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Embeddings - IBM watsonx.ai

Embeddings¶ Embeddings¶

class ibm_watsonx_ai.foundation_models.embeddings.Embeddings(*, model_id, params=None, credentials=None, project_id=None, space_id=None, api_client=None, verify=None, persistent_connection=True, batch_size=1000, concurrency_limit=5, max_retries=None, delay_time=None, retry_status_codes=None)[source]¶

Bases: BaseEmbeddings, WMLResource

Instantiate the embeddings service.

Parameters:

model_id (str, optional) – the type of model to use
params (dict, optional) – parameters to use during generate requests, use ibm_watsonx_ai.metanames.EmbedTextParamsMetaNames().show() to view the list of MetaNames
credentials (dict, optional) – credentials for the Watson Machine Learning instance
project_id (str, optional) – ID of the Watson Studio project
space_id (str, optional) – ID of the Watson Studio space
api_client (APIClient, optional) – initialized APIClient object with a set project ID or space ID. If passed, credentials and project_id/space_id are not required.
verify (bool or str, optional) –

You can pass one of following as verify:
- the path to a CA_BUNDLE file
- the path of a directory with certificates of trusted CAs
- True - default path to truststore will be taken
- False - no verification will be made
persistent_connection (bool, optional) – defines whether to keep a persistent connection when evaluating the generate, ‘embed_query’, and ‘embed_documents` methods with one prompt or batch of prompts that meet the length limit. For more details, see Generate embeddings. To close the connection, run embeddings.close_persistent_connection(), defaults to True. Added in 1.1.2.
batch_size (int, optional) – Number of elements to be embedded sending in one call (used only for sync methods), defaults to 1000
concurrency_limit (int, optional) – number of requests to be sent in parallel when generating embedding vectors (used only for sync methods), max is 10, defaults to 5
max_retries (int, optional) – number of retries performed when request was not successful and status code is in retry_status_codes, defaults to 10
delay_time (float, optional) – delay time to retry request, factor in exponential backoff formula: wx_delay_time * pow(2.0, attempt), defaults to 0.5s
retry_status_codes (list[int], optional) – list of status codes which will be considered for retry mechanism, defaults to [429, 503, 504, 520]

Note

When the credentials parameter is passed, one of these parameters is required: [project_id, space_id].

Hint

You can copy the project_id from the Project’s Manage tab (Project -> Manage -> General -> Details).

Example:

 from ibm_watsonx_ai import Credentials
 from ibm_watsonx_ai.foundation_models import Embeddings
 from ibm_watsonx_ai.metanames import EmbedTextParamsMetaNames as EmbedParams
 from ibm_watsonx_ai.foundation_models.utils.enums import EmbeddingTypes

embed_params = {
     EmbedParams.TRUNCATE_INPUT_TOKENS: 3,
     EmbedParams.RETURN_OPTIONS: {
     'input_text': True
     }
 }

 embedding = Embeddings(
     model_id=EmbeddingTypes.IBM_SLATE_30M_ENG,
     params=embed_params,
     credentials=Credentials(
         api_key = IAM_API_KEY,
         url = "https://us-south.ml.cloud.ibm.com"),
     project_id="*****"
     )

async aembed_documents(texts, params=None)[source]¶

Returns list of embedding vectors for provided texts in an asynchronous manner.

Parameters:

texts (list[str]) – list of texts for which embedding vectors will be generated, max length is determined by API (for more information, please refer to the documentation: https://cloud.ibm.com/apidocs/watsonx-ai#text-embeddings)
params (ParamsType | None, optional) – MetaProps for the embedding generation, use ibm_watsonx_ai.metanames.EmbedTextParamsMetaNames().show() to view the list of MetaNames, defaults to None

Returns:

list of embedding vectors

Return type:

list[list[float]]

Example:

q = [
    "What is a Generative AI?",
    "Generative AI refers to a type of artificial intelligence that can original content."
    ]

embedding_vectors = await embedding.aembed_documents(texts=q)
print(embedding_vectors)

async aembed_query(text, params=None)[source]¶

Returns an embedding vector for a provided text in an asynchronous manner.

Parameters:

text (str) – text for which embedding vector will be generated
params (ParamsType | None, optional) – MetaProps for the embedding generation, use ibm_watsonx_ai.metanames.EmbedTextParamsMetaNames().show() to view the list of MetaNames, defaults to None

Returns:

embedding vector

Return type:

list[float]

Example:

q = "What is a Generative AI?"
embedding_vector = await embedding.aembed_query(text=q)
print(embedding_vector)

async agenerate(inputs, params=None)[source]¶

Generate embeddings vectors for the given input with the given parameters in an asynchronous manner. Returns a REST API response.

Parameters:

inputs (list[str]) – list of texts for which embedding vectors will be generated, max length is determined by API (for more information, please refer to the documentation: https://cloud.ibm.com/apidocs/watsonx-ai#text-embeddings)
params (ParamsType | None, optional) – MetaProps for the embedding generation, use ibm_watsonx_ai.metanames.EmbedTextParamsMetaNames().show() to view the list of MetaNames, defaults to None

Returns:

scoring results containing generated embeddings vectors

Return type:

dict

close_persistent_connection()[source]¶: Only applicable if persistent_connection was set to True in Embeddings initialization. Calling this method closes the current httpx.Client and recreates a new httpx.Client with default values: timeout: httpx.Timeout(read=30 * 60, write=30 * 60, connect=10, pool=30 * 60) limit: httpx.Limits(max_connections=10, max_keepalive_connections=10, keepalive_expiry=HTTPX_KEEPALIVE_EXPIRY)

embed_documents(texts, params=None, concurrency_limit=5)[source]¶

Returns list of embedding vectors for provided texts.

Parameters:

texts (list[str]) – list of texts for which embedding vectors will be generated
params (ParamsType | None, optional) – MetaProps for the embedding generation, use ibm_watsonx_ai.metanames.EmbedTextParamsMetaNames().show() to view the list of MetaNames, defaults to None
concurrency_limit (int, optional) – number of requests to be sent in parallel, max is 10, defaults to 5

Returns:

list of embedding vectors

Return type:

list[list[float]]

Example:

q = [
    "What is a Generative AI?",
    "Generative AI refers to a type of artificial intelligence that can original content."
    ]

embedding_vectors = embedding.embed_documents(texts=q)
print(embedding_vectors)

embed_query(text, params=None)[source]¶

Returns an embedding vector for a provided text.

Parameters:

text (str) – text for which embedding vector will be generated
params (ParamsType | None, optional) – MetaProps for the embedding generation, use ibm_watsonx_ai.metanames.EmbedTextParamsMetaNames().show() to view the list of MetaNames, defaults to None

Returns:

embedding vector

Return type:

list[float]

Example:

q = "What is a Generative AI?"
embedding_vector = embedding.embed_query(text=q)
print(embedding_vector)

generate(inputs, params=None, concurrency_limit=5)[source]¶

Generate embeddings vectors for the given input with the given parameters. Returns a REST API response.

Parameters:

inputs (list[str]) – list of texts for which embedding vectors will be generated
params (ParamsType | None, optional) – MetaProps for the embedding generation, use ibm_watsonx_ai.metanames.EmbedTextParamsMetaNames().show() to view the list of MetaNames, defaults to None
concurrency_limit (int, optional) – number of requests to be sent in parallel, max is 10, defaults to 5

Returns:

scoring results containing generated embeddings vectors

Return type:

dict

to_dict()[source]¶

Serialize Embeddings.

Returns:: serializes this Embeddings so that it can be reconstructed by from_dict class method.
Return type:: dict

BaseEmbeddings¶

class ibm_watsonx_ai.foundation_models.embeddings.base_embeddings.BaseEmbeddings[source]¶

Bases: ABC

LangChain-like embedding function interface.

abstractmethod embed_documents(texts)[source]¶: Embed search docs.

abstractmethod embed_query(text)[source]¶: Embed query text.

classmethod from_dict(data, **kwargs)[source]¶

Deserialize BaseEmbeddings into a concrete one using arguments.

Returns:: concrete Embeddings or None if data is incorrect
Return type:: BaseEmbeddings | None

to_dict()[source]¶

Serialize Embeddings.

Returns:: serializes this Embeddings so that it can be reconstructed by from_dict class method.
Return type:: dict

Enums¶

class metanames.EmbedTextParamsMetaNames[source]¶

Set of MetaNames for Foundation Model Embeddings Parameters.

Available MetaNames:

MetaName

Type

Required

Example value

TRUNCATE_INPUT_TOKENS

int

2

RETURN_OPTIONS

dict

{'input_text': True}

class EmbeddingModels¶

Bases: StrEnum

This represents a dynamically generated Enum for Embedding Models.

Example of getting EmbeddingModels

# GET EmbeddingModels ENUM
client.foundation_models.EmbeddingModels

# PRINT dict of Enums
client.foundation_models.EmbeddingModels.show()

Example Output:

{'SLATE_125M_ENGLISH_RTRVR': 'ibm/slate-125m-english-rtrvr',
...
'SLATE_30M_ENGLISH_RTRVR': 'ibm/slate-30m-english-rtrvr'}

Example of initialising Embeddings with EmbeddingModels Enum:

from ibm_watsonx_ai.foundation_models import Embeddings

embeddings = Embeddings(
    model_id=client.foundation_models.EmbeddingModels.SLATE_30M_ENGLISH_RTRVR,
    credentials=Credentials(...),
    project_id=project_id,
)

class ibm_watsonx_ai.foundation_models.utils.enums.EmbeddingTypes(value)[source]¶

Bases: Enum

Deprecated since version 1.0.5: Use EmbeddingModels() instead.

Supported embedding models.

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