None,
    ) -> None:
        super().__init__(wrapped)
        self.instrumentation_settings = options or InstrumentationSettings()

    async def embed(
        self, inputs: str | Sequence[str], *, input_type: EmbedInputType, settings: EmbeddingSettings | None = None
    ) -> EmbeddingResult:
        inputs, settings = self.prepare_embed(inputs, settings)
        with self._instrument(inputs, input_type, settings) as finish:
            result = await super().embed(inputs, input_type=input_type, settings=settings)
            finish(result)
            return result

    @contextmanager
    def _instrument(
        self,
        inputs: list[str],
        input_type: EmbedInputType,
        settings: EmbeddingSettings | None,
    ) -> Iterator[Callable[[EmbeddingResult], None]]:
        operation = 'embeddings'
        span_name = f'{operation} {self.model_name}'

        inputs_count = len(inputs)

        attributes: dict[str, AttributeValue] = {
            'gen_ai.operation.name': operation,
            **self.model_attributes(self.wrapped),
            'input_type': input_type,
            'inputs_count': inputs_count,
        }

        if settings:
            attributes['embedding_settings'] = json.dumps(self.serialize_any(settings))

        if self.instrumentation_settings.include_content:
            attributes['inputs'] = json.dumps(inputs)

        attributes['logfire.json_schema'] = json.dumps(
            {
                'type': 'object',
                'properties': {
                    'input_type': {'type': 'string'},
                    'inputs_count': {'type': 'integer'},
                    'embedding_settings': {'type': 'object'},
                    **(
                        {'inputs': {'type': ['array']}, 'embeddings': {'type': 'array'}}
                        if self.instrumentation_settings.include_content
                        else {}
                    ),
                },
            }
        )

        record_metrics: Callable[[], None] | None = None
        try:
            with self.instrumentation_settings.tracer.start_as_current_span(span_name, attributes=attributes) as span:

                def finish(result: EmbeddingResult):
                    # Prepare metric recording closure first so metrics are recorded
                    # even if the span is not recording.
                    provider_name = attributes[GEN_AI_PROVIDER_NAME_ATTRIBUTE]
                    request_model = attributes[GEN_AI_REQUEST_MODEL_ATTRIBUTE]
                    response_model = result.model_name or request_model
                    price_calculation = None

                    def _record_metrics():
                        token_attributes = {
                            GEN_AI_PROVIDER_NAME_ATTRIBUTE: provider_name,
                            'gen_ai.operation.name': operation,
                            GEN_AI_REQUEST_MODEL_ATTRIBUTE: request_model,
                            'gen_ai.response.model': response_model,
                            'gen_ai.token.type': 'input',
                        }
                        tokens = result.usage.input_tokens or 0
                        if tokens:  # pragma: no branch
                            self.instrumentation_settings.tokens_histogram.record(tokens, token_attributes)
                            if price_calculation is not None:
                                self.instrumentation_settings.cost_histogram.record(
                                    float(getattr(price_calculation, 'input_price', 0.0)),
                                    token_attributes,
                                )

                    nonlocal record_metrics
                    record_metrics = _record_metrics

                    if not span.is_recording():
                        return

                    attributes_to_set: dict[str, AttributeValue] = {
                        **result.usage.opentelemetry_attributes(),
                        'gen_ai.response.model': response_model,
                    }

                    try:
                        price_calculation = result.cost()
                    except LookupError:
                        # The cost of this provider/model is unknown, which is common.
                        pass
                    except Exception as e:  # pragma: no cover
                        warnings.warn(
                            f'Failed to get cost from response: {type(e).__name__}: {e}', CostCalculationFailedWarning
                        )
                    else:
                        attributes_to_set['operation.cost'] = float(price_calculation.total_price)

                    embeddings = result.embeddings
                    if embeddings:  # pragma: no branch
                        attributes_to_set['gen_ai.embeddings.dimension.count'] = len(embeddings[0])
                        if self.instrumentation_settings.include_content:
                            attributes['embeddings'] = json.dumps(embeddings)

                    if result.provider_response_id is not None:
                        attributes_to_set['gen_ai.response.id'] = result.provider_response_id

                    span.set_attributes(attributes_to_set)

                yield finish
        finally:
            if record_metrics:  # pragma: no branch
                # Record metrics after the span finishes to avoid duplication.
                record_metrics()

    @staticmethod
    def model_attributes(model: EmbeddingModel) -> dict[str, AttributeValue]:
        attributes: dict[str, AttributeValue] = {
            GEN_AI_PROVIDER_NAME_ATTRIBUTE: model.system,
            GEN_AI_REQUEST_MODEL_ATTRIBUTE: model.model_name,
        }
        if base_url := model.base_url:
            try:
                parsed = urlparse(base_url)
            except Exception:  # pragma: no cover
                pass
            else:
                if parsed.hostname:  # pragma: no branch
                    attributes['server.address'] = parsed.hostname
                if parsed.port:
                    attributes['server.port'] = parsed.port  # pragma: no cover

        return attributes

    @staticmethod
    def serialize_any(value: Any) -> str:
        try:
            return ANY_ADAPTER.dump_python(value, mode='json')
        except Exception:  # pragma: no cover
            try:
                return str(value)
            except Exception as e:
                return f'Unable to serialize: {e}'

instrumentation_settings instance-attribute 

instrumentation_settings: InstrumentationSettings = (
    options or InstrumentationSettings()
)

Instrumentation settings for this model.

instrument_embedding_model 

instrument_embedding_model(
    model: EmbeddingModel,
    instrument: InstrumentationSettings | bool,
) -> EmbeddingModel

Instrument an embedding model with OpenTelemetry/logfire.

Source code in pydantic_ai_slim/pydantic_ai/embeddings/instrumented.py 
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def instrument_embedding_model(model: EmbeddingModel, instrument: InstrumentationSettings | bool) -> EmbeddingModel:
    """Instrument an embedding model with OpenTelemetry/logfire."""
    if instrument and not isinstance(model, InstrumentedEmbeddingModel):
        if instrument is True:
            instrument = InstrumentationSettings()

        model = InstrumentedEmbeddingModel(model, instrument)

    return model

EmbeddingResult dataclass

The result of an embedding operation.

This class contains the generated embeddings along with metadata about the operation, including the original inputs, model information, usage statistics, and timing.

Example: 

from pydantic_ai import Embedder

embedder = Embedder('openai:text-embedding-3-small')


async def main():
    result = await embedder.embed_query('What is AI?')

    # Access embeddings by index
    print(len(result.embeddings[0]))
    #> 1536

    # Access embeddings by original input text
    print(result['What is AI?'] == result.embeddings[0])
    #> True

    # Check usage
    print(f'Tokens used: {result.usage.input_tokens}')
    #> Tokens used: 3

Source code in pydantic_ai_slim/pydantic_ai/embeddings/result.py 
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@dataclass
class EmbeddingResult:
    """The result of an embedding operation.

    This class contains the generated embeddings along with metadata about
    the operation, including the original inputs, model information, usage
    statistics, and timing.

    Example:
    ```python
    from pydantic_ai import Embedder

    embedder = Embedder('openai:text-embedding-3-small')


    async def main():
        result = await embedder.embed_query('What is AI?')

        # Access embeddings by index
        print(len(result.embeddings[0]))
        #> 1536

        # Access embeddings by original input text
        print(result['What is AI?'] == result.embeddings[0])
        #> True

        # Check usage
        print(f'Tokens used: {result.usage.input_tokens}')
        #> Tokens used: 3
    ```
    """

    embeddings: Sequence[Sequence[float]]
    """The computed embedding vectors, one per input text.

    Each embedding is a sequence of floats representing the text in vector space.
    """

    _: KW_ONLY

    inputs: Sequence[str]
    """The original input texts that were embedded."""

    input_type: EmbedInputType
    """Whether the inputs were embedded as queries or documents."""

    model_name: str
    """The name of the model that generated these embeddings."""

    provider_name: str
    """The name of the provider (e.g., 'openai', 'cohere')."""

    timestamp: datetime = field(default_factory=_now_utc)
    """When the embedding request was made."""

    usage: RequestUsage = field(default_factory=RequestUsage)
    """Token usage statistics for this request."""

    provider_details: dict[str, Any] | None = None
    """Provider-specific details from the response."""

    provider_response_id: str | None = None
    """Unique identifier for this response from the provider, if available."""

    def __getitem__(self, item: int | str) -> Sequence[float]:
        """Get the embedding for an input by index or by the original input text.

        Args:
            item: Either an integer index or the original input string.

        Returns:
            The embedding vector for the specified input.

        Raises:
            IndexError: If the index is out of range.
            ValueError: If the string is not found in the inputs.
        """
        if isinstance(item, str):
            item = self.inputs.index(item)

        return self.embeddings[item]

    def cost(self) -> genai_types.PriceCalculation:
        """Calculate the cost of the embedding request.

        Uses [`genai-prices`](https://github.com/pydantic/genai-prices) for pricing data.

        Returns:
            A price calculation object with `total_price`, `input_price`, and other cost details.

        Raises:
            LookupError: If pricing data is not available for this model/provider.
        """
        assert self.model_name, 'Model name is required to calculate price'
        return calc_price(
            self.usage,
            self.model_name,
            provider_id=self.provider_name,
            genai_request_timestamp=self.timestamp,
        )

embeddings instance-attribute 

embeddings: Sequence[Sequence[float]]

The computed embedding vectors, one per input text.

Each embedding is a sequence of floats representing the text in vector space.

inputs instance-attribute 

inputs: Sequence[str]

The original input texts that were embedded.

input_type instance-attribute 

input_type: EmbedInputType

Whether the inputs were embedded as queries or documents.

model_name instance-attribute 

model_name: str

The name of the model that generated these embeddings.

provider_name instance-attribute 

provider_name: str

The name of the provider (e.g., 'openai', 'cohere').

timestamp class-attribute instance-attribute 

timestamp: datetime = field(default_factory=now_utc)

When the embedding request was made.

usage class-attribute instance-attribute 

usage: RequestUsage = field(default_factory=RequestUsage)

Token usage statistics for this request.

provider_details class-attribute instance-attribute 

provider_details: dict[str, Any] | None = None

Provider-specific details from the response.

provider_response_id class-attribute instance-attribute 

provider_response_id: str | None = None

Unique identifier for this response from the provider, if available.

__getitem__ 

__getitem__(item: int | str) -> Sequence[float]

Get the embedding for an input by index or by the original input text.

Parameters:

Name Type Description Default
item int | str

Either an integer index or the original input string.

 required

Returns:

Type Description
Sequence[float]

The embedding vector for the specified input.

Raises:

Type Description
IndexError

If the index is out of range.
ValueError

If the string is not found in the inputs.
Source code in pydantic_ai_slim/pydantic_ai/embeddings/result.py 
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def __getitem__(self, item: int | str) -> Sequence[float]:
    """Get the embedding for an input by index or by the original input text.

    Args:
        item: Either an integer index or the original input string.

    Returns:
        The embedding vector for the specified input.

    Raises:
        IndexError: If the index is out of range.
        ValueError: If the string is not found in the inputs.
    """
    if isinstance(item, str):
        item = self.inputs.index(item)

    return self.embeddings[item]

cost 

cost() -> PriceCalculation

Calculate the cost of the embedding request.

Uses genai-prices for pricing data.

Returns:

Type Description
PriceCalculation

A price calculation object with total_price, input_price, and other cost details.

Raises:

Type Description
LookupError

If pricing data is not available for this model/provider.
Source code in pydantic_ai_slim/pydantic_ai/embeddings/result.py 
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def cost(self) -> genai_types.PriceCalculation:
    """Calculate the cost of the embedding request.

    Uses [`genai-prices`](https://github.com/pydantic/genai-prices) for pricing data.

    Returns:
        A price calculation object with `total_price`, `input_price`, and other cost details.

    Raises:
        LookupError: If pricing data is not available for this model/provider.
    """
    assert self.model_name, 'Model name is required to calculate price'
    return calc_price(
        self.usage,
        self.model_name,
        provider_id=self.provider_name,
        genai_request_timestamp=self.timestamp,
    )

EmbeddingSettings

Bases: TypedDict

Common settings for configuring embedding models.

These settings apply across multiple embedding model providers. Not all settings are supported by all models - check the specific model's documentation for details.

Provider-specific settings classes (e.g., OpenAIEmbeddingSettings, CohereEmbeddingSettings) extend this with additional provider-prefixed options.

Source code in pydantic_ai_slim/pydantic_ai/embeddings/settings.py 
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class EmbeddingSettings(TypedDict, total=False):
    """Common settings for configuring embedding models.

    These settings apply across multiple embedding model providers.
    Not all settings are supported by all models - check the specific
    model's documentation for details.

    Provider-specific settings classes (e.g.,
    [`OpenAIEmbeddingSettings`][pydantic_ai.embeddings.openai.OpenAIEmbeddingSettings],
    [`CohereEmbeddingSettings`][pydantic_ai.embeddings.cohere.CohereEmbeddingSettings])
    extend this with additional provider-prefixed options.
    """

    dimensions: int
    """The number of dimensions for the output embeddings.

    Supported by:

    * OpenAI
    * Cohere
    * Google
    * Sentence Transformers
    * Bedrock
    * VoyageAI
    """

    truncate: bool
    """Whether to truncate inputs that exceed the model's context length.

    Defaults to `False`. If `True`, inputs that are too long will be truncated.
    If `False`, an error will be raised for inputs that exceed the context length.

    For more control over truncation, you can use
    [`max_input_tokens()`][pydantic_ai.embeddings.Embedder.max_input_tokens] and
    [`count_tokens()`][pydantic_ai.embeddings.Embedder.count_tokens] to implement
    your own truncation logic.

    Provider-specific truncation settings (e.g., `cohere_truncate`, `bedrock_cohere_truncate`)
    take precedence if specified.

    Supported by:

    * Cohere
    * Bedrock (Cohere and Nova models)
    * VoyageAI
    """

    extra_headers: dict[str, str]
    """Extra headers to send to the model.

    Supported by:

    * OpenAI
    * Cohere
    """

    extra_body: object
    """Extra body to send to the model.

    Supported by:

    * OpenAI
    * Cohere
    """

dimensions instance-attribute 

dimensions: int

The number of dimensions for the output embeddings.

Supported by:

  • OpenAI
  • Cohere
  • Google
  • Sentence Transformers
  • Bedrock
  • VoyageAI

truncate instance-attribute 

truncate: bool

Whether to truncate inputs that exceed the model's context length.

Defaults to False. If True, inputs that are too long will be truncated. If False, an error will be raised for inputs that exceed the context length.

For more control over truncation, you can use max_input_tokens() and count_tokens() to implement your own truncation logic.

Provider-specific truncation settings (e.g., cohere_truncate, bedrock_cohere_truncate) take precedence if specified.

Supported by:

  • Cohere
  • Bedrock (Cohere and Nova models)
  • VoyageAI

extra_headers instance-attribute 

extra_headers: dict[str, str]

Extra headers to send to the model.

Supported by:

  • OpenAI
  • Cohere

extra_body instance-attribute 

extra_body: object

Extra body to send to the model.

Supported by:

  • OpenAI
  • Cohere

merge_embedding_settings 

merge_embedding_settings(
    base: EmbeddingSettings | None,
    overrides: EmbeddingSettings | None,
) -> EmbeddingSettings | None

Merge two sets of embedding settings, with overrides taking precedence.

Parameters:

Name Type Description Default
base EmbeddingSettings | None

Base settings (typically from the embedder or model).

 required
overrides EmbeddingSettings | None

Settings that should override the base (typically per-call settings).

 required

Returns:

Type Description
EmbeddingSettings | None

Merged settings, or None if both inputs are None.
Source code in pydantic_ai_slim/pydantic_ai/embeddings/settings.py 
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def merge_embedding_settings(
    base: EmbeddingSettings | None, overrides: EmbeddingSettings | None
) -> EmbeddingSettings | None:
    """Merge two sets of embedding settings, with overrides taking precedence.

    Args:
        base: Base settings (typically from the embedder or model).
        overrides: Settings that should override the base (typically per-call settings).

    Returns:
        Merged settings, or `None` if both inputs are `None`.
    """
    # Note: we may want merge recursively if/when we add non-primitive values
    if base and overrides:
        return base | overrides
    else:
        return base or overrides

TestEmbeddingModel dataclass

Bases: EmbeddingModel

A mock embedding model for testing.

This model returns deterministic embeddings (all 1.0 values) and tracks the settings used in the last call via the last_settings attribute.

Example: 

from pydantic_ai import Embedder
from pydantic_ai.embeddings import TestEmbeddingModel

test_model = TestEmbeddingModel()
embedder = Embedder('openai:text-embedding-3-small')


async def main():
    with embedder.override(model=test_model):
        await embedder.embed_query('test')
        assert test_model.last_settings is not None

Source code in pydantic_ai_slim/pydantic_ai/embeddings/test.py 
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@dataclass(init=False)
class TestEmbeddingModel(EmbeddingModel):
    """A mock embedding model for testing.

    This model returns deterministic embeddings (all 1.0 values) and tracks
    the settings used in the last call via the `last_settings` attribute.

    Example:
    ```python
    from pydantic_ai import Embedder
    from pydantic_ai.embeddings import TestEmbeddingModel

    test_model = TestEmbeddingModel()
    embedder = Embedder('openai:text-embedding-3-small')


    async def main():
        with embedder.override(model=test_model):
            await embedder.embed_query('test')
            assert test_model.last_settings is not None
    ```
    """

    # NOTE: Avoid test discovery by pytest.
    __test__ = False

    _model_name: str
    """The model name to report in results."""

    _provider_name: str
    """The provider name to report in results."""

    _dimensions: int
    """The number of dimensions for generated embeddings."""

    last_settings: EmbeddingSettings | None = None
    """The settings used in the most recent embed call."""

    def __init__(
        self,
        model_name: str = 'test',
        *,
        provider_name: str = 'test',
        dimensions: int = 8,
        settings: EmbeddingSettings | None = None,
    ):
        """Initialize the test embedding model.

        Args:
            model_name: The model name to report in results.
            provider_name: The provider name to report in results.
            dimensions: The number of dimensions for the generated embeddings.
            settings: Optional default settings for the model.
        """
        self._model_name = model_name
        self._provider_name = provider_name
        self._dimensions = dimensions
        self.last_settings = None
        super().__init__(settings=settings)

    @property
    def model_name(self) -> str:
        """The embedding model name."""
        return self._model_name

    @property
    def system(self) -> str:
        """The embedding model provider."""
        return self._provider_name

    async def embed(
        self, inputs: str | Sequence[str], *, input_type: EmbedInputType, settings: EmbeddingSettings | None = None
    ) -> EmbeddingResult:
        inputs, settings = self.prepare_embed(inputs, settings)
        self.last_settings = settings

        dimensions = settings.get('dimensions') or self._dimensions

        return EmbeddingResult(
            embeddings=[[1.0] * dimensions] * len(inputs),
            inputs=inputs,
            input_type=input_type,
            usage=RequestUsage(input_tokens=sum(_estimate_tokens(text) for text in inputs)),
            model_name=self.model_name,
            provider_name=self.system,
            provider_response_id=str(uuid.uuid4()),
        )

    async def max_input_tokens(self) -> int | None:
        return 1024

    async def count_tokens(self, text: str) -> int:
        return _estimate_tokens(text)

last_settings class-attribute instance-attribute 

last_settings: EmbeddingSettings | None = None

The settings used in the most recent embed call.

__init__ 

__init__(
    model_name: str = "test",
    *,
    provider_name: str = "test",
    dimensions: int = 8,
    settings: EmbeddingSettings | None = None
)

Initialize the test embedding model.

Parameters:

Name Type Description Default
model_name str

The model name to report in results.

 'test'
provider_name str

The provider name to report in results.

 'test'
dimensions int

The number of dimensions for the generated embeddings.

 8
settings EmbeddingSettings | None

Optional default settings for the model.

 None
Source code in pydantic_ai_slim/pydantic_ai/embeddings/test.py 
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def __init__(
    self,
    model_name: str = 'test',
    *,
    provider_name: str = 'test',
    dimensions: int = 8,
    settings: EmbeddingSettings | None = None,
):
    """Initialize the test embedding model.

    Args:
        model_name: The model name to report in results.
        provider_name: The provider name to report in results.
        dimensions: The number of dimensions for the generated embeddings.
        settings: Optional default settings for the model.
    """
    self._model_name = model_name
    self._provider_name = provider_name
    self._dimensions = dimensions
    self.last_settings = None
    super().__init__(settings=settings)

model_name property 

model_name: str

The embedding model name.

system property 

system: str

The embedding model provider.

WrapperEmbeddingModel dataclass

Bases: EmbeddingModel

Base class for embedding models that wrap another model.

Use this as a base class to create custom embedding model wrappers that modify behavior (e.g., caching, logging, rate limiting) while delegating to an underlying model.

By default, all methods are passed through to the wrapped model. Override specific methods to customize behavior.

Source code in pydantic_ai_slim/pydantic_ai/embeddings/wrapper.py 
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@dataclass(init=False)
class WrapperEmbeddingModel(EmbeddingModel):
    """Base class for embedding models that wrap another model.

    Use this as a base class to create custom embedding model wrappers
    that modify behavior (e.g., caching, logging, rate limiting) while
    delegating to an underlying model.

    By default, all methods are passed through to the wrapped model.
    Override specific methods to customize behavior.
    """

    wrapped: EmbeddingModel
    """The underlying embedding model being wrapped."""

    def __init__(self, wrapped: EmbeddingModel | str):
        """Initialize the wrapper with an embedding model.

        Args:
            wrapped: The model to wrap. Can be an
                [`EmbeddingModel`][pydantic_ai.embeddings.EmbeddingModel] instance
                or a model name string (e.g., `'openai:text-embedding-3-small'`).
        """
        from . import infer_embedding_model

        super().__init__()
        self.wrapped = infer_embedding_model(wrapped) if isinstance(wrapped, str) else wrapped

    async def embed(
        self, inputs: str | Sequence[str], *, input_type: EmbedInputType, settings: EmbeddingSettings | None = None
    ) -> EmbeddingResult:
        return await self.wrapped.embed(inputs, input_type=input_type, settings=settings)

    async def max_input_tokens(self) -> int | None:
        return await self.wrapped.max_input_tokens()

    async def count_tokens(self, text: str) -> int:
        return await self.wrapped.count_tokens(text)

    @property
    def model_name(self) -> str:
        return self.wrapped.model_name

    @property
    def system(self) -> str:
        return self.wrapped.system

    @property
    def settings(self) -> EmbeddingSettings | None:
        """Get the settings from the wrapped embedding model."""
        return self.wrapped.settings

    @property
    def base_url(self) -> str | None:
        return self.wrapped.base_url

    def __getattr__(self, item: str):
        return getattr(self.wrapped, item)  # pragma: no cover

wrapped instance-attribute 

wrapped: EmbeddingModel = (
    infer_embedding_model(wrapped)
    if isinstance(wrapped, str)
    else wrapped
)

The underlying embedding model being wrapped.

__init__ 

__init__(wrapped: EmbeddingModel | str)

Initialize the wrapper with an embedding model.

Parameters:

Name Type Description Default
wrapped EmbeddingModel | str

The model to wrap. Can be an EmbeddingModel instance or a model name string (e.g., 'openai:text-embedding-3-small').

 required
Source code in pydantic_ai_slim/pydantic_ai/embeddings/wrapper.py 
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def __init__(self, wrapped: EmbeddingModel | str):
    """Initialize the wrapper with an embedding model.

    Args:
        wrapped: The model to wrap. Can be an
            [`EmbeddingModel`][pydantic_ai.embeddings.EmbeddingModel] instance
            or a model name string (e.g., `'openai:text-embedding-3-small'`).
    """
    from . import infer_embedding_model

    super().__init__()
    self.wrapped = infer_embedding_model(wrapped) if isinstance(wrapped, str) else wrapped

settings property 

settings: EmbeddingSettings | None

Get the settings from the wrapped embedding model.

KnownEmbeddingModelName module-attribute 

KnownEmbeddingModelName = TypeAliasType(
    "KnownEmbeddingModelName",
    Literal[
        "google-gla:gemini-embedding-001",
        "google-vertex:gemini-embedding-001",
        "google-vertex:text-embedding-005",
        "google-vertex:text-multilingual-embedding-002",
        "openai:text-embedding-ada-002",
        "openai:text-embedding-3-small",
        "openai:text-embedding-3-large",
        "cohere:embed-v4.0",
        "cohere:embed-english-v3.0",
        "cohere:embed-english-light-v3.0",
        "cohere:embed-multilingual-v3.0",
        "cohere:embed-multilingual-light-v3.0",
        "voyageai:voyage-4-large",
        "voyageai:voyage-4",
        "voyageai:voyage-4-lite",
        "voyageai:voyage-3-large",
        "voyageai:voyage-3.5",
        "voyageai:voyage-3.5-lite",
        "voyageai:voyage-code-3",
        "voyageai:voyage-finance-2",
        "voyageai:voyage-law-2",
        "voyageai:voyage-code-2",
        "bedrock:amazon.titan-embed-text-v1",
        "bedrock:amazon.titan-embed-text-v2:0",
        "bedrock:cohere.embed-english-v3",
        "bedrock:cohere.embed-multilingual-v3",
        "bedrock:cohere.embed-v4:0",
        "bedrock:amazon.nova-2-multimodal-embeddings-v1:0",
    ],
)

Known model names that can be used with the model parameter of Embedder.

KnownEmbeddingModelName is provided as a concise way to specify an embedding model.

infer_embedding_model 

infer_embedding_model(
    model: EmbeddingModel | KnownEmbeddingModelName | str,
    *,
    provider_factory: Callable[
        [str], Provider[Any]
    ] = infer_provider
) -> EmbeddingModel

Infer the model from the name.

Source code in pydantic_ai_slim/pydantic_ai/embeddings/__init__.py 
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def infer_embedding_model(
    model: EmbeddingModel | KnownEmbeddingModelName | str,
    *,
    provider_factory: Callable[[str], Provider[Any]] = infer_provider,
) -> EmbeddingModel:
    """Infer the model from the name."""
    if isinstance(model, EmbeddingModel):
        return model

    try:
        provider_name, model_name = model.split(':', maxsplit=1)
    except ValueError as e:
        raise ValueError('You must provide a provider prefix when specifying an embedding model name') from e

    provider = provider_factory(provider_name)

    model_kind = provider_name
    if model_kind.startswith('gateway/'):
        from ..providers.gateway import normalize_gateway_provider

        model_kind = normalize_gateway_provider(model_kind)

    if model_kind in (
        'openai',
        # For now, we assume that every chat and completions-compatible provider also
        # supports the embeddings endpoint, as at worst the user would get an `ModelHTTPError`.
        *get_args(OpenAIChatCompatibleProvider.__value__),
        *get_args(OpenAIResponsesCompatibleProvider.__value__),
    ):
        from .openai import OpenAIEmbeddingModel

        return OpenAIEmbeddingModel(model_name, provider=provider)
    elif model_kind == 'cohere':
        from .cohere import CohereEmbeddingModel

        return CohereEmbeddingModel(model_name, provider=provider)
    elif model_kind == 'bedrock':
        from .bedrock import BedrockEmbeddingModel

        return BedrockEmbeddingModel(model_name, provider=provider)
    elif model_kind in ('google-gla', 'google-vertex'):
        from .google import GoogleEmbeddingModel

        return GoogleEmbeddingModel(model_name, provider=provider)
    elif model_kind == 'sentence-transformers':
        from .sentence_transformers import SentenceTransformerEmbeddingModel

        return SentenceTransformerEmbeddingModel(model_name)
    elif model_kind == 'voyageai':
        from .voyageai import VoyageAIEmbeddingModel

        return VoyageAIEmbeddingModel(model_name, provider=provider)
    else:
        raise UserError(f'Unknown embeddings model: {model}')  # pragma: no cover

Embedder dataclass

High-level interface for generating text embeddings.

The Embedder class provides a convenient way to generate vector embeddings from text using various embedding model providers. It handles model inference, settings management, and optional OpenTelemetry instrumentation.

Example: 

from pydantic_ai import Embedder

embedder = Embedder('openai:text-embedding-3-small')


async def main():
    result = await embedder.embed_query('What is machine learning?')
    print(result.embeddings[0][:5])  # First 5 dimensions
    #> [1.0, 1.0, 1.0, 1.0, 1.0]

Source code in pydantic_ai_slim/pydantic_ai/embeddings/__init__.py 
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@dataclass(init=False)
class Embedder:
    """High-level interface for generating text embeddings.

    The `Embedder` class provides a convenient way to generate vector embeddings from text
    using various embedding model providers. It handles model inference, settings management,
    and optional OpenTelemetry instrumentation.

    Example:
    ```python
    from pydantic_ai import Embedder

    embedder = Embedder('openai:text-embedding-3-small')


    async def main():
        result = await embedder.embed_query('What is machine learning?')
        print(result.embeddings[0][:5])  # First 5 dimensions
        #> [1.0, 1.0, 1.0, 1.0, 1.0]
    ```
    """

    instrument: InstrumentationSettings | bool | None
    """Options to automatically instrument with OpenTelemetry.

    Set to `True` to use default instrumentation settings, which will use Logfire if it's configured.
    Set to an instance of [`InstrumentationSettings`][pydantic_ai.models.instrumented.InstrumentationSettings] to customize.
    If this isn't set, then the last value set by
    [`Embedder.instrument_all()`][pydantic_ai.embeddings.Embedder.instrument_all]
    will be used, which defaults to False.
    See the [Debugging and Monitoring guide](https://ai.pydantic.dev/logfire/) for more info.
    """

    _instrument_default: ClassVar[InstrumentationSettings | bool] = False

    def __init__(
        self,
        model: EmbeddingModel | KnownEmbeddingModelName | str,
        *,
        settings: EmbeddingSettings | None = None,
        defer_model_check: bool = True,
        instrument: InstrumentationSettings | bool | None = None,
    ) -> None:
        """Initialize an Embedder.

        Args:
            model: The embedding model to use. Can be specified as:

                - A model name string in the format `'provider:model-name'`
                  (e.g., `'openai:text-embedding-3-small'`)
                - An [`EmbeddingModel`][pydantic_ai.embeddings.EmbeddingModel] instance
            settings: Optional [`EmbeddingSettings`][pydantic_ai.embeddings.EmbeddingSettings]
                to use as defaults for all embed calls.
            defer_model_check: Whether to defer model validation until first use.
                Set to `False` to validate the model immediately on construction.
            instrument: OpenTelemetry instrumentation settings. Set to `True` to enable with defaults,
                or pass an [`InstrumentationSettings`][pydantic_ai.models.instrumented.InstrumentationSettings]
                instance to customize. If `None`, uses the value from
                [`Embedder.instrument_all()`][pydantic_ai.embeddings.Embedder.instrument_all].
        """
        self._model = model if defer_model_check else infer_embedding_model(model)
        self._settings = settings
        self.instrument = instrument

        self._override_model: ContextVar[EmbeddingModel | None] = ContextVar('_override_model', default=None)

    @staticmethod
    def instrument_all(instrument: InstrumentationSettings | bool = True) -> None:
        """Set the default instrumentation options for all embedders where `instrument` is not explicitly set.

        This is useful for enabling instrumentation globally without modifying each embedder individually.

        Args:
            instrument: Instrumentation settings to use as the default. Set to `True` for default settings,
                `False` to disable, or pass an
                [`InstrumentationSettings`][pydantic_ai.models.instrumented.InstrumentationSettings]
                instance to customize.
        """
        Embedder._instrument_default = instrument

    @property
    def model(self) -> EmbeddingModel | KnownEmbeddingModelName | str:
        """The embedding model used by this embedder."""
        return self._model

    @contextmanager
    def override(
        self,
        *,
        model: EmbeddingModel | KnownEmbeddingModelName | str | _utils.Unset = _utils.UNSET,
    ) -> Iterator[None]:
        """Context manager to temporarily override the embedding model.

        Useful for testing or dynamically switching models.

        Args:
            model: The embedding model to use within this context.

        Example:
        ```python
        from pydantic_ai import Embedder

        embedder = Embedder('openai:text-embedding-3-small')


        async def main():
            # Temporarily use a different model
            with embedder.override(model='openai:text-embedding-3-large'):
                result = await embedder.embed_query('test')
                print(len(result.embeddings[0]))  # 3072 dimensions for large model
                #> 3072
        ```
        """
        if _utils.is_set(model):
            model_token = self._override_model.set(infer_embedding_model(model))
        else:
            model_token = None

        try:
            yield
        finally:
            if model_token is not None:
                self._override_model.reset(model_token)

    async def embed_query(
        self, query: str | Sequence[str], *, settings: EmbeddingSettings | None = None
    ) -> EmbeddingResult:
        """Embed one or more query texts.

        Use this method when embedding search queries that will be compared against document embeddings.
        Some models optimize embeddings differently based on whether the input is a query or document.

        Args:
            query: A single query string or sequence of query strings to embed.
            settings: Optional settings to override the embedder's default settings for this call.

        Returns:
            An [`EmbeddingResult`][pydantic_ai.embeddings.EmbeddingResult] containing the embeddings
            and metadata about the operation.
        """
        return await self.embed(query, input_type='query', settings=settings)

    async def embed_documents(
        self, documents: str | Sequence[str], *, settings: EmbeddingSettings | None = None
    ) -> EmbeddingResult:
        """Embed one or more document texts.

        Use this method when embedding documents that will be stored and later searched against.
        Some models optimize embeddings differently based on whether the input is a query or document.

        Args:
            documents: A single document string or sequence of document strings to embed.
            settings: Optional settings to override the embedder's default settings for this call.

        Returns:
            An [`EmbeddingResult`][pydantic_ai.embeddings.EmbeddingResult] containing the embeddings
            and metadata about the operation.
        """
        return await self.embed(documents, input_type='document', settings=settings)

    async def embed(
        self, inputs: str | Sequence[str], *, input_type: EmbedInputType, settings: EmbeddingSettings | None = None
    ) -> EmbeddingResult:
        """Embed text inputs with explicit input type specification.

        This is the low-level embedding method. For most use cases, prefer
        [`embed_query()`][pydantic_ai.embeddings.Embedder.embed_query] or
        [`embed_documents()`][pydantic_ai.embeddings.Embedder.embed_documents].

        Args:
            inputs: A single string or sequence of strings to embed.
            input_type: The type of input, either `'query'` or `'document'`.
            settings: Optional settings to override the embedder's default settings for this call.

        Returns:
            An [`EmbeddingResult`][pydantic_ai.embeddings.EmbeddingResult] containing the embeddings
            and metadata about the operation.
        """
        model = self._get_model()
        settings = merge_embedding_settings(self._settings, settings)
        return await model.embed(inputs, input_type=input_type, settings=settings)

    async def max_input_tokens(self) -> int | None:
        """Get the maximum number of tokens the model can accept as input.

        Returns:
            The maximum token count, or `None` if the limit is unknown for this model.
        """
        model = self._get_model()
        return await model.max_input_tokens()

    async def count_tokens(self, text: str) -> int:
        """Count the number of tokens in the given text.

        Args:
            text: The text to tokenize and count.

        Returns:
            The number of tokens in the text.

        Raises:
            NotImplementedError: If the model doesn't support token counting.
            UserError: If the model or tokenizer is not supported.
        """
        model = self._get_model()
        return await model.count_tokens(text)

    def embed_query_sync(
        self, query: str | Sequence[str], *, settings: EmbeddingSettings | None = None
    ) -> EmbeddingResult:
        """Synchronous version of [`embed_query()`][pydantic_ai.embeddings.Embedder.embed_query]."""
        return _utils.get_event_loop().run_until_complete(self.embed_query(query, settings=settings))

    def embed_documents_sync(
        self, documents: str | Sequence[str], *, settings: EmbeddingSettings | None = None
    ) -> EmbeddingResult:
        """Synchronous version of [`embed_documents()`][pydantic_ai.embeddings.Embedder.embed_documents]."""
        return _utils.get_event_loop().run_until_complete(self.embed_documents(documents, settings=settings))

    def embed_sync(
        self, inputs: str | Sequence[str], *, input_type: EmbedInputType, settings: EmbeddingSettings | None = None
    ) -> EmbeddingResult:
        """Synchronous version of [`embed()`][pydantic_ai.embeddings.Embedder.embed]."""
        return _utils.get_event_loop().run_until_complete(self.embed(inputs, input_type=input_type, settings=settings))

    def max_input_tokens_sync(self) -> int | None:
        """Synchronous version of [`max_input_tokens()`][pydantic_ai.embeddings.Embedder.max_input_tokens]."""
        return _utils.get_event_loop().run_until_complete(self.max_input_tokens())

    def count_tokens_sync(self, text: str) -> int:
        """Synchronous version of [`count_tokens()`][pydantic_ai.embeddings.Embedder.count_tokens]."""
        return _utils.get_event_loop().run_until_complete(self.count_tokens(text))

    def _get_model(self) -> EmbeddingModel:
        """Create a model configured for this embedder.

        Returns:
            The embedding model to use, with instrumentation applied if configured.
        """
        model_: EmbeddingModel
        if some_model := self._override_model.get():
            model_ = some_model
        else:
            model_ = self._model = infer_embedding_model(self.model)

        instrument = self.instrument
        if instrument is None:
            instrument = self._instrument_default

        return instrument_embedding_model(model_, instrument)

__init__ 

__init__(
    model: EmbeddingModel | KnownEmbeddingModelName | str,
    *,
    settings: EmbeddingSettings | None = None,
    defer_model_check: bool = True,
    instrument: InstrumentationSettings | bool | None = None
) -> None

Initialize an Embedder.

Parameters:

Name Type Description Default
model EmbeddingModel | KnownEmbeddingModelName | str

The embedding model to use. Can be specified as:

  • A model name string in the format 'provider:model-name' (e.g., 'openai:text-embedding-3-small')
  • An EmbeddingModel instance
 required
settings EmbeddingSettings | None

Optional EmbeddingSettings to use as defaults for all embed calls.

 None
defer_model_check bool

Whether to defer model validation until first use. Set to False to validate the model immediately on construction.

 True
instrument InstrumentationSettings | bool | None

OpenTelemetry instrumentation settings. Set to True to enable with defaults, or pass an InstrumentationSettings instance to customize. If None, uses the value from Embedder.instrument_all().

 None
Source code in pydantic_ai_slim/pydantic_ai/embeddings/__init__.py 
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def __init__(
    self,
    model: EmbeddingModel | KnownEmbeddingModelName | str,
    *,
    settings: EmbeddingSettings | None = None,
    defer_model_check: bool = True,
    instrument: InstrumentationSettings | bool | None = None,
) -> None:
    """Initialize an Embedder.

    Args:
        model: The embedding model to use. Can be specified as:

            - A model name string in the format `'provider:model-name'`
              (e.g., `'openai:text-embedding-3-small'`)
            - An [`EmbeddingModel`][pydantic_ai.embeddings.EmbeddingModel] instance
        settings: Optional [`EmbeddingSettings`][pydantic_ai.embeddings.EmbeddingSettings]
            to use as defaults for all embed calls.
        defer_model_check: Whether to defer model validation until first use.
            Set to `False` to validate the model immediately on construction.
        instrument: OpenTelemetry instrumentation settings. Set to `True` to enable with defaults,
            or pass an [`InstrumentationSettings`][pydantic_ai.models.instrumented.InstrumentationSettings]
            instance to customize. If `None`, uses the value from
            [`Embedder.instrument_all()`][pydantic_ai.embeddings.Embedder.instrument_all].
    """
    self._model = model if defer_model_check else infer_embedding_model(model)
    self._settings = settings
    self.instrument = instrument

    self._override_model: ContextVar[EmbeddingModel | None] = ContextVar('_override_model', default=None)

instrument instance-attribute 

instrument: InstrumentationSettings | bool | None = (
    instrument
)

Options to automatically instrument with OpenTelemetry.

Set to True to use default instrumentation settings, which will use Logfire if it's configured. Set to an instance of InstrumentationSettings to customize. If this isn't set, then the last value set by Embedder.instrument_all() will be used, which defaults to False. See the Debugging and Monitoring guide for more info.

instrument_all staticmethod 

instrument_all(
    instrument: InstrumentationSettings | bool = True,
) -> None

Set the default instrumentation options for all embedders where instrument is not explicitly set.

This is useful for enabling instrumentation globally without modifying each embedder individually.

Parameters:

Name Type Description Default
instrument InstrumentationSettings | bool

Instrumentation settings to use as the default. Set to True for default settings, False to disable, or pass an InstrumentationSettings instance to customize.

 True
Source code in pydantic_ai_slim/pydantic_ai/embeddings/__init__.py 
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@staticmethod
def instrument_all(instrument: InstrumentationSettings | bool = True) -> None:
    """Set the default instrumentation options for all embedders where `instrument` is not explicitly set.

    This is useful for enabling instrumentation globally without modifying each embedder individually.

    Args:
        instrument: Instrumentation settings to use as the default. Set to `True` for default settings,
            `False` to disable, or pass an
            [`InstrumentationSettings`][pydantic_ai.models.instrumented.InstrumentationSettings]
            instance to customize.
    """
    Embedder._instrument_default = instrument

model property 

model: EmbeddingModel | KnownEmbeddingModelName | str

The embedding model used by this embedder.

override 

override(
    *,
    model: (
        EmbeddingModel
        | KnownEmbeddingModelName
        | str
        | Unset
    ) = UNSET
) -> Iterator[None]

Context manager to temporarily override the embedding model.

Useful for testing or dynamically switching models.

Parameters:

Name Type Description Default
model EmbeddingModel | KnownEmbeddingModelName | str | Unset

The embedding model to use within this context.

 UNSET

Example: 

from pydantic_ai import Embedder

embedder = Embedder('openai:text-embedding-3-small')


async def main():
    # Temporarily use a different model
    with embedder.override(model='openai:text-embedding-3-large'):
        result = await embedder.embed_query('test')
        print(len(result.embeddings[0]))  # 3072 dimensions for large model
        #> 3072

Source code in pydantic_ai_slim/pydantic_ai/embeddings/__init__.py 
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@contextmanager
def override(
    self,
    *,
    model: EmbeddingModel | KnownEmbeddingModelName | str | _utils.Unset = _utils.UNSET,
) -> Iterator[None]:
    """Context manager to temporarily override the embedding model.

    Useful for testing or dynamically switching models.

    Args:
        model: The embedding model to use within this context.

    Example:
    ```python
    from pydantic_ai import Embedder

    embedder = Embedder('openai:text-embedding-3-small')


    async def main():
        # Temporarily use a different model
        with embedder.override(model='openai:text-embedding-3-large'):
            result = await embedder.embed_query('test')
            print(len(result.embeddings[0]))  # 3072 dimensions for large model
            #> 3072
    ```
    """
    if _utils.is_set(model):
        model_token = self._override_model.set(infer_embedding_model(model))
    else:
        model_token = None

    try:
        yield
    finally:
        if model_token is not None:
            self._override_model.reset(model_token)

embed_query async 

embed_query(
    query: str | Sequence[str],
    *,
    settings: EmbeddingSettings | None = None
) -> EmbeddingResult

Embed one or more query texts.

Use this method when embedding search queries that will be compared against document embeddings. Some models optimize embeddings differently based on whether the input is a query or document.

Parameters:

Name Type Description Default
query str | Sequence[str]

A single query string or sequence of query strings to embed.

 required
settings EmbeddingSettings | None

Optional settings to override the embedder's default settings for this call.

 None

Returns:

Type Description
EmbeddingResult

An EmbeddingResult containing the embeddings
EmbeddingResult

and metadata about the operation.
Source code in pydantic_ai_slim/pydantic_ai/embeddings/__init__.py 
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async def embed_query(
    self, query: str | Sequence[str], *, settings: EmbeddingSettings | None = None
) -> EmbeddingResult:
    """Embed one or more query texts.

    Use this method when embedding search queries that will be compared against document embeddings.
    Some models optimize embeddings differently based on whether the input is a query or document.

    Args:
        query: A single query string or sequence of query strings to embed.
        settings: Optional settings to override the embedder's default settings for this call.

    Returns:
        An [`EmbeddingResult`][pydantic_ai.embeddings.EmbeddingResult] containing the embeddings
        and metadata about the operation.
    """
    return await self.embed(query, input_type='query', settings=settings)

embed_documents async 

embed_documents(
    documents: str | Sequence[str],
    *,
    settings: EmbeddingSettings | None = None
) -> EmbeddingResult

Embed one or more document texts.

Use this method when embedding documents that will be stored and later searched against. Some models optimize embeddings differently based on whether the input is a query or document.

Parameters:

Name Type Description Default
documents str | Sequence[str]

A single document string or sequence of document strings to embed.

 required
settings EmbeddingSettings | None

Optional settings to override the embedder's default settings for this call.

 None

Returns:

Type Description
EmbeddingResult

An EmbeddingResult containing the embeddings
EmbeddingResult

and metadata about the operation.
Source code in pydantic_ai_slim/pydantic_ai/embeddings/__init__.py 
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async def embed_documents(
    self, documents: str | Sequence[str], *, settings: EmbeddingSettings | None = None
) -> EmbeddingResult:
    """Embed one or more document texts.

    Use this method when embedding documents that will be stored and later searched against.
    Some models optimize embeddings differently based on whether the input is a query or document.

    Args:
        documents: A single document string or sequence of document strings to embed.
        settings: Optional settings to override the embedder's default settings for this call.

    Returns:
        An [`EmbeddingResult`][pydantic_ai.embeddings.EmbeddingResult] containing the embeddings
        and metadata about the operation.
    """
    return await self.embed(documents, input_type='document', settings=settings)

embed async 

embed(
    inputs: str | Sequence[str],
    *,
    input_type: EmbedInputType,
    settings: EmbeddingSettings | None = None
) -> EmbeddingResult

Embed text inputs with explicit input type specification.

This is the low-level embedding method. For most use cases, prefer embed_query() or embed_documents().

Parameters:

Name Type Description Default
inputs str | Sequence[str]

A single string or sequence of strings to embed.

 required
input_type EmbedInputType

The type of input, either 'query' or 'document'.

 required
settings EmbeddingSettings | None

Optional settings to override the embedder's default settings for this call.

 None

Returns:

Type Description
EmbeddingResult

An EmbeddingResult containing the embeddings
EmbeddingResult

and metadata about the operation.
Source code in pydantic_ai_slim/pydantic_ai/embeddings/__init__.py 
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async def embed(
    self, inputs: str | Sequence[str], *, input_type: EmbedInputType, settings: EmbeddingSettings | None = None
) -> EmbeddingResult:
    """Embed text inputs with explicit input type specification.

    This is the low-level embedding method. For most use cases, prefer
    [`embed_query()`][pydantic_ai.embeddings.Embedder.embed_query] or
    [`embed_documents()`][pydantic_ai.embeddings.Embedder.embed_documents].

    Args:
        inputs: A single string or sequence of strings to embed.
        input_type: The type of input, either `'query'` or `'document'`.
        settings: Optional settings to override the embedder's default settings for this call.

    Returns:
        An [`EmbeddingResult`][pydantic_ai.embeddings.EmbeddingResult] containing the embeddings
        and metadata about the operation.
    """
    model = self._get_model()
    settings = merge_embedding_settings(self._settings, settings)
    return await model.embed(inputs, input_type=input_type, settings=settings)

max_input_tokens async 

max_input_tokens() -> int | None

Get the maximum number of tokens the model can accept as input.

Returns:

Type Description
int | None

The maximum token count, or None if the limit is unknown for this model.
Source code in pydantic_ai_slim/pydantic_ai/embeddings/__init__.py 
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async def max_input_tokens(self) -> int | None:
    """Get the maximum number of tokens the model can accept as input.

    Returns:
        The maximum token count, or `None` if the limit is unknown for this model.
    """
    model = self._get_model()
    return await model.max_input_tokens()

count_tokens async 

count_tokens(text: str) -> int

Count the number of tokens in the given text.

Parameters:

Name Type Description Default
text str

The text to tokenize and count.

 required

Returns:

Type Description
int

The number of tokens in the text.

Raises:

Type Description
NotImplementedError

If the model doesn't support token counting.
UserError

If the model or tokenizer is not supported.
Source code in pydantic_ai_slim/pydantic_ai/embeddings/__init__.py 
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async def count_tokens(self, text: str) -> int:
    """Count the number of tokens in the given text.

    Args:
        text: The text to tokenize and count.

    Returns:
        The number of tokens in the text.

    Raises:
        NotImplementedError: If the model doesn't support token counting.
        UserError: If the model or tokenizer is not supported.
    """
    model = self._get_model()
    return await model.count_tokens(text)

embed_query_sync 

embed_query_sync(
    query: str | Sequence[str],
    *,
    settings: EmbeddingSettings | None = None
) -> EmbeddingResult

Synchronous version of embed_query().

Source code in pydantic_ai_slim/pydantic_ai/embeddings/__init__.py 
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def embed_query_sync(
    self, query: str | Sequence[str], *, settings: EmbeddingSettings | None = None
) -> EmbeddingResult:
    """Synchronous version of [`embed_query()`][pydantic_ai.embeddings.Embedder.embed_query]."""
    return _utils.get_event_loop().run_until_complete(self.embed_query(query, settings=settings))

embed_documents_sync 

embed_documents_sync(
    documents: str | Sequence[str],
    *,
    settings: EmbeddingSettings | None = None
) -> EmbeddingResult

Synchronous version of embed_documents().

Source code in pydantic_ai_slim/pydantic_ai/embeddings/__init__.py 
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def embed_documents_sync(
    self, documents: str | Sequence[str], *, settings: EmbeddingSettings | None = None
) -> EmbeddingResult:
    """Synchronous version of [`embed_documents()`][pydantic_ai.embeddings.Embedder.embed_documents]."""
    return _utils.get_event_loop().run_until_complete(self.embed_documents(documents, settings=settings))

embed_sync 

embed_sync(
    inputs: str | Sequence[str],
    *,
    input_type: EmbedInputType,
    settings: EmbeddingSettings | None = None
) -> EmbeddingResult

Synchronous version of embed().

Source code in pydantic_ai_slim/pydantic_ai/embeddings/__init__.py 
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def embed_sync(
    self, inputs: str | Sequence[str], *, input_type: EmbedInputType, settings: EmbeddingSettings | None = None
) -> EmbeddingResult:
    """Synchronous version of [`embed()`][pydantic_ai.embeddings.Embedder.embed]."""
    return _utils.get_event_loop().run_until_complete(self.embed(inputs, input_type=input_type, settings=settings))

max_input_tokens_sync 

max_input_tokens_sync() -> int | None

Synchronous version of max_input_tokens().

Source code in pydantic_ai_slim/pydantic_ai/embeddings/__init__.py 
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def max_input_tokens_sync(self) -> int | None:
    """Synchronous version of [`max_input_tokens()`][pydantic_ai.embeddings.Embedder.max_input_tokens]."""
    return _utils.get_event_loop().run_until_complete(self.max_input_tokens())

count_tokens_sync 

count_tokens_sync(text: str) -> int

Synchronous version of count_tokens().

Source code in pydantic_ai_slim/pydantic_ai/embeddings/__init__.py 
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def count_tokens_sync(self, text: str) -> int:
    """Synchronous version of [`count_tokens()`][pydantic_ai.embeddings.Embedder.count_tokens]."""
    return _utils.get_event_loop().run_until_complete(self.count_tokens(text))

EmbeddingModel

Bases: ABC

Abstract base class for embedding models.

Implement this class to create a custom embedding model. For most use cases, use one of the built-in implementations:

Source code in pydantic_ai_slim/pydantic_ai/embeddings/base.py 
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class EmbeddingModel(ABC):
    """Abstract base class for embedding models.

    Implement this class to create a custom embedding model. For most use cases,
    use one of the built-in implementations:

    - [`OpenAIEmbeddingModel`][pydantic_ai.embeddings.openai.OpenAIEmbeddingModel]
    - [`CohereEmbeddingModel`][pydantic_ai.embeddings.cohere.CohereEmbeddingModel]
    - [`GoogleEmbeddingModel`][pydantic_ai.embeddings.google.GoogleEmbeddingModel]
    - [`BedrockEmbeddingModel`][pydantic_ai.embeddings.bedrock.BedrockEmbeddingModel]
    - [`SentenceTransformerEmbeddingModel`][pydantic_ai.embeddings.sentence_transformers.SentenceTransformerEmbeddingModel]
    """

    _settings: EmbeddingSettings | None = None

    def __init__(
        self,
        *,
        settings: EmbeddingSettings | None = None,
    ) -> None:
        """Initialize the model with optional settings.

        Args:
            settings: Model-specific settings that will be used as defaults for this model.
        """
        self._settings = settings

    @property
    def settings(self) -> EmbeddingSettings | None:
        """Get the default settings for this model."""
        return self._settings

    @property
    def base_url(self) -> str | None:
        """The base URL for the provider API, if available."""
        return None

    @property
    @abstractmethod
    def model_name(self) -> str:
        """The name of the embedding model."""
        raise NotImplementedError()

    @property
    @abstractmethod
    def system(self) -> str:
        """The embedding model provider/system identifier (e.g., 'openai', 'cohere')."""
        raise NotImplementedError()

    @abstractmethod
    async def embed(
        self, inputs: str | Sequence[str], *, input_type: EmbedInputType, settings: EmbeddingSettings | None = None
    ) -> EmbeddingResult:
        """Generate embeddings for the given inputs.

        Args:
            inputs: A single string or sequence of strings to embed.
            input_type: Whether the inputs are queries or documents.
            settings: Optional settings to override the model's defaults.

        Returns:
            An [`EmbeddingResult`][pydantic_ai.embeddings.EmbeddingResult] containing
            the embeddings and metadata.
        """
        raise NotImplementedError

    def prepare_embed(
        self, inputs: str | Sequence[str], settings: EmbeddingSettings | None = None
    ) -> tuple[list[str], EmbeddingSettings]:
        """Prepare the inputs and settings for embedding.

        This method normalizes inputs to a list and merges settings.
        Subclasses should call this at the start of their `embed()` implementation.

        Args:
            inputs: A single string or sequence of strings.
            settings: Optional settings to merge with defaults.

        Returns:
            A tuple of (normalized inputs list, merged settings).
        """
        inputs = [inputs] if isinstance(inputs, str) else list(inputs)

        settings = merge_embedding_settings(self._settings, settings) or {}

        return inputs, settings

    async def max_input_tokens(self) -> int | None:
        """Get the maximum number of tokens that can be input to the model.

        Returns:
            The maximum token count, or `None` if unknown.
        """
        return None  # pragma: no cover

    async def count_tokens(self, text: str) -> int:
        """Count the number of tokens in the given text.

        Args:
            text: The text to tokenize and count.

        Returns:
            The number of tokens.

        Raises:
            NotImplementedError: If the model doesn't support token counting.
            UserError: If the model or tokenizer is not supported.
        """
        raise NotImplementedError

__init__ 

__init__(
    *, settings: EmbeddingSettings | None = None
) -> None

Initialize the model with optional settings.

Parameters:

Name Type Description Default
settings EmbeddingSettings | None

Model-specific settings that will be used as defaults for this model.

 None
Source code in pydantic_ai_slim/pydantic_ai/embeddings/base.py 
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def __init__(
    self,
    *,
    settings: EmbeddingSettings | None = None,
) -> None:
    """Initialize the model with optional settings.

    Args:
        settings: Model-specific settings that will be used as defaults for this model.
    """
    self._settings = settings

settings property 

settings: EmbeddingSettings | None

Get the default settings for this model.

base_url property 

base_url: str | None

The base URL for the provider API, if available.

model_name abstractmethod property 

model_name: str

The name of the embedding model.

system abstractmethod property 

system: str

The embedding model provider/system identifier (e.g., 'openai', 'cohere').

embed abstractmethod async 

embed(
    inputs: str | Sequence[str],
    *,
    input_type: EmbedInputType,
    settings: EmbeddingSettings | None = None
) -> EmbeddingResult

Generate embeddings for the given inputs.

Parameters:

Name Type Description Default
inputs str | Sequence[str]

A single string or sequence of strings to embed.

 required
input_type EmbedInputType

Whether the inputs are queries or documents.

 required
settings EmbeddingSettings | None

Optional settings to override the model's defaults.

 None

Returns:

Type Description
EmbeddingResult

An EmbeddingResult containing
EmbeddingResult

the embeddings and metadata.
Source code in pydantic_ai_slim/pydantic_ai/embeddings/base.py 
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@abstractmethod
async def embed(
    self, inputs: str | Sequence[str], *, input_type: EmbedInputType, settings: EmbeddingSettings | None = None
) -> EmbeddingResult:
    """Generate embeddings for the given inputs.

    Args:
        inputs: A single string or sequence of strings to embed.
        input_type: Whether the inputs are queries or documents.
        settings: Optional settings to override the model's defaults.

    Returns:
        An [`EmbeddingResult`][pydantic_ai.embeddings.EmbeddingResult] containing
        the embeddings and metadata.
    """
    raise NotImplementedError

prepare_embed 

prepare_embed(
    inputs: str | Sequence[str],
    settings: EmbeddingSettings | None = None,
) -> tuple[list[str], EmbeddingSettings]

Prepare the inputs and settings for embedding.

This method normalizes inputs to a list and merges settings. Subclasses should call this at the start of their embed() implementation.

Parameters:

Name Type Description Default
inputs str | Sequence[str]

A single string or sequence of strings.

 required
settings EmbeddingSettings | None

Optional settings to merge with defaults.

 None

Returns:

Type Description
tuple[list[str], EmbeddingSettings]

A tuple of (normalized inputs list, merged settings).
Source code in pydantic_ai_slim/pydantic_ai/embeddings/base.py 
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def prepare_embed(
    self, inputs: str | Sequence[str], settings: EmbeddingSettings | None = None
) -> tuple[list[str], EmbeddingSettings]:
    """Prepare the inputs and settings for embedding.

    This method normalizes inputs to a list and merges settings.
    Subclasses should call this at the start of their `embed()` implementation.

    Args:
        inputs: A single string or sequence of strings.
        settings: Optional settings to merge with defaults.

    Returns:
        A tuple of (normalized inputs list, merged settings).
    """
    inputs = [inputs] if isinstance(inputs, str) else list(inputs)

    settings = merge_embedding_settings(self._settings, settings) or {}

    return inputs, settings

max_input_tokens async 

max_input_tokens() -> int | None

Get the maximum number of tokens that can be input to the model.

Returns:

Type Description
int | None

The maximum token count, or None if unknown.
Source code in pydantic_ai_slim/pydantic_ai/embeddings/base.py 
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async def max_input_tokens(self) -> int | None:
    """Get the maximum number of tokens that can be input to the model.

    Returns:
        The maximum token count, or `None` if unknown.
    """
    return None  # pragma: no cover

count_tokens async 

count_tokens(text: str) -> int

Count the number of tokens in the given text.

Parameters:

Name Type Description Default
text str

The text to tokenize and count.

 required

Returns:

Type Description
int

The number of tokens.

Raises:

Type Description
NotImplementedError

If the model doesn't support token counting.
UserError

If the model or tokenizer is not supported.
Source code in pydantic_ai_slim/pydantic_ai/embeddings/base.py 
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async def count_tokens(self, text: str) -> int:
    """Count the number of tokens in the given text.

    Args:
        text: The text to tokenize and count.

    Returns:
        The number of tokens.

    Raises:
        NotImplementedError: If the model doesn't support token counting.
        UserError: If the model or tokenizer is not supported.
    """
    raise NotImplementedError

EmbedInputType module-attribute 

EmbedInputType = Literal['query', 'document']

The type of input to the embedding model.

  • 'query': Text that will be used as a search query
  • 'document': Text that will be stored and searched against

Some embedding models optimize differently for queries vs documents.

EmbeddingResult dataclass

The result of an embedding operation.

This class contains the generated embeddings along with metadata about the operation, including the original inputs, model information, usage statistics, and timing.

Example: 

from pydantic_ai import Embedder

embedder = Embedder('openai:text-embedding-3-small')


async def main():
    result = await embedder.embed_query('What is AI?')

    # Access embeddings by index
    print(len(result.embeddings[0]))
    #> 1536

    # Access embeddings by original input text
    print(result['What is AI?'] == result.embeddings[0])
    #> True

    # Check usage
    print(f'Tokens used: {result.usage.input_tokens}')
    #> Tokens used: 3

Source code in pydantic_ai_slim/pydantic_ai/embeddings/result.py 
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@dataclass
class EmbeddingResult:
    """The result of an embedding operation.

    This class contains the generated embeddings along with metadata about
    the operation, including the original inputs, model information, usage
    statistics, and timing.

    Example:
    ```python
    from pydantic_ai import Embedder

    embedder = Embedder('openai:text-embedding-3-small')


    async def main():
        result = await embedder.embed_query('What is AI?')

        # Access embeddings by index
        print(len(result.embeddings[0]))
        #> 1536

        # Access embeddings by original input text
        print(result['What is AI?'] == result.embeddings[0])
        #> True

        # Check usage
        print(f'Tokens used: {result.usage.input_tokens}')
        #> Tokens used: 3
    ```
    """

    embeddings: Sequence[Sequence[float]]
    """The computed embedding vectors, one per input text.

    Each embedding is a sequence of floats representing the text in vector space.
    """

    _: KW_ONLY

    inputs: Sequence[str]
    """The original input texts that were embedded."""

    input_type: EmbedInputType
    """Whether the inputs were embedded as queries or documents."""

    model_name: str
    """The name of the model that generated these embeddings."""

    provider_name: str
    """The name of the provider (e.g., 'openai', 'cohere')."""

    timestamp: datetime = field(default_factory=_now_utc)
    """When the embedding request was made."""

    usage: RequestUsage = field(default_factory=RequestUsage)
    """Token usage statistics for this request."""

    provider_details: dict[str, Any] | None = None
    """Provider-specific details from the response."""

    provider_response_id: str | None = None
    """Unique identifier for this response from the provider, if available."""

    def __getitem__(self, item: int | str) -> Sequence[float]:
        """Get the embedding for an input by index or by the original input text.

        Args:
            item: Either an integer index or the original input string.

        Returns:
            The embedding vector for the specified input.

        Raises:
            IndexError: If the index is out of range.
            ValueError: If the string is not found in the inputs.
        """
        if isinstance(item, str):
            item = self.inputs.index(item)

        return self.embeddings[item]

    def cost(self) -> genai_types.PriceCalculation:
        """Calculate the cost of the embedding request.

        Uses [`genai-prices`](https://github.com/pydantic/genai-prices) for pricing data.

        Returns:
            A price calculation object with `total_price`, `input_price`, and other cost details.

        Raises:
            LookupError: If pricing data is not available for this model/provider.
        """
        assert self.model_name, 'Model name is required to calculate price'
        return calc_price(
            self.usage,
            self.model_name,
            provider_id=self.provider_name,
            genai_request_timestamp=self.timestamp,
        )

embeddings instance-attribute 

embeddings: Sequence[Sequence[float]]

The computed embedding vectors, one per input text.

Each embedding is a sequence of floats representing the text in vector space.

inputs instance-attribute 

inputs: Sequence[str]

The original input texts that were embedded.

input_type instance-attribute 

input_type: EmbedInputType

Whether the inputs were embedded as queries or documents.

model_name instance-attribute 

model_name: str

The name of the model that generated these embeddings.

provider_name instance-attribute 

provider_name: str

The name of the provider (e.g., 'openai', 'cohere').

timestamp class-attribute instance-attribute 

timestamp: datetime = field(default_factory=now_utc)

When the embedding request was made.

usage class-attribute instance-attribute 

usage: RequestUsage = field(default_factory=RequestUsage)

Token usage statistics for this request.

provider_details class-attribute instance-attribute 

provider_details: dict[str, Any] | None = None

Provider-specific details from the response.

provider_response_id class-attribute instance-attribute 

provider_response_id: str | None = None

Unique identifier for this response from the provider, if available.

__getitem__ 

__getitem__(item: int | str) -> Sequence[float]

Get the embedding for an input by index or by the original input text.

Parameters:

Name Type Description Default
item int | str

Either an integer index or the original input string.

 required

Returns:

Type Description
Sequence[float]

The embedding vector for the specified input.

Raises:

Type Description
IndexError

If the index is out of range.
ValueError

If the string is not found in the inputs.
Source code in pydantic_ai_slim/pydantic_ai/embeddings/result.py 
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def __getitem__(self, item: int | str) -> Sequence[float]:
    """Get the embedding for an input by index or by the original input text.

    Args:
        item: Either an integer index or the original input string.

    Returns:
        The embedding vector for the specified input.

    Raises:
        IndexError: If the index is out of range.
        ValueError: If the string is not found in the inputs.
    """
    if isinstance(item, str):
        item = self.inputs.index(item)

    return self.embeddings[item]

cost 

cost() -> PriceCalculation

Calculate the cost of the embedding request.

Uses genai-prices for pricing data.

Returns:

Type Description
PriceCalculation

A price calculation object with total_price, input_price, and other cost details.

Raises:

Type Description
LookupError

If pricing data is not available for this model/provider.
Source code in pydantic_ai_slim/pydantic_ai/embeddings/result.py 
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def cost(self) -> genai_types.PriceCalculation:
    """Calculate the cost of the embedding request.

    Uses [`genai-prices`](https://github.com/pydantic/genai-prices) for pricing data.

    Returns:
        A price calculation object with `total_price`, `input_price`, and other cost details.

    Raises:
        LookupError: If pricing data is not available for this model/provider.
    """
    assert self.model_name, 'Model name is required to calculate price'
    return calc_price(
        self.usage,
        self.model_name,
        provider_id=self.provider_name,
        genai_request_timestamp=self.timestamp,
    )

EmbeddingSettings

Bases: TypedDict

Common settings for configuring embedding models.

These settings apply across multiple embedding model providers. Not all settings are supported by all models - check the specific model's documentation for details.

Provider-specific settings classes (e.g., OpenAIEmbeddingSettings, CohereEmbeddingSettings) extend this with additional provider-prefixed options.

Source code in pydantic_ai_slim/pydantic_ai/embeddings/settings.py 
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class EmbeddingSettings(TypedDict, total=False):
    """Common settings for configuring embedding models.

    These settings apply across multiple embedding model providers.
    Not all settings are supported by all models - check the specific
    model's documentation for details.

    Provider-specific settings classes (e.g.,
    [`OpenAIEmbeddingSettings`][pydantic_ai.embeddings.openai.OpenAIEmbeddingSettings],
    [`CohereEmbeddingSettings`][pydantic_ai.embeddings.cohere.CohereEmbeddingSettings])
    extend this with additional provider-prefixed options.
    """

    dimensions: int
    """The number of dimensions for the output embeddings.

    Supported by:

    * OpenAI
    * Cohere
    * Google
    * Sentence Transformers
    * Bedrock
    * VoyageAI
    """

    truncate: bool
    """Whether to truncate inputs that exceed the model's context length.

    Defaults to `False`. If `True`, inputs that are too long will be truncated.
    If `False`, an error will be raised for inputs that exceed the context length.

    For more control over truncation, you can use
    [`max_input_tokens()`][pydantic_ai.embeddings.Embedder.max_input_tokens] and
    [`count_tokens()`][pydantic_ai.embeddings.Embedder.count_tokens] to implement
    your own truncation logic.

    Provider-specific truncation settings (e.g., `cohere_truncate`, `bedrock_cohere_truncate`)
    take precedence if specified.

    Supported by:

    * Cohere
    * Bedrock (Cohere and Nova models)
    * VoyageAI
    """

    extra_headers: dict[str, str]
    """Extra headers to send to the model.

    Supported by:

    * OpenAI
    * Cohere
    """

    extra_body: object
    """Extra body to send to the model.

    Supported by:

    * OpenAI
    * Cohere
    """

dimensions instance-attribute 

dimensions: int

The number of dimensions for the output embeddings.

Supported by:

  • OpenAI
  • Cohere
  • Google
  • Sentence Transformers
  • Bedrock
  • VoyageAI

truncate instance-attribute 

truncate: bool

Whether to truncate inputs that exceed the model's context length.

Defaults to False. If True, inputs that are too long will be truncated. If False, an error will be raised for inputs that exceed the context length.

For more control over truncation, you can use max_input_tokens() and count_tokens() to implement your own truncation logic.

Provider-specific truncation settings (e.g., cohere_truncate, bedrock_cohere_truncate) take precedence if specified.

Supported by:

  • Cohere
  • Bedrock (Cohere and Nova models)
  • VoyageAI

extra_headers instance-attribute 

extra_headers: dict[str, str]

Extra headers to send to the model.

Supported by:

  • OpenAI
  • Cohere

extra_body instance-attribute 

extra_body: object

Extra body to send to the model.

Supported by:

  • OpenAI
  • Cohere

merge_embedding_settings 

merge_embedding_settings(
    base: EmbeddingSettings | None,
    overrides: EmbeddingSettings | None,
) -> EmbeddingSettings | None

Merge two sets of embedding settings, with overrides taking precedence.

Parameters:

Name Type Description Default
base EmbeddingSettings | None

Base settings (typically from the embedder or model).

 required
overrides EmbeddingSettings | None

Settings that should override the base (typically per-call settings).

 required

Returns:

Type Description
EmbeddingSettings | None

Merged settings, or None if both inputs are None.
Source code in pydantic_ai_slim/pydantic_ai/embeddings/settings.py 
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def merge_embedding_settings(
    base: EmbeddingSettings | None, overrides: EmbeddingSettings | None
) -> EmbeddingSettings | None:
    """Merge two sets of embedding settings, with overrides taking precedence.

    Args:
        base: Base settings (typically from the embedder or model).
        overrides: Settings that should override the base (typically per-call settings).

    Returns:
        Merged settings, or `None` if both inputs are `None`.
    """
    # Note: we may want merge recursively if/when we add non-primitive values
    if base and overrides:
        return base | overrides
    else:
        return base or overrides

OpenAIEmbeddingModelName module-attribute 

OpenAIEmbeddingModelName = str | EmbeddingModel

Possible OpenAI embeddings model names.

See the OpenAI embeddings documentation for available models.

OpenAIEmbeddingSettings

Bases: EmbeddingSettings

Settings used for an OpenAI embedding model request.

All fields from EmbeddingSettings are supported.

Source code in pydantic_ai_slim/pydantic_ai/embeddings/openai.py 
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class OpenAIEmbeddingSettings(EmbeddingSettings, total=False):
    """Settings used for an OpenAI embedding model request.

    All fields from [`EmbeddingSettings`][pydantic_ai.embeddings.EmbeddingSettings] are supported.
    """

OpenAIEmbeddingModel dataclass

Bases: EmbeddingModel

OpenAI embedding model implementation.

This model works with OpenAI's embeddings API and any OpenAI-compatible providers.

Example: 

from pydantic_ai.embeddings.openai import OpenAIEmbeddingModel
from pydantic_ai.providers.openai import OpenAIProvider

# Using OpenAI directly
model = OpenAIEmbeddingModel('text-embedding-3-small')

# Using an OpenAI-compatible provider
model = OpenAIEmbeddingModel(
    'text-embedding-3-small',
    provider=OpenAIProvider(base_url='https://my-provider.com/v1'),
)

Source code in pydantic_ai_slim/pydantic_ai/embeddings/openai.py 
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@dataclass(init=False)
class OpenAIEmbeddingModel(EmbeddingModel):
    """OpenAI embedding model implementation.

    This model works with OpenAI's embeddings API and any
    [OpenAI-compatible providers](../models/openai.md#openai-compatible-models).

    Example:
    ```python
    from pydantic_ai.embeddings.openai import OpenAIEmbeddingModel
    from pydantic_ai.providers.openai import OpenAIProvider

    # Using OpenAI directly
    model = OpenAIEmbeddingModel('text-embedding-3-small')

    # Using an OpenAI-compatible provider
    model = OpenAIEmbeddingModel(
        'text-embedding-3-small',
        provider=OpenAIProvider(base_url='https://my-provider.com/v1'),
    )
    ```
    """

    _model_name: OpenAIEmbeddingModelName = field(repr=False)
    _provider: Provider[AsyncOpenAI] = field(repr=False)

    def __init__(
        self,
        model_name: OpenAIEmbeddingModelName,
        *,
        provider: OpenAIEmbeddingsCompatibleProvider | Literal['openai'] | Provider[AsyncOpenAI] = 'openai',
        settings: EmbeddingSettings | None = None,
    ):
        """Initialize an OpenAI embedding model.

        Args:
            model_name: The name of the OpenAI model to use.
                See [OpenAI's embedding models](https://platform.openai.com/docs/guides/embeddings)
                for available options.
            provider: The provider to use for authentication and API access. Can be:

                - `'openai'` (default): Uses the standard OpenAI API
                - A provider name string (e.g., `'azure'`, `'deepseek'`)
                - A [`Provider`][pydantic_ai.providers.Provider] instance for custom configuration

                See [OpenAI-compatible providers](../models/openai.md#openai-compatible-models)
                for a list of supported providers.
            settings: Model-specific [`EmbeddingSettings`][pydantic_ai.embeddings.EmbeddingSettings]
                to use as defaults for this model.
        """
        self._model_name = model_name

        if isinstance(provider, str):
            provider = infer_provider(provider)
        self._provider = provider
        self._client = provider.client

        super().__init__(settings=settings)

    @property
    def base_url(self) -> str:
        return str(self._client.base_url)

    @property
    def model_name(self) -> OpenAIEmbeddingModelName:
        """The embedding model name."""
        return self._model_name

    @property
    def system(self) -> str:
        """The embedding model provider."""
        return self._provider.name

    async def embed(
        self, inputs: str | Sequence[str], *, input_type: EmbedInputType, settings: EmbeddingSettings | None = None
    ) -> EmbeddingResult:
        inputs, settings = self.prepare_embed(inputs, settings)
        settings = cast(OpenAIEmbeddingSettings, settings)

        try:
            response = await self._client.embeddings.create(
                input=inputs,
                model=self.model_name,
                dimensions=settings.get('dimensions') or OMIT,
                extra_headers=settings.get('extra_headers'),
                extra_body=settings.get('extra_body'),
            )
        except APIStatusError as e:
            if (status_code := e.status_code) >= 400:
                raise ModelHTTPError(status_code=status_code, model_name=self.model_name, body=e.body) from e
            raise  # pragma: lax no cover
        except APIConnectionError as e:  # pragma: no cover
            raise ModelAPIError(model_name=self.model_name, message=e.message) from e

        embeddings = [item.embedding for item in response.data]

        return EmbeddingResult(
            embeddings=embeddings,
            inputs=inputs,
            input_type=input_type,
            usage=_map_usage(response.usage, self.system, self.base_url, response.model),
            model_name=response.model,
            provider_name=self.system,
        )

    async def max_input_tokens(self) -> int | None:
        if self.system != 'openai':
            return None

        # https://platform.openai.com/docs/guides/embeddings#embedding-models
        return 8192

    async def count_tokens(self, text: str) -> int:
        if self.system != 'openai':
            raise UserError(
                'Counting tokens is not supported for non-OpenAI embedding models',
            )
        try:
            encoding = await _utils.run_in_executor(tiktoken.encoding_for_model, self.model_name)
        except KeyError as e:  # pragma: no cover
            raise ValueError(
                f'The embedding model {self.model_name!r} is not supported by tiktoken',
            ) from e
        return len(encoding.encode(text))

__init__ 

__init__(
    model_name: OpenAIEmbeddingModelName,
    *,
    provider: (
        OpenAIEmbeddingsCompatibleProvider
        | Literal["openai"]
        | Provider[AsyncOpenAI]
    ) = "openai",
    settings: EmbeddingSettings | None = None
)

Initialize an OpenAI embedding model.

Parameters:

Name Type Description Default
model_name OpenAIEmbeddingModelName

The name of the OpenAI model to use. See OpenAI's embedding models for available options.

 required
provider OpenAIEmbeddingsCompatibleProvider | Literal['openai'] | Provider[AsyncOpenAI]

The provider to use for authentication and API access. Can be:

  • 'openai' (default): Uses the standard OpenAI API
  • A provider name string (e.g., 'azure', 'deepseek')
  • A Provider instance for custom configuration

See OpenAI-compatible providers for a list of supported providers.

 'openai'
settings EmbeddingSettings | None

Model-specific EmbeddingSettings to use as defaults for this model.

 None
Source code in pydantic_ai_slim/pydantic_ai/embeddings/openai.py 
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def __init__(
    self,
    model_name: OpenAIEmbeddingModelName,
    *,
    provider: OpenAIEmbeddingsCompatibleProvider | Literal['openai'] | Provider[AsyncOpenAI] = 'openai',
    settings: EmbeddingSettings | None = None,
):
    """Initialize an OpenAI embedding model.

    Args:
        model_name: The name of the OpenAI model to use.
            See [OpenAI's embedding models](https://platform.openai.com/docs/guides/embeddings)
            for available options.
        provider: The provider to use for authentication and API access. Can be:

            - `'openai'` (default): Uses the standard OpenAI API
            - A provider name string (e.g., `'azure'`, `'deepseek'`)
            - A [`Provider`][pydantic_ai.providers.Provider] instance for custom configuration

            See [OpenAI-compatible providers](../models/openai.md#openai-compatible-models)
            for a list of supported providers.
        settings: Model-specific [`EmbeddingSettings`][pydantic_ai.embeddings.EmbeddingSettings]
            to use as defaults for this model.
    """
    self._model_name = model_name

    if isinstance(provider, str):
        provider = infer_provider(provider)
    self._provider = provider
    self._client = provider.client

    super().__init__(settings=settings)

model_name property 

model_name: OpenAIEmbeddingModelName

The embedding model name.

system property 

system: str

The embedding model provider.

LatestCohereEmbeddingModelNames module-attribute 

LatestCohereEmbeddingModelNames = Literal[
    "embed-v4.0",
    "embed-english-v3.0",
    "embed-english-light-v3.0",
    "embed-multilingual-v3.0",
    "embed-multilingual-light-v3.0",
]

Latest Cohere embeddings models.

See the Cohere Embed documentation for available models and their capabilities.

CohereEmbeddingModelName module-attribute 

CohereEmbeddingModelName = (
    str | LatestCohereEmbeddingModelNames
)

Possible Cohere embeddings model names.

CohereEmbeddingSettings

Bases: EmbeddingSettings

Settings used for a Cohere embedding model request.

All fields from EmbeddingSettings are supported, plus Cohere-specific settings prefixed with cohere_.

Source code in pydantic_ai_slim/pydantic_ai/embeddings/cohere.py 
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class CohereEmbeddingSettings(EmbeddingSettings, total=False):
    """Settings used for a Cohere embedding model request.

    All fields from [`EmbeddingSettings`][pydantic_ai.embeddings.EmbeddingSettings] are supported,
    plus Cohere-specific settings prefixed with `cohere_`.
    """

    # ALL FIELDS MUST BE `cohere_` PREFIXED SO YOU CAN MERGE THEM WITH OTHER MODELS.

    cohere_max_tokens: int
    """The maximum number of tokens to embed."""

    cohere_input_type: CohereEmbedInputType
    """The Cohere-specific input type for the embedding.

    Overrides the standard `input_type` argument. Options include:
    `'search_query'`, `'search_document'`, `'classification'`, `'clustering'`, and `'image'`.
    """

    cohere_truncate: V2EmbedRequestTruncate
    """The truncation strategy to use:

    - `'NONE'` (default): Raise an error if input exceeds max tokens.
    - `'END'`: Truncate the end of the input text.
    - `'START'`: Truncate the start of the input text.

    Note: This setting overrides the standard `truncate` boolean setting when specified.
    """

cohere_max_tokens instance-attribute 

cohere_max_tokens: int

The maximum number of tokens to embed.

cohere_input_type instance-attribute 

cohere_input_type: EmbedInputType

The Cohere-specific input type for the embedding.

Overrides the standard input_type argument. Options include: 'search_query', 'search_document', 'classification', 'clustering', and 'image'.

cohere_truncate instance-attribute 

cohere_truncate: V2EmbedRequestTruncate

The truncation strategy to use:

  • 'NONE' (default): Raise an error if input exceeds max tokens.
  • 'END': Truncate the end of the input text.
  • 'START': Truncate the start of the input text.

Note: This setting overrides the standard truncate boolean setting when specified.

CohereEmbeddingModel dataclass

Bases: EmbeddingModel

Cohere embedding model implementation.

This model works with Cohere's embeddings API, which offers multilingual support and various model sizes.

Example: 

from pydantic_ai.embeddings.cohere import CohereEmbeddingModel

model = CohereEmbeddingModel('embed-v4.0')

Source code in pydantic_ai_slim/pydantic_ai/embeddings/cohere.py 
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@dataclass(init=False)
class CohereEmbeddingModel(EmbeddingModel):
    """Cohere embedding model implementation.

    This model works with Cohere's embeddings API, which offers
    multilingual support and various model sizes.

    Example:
    ```python
    from pydantic_ai.embeddings.cohere import CohereEmbeddingModel

    model = CohereEmbeddingModel('embed-v4.0')
    ```
    """

    _model_name: CohereEmbeddingModelName = field(repr=False)
    _provider: Provider[AsyncClientV2] = field(repr=False)

    def __init__(
        self,
        model_name: CohereEmbeddingModelName,
        *,
        provider: Literal['cohere'] | Provider[AsyncClientV2] = 'cohere',
        settings: EmbeddingSettings | None = None,
    ):
        """Initialize a Cohere embedding model.

        Args:
            model_name: The name of the Cohere model to use.
                See [Cohere Embed documentation](https://docs.cohere.com/docs/cohere-embed)
                for available models.
            provider: The provider to use for authentication and API access. Can be:

                - `'cohere'` (default): Uses the standard Cohere API
                - A [`CohereProvider`][pydantic_ai.providers.cohere.CohereProvider] instance
                  for custom configuration
            settings: Model-specific [`EmbeddingSettings`][pydantic_ai.embeddings.EmbeddingSettings]
                to use as defaults for this model.
        """
        self._model_name = model_name

        if isinstance(provider, str):
            provider = infer_provider(provider)
        self._provider = provider
        self._client = provider.client
        self._v1_client = provider.v1_client if isinstance(provider, CohereProvider) else None

        super().__init__(settings=settings)

    @property
    def base_url(self) -> str:
        """The base URL for the provider API, if available."""
        return self._provider.base_url

    @property
    def model_name(self) -> CohereEmbeddingModelName:
        """The embedding model name."""
        return self._model_name

    @property
    def system(self) -> str:
        """The embedding model provider."""
        return self._provider.name

    async def embed(
        self, inputs: str | Sequence[str], *, input_type: EmbedInputType, settings: EmbeddingSettings | None = None
    ) -> EmbeddingResult:
        inputs, settings = self.prepare_embed(inputs, settings)
        settings = cast(CohereEmbeddingSettings, settings)

        cohere_input_type = settings.get(
            'cohere_input_type', 'search_document' if input_type == 'document' else 'search_query'
        )

        request_options: RequestOptions = {}
        if extra_headers := settings.get('extra_headers'):  # pragma: no cover
            request_options['additional_headers'] = extra_headers
        if extra_body := settings.get('extra_body'):  # pragma: no cover
            request_options['additional_body_parameters'] = cast(dict[str, Any], extra_body)

        # Determine truncation strategy: cohere_truncate takes precedence over truncate
        if 'cohere_truncate' in settings:
            truncate = settings['cohere_truncate']
        elif settings.get('truncate'):
            truncate = 'END'
        else:
            truncate = 'NONE'

        try:
            response = await self._client.embed(
                model=self.model_name,
                texts=inputs,
                output_dimension=settings.get('dimensions'),
                input_type=cohere_input_type,
                max_tokens=settings.get('cohere_max_tokens'),
                truncate=truncate,
                request_options=request_options,
            )
        except ApiError as e:
            if (status_code := e.status_code) and status_code >= 400:
                raise ModelHTTPError(status_code=status_code, model_name=self.model_name, body=e.body) from e
            raise ModelAPIError(model_name=self.model_name, message=str(e)) from e  # pragma: no cover

        embeddings = response.embeddings.float_
        if embeddings is None:
            raise UnexpectedModelBehavior(  # pragma: no cover
                'The Cohere embeddings response did not have an `embeddings` field holding a list of floats',
                str(response),
            )

        return EmbeddingResult(
            embeddings=embeddings,
            inputs=inputs,
            input_type=input_type,
            usage=_map_usage(response, self.system, self.base_url, self.model_name),
            model_name=self.model_name,
            provider_name=self.system,
            provider_response_id=response.id,
        )

    async def max_input_tokens(self) -> int | None:
        return _MAX_INPUT_TOKENS.get(self.model_name)

    async def count_tokens(self, text: str) -> int:
        if self._v1_client is None:
            raise NotImplementedError('Counting tokens requires the Cohere v1 client')
        try:
            result = await self._v1_client.tokenize(
                model=self.model_name,
                text=text,  # Has a max length of 65536 characters
                offline=False,
            )
        except ApiError as e:  # pragma: no cover
            if (status_code := e.status_code) and status_code >= 400:
                raise ModelHTTPError(status_code=status_code, model_name=self.model_name, body=e.body) from e
            raise ModelAPIError(model_name=self.model_name, message=str(e)) from e

        return len(result.tokens)

__init__ 

__init__(
    model_name: CohereEmbeddingModelName,
    *,
    provider: (
        Literal["cohere"] | Provider[AsyncClientV2]
    ) = "cohere",
    settings: EmbeddingSettings | None = None
)

Initialize a Cohere embedding model.

Parameters:

Name Type Description Default
model_name CohereEmbeddingModelName

The name of the Cohere model to use. See Cohere Embed documentation for available models.

 required
provider Literal['cohere'] | Provider[AsyncClientV2]

The provider to use for authentication and API access. Can be:

  • 'cohere' (default): Uses the standard Cohere API
  • A CohereProvider instance for custom configuration
 'cohere'
settings EmbeddingSettings | None

Model-specific EmbeddingSettings to use as defaults for this model.

 None
Source code in pydantic_ai_slim/pydantic_ai/embeddings/cohere.py 
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def __init__(
    self,
    model_name: CohereEmbeddingModelName,
    *,
    provider: Literal['cohere'] | Provider[AsyncClientV2] = 'cohere',
    settings: EmbeddingSettings | None = None,
):
    """Initialize a Cohere embedding model.

    Args:
        model_name: The name of the Cohere model to use.
            See [Cohere Embed documentation](https://docs.cohere.com/docs/cohere-embed)
            for available models.
        provider: The provider to use for authentication and API access. Can be:

            - `'cohere'` (default): Uses the standard Cohere API
            - A [`CohereProvider`][pydantic_ai.providers.cohere.CohereProvider] instance
              for custom configuration
        settings: Model-specific [`EmbeddingSettings`][pydantic_ai.embeddings.EmbeddingSettings]
            to use as defaults for this model.
    """
    self._model_name = model_name

    if isinstance(provider, str):
        provider = infer_provider(provider)
    self._provider = provider
    self._client = provider.client
    self._v1_client = provider.v1_client if isinstance(provider, CohereProvider) else None

    super().__init__(settings=settings)

base_url property 

base_url: str

The base URL for the provider API, if available.

model_name property 

model_name: CohereEmbeddingModelName

The embedding model name.

system property 

system: str

The embedding model provider.

LatestGoogleGLAEmbeddingModelNames module-attribute 

LatestGoogleGLAEmbeddingModelNames = Literal[
    "gemini-embedding-001"
]

Latest Google Gemini API (GLA) embedding models.

See the Google Embeddings documentation for available models and their capabilities.

LatestGoogleVertexEmbeddingModelNames module-attribute 

LatestGoogleVertexEmbeddingModelNames = Literal[
    "gemini-embedding-001",
    "text-embedding-005",
    "text-multilingual-embedding-002",
]

Latest Google Vertex AI embedding models.

See the Vertex AI Embeddings documentation for available models and their capabilities.

LatestGoogleEmbeddingModelNames module-attribute 

LatestGoogleEmbeddingModelNames = (
    LatestGoogleGLAEmbeddingModelNames
    | LatestGoogleVertexEmbeddingModelNames
)

All latest Google embedding models (union of GLA and Vertex AI models).

GoogleEmbeddingModelName module-attribute 

GoogleEmbeddingModelName = (
    str | LatestGoogleEmbeddingModelNames
)

Possible Google embeddings model names.

GoogleEmbeddingSettings

Bases: EmbeddingSettings

Settings used for a Google embedding model request.

All fields from EmbeddingSettings are supported, plus Google-specific settings prefixed with google_.

Source code in pydantic_ai_slim/pydantic_ai/embeddings/google.py 
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class GoogleEmbeddingSettings(EmbeddingSettings, total=False):
    """Settings used for a Google embedding model request.

    All fields from [`EmbeddingSettings`][pydantic_ai.embeddings.EmbeddingSettings] are supported,
    plus Google-specific settings prefixed with `google_`.
    """

    # ALL FIELDS MUST BE `google_` PREFIXED SO YOU CAN MERGE THEM WITH OTHER MODELS.

    google_task_type: str
    """The task type for the embedding.

    Overrides the automatic task type selection based on `input_type`.
    See [Google's task type documentation](https://ai.google.dev/gemini-api/docs/embeddings#task-types)
    for available options.
    """

    google_title: str
    """Optional title for the content being embedded.

    Only applicable when task_type is `RETRIEVAL_DOCUMENT`.
    """

google_task_type instance-attribute 

google_task_type: str

The task type for the embedding.

Overrides the automatic task type selection based on input_type. See Google's task type documentation for available options.

google_title instance-attribute 

google_title: str

Optional title for the content being embedded.

Only applicable when task_type is RETRIEVAL_DOCUMENT.

GoogleEmbeddingModel dataclass

Bases: EmbeddingModel

Google embedding model implementation.

This model works with Google's embeddings API via the google-genai SDK, supporting both the Gemini API (Google AI Studio) and Vertex AI.

Example: 

from pydantic_ai.embeddings.google import GoogleEmbeddingModel
from pydantic_ai.providers.google import GoogleProvider

# Using Gemini API (requires GOOGLE_API_KEY env var)
model = GoogleEmbeddingModel('gemini-embedding-001')

# Using Vertex AI
model = GoogleEmbeddingModel(
    'gemini-embedding-001',
    provider=GoogleProvider(vertexai=True, project='my-project', location='us-central1'),
)

Source code in pydantic_ai_slim/pydantic_ai/embeddings/google.py 
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@dataclass(init=False)
class GoogleEmbeddingModel(EmbeddingModel):
    """Google embedding model implementation.

    This model works with Google's embeddings API via the `google-genai` SDK,
    supporting both the Gemini API (Google AI Studio) and Vertex AI.

    Example:
    ```python
    from pydantic_ai.embeddings.google import GoogleEmbeddingModel
    from pydantic_ai.providers.google import GoogleProvider

    # Using Gemini API (requires GOOGLE_API_KEY env var)
    model = GoogleEmbeddingModel('gemini-embedding-001')

    # Using Vertex AI
    model = GoogleEmbeddingModel(
        'gemini-embedding-001',
        provider=GoogleProvider(vertexai=True, project='my-project', location='us-central1'),
    )
    ```
    """

    _model_name: GoogleEmbeddingModelName = field(repr=False)
    _provider: Provider[Client] = field(repr=False)

    def __init__(
        self,
        model_name: GoogleEmbeddingModelName,
        *,
        provider: Literal['google-gla', 'google-vertex'] | Provider[Client] = 'google-gla',
        settings: EmbeddingSettings | None = None,
    ):
        """Initialize a Google embedding model.

        Args:
            model_name: The name of the Google model to use.
                See [Google Embeddings documentation](https://ai.google.dev/gemini-api/docs/embeddings)
                for available models.
            provider: The provider to use for authentication and API access. Can be:

                - `'google-gla'` (default): Uses the Gemini API (Google AI Studio)
                - `'google-vertex'`: Uses Vertex AI
                - A [`GoogleProvider`][pydantic_ai.providers.google.GoogleProvider] instance
                  for custom configuration
            settings: Model-specific [`EmbeddingSettings`][pydantic_ai.embeddings.EmbeddingSettings]
                to use as defaults for this model.
        """
        self._model_name = model_name

        if isinstance(provider, str):
            provider = infer_provider(provider)
        self._provider = provider
        self._client = provider.client

        super().__init__(settings=settings)

    @property
    def base_url(self) -> str:
        return self._provider.base_url

    @property
    def model_name(self) -> GoogleEmbeddingModelName:
        """The embedding model name."""
        return self._model_name

    @property
    def system(self) -> str:
        """The embedding model provider."""
        return self._provider.name

    async def embed(
        self, inputs: str | Sequence[str], *, input_type: EmbedInputType, settings: EmbeddingSettings | None = None
    ) -> EmbeddingResult:
        inputs, settings = self.prepare_embed(inputs, settings)
        settings = cast(GoogleEmbeddingSettings, settings)

        google_task_type = settings.get('google_task_type')
        if google_task_type is None:
            google_task_type = 'RETRIEVAL_DOCUMENT' if input_type == 'document' else 'RETRIEVAL_QUERY'

        config = EmbedContentConfig(
            task_type=google_task_type,
            output_dimensionality=settings.get('dimensions'),
            title=settings.get('google_title'),
        )

        try:
            response = await self._client.aio.models.embed_content(
                model=self._model_name,
                contents=cast(ContentListUnion, inputs),
                config=config,
            )
        except errors.APIError as e:
            if (status_code := e.code) >= 400:
                raise ModelHTTPError(
                    status_code=status_code,
                    model_name=self._model_name,
                    body=cast(object, e.details),  # pyright: ignore[reportUnknownMemberType]
                ) from e
            raise  # pragma: no cover

        embeddings: list[list[float]] = [emb.values for emb in (response.embeddings or []) if emb.values is not None]

        return EmbeddingResult(
            embeddings=embeddings,
            inputs=inputs,
            input_type=input_type,
            usage=_map_usage(response, self.system, self.base_url, self._model_name),
            model_name=self._model_name,
            provider_name=self.system,
        )

    async def max_input_tokens(self) -> int | None:
        return _MAX_INPUT_TOKENS.get(self._model_name)

    async def count_tokens(self, text: str) -> int:
        try:
            response = await self._client.aio.models.count_tokens(
                model=self._model_name,
                contents=text,
            )
        except errors.APIError as e:
            if (status_code := e.code) >= 400:
                raise ModelHTTPError(
                    status_code=status_code,
                    model_name=self._model_name,
                    body=cast(object, e.details),  # pyright: ignore[reportUnknownMemberType]
                ) from e
            raise  # pragma: no cover

        if response.total_tokens is None:
            raise UnexpectedModelBehavior('Token counting returned no result')  # pragma: no cover
        return response.total_tokens

__init__ 

__init__(
    model_name: GoogleEmbeddingModelName,
    *,
    provider: (
        Literal["google-gla", "google-vertex"]
        | Provider[Client]
    ) = "google-gla",
    settings: EmbeddingSettings | None = None
)

Initialize a Google embedding model.

Parameters:

Name Type Description Default
model_name GoogleEmbeddingModelName

The name of the Google model to use. See Google Embeddings documentation for available models.

 required
provider Literal['google-gla', 'google-vertex'] | Provider[Client]

The provider to use for authentication and API access. Can be:

  • 'google-gla' (default): Uses the Gemini API (Google AI Studio)
  • 'google-vertex': Uses Vertex AI
  • A GoogleProvider instance for custom configuration
 'google-gla'
settings EmbeddingSettings | None

Model-specific EmbeddingSettings to use as defaults for this model.

 None
Source code in pydantic_ai_slim/pydantic_ai/embeddings/google.py 
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def __init__(
    self,
    model_name: GoogleEmbeddingModelName,
    *,
    provider: Literal['google-gla', 'google-vertex'] | Provider[Client] = 'google-gla',
    settings: EmbeddingSettings | None = None,
):
    """Initialize a Google embedding model.

    Args:
        model_name: The name of the Google model to use.
            See [Google Embeddings documentation](https://ai.google.dev/gemini-api/docs/embeddings)
            for available models.
        provider: The provider to use for authentication and API access. Can be:

            - `'google-gla'` (default): Uses the Gemini API (Google AI Studio)
            - `'google-vertex'`: Uses Vertex AI
            - A [`GoogleProvider`][pydantic_ai.providers.google.GoogleProvider] instance
              for custom configuration
        settings: Model-specific [`EmbeddingSettings`][pydantic_ai.embeddings.EmbeddingSettings]
            to use as defaults for this model.
    """
    self._model_name = model_name

    if isinstance(provider, str):
        provider = infer_provider(provider)
    self._provider = provider
    self._client = provider.client

    super().__init__(settings=settings)

model_name property 

model_name: GoogleEmbeddingModelName

The embedding model name.

system property 

system: str

The embedding model provider.

LatestBedrockEmbeddingModelNames module-attribute 

LatestBedrockEmbeddingModelNames = Literal[
    "amazon.titan-embed-text-v1",
    "amazon.titan-embed-text-v2:0",
    "cohere.embed-english-v3",
    "cohere.embed-multilingual-v3",
    "cohere.embed-v4:0",
    "amazon.nova-2-multimodal-embeddings-v1:0",
]

Latest Bedrock embedding model names.

See the Bedrock docs for available embedding models.

BedrockEmbeddingModelName module-attribute 

BedrockEmbeddingModelName = (
    str | LatestBedrockEmbeddingModelNames
)

Possible Bedrock embedding model names.

BedrockEmbeddingSettings

Bases: EmbeddingSettings

Settings used for a Bedrock embedding model request.

All fields from EmbeddingSettings are supported, plus Bedrock-specific settings prefixed with bedrock_.

All settings are optional - if not specified, model defaults are used.

Note on dimensions parameter support:

  • Titan v1 (amazon.titan-embed-text-v1): Not supported (fixed: 1536)
  • Titan v2 (amazon.titan-embed-text-v2:0): Supported (default: 1024, accepts 256/384/1024)
  • Cohere v3 (cohere.embed-english-v3, cohere.embed-multilingual-v3): Not supported (fixed: 1024)
  • Cohere v4 (cohere.embed-v4:0): Supported (default: 1536, accepts 256/512/1024/1536)
  • Nova (amazon.nova-2-multimodal-embeddings-v1:0): Supported (default: 3072, accepts 256/384/1024/3072)

Unsupported settings are silently ignored.

Note on truncate parameter support:

  • Titan models (amazon.titan-embed-text-v1, amazon.titan-embed-text-v2:0): Not supported
  • Cohere models (all versions): Supported (default: False, maps to 'END' when True)
  • Nova (amazon.nova-2-multimodal-embeddings-v1:0): Supported (default: False, maps to 'END' when True)

For fine-grained truncation control, use model-specific settings: bedrock_cohere_truncate or bedrock_nova_truncate.

Example 
from pydantic_ai.embeddings.bedrock import BedrockEmbeddingSettings

# Use model defaults
settings = BedrockEmbeddingSettings()

# Customize specific settings for Titan v2:0
settings = BedrockEmbeddingSettings(
    dimensions=512,
    bedrock_titan_normalize=True,
)

# Customize specific settings for Cohere v4
settings = BedrockEmbeddingSettings(
    dimensions=512,
    bedrock_cohere_max_tokens=1000,
)
Source code in pydantic_ai_slim/pydantic_ai/embeddings/bedrock.py 
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class BedrockEmbeddingSettings(EmbeddingSettings, total=False):
    """Settings used for a Bedrock embedding model request.

    All fields from [`EmbeddingSettings`][pydantic_ai.embeddings.EmbeddingSettings] are supported,
    plus Bedrock-specific settings prefixed with `bedrock_`.

    All settings are optional - if not specified, model defaults are used.

    **Note on `dimensions` parameter support:**

    - **Titan v1** (`amazon.titan-embed-text-v1`): Not supported (fixed: 1536)
    - **Titan v2** (`amazon.titan-embed-text-v2:0`): Supported (default: 1024, accepts 256/384/1024)
    - **Cohere v3** (`cohere.embed-english-v3`, `cohere.embed-multilingual-v3`): Not supported (fixed: 1024)
    - **Cohere v4** (`cohere.embed-v4:0`): Supported (default: 1536, accepts 256/512/1024/1536)
    - **Nova** (`amazon.nova-2-multimodal-embeddings-v1:0`): Supported (default: 3072, accepts 256/384/1024/3072)

    Unsupported settings are silently ignored.

    **Note on `truncate` parameter support:**

    - **Titan models** (`amazon.titan-embed-text-v1`, `amazon.titan-embed-text-v2:0`): Not supported
    - **Cohere models** (all versions): Supported (default: `False`, maps to `'END'` when `True`)
    - **Nova** (`amazon.nova-2-multimodal-embeddings-v1:0`): Supported (default: `False`, maps to `'END'` when `True`)

    For fine-grained truncation control, use model-specific settings: `bedrock_cohere_truncate` or `bedrock_nova_truncate`.

    Example:
        ```python
        from pydantic_ai.embeddings.bedrock import BedrockEmbeddingSettings

        # Use model defaults
        settings = BedrockEmbeddingSettings()

        # Customize specific settings for Titan v2:0
        settings = BedrockEmbeddingSettings(
            dimensions=512,
            bedrock_titan_normalize=True,
        )

        # Customize specific settings for Cohere v4
        settings = BedrockEmbeddingSettings(
            dimensions=512,
            bedrock_cohere_max_tokens=1000,
        )
        ```
    """

    # ALL FIELDS MUST BE `bedrock_` PREFIXED SO YOU CAN MERGE THEM WITH OTHER MODELS.

    # ==================== Amazon Titan Settings ====================

    bedrock_titan_normalize: bool
    """Whether to normalize embedding vectors for Titan models.

    **Supported by:** `amazon.titan-embed-text-v2:0` (default: `True`)

    **Not supported by:** `amazon.titan-embed-text-v1` (silently ignored)

    When enabled, vectors are normalized for direct cosine similarity calculations.
    """

    # ==================== Cohere Settings ====================

    bedrock_cohere_max_tokens: int
    """The maximum number of tokens to embed for Cohere models.

    **Supported by:** `cohere.embed-v4:0` (default: 128000)

    **Not supported by:** `cohere.embed-english-v3`, `cohere.embed-multilingual-v3`
    (silently ignored)
    """

    bedrock_cohere_input_type: Literal['search_document', 'search_query', 'classification', 'clustering']
    """The input type for Cohere models.

    **Supported by:** All Cohere models (`cohere.embed-english-v3`, `cohere.embed-multilingual-v3`, `cohere.embed-v4:0`)

    By default, `embed_query()` uses `'search_query'` and `embed_documents()` uses `'search_document'`.
    Also accepts `'classification'` or `'clustering'`.
    """

    bedrock_cohere_truncate: Literal['NONE', 'START', 'END']
    """The truncation strategy for Cohere models. Overrides base `truncate` setting.

    **Supported by:** All Cohere models (`cohere.embed-english-v3`, `cohere.embed-multilingual-v3`, `cohere.embed-v4:0`)

    Default: `'NONE'`

    - `'NONE'`: Raise an error if input exceeds max tokens.
    - `'START'`: Truncate the start of the input.
    - `'END'`: Truncate the end of the input.
    """

    # ==================== Amazon Nova Settings ====================

    bedrock_nova_truncate: Literal['NONE', 'START', 'END']
    """The truncation strategy for Nova models. Overrides base `truncate` setting.

    **Supported by:** `amazon.nova-2-multimodal-embeddings-v1:0`

    Default: `'NONE'`

    - `'NONE'`: Raise an error if input exceeds max tokens.
    - `'START'`: Truncate the start of the input.
    - `'END'`: Truncate the end of the input.
    """

    bedrock_nova_embedding_purpose: Literal[
        'GENERIC_INDEX',
        'GENERIC_RETRIEVAL',
        'TEXT_RETRIEVAL',
        'CLASSIFICATION',
        'CLUSTERING',
    ]
    """The embedding purpose for Nova models.

    **Supported by:** `amazon.nova-2-multimodal-embeddings-v1:0`

    By default, `embed_query()` uses `'GENERIC_RETRIEVAL'` and `embed_documents()` uses `'GENERIC_INDEX'`.
    Also accepts `'TEXT_RETRIEVAL'`, `'CLASSIFICATION'`, or `'CLUSTERING'`.

    Note: Multimodal-specific purposes (`'IMAGE_RETRIEVAL'`, `'VIDEO_RETRIEVAL'`,
    `'DOCUMENT_RETRIEVAL'`, `'AUDIO_RETRIEVAL'`) are not supported as this
    embedding client only accepts text input.
    """

    # ==================== Concurrency Settings ====================

    bedrock_max_concurrency: int
    """Maximum number of concurrent requests for models that don't support batch embedding.

    **Applies to:** `amazon.titan-embed-text-v1`, `amazon.titan-embed-text-v2:0`,
    `amazon.nova-2-multimodal-embeddings-v1:0`

    When embedding multiple texts with models that only support single-text requests,
    this controls how many requests run in parallel. Defaults to 5.
    """

bedrock_titan_normalize instance-attribute 

bedrock_titan_normalize: bool

Whether to normalize embedding vectors for Titan models.

Supported by: amazon.titan-embed-text-v2:0 (default: True)

Not supported by: amazon.titan-embed-text-v1 (silently ignored)

When enabled, vectors are normalized for direct cosine similarity calculations.

bedrock_cohere_max_tokens instance-attribute 

bedrock_cohere_max_tokens: int

The maximum number of tokens to embed for Cohere models.

Supported by: cohere.embed-v4:0 (default: 128000)

Not supported by: cohere.embed-english-v3, cohere.embed-multilingual-v3 (silently ignored)

bedrock_cohere_input_type instance-attribute 

bedrock_cohere_input_type: Literal[
    "search_document",
    "search_query",
    "classification",
    "clustering",
]

The input type for Cohere models.

Supported by: All Cohere models (cohere.embed-english-v3, cohere.embed-multilingual-v3, cohere.embed-v4:0)

By default, embed_query() uses 'search_query' and embed_documents() uses 'search_document'. Also accepts 'classification' or 'clustering'.

bedrock_cohere_truncate instance-attribute 

bedrock_cohere_truncate: Literal['NONE', 'START', 'END']

The truncation strategy for Cohere models. Overrides base truncate setting.

Supported by: All Cohere models (cohere.embed-english-v3, cohere.embed-multilingual-v3, cohere.embed-v4:0)

Default: 'NONE'

  • 'NONE': Raise an error if input exceeds max tokens.
  • 'START': Truncate the start of the input.
  • 'END': Truncate the end of the input.

bedrock_nova_truncate instance-attribute 

bedrock_nova_truncate: Literal['NONE', 'START', 'END']

The truncation strategy for Nova models. Overrides base truncate setting.

Supported by: amazon.nova-2-multimodal-embeddings-v1:0

Default: 'NONE'

  • 'NONE': Raise an error if input exceeds max tokens.
  • 'START': Truncate the start of the input.
  • 'END': Truncate the end of the input.

bedrock_nova_embedding_purpose instance-attribute 

bedrock_nova_embedding_purpose: Literal[
    "GENERIC_INDEX",
    "GENERIC_RETRIEVAL",
    "TEXT_RETRIEVAL",
    "CLASSIFICATION",
    "CLUSTERING",
]

The embedding purpose for Nova models.

Supported by: amazon.nova-2-multimodal-embeddings-v1:0

By default, embed_query() uses 'GENERIC_RETRIEVAL' and embed_documents() uses 'GENERIC_INDEX'. Also accepts 'TEXT_RETRIEVAL', 'CLASSIFICATION', or 'CLUSTERING'.

Note: Multimodal-specific purposes ('IMAGE_RETRIEVAL', 'VIDEO_RETRIEVAL', 'DOCUMENT_RETRIEVAL', 'AUDIO_RETRIEVAL') are not supported as this embedding client only accepts text input.

bedrock_max_concurrency instance-attribute 

bedrock_max_concurrency: int

Maximum number of concurrent requests for models that don't support batch embedding.

Applies to: amazon.titan-embed-text-v1, amazon.titan-embed-text-v2:0, amazon.nova-2-multimodal-embeddings-v1:0

When embedding multiple texts with models that only support single-text requests, this controls how many requests run in parallel. Defaults to 5.

BedrockEmbeddingModel dataclass

Bases: EmbeddingModel

Bedrock embedding model implementation.

This model works with AWS Bedrock's embedding models including Amazon Titan Embeddings and Cohere Embed models.

Example: 

from pydantic_ai.embeddings.bedrock import BedrockEmbeddingModel
from pydantic_ai.providers.bedrock import BedrockProvider

# Using default AWS credentials
model = BedrockEmbeddingModel('amazon.titan-embed-text-v2:0')

# Using explicit credentials
model = BedrockEmbeddingModel(
    'cohere.embed-english-v3',
    provider=BedrockProvider(
        region_name='us-east-1',
        aws_access_key_id='...',
        aws_secret_access_key='...',
    ),
)

Source code in pydantic_ai_slim/pydantic_ai/embeddings/bedrock.py 
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@dataclass(init=False)
class BedrockEmbeddingModel(EmbeddingModel):
    """Bedrock embedding model implementation.

    This model works with AWS Bedrock's embedding models including
    Amazon Titan Embeddings and Cohere Embed models.

    Example:
    ```python
    from pydantic_ai.embeddings.bedrock import BedrockEmbeddingModel
    from pydantic_ai.providers.bedrock import BedrockProvider

    # Using default AWS credentials
    model = BedrockEmbeddingModel('amazon.titan-embed-text-v2:0')

    # Using explicit credentials
    model = BedrockEmbeddingModel(
        'cohere.embed-english-v3',
        provider=BedrockProvider(
            region_name='us-east-1',
            aws_access_key_id='...',
            aws_secret_access_key='...',
        ),
    )
    ```
    """

    client: BedrockRuntimeClient

    _model_name: BedrockEmbeddingModelName = field(repr=False)
    _provider: Provider[BaseClient] = field(repr=False)
    _handler: _BedrockEmbeddingHandler = field(repr=False)

    def __init__(
        self,
        model_name: BedrockEmbeddingModelName,
        *,
        provider: Literal['bedrock'] | Provider[BaseClient] = 'bedrock',
        settings: EmbeddingSettings | None = None,
    ):
        """Initialize a Bedrock embedding model.

        Args:
            model_name: The name of the Bedrock embedding model to use.
                See [Bedrock embedding models](https://docs.aws.amazon.com/bedrock/latest/userguide/models-supported.html)
                for available options.
            provider: The provider to use for authentication and API access. Can be:

                - `'bedrock'` (default): Uses default AWS credentials
                - A [`BedrockProvider`][pydantic_ai.providers.bedrock.BedrockProvider] instance
                  for custom configuration

            settings: Model-specific [`EmbeddingSettings`][pydantic_ai.embeddings.EmbeddingSettings]
                to use as defaults for this model.
        """
        self._model_name = model_name

        if isinstance(provider, str):
            provider = infer_provider(provider)
        self._provider = provider
        self.client = cast('BedrockRuntimeClient', provider.client)
        self._handler = _get_handler_for_model(model_name)

        super().__init__(settings=settings)

    @property
    def base_url(self) -> str:
        """The base URL for the provider API."""
        return str(self.client.meta.endpoint_url)

    @property
    def model_name(self) -> BedrockEmbeddingModelName:
        """The embedding model name."""
        return self._model_name

    @property
    def system(self) -> str:
        """The embedding model provider."""
        return self._provider.name

    async def embed(
        self, inputs: str | Sequence[str], *, input_type: EmbedInputType, settings: EmbeddingSettings | None = None
    ) -> EmbeddingResult:
        inputs_list, settings_dict = self.prepare_embed(inputs, settings)
        settings_typed = cast(BedrockEmbeddingSettings, settings_dict)

        if self._handler.supports_batch:
            # Models like Cohere support batch requests
            return await self._embed_batch(inputs_list, input_type, settings_typed)
        else:
            # Models like Titan require individual requests
            return await self._embed_concurrent(inputs_list, input_type, settings_typed)

    async def _embed_batch(
        self,
        inputs: list[str],
        input_type: EmbedInputType,
        settings: BedrockEmbeddingSettings,
    ) -> EmbeddingResult:
        """Embed all inputs in a single batch request."""
        body = self._handler.prepare_request(inputs, input_type, settings)
        response, input_tokens = await self._invoke_model(body)
        embeddings, response_id = self._handler.parse_response(response)

        return EmbeddingResult(
            embeddings=embeddings,
            inputs=inputs,
            input_type=input_type,
            usage=RequestUsage(input_tokens=input_tokens),
            model_name=self.model_name,
            provider_name=self.system,
            provider_response_id=response_id,
        )

    async def _embed_concurrent(
        self,
        inputs: list[str],
        input_type: EmbedInputType,
        settings: BedrockEmbeddingSettings,
    ) -> EmbeddingResult:
        """Embed inputs concurrently with controlled parallelism and combine results."""
        max_concurrency = settings.get('bedrock_max_concurrency', 5)
        semaphore = anyio.Semaphore(max_concurrency)

        results: list[tuple[Sequence[float], int]] = [None] * len(inputs)  # type: ignore[list-item]

        async def embed_single(index: int, text: str) -> None:
            async with semaphore:
                body = self._handler.prepare_request([text], input_type, settings)
                response, input_tokens = await self._invoke_model(body)
                embeddings, _ = self._handler.parse_response(response)
                results[index] = (embeddings[0], input_tokens)

        async with anyio.create_task_group() as tg:
            for i, text in enumerate(inputs):
                tg.start_soon(embed_single, i, text)

        all_embeddings = [embedding for embedding, _ in results]
        total_input_tokens = sum(tokens for _, tokens in results)

        return EmbeddingResult(
            embeddings=all_embeddings,
            inputs=inputs,
            input_type=input_type,
            usage=RequestUsage(input_tokens=total_input_tokens),
            model_name=self.model_name,
            provider_name=self.system,
        )

    async def _invoke_model(self, body: dict[str, Any]) -> tuple[dict[str, Any], int]:
        """Invoke the Bedrock model and return parsed response with token count.

        Returns:
            A tuple of (response_body, input_token_count).
        """
        try:
            response: InvokeModelResponseTypeDef = await anyio.to_thread.run_sync(
                functools.partial(
                    self.client.invoke_model,
                    modelId=self._model_name,
                    body=json.dumps(body),
                    contentType='application/json',
                    accept='application/json',
                )
            )
        except ClientError as e:
            status_code = e.response.get('ResponseMetadata', {}).get('HTTPStatusCode')
            if isinstance(status_code, int):
                raise ModelHTTPError(status_code=status_code, model_name=self.model_name, body=e.response) from e
            raise ModelAPIError(model_name=self.model_name, message=str(e)) from e

        # Extract input token count from HTTP headers
        input_tokens = int(
            response.get('ResponseMetadata', {}).get('HTTPHeaders', {}).get('x-amzn-bedrock-input-token-count', '0')
        )

        response_body = json.loads(response['body'].read())
        return response_body, input_tokens

    async def max_input_tokens(self) -> int | None:
        """Get the maximum number of tokens that can be input to the model."""
        return _MAX_INPUT_TOKENS.get(self._handler.model_name, None)

__init__ 

__init__(
    model_name: BedrockEmbeddingModelName,
    *,
    provider: (
        Literal["bedrock"] | Provider[BaseClient]
    ) = "bedrock",
    settings: EmbeddingSettings | None = None
)

Initialize a Bedrock embedding model.

Parameters:

Name Type Description Default
model_name BedrockEmbeddingModelName

The name of the Bedrock embedding model to use. See Bedrock embedding models for available options.

 required
provider Literal['bedrock'] | Provider[BaseClient]

The provider to use for authentication and API access. Can be:

  • 'bedrock' (default): Uses default AWS credentials
  • A BedrockProvider instance for custom configuration
 'bedrock'
settings EmbeddingSettings | None

Model-specific EmbeddingSettings to use as defaults for this model.

 None
Source code in pydantic_ai_slim/pydantic_ai/embeddings/bedrock.py 
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def __init__(
    self,
    model_name: BedrockEmbeddingModelName,
    *,
    provider: Literal['bedrock'] | Provider[BaseClient] = 'bedrock',
    settings: EmbeddingSettings | None = None,
):
    """Initialize a Bedrock embedding model.

    Args:
        model_name: The name of the Bedrock embedding model to use.
            See [Bedrock embedding models](https://docs.aws.amazon.com/bedrock/latest/userguide/models-supported.html)
            for available options.
        provider: The provider to use for authentication and API access. Can be:

            - `'bedrock'` (default): Uses default AWS credentials
            - A [`BedrockProvider`][pydantic_ai.providers.bedrock.BedrockProvider] instance
              for custom configuration

        settings: Model-specific [`EmbeddingSettings`][pydantic_ai.embeddings.EmbeddingSettings]
            to use as defaults for this model.
    """
    self._model_name = model_name

    if isinstance(provider, str):
        provider = infer_provider(provider)
    self._provider = provider
    self.client = cast('BedrockRuntimeClient', provider.client)
    self._handler = _get_handler_for_model(model_name)

    super().__init__(settings=settings)

base_url property 

base_url: str

The base URL for the provider API.

model_name property 

model_name: BedrockEmbeddingModelName

The embedding model name.

system property 

system: str

The embedding model provider.

max_input_tokens async 

max_input_tokens() -> int | None

Get the maximum number of tokens that can be input to the model.

Source code in pydantic_ai_slim/pydantic_ai/embeddings/bedrock.py 
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async def max_input_tokens(self) -> int | None:
    """Get the maximum number of tokens that can be input to the model."""
    return _MAX_INPUT_TOKENS.get(self._handler.model_name, None)

LatestVoyageAIEmbeddingModelNames module-attribute 

LatestVoyageAIEmbeddingModelNames = Literal[
    "voyage-4-large",
    "voyage-4",
    "voyage-4-lite",
    "voyage-3-large",
    "voyage-3.5",
    "voyage-3.5-lite",
    "voyage-code-3",
    "voyage-finance-2",
    "voyage-law-2",
    "voyage-code-2",
]

Latest VoyageAI embedding models.

See VoyageAI Embeddings for available models and their capabilities.

VoyageAIEmbeddingModelName module-attribute 

VoyageAIEmbeddingModelName = (
    str | LatestVoyageAIEmbeddingModelNames
)

Possible VoyageAI embedding model names.

VoyageAIEmbedInputType module-attribute 

VoyageAIEmbedInputType = Literal[
    "query", "document", "none"
]

VoyageAI embedding input types.

  • 'query': For search queries; prepends retrieval-optimized prefix.
  • 'document': For documents; prepends document retrieval prefix.
  • 'none': Direct embedding without any prefix.

VoyageAIEmbeddingSettings

Bases: EmbeddingSettings

Settings used for a VoyageAI embedding model request.

All fields from EmbeddingSettings are supported, plus VoyageAI-specific settings prefixed with voyageai_.

Source code in pydantic_ai_slim/pydantic_ai/embeddings/voyageai.py 
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class VoyageAIEmbeddingSettings(EmbeddingSettings, total=False):
    """Settings used for a VoyageAI embedding model request.

    All fields from [`EmbeddingSettings`][pydantic_ai.embeddings.EmbeddingSettings] are supported,
    plus VoyageAI-specific settings prefixed with `voyageai_`.
    """

    # ALL FIELDS MUST BE `voyageai_` PREFIXED SO YOU CAN MERGE THEM WITH OTHER MODELS.

    voyageai_input_type: VoyageAIEmbedInputType
    """The VoyageAI-specific input type for the embedding.

    Overrides the standard `input_type` argument. Options include:
    `'query'`, `'document'`, or `'none'` for direct embedding without prefix.
    """

voyageai_input_type instance-attribute 

voyageai_input_type: VoyageAIEmbedInputType

The VoyageAI-specific input type for the embedding.

Overrides the standard input_type argument. Options include: 'query', 'document', or 'none' for direct embedding without prefix.

VoyageAIEmbeddingModel dataclass

Bases: EmbeddingModel

VoyageAI embedding model implementation.

VoyageAI provides state-of-the-art embedding models optimized for retrieval, with specialized models for code, finance, and legal domains.

Example: 

from pydantic_ai.embeddings.voyageai import VoyageAIEmbeddingModel

model = VoyageAIEmbeddingModel('voyage-3.5')

Source code in pydantic_ai_slim/pydantic_ai/embeddings/voyageai.py 
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@dataclass(init=False)
class VoyageAIEmbeddingModel(EmbeddingModel):
    """VoyageAI embedding model implementation.

    VoyageAI provides state-of-the-art embedding models optimized for
    retrieval, with specialized models for code, finance, and legal domains.

    Example:
    ```python
    from pydantic_ai.embeddings.voyageai import VoyageAIEmbeddingModel

    model = VoyageAIEmbeddingModel('voyage-3.5')
    ```
    """

    _model_name: VoyageAIEmbeddingModelName = field(repr=False)
    _provider: Provider[AsyncClient] = field(repr=False)

    def __init__(
        self,
        model_name: VoyageAIEmbeddingModelName,
        *,
        provider: Literal['voyageai'] | Provider[AsyncClient] = 'voyageai',
        settings: EmbeddingSettings | None = None,
    ):
        """Initialize a VoyageAI embedding model.

        Args:
            model_name: The name of the VoyageAI model to use.
                See [VoyageAI models](https://docs.voyageai.com/docs/embeddings)
                for available options.
            provider: The provider to use for authentication and API access. Can be:

                - `'voyageai'` (default): Uses the standard VoyageAI API
                - A [`VoyageAIProvider`][pydantic_ai.providers.voyageai.VoyageAIProvider] instance
                  for custom configuration
            settings: Model-specific [`EmbeddingSettings`][pydantic_ai.embeddings.EmbeddingSettings]
                to use as defaults for this model.
        """
        self._model_name = model_name

        if isinstance(provider, str):
            provider = infer_provider(provider)
        self._provider = provider

        super().__init__(settings=settings)

    @property
    def base_url(self) -> str:
        """The base URL for the provider API."""
        return self._provider.base_url

    @property
    def model_name(self) -> VoyageAIEmbeddingModelName:
        """The embedding model name."""
        return self._model_name

    @property
    def system(self) -> str:
        """The embedding model provider."""
        return self._provider.name

    async def embed(
        self,
        inputs: str | Sequence[str],
        *,
        input_type: EmbedInputType,
        settings: EmbeddingSettings | None = None,
    ) -> EmbeddingResult:
        inputs, settings = self.prepare_embed(inputs, settings)
        settings = cast(VoyageAIEmbeddingSettings, settings)

        voyageai_input_type: VoyageAIEmbedInputType = settings.get(
            'voyageai_input_type', 'document' if input_type == 'document' else 'query'
        )
        # Convert 'none' string to None for the API
        api_input_type = None if voyageai_input_type == 'none' else voyageai_input_type

        try:
            response = await self._provider.client.embed(
                texts=list(inputs),
                model=self.model_name,
                input_type=api_input_type,
                truncation=settings.get('truncate', False),
                output_dimension=settings.get('dimensions'),
            )
        except VoyageError as e:
            raise ModelAPIError(model_name=self.model_name, message=str(e)) from e

        return EmbeddingResult(
            embeddings=response.embeddings,
            inputs=inputs,
            input_type=input_type,
            usage=_map_usage(response.total_tokens),
            model_name=self.model_name,
            provider_name=self.system,
        )

    async def max_input_tokens(self) -> int | None:
        return _MAX_INPUT_TOKENS.get(self.model_name)

__init__ 

__init__(
    model_name: VoyageAIEmbeddingModelName,
    *,
    provider: (
        Literal["voyageai"] | Provider[AsyncClient]
    ) = "voyageai",
    settings: EmbeddingSettings | None = None
)

Initialize a VoyageAI embedding model.

Parameters:

Name Type Description Default
model_name VoyageAIEmbeddingModelName

The name of the VoyageAI model to use. See VoyageAI models for available options.

 required
provider Literal['voyageai'] | Provider[AsyncClient]

The provider to use for authentication and API access. Can be:

  • 'voyageai' (default): Uses the standard VoyageAI API
  • A VoyageAIProvider instance for custom configuration
 'voyageai'
settings EmbeddingSettings | None

Model-specific EmbeddingSettings to use as defaults for this model.

 None
Source code in pydantic_ai_slim/pydantic_ai/embeddings/voyageai.py 
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def __init__(
    self,
    model_name: VoyageAIEmbeddingModelName,
    *,
    provider: Literal['voyageai'] | Provider[AsyncClient] = 'voyageai',
    settings: EmbeddingSettings | None = None,
):
    """Initialize a VoyageAI embedding model.

    Args:
        model_name: The name of the VoyageAI model to use.
            See [VoyageAI models](https://docs.voyageai.com/docs/embeddings)
            for available options.
        provider: The provider to use for authentication and API access. Can be:

            - `'voyageai'` (default): Uses the standard VoyageAI API
            - A [`VoyageAIProvider`][pydantic_ai.providers.voyageai.VoyageAIProvider] instance
              for custom configuration
        settings: Model-specific [`EmbeddingSettings`][pydantic_ai.embeddings.EmbeddingSettings]
            to use as defaults for this model.
    """
    self._model_name = model_name

    if isinstance(provider, str):
        provider = infer_provider(provider)
    self._provider = provider

    super().__init__(settings=settings)

base_url property 

base_url: str

The base URL for the provider API.

model_name property 

model_name: VoyageAIEmbeddingModelName

The embedding model name.

system property 

system: str

The embedding model provider.

SentenceTransformersEmbeddingSettings

Bases: EmbeddingSettings

Settings used for a Sentence-Transformers embedding model request.

All fields from EmbeddingSettings are supported, plus Sentence-Transformers-specific settings prefixed with sentence_transformers_.

Source code in pydantic_ai_slim/pydantic_ai/embeddings/sentence_transformers.py 
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class SentenceTransformersEmbeddingSettings(EmbeddingSettings, total=False):
    """Settings used for a Sentence-Transformers embedding model request.

    All fields from [`EmbeddingSettings`][pydantic_ai.embeddings.EmbeddingSettings] are supported,
    plus Sentence-Transformers-specific settings prefixed with `sentence_transformers_`.
    """

    sentence_transformers_device: str
    """Device to run inference on.

    Examples: `'cpu'`, `'cuda'`, `'cuda:0'`, `'mps'` (Apple Silicon).
    """

    sentence_transformers_normalize_embeddings: bool
    """Whether to L2-normalize embeddings.

    When `True`, all embeddings will have unit length, which is useful for
    cosine similarity calculations.
    """

    sentence_transformers_batch_size: int
    """Batch size to use during encoding.

    Larger batches may be faster but require more memory.
    """

sentence_transformers_device instance-attribute 

sentence_transformers_device: str

Device to run inference on.

Examples: 'cpu', 'cuda', 'cuda:0', 'mps' (Apple Silicon).

sentence_transformers_normalize_embeddings instance-attribute 

sentence_transformers_normalize_embeddings: bool

Whether to L2-normalize embeddings.

When True, all embeddings will have unit length, which is useful for cosine similarity calculations.

sentence_transformers_batch_size instance-attribute 

sentence_transformers_batch_size: int

Batch size to use during encoding.

Larger batches may be faster but require more memory.

SentenceTransformerEmbeddingModel dataclass

Bases: EmbeddingModel

Local embedding model using the sentence-transformers library.

This model runs embeddings locally on your machine, which is useful for:

  • Privacy-sensitive applications where data shouldn't leave your infrastructure
  • Reducing API costs for high-volume embedding workloads
  • Offline or air-gapped environments

Models are downloaded from Hugging Face on first use. See the Sentence-Transformers documentation for available models.

Example: 

from sentence_transformers import SentenceTransformer

from pydantic_ai.embeddings.sentence_transformers import (
    SentenceTransformerEmbeddingModel,
)

# Using a model name (downloads from Hugging Face)
model = SentenceTransformerEmbeddingModel('all-MiniLM-L6-v2')

# Using an existing SentenceTransformer instance
st_model = SentenceTransformer('all-MiniLM-L6-v2')
model = SentenceTransformerEmbeddingModel(st_model)

Source code in pydantic_ai_slim/pydantic_ai/embeddings/sentence_transformers.py 
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@dataclass(init=False)
class SentenceTransformerEmbeddingModel(EmbeddingModel):
    """Local embedding model using the `sentence-transformers` library.

    This model runs embeddings locally on your machine, which is useful for:

    - Privacy-sensitive applications where data shouldn't leave your infrastructure
    - Reducing API costs for high-volume embedding workloads
    - Offline or air-gapped environments

    Models are downloaded from Hugging Face on first use.
    See the [Sentence-Transformers documentation](https://www.sbert.net/docs/sentence_transformer/pretrained_models.html)
    for available models.

    Example:
    ```python
    from sentence_transformers import SentenceTransformer

    from pydantic_ai.embeddings.sentence_transformers import (
        SentenceTransformerEmbeddingModel,
    )

    # Using a model name (downloads from Hugging Face)
    model = SentenceTransformerEmbeddingModel('all-MiniLM-L6-v2')

    # Using an existing SentenceTransformer instance
    st_model = SentenceTransformer('all-MiniLM-L6-v2')
    model = SentenceTransformerEmbeddingModel(st_model)
    ```
    """

    _model_name: str = field(repr=False)
    _model: SentenceTransformer | None = field(repr=False, default=None)

    def __init__(self, model: SentenceTransformer | str, *, settings: EmbeddingSettings | None = None) -> None:
        """Initialize a Sentence-Transformers embedding model.

        Args:
            model: The model to use. Can be:

                - A model name from Hugging Face (e.g., `'all-MiniLM-L6-v2'`)
                - A local path to a saved model
                - An existing `SentenceTransformer` instance
            settings: Model-specific
                [`SentenceTransformersEmbeddingSettings`][pydantic_ai.embeddings.sentence_transformers.SentenceTransformersEmbeddingSettings]
                to use as defaults for this model.
        """
        if isinstance(model, str):
            self._model_name = model
        else:
            self._model = deepcopy(model)
            self._model_name = model.model_card_data.model_id or model.model_card_data.base_model or 'unknown'

        super().__init__(settings=settings)

    @property
    def base_url(self) -> str | None:
        """No base URL — runs locally."""
        return None

    @property
    def model_name(self) -> str:
        """The embedding model name."""
        return self._model_name

    @property
    def system(self) -> str:
        """The embedding model provider/system identifier."""
        return 'sentence-transformers'

    async def embed(
        self, inputs: str | Sequence[str], *, input_type: EmbedInputType, settings: EmbeddingSettings | None = None
    ) -> EmbeddingResult:
        inputs, settings = self.prepare_embed(inputs, settings)
        settings = cast(SentenceTransformersEmbeddingSettings, settings)

        device = settings.get('sentence_transformers_device', None)
        normalize = settings.get('sentence_transformers_normalize_embeddings', False)
        batch_size = settings.get('sentence_transformers_batch_size', None)
        dimensions = settings.get('dimensions', None)

        model = await self._get_model()
        encode_func = model.encode_query if input_type == 'query' else model.encode_document  # type: ignore[reportUnknownReturnType]

        np_embeddings: np.ndarray[Any, float] = await _utils.run_in_executor(  # type: ignore[reportAssignmentType]
            encode_func,  # type: ignore[reportArgumentType]
            inputs,
            show_progress_bar=False,
            convert_to_numpy=True,
            convert_to_tensor=False,
            device=device,
            normalize_embeddings=normalize,
            truncate_dim=dimensions,
            **{'batch_size': batch_size} if batch_size is not None else {},  # type: ignore[reportArgumentType]
        )
        embeddings = np_embeddings.tolist()

        return EmbeddingResult(
            embeddings=embeddings,
            inputs=inputs,
            input_type=input_type,
            model_name=self.model_name,
            provider_name=self.system,
        )

    async def max_input_tokens(self) -> int | None:
        model = await self._get_model()
        return model.get_max_seq_length()

    async def count_tokens(self, text: str) -> int:
        model = await self._get_model()
        result: dict[str, torch.Tensor] = await _utils.run_in_executor(
            model.tokenize,  # type: ignore[reportArgumentType]
            [text],
        )
        if 'input_ids' not in result or not isinstance(result['input_ids'], torch.Tensor):  # pragma: no cover
            raise UnexpectedModelBehavior(
                'The SentenceTransformers tokenizer output did not have an `input_ids` field holding a tensor',
                str(result),
            )
        return len(result['input_ids'][0])

    async def _get_model(self) -> SentenceTransformer:
        if self._model is None:
            # This may download the model from Hugging Face, so we do it in a thread
            self._model = await _utils.run_in_executor(SentenceTransformer, self.model_name)  # pragma: no cover
        return self._model

__init__ 

__init__(
    model: SentenceTransformer | str,
    *,
    settings: EmbeddingSettings | None = None
) -> None

Initialize a Sentence-Transformers embedding model.

Parameters:

Name Type Description Default
model SentenceTransformer | str

The model to use. Can be:

  • A model name from Hugging Face (e.g., 'all-MiniLM-L6-v2')
  • A local path to a saved model
  • An existing SentenceTransformer instance
 required
settings EmbeddingSettings | None

Model-specific SentenceTransformersEmbeddingSettings to use as defaults for this model.

 None
Source code in pydantic_ai_slim/pydantic_ai/embeddings/sentence_transformers.py 
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def __init__(self, model: SentenceTransformer | str, *, settings: EmbeddingSettings | None = None) -> None:
    """Initialize a Sentence-Transformers embedding model.

    Args:
        model: The model to use. Can be:

            - A model name from Hugging Face (e.g., `'all-MiniLM-L6-v2'`)
            - A local path to a saved model
            - An existing `SentenceTransformer` instance
        settings: Model-specific
            [`SentenceTransformersEmbeddingSettings`][pydantic_ai.embeddings.sentence_transformers.SentenceTransformersEmbeddingSettings]
            to use as defaults for this model.
    """
    if isinstance(model, str):
        self._model_name = model
    else:
        self._model = deepcopy(model)
        self._model_name = model.model_card_data.model_id or model.model_card_data.base_model or 'unknown'

    super().__init__(settings=settings)

base_url property 

base_url: str | None

No base URL — runs locally.

model_name property 

model_name: str

The embedding model name.

system property 

system: str

The embedding model provider/system identifier.

TestEmbeddingModel dataclass

Bases: EmbeddingModel

A mock embedding model for testing.

This model returns deterministic embeddings (all 1.0 values) and tracks the settings used in the last call via the last_settings attribute.

Example: 

from pydantic_ai import Embedder
from pydantic_ai.embeddings import TestEmbeddingModel

test_model = TestEmbeddingModel()
embedder = Embedder('openai:text-embedding-3-small')


async def main():
    with embedder.override(model=test_model):
        await embedder.embed_query('test')
        assert test_model.last_settings is not None

Source code in pydantic_ai_slim/pydantic_ai/embeddings/test.py 
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@dataclass(init=False)
class TestEmbeddingModel(EmbeddingModel):
    """A mock embedding model for testing.

    This model returns deterministic embeddings (all 1.0 values) and tracks
    the settings used in the last call via the `last_settings` attribute.

    Example:
    ```python
    from pydantic_ai import Embedder
    from pydantic_ai.embeddings import TestEmbeddingModel

    test_model = TestEmbeddingModel()
    embedder = Embedder('openai:text-embedding-3-small')


    async def main():
        with embedder.override(model=test_model):
            await embedder.embed_query('test')
            assert test_model.last_settings is not None
    ```
    """

    # NOTE: Avoid test discovery by pytest.
    __test__ = False

    _model_name: str
    """The model name to report in results."""

    _provider_name: str
    """The provider name to report in results."""

    _dimensions: int
    """The number of dimensions for generated embeddings."""

    last_settings: EmbeddingSettings | None = None
    """The settings used in the most recent embed call."""

    def __init__(
        self,
        model_name: str = 'test',
        *,
        provider_name: str = 'test',
        dimensions: int = 8,
        settings: EmbeddingSettings | None = None,
    ):
        """Initialize the test embedding model.

        Args:
            model_name: The model name to report in results.
            provider_name: The provider name to report in results.
            dimensions: The number of dimensions for the generated embeddings.
            settings: Optional default settings for the model.
        """
        self._model_name = model_name
        self._provider_name = provider_name
        self._dimensions = dimensions
        self.last_settings = None
        super().__init__(settings=settings)

    @property
    def model_name(self) -> str:
        """The embedding model name."""
        return self._model_name

    @property
    def system(self) -> str:
        """The embedding model provider."""
        return self._provider_name

    async def embed(
        self, inputs: str | Sequence[str], *, input_type: EmbedInputType, settings: EmbeddingSettings | None = None
    ) -> EmbeddingResult:
        inputs, settings = self.prepare_embed(inputs, settings)
        self.last_settings = settings

        dimensions = settings.get('dimensions') or self._dimensions

        return EmbeddingResult(
            embeddings=[[1.0] * dimensions] * len(inputs),
            inputs=inputs,
            input_type=input_type,
            usage=RequestUsage(input_tokens=sum(_estimate_tokens(text) for text in inputs)),
            model_name=self.model_name,
            provider_name=self.system,
            provider_response_id=str(uuid.uuid4()),
        )

    async def max_input_tokens(self) -> int | None:
        return 1024

    async def count_tokens(self, text: str) -> int:
        return _estimate_tokens(text)

__init__ 

__init__(
    model_name: str = "test",
    *,
    provider_name: str = "test",
    dimensions: int = 8,
    settings: EmbeddingSettings | None = None
)

Initialize the test embedding model.

Parameters:

Name Type Description Default
model_name str

The model name to report in results.

 'test'
provider_name str

The provider name to report in results.

 'test'
dimensions int

The number of dimensions for the generated embeddings.

 8
settings EmbeddingSettings | None

Optional default settings for the model.

 None
Source code in pydantic_ai_slim/pydantic_ai/embeddings/test.py 
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def __init__(
    self,
    model_name: str = 'test',
    *,
    provider_name: str = 'test',
    dimensions: int = 8,
    settings: EmbeddingSettings | None = None,
):
    """Initialize the test embedding model.

    Args:
        model_name: The model name to report in results.
        provider_name: The provider name to report in results.
        dimensions: The number of dimensions for the generated embeddings.
        settings: Optional default settings for the model.
    """
    self._model_name = model_name
    self._provider_name = provider_name
    self._dimensions = dimensions
    self.last_settings = None
    super().__init__(settings=settings)

last_settings class-attribute instance-attribute 

last_settings: EmbeddingSettings | None = None

The settings used in the most recent embed call.

model_name property 

model_name: str

The embedding model name.

system property 

system: str

The embedding model provider.

WrapperEmbeddingModel dataclass

Bases: EmbeddingModel

Base class for embedding models that wrap another model.

Use this as a base class to create custom embedding model wrappers that modify behavior (e.g., caching, logging, rate limiting) while delegating to an underlying model.

By default, all methods are passed through to the wrapped model. Override specific methods to customize behavior.

Source code in pydantic_ai_slim/pydantic_ai/embeddings/wrapper.py 
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@dataclass(init=False)
class WrapperEmbeddingModel(EmbeddingModel):
    """Base class for embedding models that wrap another model.

    Use this as a base class to create custom embedding model wrappers
    that modify behavior (e.g., caching, logging, rate limiting) while
    delegating to an underlying model.

    By default, all methods are passed through to the wrapped model.
    Override specific methods to customize behavior.
    """

    wrapped: EmbeddingModel
    """The underlying embedding model being wrapped."""

    def __init__(self, wrapped: EmbeddingModel | str):
        """Initialize the wrapper with an embedding model.

        Args:
            wrapped: The model to wrap. Can be an
                [`EmbeddingModel`][pydantic_ai.embeddings.EmbeddingModel] instance
                or a model name string (e.g., `'openai:text-embedding-3-small'`).
        """
        from . import infer_embedding_model

        super().__init__()
        self.wrapped = infer_embedding_model(wrapped) if isinstance(wrapped, str) else wrapped

    async def embed(
        self, inputs: str | Sequence[str], *, input_type: EmbedInputType, settings: EmbeddingSettings | None = None
    ) -> EmbeddingResult:
        return await self.wrapped.embed(inputs, input_type=input_type, settings=settings)

    async def max_input_tokens(self) -> int | None:
        return await self.wrapped.max_input_tokens()

    async def count_tokens(self, text: str) -> int:
        return await self.wrapped.count_tokens(text)

    @property
    def model_name(self) -> str:
        return self.wrapped.model_name

    @property
    def system(self) -> str:
        return self.wrapped.system

    @property
    def settings(self) -> EmbeddingSettings | None:
        """Get the settings from the wrapped embedding model."""
        return self.wrapped.settings

    @property
    def base_url(self) -> str | None:
        return self.wrapped.base_url

    def __getattr__(self, item: str):
        return getattr(self.wrapped, item)  # pragma: no cover

__init__ 

__init__(wrapped: EmbeddingModel | str)

Initialize the wrapper with an embedding model.

Parameters:

Name Type Description Default
wrapped EmbeddingModel | str

The model to wrap. Can be an EmbeddingModel instance or a model name string (e.g., 'openai:text-embedding-3-small').

 required
Source code in pydantic_ai_slim/pydantic_ai/embeddings/wrapper.py 
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def __init__(self, wrapped: EmbeddingModel | str):
    """Initialize the wrapper with an embedding model.

    Args:
        wrapped: The model to wrap. Can be an
            [`EmbeddingModel`][pydantic_ai.embeddings.EmbeddingModel] instance
            or a model name string (e.g., `'openai:text-embedding-3-small'`).
    """
    from . import infer_embedding_model

    super().__init__()
    self.wrapped = infer_embedding_model(wrapped) if isinstance(wrapped, str) else wrapped

wrapped instance-attribute 

wrapped: EmbeddingModel = (
    infer_embedding_model(wrapped)
    if isinstance(wrapped, str)
    else wrapped
)

The underlying embedding model being wrapped.

settings property 

settings: EmbeddingSettings | None

Get the settings from the wrapped embedding model.

instrument_embedding_model 

instrument_embedding_model(
    model: EmbeddingModel,
    instrument: InstrumentationSettings | bool,
) -> EmbeddingModel

Instrument an embedding model with OpenTelemetry/logfire.

Source code in pydantic_ai_slim/pydantic_ai/embeddings/instrumented.py 
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def instrument_embedding_model(model: EmbeddingModel, instrument: InstrumentationSettings | bool) -> EmbeddingModel:
    """Instrument an embedding model with OpenTelemetry/logfire."""
    if instrument and not isinstance(model, InstrumentedEmbeddingModel):
        if instrument is True:
            instrument = InstrumentationSettings()

        model = InstrumentedEmbeddingModel(model, instrument)

    return model

InstrumentedEmbeddingModel dataclass

Bases: WrapperEmbeddingModel

Embedding model which wraps another model so that requests are instrumented with OpenTelemetry.

See the Debugging and Monitoring guide for more info.

Source code in pydantic_ai_slim/pydantic_ai/embeddings/instrumented.py 
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@dataclass(init=False)
class InstrumentedEmbeddingModel(WrapperEmbeddingModel):
    """Embedding model which wraps another model so that requests are instrumented with OpenTelemetry.

    See the [Debugging and Monitoring guide](https://ai.pydantic.dev/logfire/) for more info.
    """

    instrumentation_settings: InstrumentationSettings
    """Instrumentation settings for this model."""

    def __init__(
        self,
        wrapped: EmbeddingModel | str,
        options: InstrumentationSettings | None = None,
    ) -> None:
        super().__init__(wrapped)
        self.instrumentation_settings = options or InstrumentationSettings()

    async def embed(
        self, inputs: str | Sequence[str], *, input_type: EmbedInputType, settings: EmbeddingSettings | None = None
    ) -> EmbeddingResult:
        inputs, settings = self.prepare_embed(inputs, settings)
        with self._instrument(inputs, input_type, settings) as finish:
            result = await super().embed(inputs, input_type=input_type, settings=settings)
            finish(result)
            return result

    @contextmanager
    def _instrument(
        self,
        inputs: list[str],
        input_type: EmbedInputType,
        settings: EmbeddingSettings | None,
    ) -> Iterator[Callable[[EmbeddingResult], None]]:
        operation = 'embeddings'
        span_name = f'{operation} {self.model_name}'

        inputs_count = len(inputs)

        attributes: dict[str, AttributeValue] = {
            'gen_ai.operation.name': operation,
            **self.model_attributes(self.wrapped),
            'input_type': input_type,
            'inputs_count': inputs_count,
        }

        if settings:
            attributes['embedding_settings'] = json.dumps(self.serialize_any(settings))

        if self.instrumentation_settings.include_content:
            attributes['inputs'] = json.dumps(inputs)

        attributes['logfire.json_schema'] = json.dumps(
            {
                'type': 'object',
                'properties': {
                    'input_type': {'type': 'string'},
                    'inputs_count': {'type': 'integer'},
                    'embedding_settings': {'type': 'object'},
                    **(
                        {'inputs': {'type': ['array']}, 'embeddings': {'type': 'array'}}
                        if self.instrumentation_settings.include_content
                        else {}
                    ),
                },
            }
        )

        record_metrics: Callable[[], None] | None = None
        try:
            with self.instrumentation_settings.tracer.start_as_current_span(span_name, attributes=attributes) as span:

                def finish(result: EmbeddingResult):
                    # Prepare metric recording closure first so metrics are recorded
                    # even if the span is not recording.
                    provider_name = attributes[GEN_AI_PROVIDER_NAME_ATTRIBUTE]
                    request_model = attributes[GEN_AI_REQUEST_MODEL_ATTRIBUTE]
                    response_model = result.model_name or request_model
                    price_calculation = None

                    def _record_metrics():
                        token_attributes = {
                            GEN_AI_PROVIDER_NAME_ATTRIBUTE: provider_name,
                            'gen_ai.operation.name': operation,
                            GEN_AI_REQUEST_MODEL_ATTRIBUTE: request_model,
                            'gen_ai.response.model': response_model,
                            'gen_ai.token.type': 'input',
                        }
                        tokens = result.usage.input_tokens or 0
                        if tokens:  # pragma: no branch
                            self.instrumentation_settings.tokens_histogram.record(tokens, token_attributes)
                            if price_calculation is not None:
                                self.instrumentation_settings.cost_histogram.record(
                                    float(getattr(price_calculation, 'input_price', 0.0)),
                                    token_attributes,
                                )

                    nonlocal record_metrics
                    record_metrics = _record_metrics

                    if not span.is_recording():
                        return

                    attributes_to_set: dict[str, AttributeValue] = {
                        **result.usage.opentelemetry_attributes(),
                        'gen_ai.response.model': response_model,
                    }

                    try:
                        price_calculation = result.cost()
                    except LookupError:
                        # The cost of this provider/model is unknown, which is common.
                        pass
                    except Exception as e:  # pragma: no cover
                        warnings.warn(
                            f'Failed to get cost from response: {type(e).__name__}: {e}', CostCalculationFailedWarning
                        )
                    else:
                        attributes_to_set['operation.cost'] = float(price_calculation.total_price)

                    embeddings = result.embeddings
                    if embeddings:  # pragma: no branch
                        attributes_to_set['gen_ai.embeddings.dimension.count'] = len(embeddings[0])
                        if self.instrumentation_settings.include_content:
                            attributes['embeddings'] = json.dumps(embeddings)

                    if result.provider_response_id is not None:
                        attributes_to_set['gen_ai.response.id'] = result.provider_response_id

                    span.set_attributes(attributes_to_set)

                yield finish
        finally:
            if record_metrics:  # pragma: no branch
                # Record metrics after the span finishes to avoid duplication.
                record_metrics()

    @staticmethod
    def model_attributes(model: EmbeddingModel) -> dict[str, AttributeValue]:
        attributes: dict[str, AttributeValue] = {
            GEN_AI_PROVIDER_NAME_ATTRIBUTE: model.system,
            GEN_AI_REQUEST_MODEL_ATTRIBUTE: model.model_name,
        }
        if base_url := model.base_url:
            try:
                parsed = urlparse(base_url)
            except Exception:  # pragma: no cover
                pass
            else:
                if parsed.hostname:  # pragma: no branch
                    attributes['server.address'] = parsed.hostname
                if parsed.port:
                    attributes['server.port'] = parsed.port  # pragma: no cover

        return attributes

    @staticmethod
    def serialize_any(value: Any) -> str:
        try:
            return ANY_ADAPTER.dump_python(value, mode='json')
        except Exception:  # pragma: no cover
            try:
                return str(value)
            except Exception as e:
                return f'Unable to serialize: {e}'

instrumentation_settings instance-attribute 

instrumentation_settings: InstrumentationSettings = (
    options or InstrumentationSettings()
)

Instrumentation settings for this model.