Module azure_document_embedder

AzureOpenAIDocumentEmbedder

A component for computing Document embeddings using OpenAI models on Azure.

Usage example:

from haystack import Document
from haystack.components.embedders import AzureOpenAIDocumentEmbedder

doc = Document(content="I love pizza!")

document_embedder = AzureOpenAIDocumentEmbedder()

result = document_embedder.run([doc])
print(result['documents'][0].embedding)

# [0.017020374536514282, -0.023255806416273117, ...]

AzureOpenAIDocumentEmbedder.__init__

def __init__(azure_endpoint: Optional[str] = None,
             api_version: Optional[str] = "2023-05-15",
             azure_deployment: str = "text-embedding-ada-002",
             dimensions: Optional[int] = None,
             api_key: Optional[Secret] = Secret.from_env_var(
                 "AZURE_OPENAI_API_KEY", strict=False),
             azure_ad_token: Optional[Secret] = Secret.from_env_var(
                 "AZURE_OPENAI_AD_TOKEN", strict=False),
             organization: Optional[str] = None,
             prefix: str = "",
             suffix: str = "",
             batch_size: int = 32,
             progress_bar: bool = True,
             meta_fields_to_embed: Optional[List[str]] = None,
             embedding_separator: str = "\n",
             timeout: Optional[float] = None,
             max_retries: Optional[int] = None)

Create an AzureOpenAIDocumentEmbedder component.

Arguments:

• azure_endpoint: The endpoint of the deployed model.
• api_version: The version of the API to use.
• azure_deployment: The deployment of the model, usually matches the model name.
• dimensions: The number of dimensions the resulting output embeddings should have. Only supported in text-embedding-3 and later models.
• api_key: The API key used for authentication.
• azure_ad_token: Microsoft Entra ID token, see Microsoft's official Entra ID documentation for more information. Used to be called Azure Active Directory.
• organization: The Organization ID. See OpenAI's production best practices for more information.
• prefix: A string to add at the beginning of each text.
• suffix: A string to add at the end of each text.
• batch_size: Number of Documents to encode at once.
• progress_bar: If True shows a progress bar when running.
• meta_fields_to_embed: List of meta fields that will be embedded along with the Document text.
• embedding_separator: Separator used to concatenate the meta fields to the Document text.
• timeout: The timeout in seconds to be passed to the underlying AzureOpenAI client, if not set it is inferred from the OPENAI_TIMEOUT environment variable or set to 30.
• max_retries: Maximum retries to establish a connection with AzureOpenAI if it returns an internal error, if not set it is inferred from the OPENAI_MAX_RETRIES environment variable or set to 5.

AzureOpenAIDocumentEmbedder.to_dict

def to_dict() -> Dict[str, Any]

Serializes the component to a dictionary.

Returns:

Dictionary with serialized data.

AzureOpenAIDocumentEmbedder.from_dict

@classmethod
def from_dict(cls, data: Dict[str, Any]) -> "AzureOpenAIDocumentEmbedder"

Deserializes the component from a dictionary.

Arguments:

• data: Dictionary to deserialize from.

Returns:

Deserialized component.

AzureOpenAIDocumentEmbedder.run

@component.output_types(documents=List[Document], meta=Dict[str, Any])
def run(documents: List[Document]) -> Dict[str, Any]

Embed a list of Documents.

Arguments:

• documents: Documents to embed.

Returns:

A dictionary with the following keys:

• documents: Documents with embeddings
• meta: Information about the usage of the model.

Module azure_text_embedder

AzureOpenAITextEmbedder

A component for embedding strings using OpenAI models on Azure.

Usage example:

from haystack.components.embedders import AzureOpenAITextEmbedder

text_to_embed = "I love pizza!"

text_embedder = AzureOpenAITextEmbedder()

print(text_embedder.run(text_to_embed))

# {'embedding': [0.017020374536514282, -0.023255806416273117, ...],
# 'meta': {'model': 'text-embedding-ada-002-v2',
#          'usage': {'prompt_tokens': 4, 'total_tokens': 4}}}

AzureOpenAITextEmbedder.__init__

def __init__(azure_endpoint: Optional[str] = None,
             api_version: Optional[str] = "2023-05-15",
             azure_deployment: str = "text-embedding-ada-002",
             dimensions: Optional[int] = None,
             api_key: Optional[Secret] = Secret.from_env_var(
                 "AZURE_OPENAI_API_KEY", strict=False),
             azure_ad_token: Optional[Secret] = Secret.from_env_var(
                 "AZURE_OPENAI_AD_TOKEN", strict=False),
             organization: Optional[str] = None,
             timeout: Optional[float] = None,
             max_retries: Optional[int] = None,
             prefix: str = "",
             suffix: str = "")

Create an AzureOpenAITextEmbedder component.

Arguments:

• azure_endpoint: The endpoint of the deployed model.
• api_version: The version of the API to use.
• azure_deployment: The deployment of the model, usually matches the model name.
• dimensions: The number of dimensions the resulting output embeddings should have. Only supported in text-embedding-3 and later models.
• api_key: The API key used for authentication.
• azure_ad_token: Microsoft Entra ID token, see Microsoft's official Entra ID documentation for more information. Used to be called Azure Active Directory.
• organization: The Organization ID. See OpenAI's production best practices for more information.
• timeout: The timeout in seconds to be passed to the underlying AzureOpenAI client, if not set it is inferred from the OPENAI_TIMEOUT environment variable or set to 30.
• max_retries: Maximum retries to establish a connection with AzureOpenAI if it returns an internal error, if not set it is inferred from the OPENAI_MAX_RETRIES environment variable or set to 5.
• prefix: A string to add at the beginning of each text.
• suffix: A string to add at the end of each text.

AzureOpenAITextEmbedder.to_dict

def to_dict() -> Dict[str, Any]

Serializes the component to a dictionary.

Returns:

Dictionary with serialized data.

AzureOpenAITextEmbedder.from_dict

@classmethod
def from_dict(cls, data: Dict[str, Any]) -> "AzureOpenAITextEmbedder"

Deserializes the component from a dictionary.

Arguments:

• data: Dictionary to deserialize from.

Returns:

Deserialized component.

AzureOpenAITextEmbedder.run

@component.output_types(embedding=List[float], meta=Dict[str, Any])
def run(text: str)

Embed a single string.

Arguments:

• text: Text to embed.

Returns:

A dictionary with the following keys:

• embedding: The embedding of the input text.
• meta: Information about the usage of the model.

Module hugging_face_api_document_embedder

HuggingFaceAPIDocumentEmbedder

A component that embeds documents using Hugging Face APIs.

This component can be used to compute Document embeddings using different Hugging Face APIs:

• [Free Serverless Inference API]((https://huggingface.co/inference-api)
• Paid Inference Endpoints
• Self-hosted Text Embeddings Inference

Example usage with the free Serverless Inference API:

from haystack.components.embedders import HuggingFaceAPIDocumentEmbedder
from haystack.utils import Secret
from haystack.dataclasses import Document

doc = Document(content="I love pizza!")

doc_embedder = HuggingFaceAPIDocumentEmbedder(api_type="serverless_inference_api",
                                              api_params={"model": "BAAI/bge-small-en-v1.5"},
                                              token=Secret.from_token("<your-api-key>"))

result = document_embedder.run([doc])
print(result["documents"][0].embedding)

# [0.017020374536514282, -0.023255806416273117, ...]

Example usage with paid Inference Endpoints:

from haystack.components.embedders import HuggingFaceAPIDocumentEmbedder
from haystack.utils import Secret
from haystack.dataclasses import Document

doc = Document(content="I love pizza!")

doc_embedder = HuggingFaceAPIDocumentEmbedder(api_type="inference_endpoints",
                                              api_params={"url": "<your-inference-endpoint-url>"},
                                              token=Secret.from_token("<your-api-key>"))

result = document_embedder.run([doc])
print(result["documents"][0].embedding)

# [0.017020374536514282, -0.023255806416273117, ...]

Example usage with self-hosted Text Embeddings Inference:

from haystack.components.embedders import HuggingFaceAPIDocumentEmbedder
from haystack.dataclasses import Document

doc = Document(content="I love pizza!")

doc_embedder = HuggingFaceAPIDocumentEmbedder(api_type="text_embeddings_inference",
                                              api_params={"url": "http://localhost:8080"})

result = document_embedder.run([doc])
print(result["documents"][0].embedding)

# [0.017020374536514282, -0.023255806416273117, ...]

HuggingFaceAPIDocumentEmbedder.__init__

def __init__(api_type: Union[HFEmbeddingAPIType, str],
             api_params: Dict[str, str],
             token: Optional[Secret] = Secret.from_env_var(
                 ["HF_API_TOKEN", "HF_TOKEN"], strict=False),
             prefix: str = "",
             suffix: str = "",
             truncate: bool = True,
             normalize: bool = False,
             batch_size: int = 32,
             progress_bar: bool = True,
             meta_fields_to_embed: Optional[List[str]] = None,
             embedding_separator: str = "\n")

Create an HuggingFaceAPITextEmbedder component.

Arguments:

• api_type: The type of Hugging Face API to use.
• api_params: A dictionary containing the following keys:
• model: model ID on the Hugging Face Hub. Required when api_type is SERVERLESS_INFERENCE_API.
• url: URL of the inference endpoint. Required when api_type is INFERENCE_ENDPOINTS or TEXT_EMBEDDINGS_INFERENCE.
• token: The HuggingFace token to use as HTTP bearer authorization. You can find your HF token in your account settings.
• prefix: A string to add at the beginning of each text.
• suffix: A string to add at the end of each text.
• truncate: Truncate input text from the end to the maximum length supported by the model. This parameter takes effect when the api_type is TEXT_EMBEDDINGS_INFERENCE. It also takes effect when the api_type is INFERENCE_ENDPOINTS and the backend is based on Text Embeddings Inference. This parameter is ignored when the api_type is SERVERLESS_INFERENCE_API (it is always set to True and cannot be changed).
• normalize: Normalize the embeddings to unit length. This parameter takes effect when the api_type is TEXT_EMBEDDINGS_INFERENCE. It also takes effect when the api_type is INFERENCE_ENDPOINTS and the backend is based on Text Embeddings Inference. This parameter is ignored when the api_type is SERVERLESS_INFERENCE_API (it is always set to False and cannot be changed).
• batch_size: Number of Documents to process at once.
• progress_bar: If True shows a progress bar when running.
• meta_fields_to_embed: List of meta fields that will be embedded along with the Document text.
• embedding_separator: Separator used to concatenate the meta fields to the Document text.

HuggingFaceAPIDocumentEmbedder.to_dict

def to_dict() -> Dict[str, Any]

Serializes the component to a dictionary.

Returns:

Dictionary with serialized data.

HuggingFaceAPIDocumentEmbedder.from_dict

@classmethod
def from_dict(cls, data: Dict[str, Any]) -> "HuggingFaceAPIDocumentEmbedder"

Deserializes the component from a dictionary.

Arguments:

• data: Dictionary to deserialize from.

Returns:

Deserialized component.

HuggingFaceAPIDocumentEmbedder.run

@component.output_types(documents=List[Document])
def run(documents: List[Document])

Embed a list of Documents.

Arguments:

• documents: Documents to embed.

Returns:

A dictionary with the following keys:

• documents: Documents with embeddings

Module hugging_face_api_text_embedder

HuggingFaceAPITextEmbedder

A component that embeds text using Hugging Face APIs.

This component can be used to embed strings using different Hugging Face APIs:

• [Free Serverless Inference API]((https://huggingface.co/inference-api)
• Paid Inference Endpoints
• Self-hosted Text Embeddings Inference

Example usage with the free Serverless Inference API:

from haystack.components.embedders import HuggingFaceAPITextEmbedder
from haystack.utils import Secret

text_embedder = HuggingFaceAPITextEmbedder(api_type="serverless_inference_api",
                                           api_params={"model": "BAAI/bge-small-en-v1.5"},
                                           token=Secret.from_token("<your-api-key>"))

print(text_embedder.run("I love pizza!"))

# {'embedding': [0.017020374536514282, -0.023255806416273117, ...],

Example usage with paid Inference Endpoints:

from haystack.components.embedders import HuggingFaceAPITextEmbedder
from haystack.utils import Secret
text_embedder = HuggingFaceAPITextEmbedder(api_type="inference_endpoints",
                                           api_params={"model": "BAAI/bge-small-en-v1.5"},
                                           token=Secret.from_token("<your-api-key>"))

print(text_embedder.run("I love pizza!"))

# {'embedding': [0.017020374536514282, -0.023255806416273117, ...],

Example usage with self-hosted Text Embeddings Inference:

from haystack.components.embedders import HuggingFaceAPITextEmbedder
from haystack.utils import Secret

text_embedder = HuggingFaceAPITextEmbedder(api_type="text_embeddings_inference",
                                           api_params={"url": "http://localhost:8080"})

print(text_embedder.run("I love pizza!"))

# {'embedding': [0.017020374536514282, -0.023255806416273117, ...],

HuggingFaceAPITextEmbedder.__init__

def __init__(api_type: Union[HFEmbeddingAPIType, str],
             api_params: Dict[str, str],
             token: Optional[Secret] = Secret.from_env_var(
                 ["HF_API_TOKEN", "HF_TOKEN"], strict=False),
             prefix: str = "",
             suffix: str = "",
             truncate: bool = True,
             normalize: bool = False)

Create an HuggingFaceAPITextEmbedder component.

Arguments:

• api_type: The type of Hugging Face API to use.
• api_params: A dictionary containing the following keys:
• model: model ID on the Hugging Face Hub. Required when api_type is SERVERLESS_INFERENCE_API.
• url: URL of the inference endpoint. Required when api_type is INFERENCE_ENDPOINTS or TEXT_EMBEDDINGS_INFERENCE.
• token: The HuggingFace token to use as HTTP bearer authorization You can find your HF token in your account settings
• prefix: A string to add at the beginning of each text.
• suffix: A string to add at the end of each text.
• truncate: Truncate input text from the end to the maximum length supported by the model. This parameter takes effect when the api_type is TEXT_EMBEDDINGS_INFERENCE. It also takes effect when the api_type is INFERENCE_ENDPOINTS and the backend is based on Text Embeddings Inference. This parameter is ignored when the api_type is SERVERLESS_INFERENCE_API (it is always set to True and cannot be changed).
• normalize: Normalize the embeddings to unit length. This parameter takes effect when the api_type is TEXT_EMBEDDINGS_INFERENCE. It also takes effect when the api_type is INFERENCE_ENDPOINTS and the backend is based on Text Embeddings Inference. This parameter is ignored when the api_type is SERVERLESS_INFERENCE_API (it is always set to False and cannot be changed).

HuggingFaceAPITextEmbedder.to_dict

def to_dict() -> Dict[str, Any]

Serializes the component to a dictionary.

Returns:

Dictionary with serialized data.

HuggingFaceAPITextEmbedder.from_dict

@classmethod
def from_dict(cls, data: Dict[str, Any]) -> "HuggingFaceAPITextEmbedder"

Deserializes the component from a dictionary.

Arguments:

• data: Dictionary to deserialize from.

Returns:

Deserialized component.

HuggingFaceAPITextEmbedder.run

@component.output_types(embedding=List[float])
def run(text: str)

Embed a single string.

Arguments:

• text: Text to embed.

Returns:

A dictionary with the following keys:

• embedding: The embedding of the input text.

Module openai_document_embedder

OpenAIDocumentEmbedder

A component for computing Document embeddings using OpenAI models.

Usage example:

from haystack import Document
from haystack.components.embedders import OpenAIDocumentEmbedder

doc = Document(content="I love pizza!")

document_embedder = OpenAIDocumentEmbedder()

result = document_embedder.run([doc])
print(result['documents'][0].embedding)

# [0.017020374536514282, -0.023255806416273117, ...]

OpenAIDocumentEmbedder.__init__

def __init__(api_key: Secret = Secret.from_env_var("OPENAI_API_KEY"),
             model: str = "text-embedding-ada-002",
             dimensions: Optional[int] = None,
             api_base_url: Optional[str] = None,
             organization: Optional[str] = None,
             prefix: str = "",
             suffix: str = "",
             batch_size: int = 32,
             progress_bar: bool = True,
             meta_fields_to_embed: Optional[List[str]] = None,
             embedding_separator: str = "\n",
             timeout: Optional[float] = None,
             max_retries: Optional[int] = None)

Create a OpenAIDocumentEmbedder component.

By setting the 'OPENAI_TIMEOUT' and 'OPENAI_MAX_RETRIES' you can change the timeout and max_retries parameters in the OpenAI client.

Arguments:

• api_key: The OpenAI API key.
• model: The name of the model to use.
• dimensions: The number of dimensions the resulting output embeddings should have. Only supported in text-embedding-3 and later models.
• api_base_url: Overrides default base url for all HTTP requests.
• organization: The Organization ID. See OpenAI's production best practices for more information.
• prefix: A string to add at the beginning of each text.
• suffix: A string to add at the end of each text.
• batch_size: Number of Documents to encode at once.
• progress_bar: If True shows a progress bar when running.
• meta_fields_to_embed: List of meta fields that will be embedded along with the Document text.
• embedding_separator: Separator used to concatenate the meta fields to the Document text.
• timeout: Timeout for OpenAI Client calls, if not set it is inferred from the OPENAI_TIMEOUT environment variable or set to 30.
• max_retries: Maximum retries to stablish contact with OpenAI if it returns an internal error, if not set it is inferred from the OPENAI_MAX_RETRIES environment variable or set to 5.

OpenAIDocumentEmbedder.to_dict

def to_dict() -> Dict[str, Any]

Serializes the component to a dictionary.

Returns:

Dictionary with serialized data.

OpenAIDocumentEmbedder.from_dict

@classmethod
def from_dict(cls, data: Dict[str, Any]) -> "OpenAIDocumentEmbedder"

Deserializes the component from a dictionary.

Arguments:

• data: Dictionary to deserialize from.

Returns:

Deserialized component.

OpenAIDocumentEmbedder.run

@component.output_types(documents=List[Document], meta=Dict[str, Any])
def run(documents: List[Document])

Embed a list of Documents.

Arguments:

• documents: Documents to embed.

Returns:

A dictionary with the following keys:

• documents: Documents with embeddings
• meta: Information about the usage of the model.

Module openai_text_embedder

OpenAITextEmbedder

Embeds strings using OpenAI models.

You can use it to embed user query and send it to an embedding Retriever.

Usage example

from haystack.components.embedders import OpenAITextEmbedder

text_to_embed = "I love pizza!"

text_embedder = OpenAITextEmbedder()

print(text_embedder.run(text_to_embed))

# {'embedding': [0.017020374536514282, -0.023255806416273117, ...],
# 'meta': {'model': 'text-embedding-ada-002-v2',
#          'usage': {'prompt_tokens': 4, 'total_tokens': 4}}}

OpenAITextEmbedder.__init__

def __init__(api_key: Secret = Secret.from_env_var("OPENAI_API_KEY"),
             model: str = "text-embedding-ada-002",
             dimensions: Optional[int] = None,
             api_base_url: Optional[str] = None,
             organization: Optional[str] = None,
             prefix: str = "",
             suffix: str = "",
             timeout: Optional[float] = None,
             max_retries: Optional[int] = None)

Creates an OpenAITextEmbedder component.

Before initializing the component, you can set the 'OPENAI_TIMEOUT' and 'OPENAI_MAX_RETRIES' environment variables to override the timeout and max_retries parameters respectively in the OpenAI client.

Arguments:

• api_key: The OpenAI API key. You can set it with an environment variable OPENAI_API_KEY, or pass with this parameter during initialization.
• model: The name of the model to use for calculating embeddings. The default model is text-embedding-ada-002.
• dimensions: The number of dimensions of the resulting embeddings. Only text-embedding-3 and later models support this parameter.
• api_base_url: Overrides default base URL for all HTTP requests.
• organization: Your organization ID. See OpenAI's production best practices for more information.
• prefix: A string to add at the beginning of each text to embed.
• suffix: A string to add at the end of each text to embed.
• timeout: Timeout for OpenAI client calls. If not set, it defaults to either the OPENAI_TIMEOUT environment variable, or 30 seconds.
• max_retries: Maximum number of retries to contact OpenAI after an internal error. If not set, it defaults to either the OPENAI_MAX_RETRIES environment variable, or set to 5.

OpenAITextEmbedder.to_dict

def to_dict() -> Dict[str, Any]

Serializes the component to a dictionary.

Returns:

Dictionary with serialized data.

OpenAITextEmbedder.from_dict

@classmethod
def from_dict(cls, data: Dict[str, Any]) -> "OpenAITextEmbedder"

Deserializes the component from a dictionary.

Arguments:

• data: Dictionary to deserialize from.

Returns:

Deserialized component.

OpenAITextEmbedder.run

@component.output_types(embedding=List[float], meta=Dict[str, Any])
def run(text: str)

Embeds a single string.

Arguments:

• text: Text to embed.

Returns:

A dictionary with the following keys:

• embedding: The embedding of the input text.
• meta: Information about the usage of the model.

Module sentence_transformers_document_embedder

SentenceTransformersDocumentEmbedder

Calculates document embeddings using Sentence Transformers models.

It stores the embeddings in the embedding metadata field of each document. You can also embed documents' metadata. Use this component in indexing pipelines to embed input documents and send them to DocumentWriter to write a into a Document Store.

Usage example:

from haystack import Document
from haystack.components.embedders import SentenceTransformersDocumentEmbedder
doc = Document(content="I love pizza!")
doc_embedder = SentenceTransformersDocumentEmbedder()
doc_embedder.warm_up()

result = doc_embedder.run([doc])
print(result['documents'][0].embedding)

# [-0.07804739475250244, 0.1498992145061493, ...]

SentenceTransformersDocumentEmbedder.__init__

def __init__(model: str = "sentence-transformers/all-mpnet-base-v2",
             device: Optional[ComponentDevice] = None,
             token: Optional[Secret] = Secret.from_env_var(
                 ["HF_API_TOKEN", "HF_TOKEN"], strict=False),
             prefix: str = "",
             suffix: str = "",
             batch_size: int = 32,
             progress_bar: bool = True,
             normalize_embeddings: bool = False,
             meta_fields_to_embed: Optional[List[str]] = None,
             embedding_separator: str = "\n",
             trust_remote_code: bool = False,
             truncate_dim: Optional[int] = None,
             model_kwargs: Optional[Dict[str, Any]] = None,
             tokenizer_kwargs: Optional[Dict[str, Any]] = None,
             precision: Literal["float32", "int8", "uint8", "binary",
                                "ubinary"] = "float32")

Creates a SentenceTransformersDocumentEmbedder component.

Arguments:

• model: The model to use for calculating embeddings. Pass a local path or ID of the model on Hugging Face.
• device: The device to use for loading the model. Overrides the default device.
• token: The API token to download private models from Hugging Face.
• prefix: A string to add at the beginning of each document text. Can be used to prepend the text with an instruction, as required by some embedding models, such as E5 and bge.
• suffix: A string to add at the end of each document text.
• batch_size: Number of documents to embed at once.
• progress_bar: If True, shows a progress bar when embedding documents.
• normalize_embeddings: If True, returns vectors with length 1.
• meta_fields_to_embed: List of metadata fields to embed along with the document text.
• embedding_separator: Separator used to concatenate the metadata fields to the document text.
• trust_remote_code: If False, allows only Hugging Face verified model architectures. If True, allows custom models and scripts.
• truncate_dim: The dimension to truncate sentence embeddings to. None does no truncation. If the model wasn't trained with Matryoshka Representation Learning, truncating embeddings can significantly affect performance.
• model_kwargs: Additional keyword arguments for AutoModelForSequenceClassification.from_pretrained when loading the model. Refer to specific model documentation for available kwargs.
• tokenizer_kwargs: Additional keyword arguments for AutoTokenizer.from_pretrained when loading the tokenizer. Refer to specific model documentation for available kwargs.
• precision: The precision to use for the embeddings. All non-float32 precisions are quantized embeddings. Quantized embeddings are smaller in size and faster to compute, but may have a lower accuracy. They are useful for reducing the size of the embeddings of a corpus for semantic search, among other tasks.

SentenceTransformersDocumentEmbedder.to_dict

def to_dict() -> Dict[str, Any]

Serializes the component to a dictionary.

Returns:

Dictionary with serialized data.

SentenceTransformersDocumentEmbedder.from_dict

@classmethod
def from_dict(cls, data: Dict[str,
                              Any]) -> "SentenceTransformersDocumentEmbedder"

Deserializes the component from a dictionary.

Arguments:

• data: Dictionary to deserialize from.

Returns:

Deserialized component.

SentenceTransformersDocumentEmbedder.warm_up

def warm_up()

Initializes the component.

SentenceTransformersDocumentEmbedder.run

@component.output_types(documents=List[Document])
def run(documents: List[Document])

Embed a list of documents.

Arguments:

• documents: Documents to embed.

Returns:

A dictionary with the following keys:

• documents: Documents with embeddings.

Module sentence_transformers_text_embedder

SentenceTransformersTextEmbedder

Embeds strings using Sentence Transformers models.

You can use it to embed user query and send it to an embedding retriever.

Usage example:

from haystack.components.embedders import SentenceTransformersTextEmbedder

text_to_embed = "I love pizza!"

text_embedder = SentenceTransformersTextEmbedder()
text_embedder.warm_up()

print(text_embedder.run(text_to_embed))

# {'embedding': [-0.07804739475250244, 0.1498992145061493,, ...]}

SentenceTransformersTextEmbedder.__init__

def __init__(model: str = "sentence-transformers/all-mpnet-base-v2",
             device: Optional[ComponentDevice] = None,
             token: Optional[Secret] = Secret.from_env_var(
                 ["HF_API_TOKEN", "HF_TOKEN"], strict=False),
             prefix: str = "",
             suffix: str = "",
             batch_size: int = 32,
             progress_bar: bool = True,
             normalize_embeddings: bool = False,
             trust_remote_code: bool = False,
             truncate_dim: Optional[int] = None,
             model_kwargs: Optional[Dict[str, Any]] = None,
             tokenizer_kwargs: Optional[Dict[str, Any]] = None,
             precision: Literal["float32", "int8", "uint8", "binary",
                                "ubinary"] = "float32")

Create a SentenceTransformersTextEmbedder component.

Arguments:

• model: The model to use for calculating embeddings. Specify the path to a local model or the ID of the model on Hugging Face.
• device: Overrides the default device used to load the model.
• token: An API token to use private models from Hugging Face.
• prefix: A string to add at the beginning of each text to be embedded. You can use it to prepend the text with an instruction, as required by some embedding models, such as E5 and bge.
• suffix: A string to add at the end of each text to embed.
• batch_size: Number of texts to embed at once.
• progress_bar: If True, shows a progress bar for calculating embeddings. If False, disables the progress bar.
• normalize_embeddings: If True, returned vectors have a length of 1.
• trust_remote_code: If False, permits only Hugging Face verified model architectures. If True, permits custom models and scripts.
• truncate_dim: The dimension to truncate sentence embeddings to. None does no truncation. If the model has not been trained with Matryoshka Representation Learning, truncation of embeddings can significantly affect performance.
• model_kwargs: Additional keyword arguments for AutoModelForSequenceClassification.from_pretrained when loading the model. Refer to specific model documentation for available kwargs.
• tokenizer_kwargs: Additional keyword arguments for AutoTokenizer.from_pretrained when loading the tokenizer. Refer to specific model documentation for available kwargs.
• precision: The precision to use for the embeddings. All non-float32 precisions are quantized embeddings. Quantized embeddings are smaller in size and faster to compute, but may have a lower accuracy. They are useful for reducing the size of the embeddings of a corpus for semantic search, among other tasks.

SentenceTransformersTextEmbedder.to_dict

def to_dict() -> Dict[str, Any]

Serializes the component to a dictionary.

Returns:

Dictionary with serialized data.

SentenceTransformersTextEmbedder.from_dict

@classmethod
def from_dict(cls, data: Dict[str, Any]) -> "SentenceTransformersTextEmbedder"

Deserializes the component from a dictionary.

Arguments:

• data: Dictionary to deserialize from.

Returns:

Deserialized component.

SentenceTransformersTextEmbedder.warm_up

def warm_up()

Initializes the component.

SentenceTransformersTextEmbedder.run

@component.output_types(embedding=List[float])
def run(text: str)

Embed a single string.

Arguments:

• text: Text to embed.

Returns:

A dictionary with the following keys:

• embedding: The embedding of the input text.