Em Invoker

Modules concerning the embedding model invokers used in Gen AI applications.

`AzureOpenAIEMInvoker(azure_endpoint, azure_deployment, api_key=None, model_kwargs=None, default_hyperparameters=None, retry_config=None, truncation_config=None, vector_fuser=None)`

Bases: OpenAIEMInvoker

An embedding model invoker to interact with Azure OpenAI embedding models.

Examples:

em_invoker = AzureOpenAIEMInvoker(
    azure_endpoint="https://<your-azure-openai-endpoint>.openai.azure.com/openai/v1",
    azure_deployment="<your-azure-openai-deployment>",
)
result = await em_invoker.invoke("Hi there!")

Supported features

Basic invocation
Batch invocation
Text input only
Text truncation
Vector fusion
Retry and timeout

Attributes:

Name	Type	Description
`model_id`	`str`	The model ID of the embedding model.
`model_provider`	`str`	The provider of the embedding model.
`model_name`	`str`	The name of the Azure OpenAI embedding model deployment.
`client_kwargs`	`dict[str, Any]`	The keyword arguments for the Azure OpenAI client.
`default_hyperparameters`	`dict[str, Any]`	Default hyperparameters for invoking the embedding model.
`retry_config`	`RetryConfig`	The retry configuration for the embedding model.
`truncation_config`	`TruncationConfig \| None`	The truncation configuration for the embedding model.
`vector_fuser`	`BaseVectorFuser \| None`	The vector fuser to handle mixed content.

Initializes a new instance of the AzureOpenAIEMInvoker class.

Parameters:

Name	Type	Description	Default
`azure_endpoint`	`str`	The endpoint of the Azure OpenAI service.	required
`azure_deployment`	`str`	The deployment name of the Azure OpenAI service.	required
`api_key`	`str \| None`	The API key for authenticating with Azure OpenAI. Defaults to None, in which case the `AZURE_OPENAI_API_KEY` environment variable will be used.	`None`
`model_kwargs`	`dict[str, Any] \| None`	Additional model parameters. Defaults to None.	`None`
`default_hyperparameters`	`dict[str, Any] \| None`	Default hyperparameters for invoking the model. Defaults to None.	`None`
`retry_config`	`RetryConfig \| dict[str, Any] \| None`	The retry config for the embedding model. Defaults to None, in which case a default config with no retry and 30.0 seconds timeout will be used.	`None`
`truncation_config`	`TruncationConfig \| dict[str, Any] \| None`	Config for text truncation behavior. Defaults to None, in which case no truncation is applied.	`None`
`vector_fuser`	`BaseVectorFuser \| VectorFuserType \| dict[str, Any] \| None`	The vector fuser to handle mixed content. Defaults to None, in which case handling the mixed modality content depends on the EM's capabilities.	`None`

`BedrockEMInvoker(model_name, access_key_id=None, secret_access_key=None, region_name='us-east-1', model_kwargs=None, default_hyperparameters=None, retry_config=None, truncation_config=None, vector_fuser=None)`

Bases: BaseEMInvoker

An embedding model invoker to interact with AWS Bedrock embedding models.

Examples:

em_invoker = BedrockEMInvoker(model_name="cohere.embed-english-v3")
result = await em_invoker.invoke("Hi there!")

Supported features

Basic invocation
Batch invocation
Multimodal input a. Text b. Image (For Marengo models only)
Text truncation
Vector fusion
Retry and timeout

Attributes:

Name	Type	Description
`model_id`	`str`	The model ID of the embedding model.
`model_provider`	`str`	The provider of the embedding model.
`model_name`	`str`	The name of the embedding model.
`session`	`Session`	The Bedrock client session.
`client_kwargs`	`dict[str, Any]`	The Bedrock client kwargs.
`default_hyperparameters`	`dict[str, Any]`	Default hyperparameters for invoking the embedding model.
`retry_config`	`RetryConfig`	The retry configuration for the embedding model.
`truncation_config`	`TruncationConfig \| None`	The truncation configuration for the embedding model.
`vector_fuser`	`BaseVectorFuser \| None`	The vector fuser to handle mixed content.

Initializes a new instance of the BedrockEMInvoker class.

Parameters:

Name	Type	Description	Default
`model_name`	`str`	The name of the Bedrock embedding model to be used.	required
`access_key_id`	`str \| None`	The AWS access key ID. Defaults to None, in which case the `AWS_ACCESS_KEY_ID` environment variable will be used.	`None`
`secret_access_key`	`str \| None`	The AWS secret access key. Defaults to None, in which case the `AWS_SECRET_ACCESS_KEY` environment variable will be used.	`None`
`region_name`	`str`	The AWS region name. Defaults to "us-east-1".	`'us-east-1'`
`model_kwargs`	`dict[str, Any] \| None`	Additional keyword arguments for the Bedrock client. Defaults to None.	`None`
`default_hyperparameters`	`dict[str, Any] \| None`	Default hyperparameters for invoking the model. Defaults to None.	`None`
`retry_config`	`RetryConfig \| dict[str, Any] \| None`	The retry config for the embedding model. Defaults to None, in which case a default config with no retry and 30.0 seconds timeout will be used.	`None`
`truncation_config`	`TruncationConfig \| dict[str, Any] \| None`	Config for text truncation behavior. Defaults to None, in which case no truncation is applied.	`None`
`vector_fuser`	`BaseVectorFuser \| VectorFuserType \| dict[str, Any] \| None`	The vector fuser to handle mixed content. Defaults to None, in which case handling the mixed modality content depends on the EM's capabilities.	`None`

Raises:

Type	Description
`ValueError`	If the model name is not supported.
`ValueError`	If `access_key_id` or `secret_access_key` is neither provided nor set in the `AWS_ACCESS_KEY_ID` or `AWS_SECRET_ACCESS_KEY` environment variables, respectively.

`CohereEMInvoker(model_name, api_key=None, base_url=None, model_kwargs=None, default_hyperparameters=None, retry_config=None, truncation_config=None, vector_fuser=None, input_type=CohereInputType.SEARCH_DOCUMENT)`

Bases: BaseEMInvoker

An embedding model invoker to interact with Cohere embedding models.

Examples:

em_invoker = CohereEMInvoker(model_name="embed-english-v4.0")
result = await em_invoker.invoke("Hi there!")

Supported features

Basic invocation
Batch invocation
Multimodal input a. Text b. Image
Text truncation
Vector fusion
Retry and timeout

Attributes:

Name	Type	Description
`model_id`	`str`	The model ID of the embedding model.
`model_provider`	`str`	The provider of the embedding model (Cohere).
`model_name`	`str`	The name of the Cohere embedding model.
`client`	`AsyncClient`	The asynchronous client for the Cohere API.
`default_hyperparameters`	`dict[str, Any]`	Default hyperparameters for invoking the embedding model.
`retry_config`	`RetryConfig`	The retry configuration for the embedding model.
`truncation_config`	`TruncationConfig \| None`	The truncation configuration for the embedding model.
`vector_fuser`	`BaseVectorFuser \| None`	The vector fuser to handle mixed content.
`input_type`	`CohereInputType`	The input type for the embedding model. Supported values include: 1. `CohereInputType.SEARCH_DOCUMENT`, 2. `CohereInputType.SEARCH_QUERY`, 3. `CohereInputType.CLASSIFICATION`, 4. `CohereInputType.CLUSTERING`, 5. `CohereInputType.IMAGE`.

Initializes a new instance of the CohereEMInvoker class.

Parameters:

Name	Type	Description	Default
`model_name`	`str`	The name of the Cohere embedding model to be used.	required
`api_key`	`str \| None`	The API key for authenticating with Cohere. Defaults to None, in which case the `COHERE_API_KEY` environment variable will be used.	`None`
`base_url`	`str \| None`	The base URL for a custom Cohere-compatible endpoint. Defaults to None, in which case Cohere's default URL will be used.	`None`
`model_kwargs`	`dict[str, Any] \| None`	Additional keyword arguments for the Cohere client. Defaults to None.	`None`
`default_hyperparameters`	`dict[str, Any] \| None`	Default hyperparameters for invoking the model. Defaults to None.	`None`
`retry_config`	`RetryConfig \| dict[str, Any] \| None`	The retry config for the embedding model. Defaults to None, in which case a default config with no retry and 30.0 seconds timeout will be used.	`None`
`truncation_config`	`TruncationConfig \| dict[str, Any] \| None`	Config for text truncation behavior. Defaults to None, in which case no truncation is applied.	`None`
`vector_fuser`	`BaseVectorFuser \| VectorFuserType \| dict[str, Any] \| None`	The vector fuser to handle mixed content. Defaults to None, in which case handling the mixed modality content depends on the EM's capabilities.	`None`
`input_type`	`CohereInputType`	The input type for the embedding model. Defaults to `CohereInputType.SEARCH_DOCUMENT`. Valid values are: "search_document", "search_query", "classification", "clustering", and "image". For more information about, please refer to https://docs.cohere.com/docs/embeddings#the-input_type-parameter.	`SEARCH_DOCUMENT`

`GoogleEMInvoker(model_name, api_key=None, credentials_path=None, project_id=None, location='us-central1', model_kwargs=None, default_hyperparameters=None, retry_config=None, truncation_config=None, vector_fuser=None)`

Bases: BaseEMInvoker

An embedding model invoker to interact with Google embedding models.

Examples:

em_invoker = GoogleEMInvoker(model_name="gemini-embedding-001")
result = await em_invoker.invoke("Hi there!")

Initialization

The GoogleEMInvoker can use either Google Gen AI or Google Vertex AI.

Google Gen AI is recommended for quick prototyping and development. It requires a Gemini API key for authentication.

Usage example:

em_invoker = GoogleEMInvoker(
    model_name="gemini-embedding-001",
    api_key="your_api_key"
)

Google Vertex AI is recommended to build production-ready applications. It requires a service account JSON file for authentication.

Usage example:

em_invoker = GoogleEMInvoker(
    model_name="gemini-embedding-001",
    credentials_path="path/to/service_account.json"
)

If neither api_key nor credentials_path is provided, Google Gen AI will be used by default. The GOOGLE_API_KEY environment variable will be used for authentication.

Supported features

Basic invocation
Batch invocation
Multimodal input a. Text b. Image c. Audio d. Video e. Document
Text truncation
Vector fusion
Retry and timeout

Attributes:

Name	Type	Description
`model_id`	`str`	The model ID of the embedding model.
`model_provider`	`str`	The provider of the embedding model.
`model_name`	`str`	The name of the embedding model.
`client_params`	`dict[str, Any]`	The Google client instance init parameters.
`default_hyperparameters`	`dict[str, Any]`	Default hyperparameters for invoking the embedding model.
`retry_config`	`RetryConfig`	The retry configuration for the embedding model.
`truncation_config`	`TruncationConfig \| None`	The truncation configuration for the embedding model.
`vector_fuser`	`BaseVectorFuser \| None`	The vector fuser to handle mixed content.

Initializes a new instance of the GoogleEMInvoker class.

Parameters:

Name	Type	Description	Default
`model_name`	`str`	The name of the model to use.	required
`api_key`	`str \| None`	Required for Google Gen AI authentication. Cannot be used together with `credentials_path`. Defaults to None.	`None`
`credentials_path`	`str \| None`	Required for Google Vertex AI authentication. Path to the service account credentials JSON file. Cannot be used together with `api_key`. Defaults to None.	`None`
`project_id`	`str \| None`	The Google Cloud project ID for Vertex AI. Only used when authenticating with `credentials_path`. Defaults to None, in which case it will be loaded from the credentials file.	`None`
`location`	`str`	The location of the Google Cloud project for Vertex AI. Only used when authenticating with `credentials_path`. Defaults to "us-central1".	`'us-central1'`
`model_kwargs`	`dict[str, Any] \| None`	Additional keyword arguments for the Google client. Defaults to None.	`None`
`default_hyperparameters`	`dict[str, Any] \| None`	Default hyperparameters for invoking the model. Defaults to None.	`None`
`retry_config`	`RetryConfig \| dict[str, Any] \| None`	The retry config for the embedding model. Defaults to None, in which case a default config with no retry and 30.0 seconds timeout will be used.	`None`
`truncation_config`	`TruncationConfig \| dict[str, Any] \| None`	Config for text truncation behavior. Defaults to None, in which case no truncation is applied.	`None`
`vector_fuser`	`BaseVectorFuser \| VectorFuserType \| dict[str, Any] \| None`	The vector fuser to handle mixed content. Defaults to None, in which case handling the mixed modality content depends on the EM's capabilities.	`None`

Note

If neither api_key nor credentials_path is provided, Google Gen AI will be used by default. The GOOGLE_API_KEY environment variable will be used for authentication.

`HuggingFaceEMInvoker(model_name, api_key=None, model_kwargs=None, default_hyperparameters=None, retry_config=None, truncation_config=None, vector_fuser=None)`

Bases: BaseEMInvoker

An embedding model invoker to interact with HuggingFace embedding models via the HF Inference API.

This invoker uses HuggingFace's native inference client to call feature extraction on models hosted on the HuggingFace Hub. It supports any embedding model that is deployed on HuggingFace's serverless inference infrastructure or through Text Embeddings Inference (TEI).

Examples:

em_invoker = HuggingFaceEMInvoker(
    model_name="sentence-transformers/all-MiniLM-L6-v2",
)
result = await em_invoker.invoke("Hi there!")

Supported features

Basic invocation
Batch invocation
Text input only
Text truncation
Vector fusion
Retry and timeout

Attributes:

Name	Type	Description
`model_id`	`str`	The model ID of the embedding model.
`model_provider`	`str`	The provider of the embedding model.
`model_name`	`str`	The name of the embedding model.
`default_hyperparameters`	`dict[str, Any]`	Default hyperparameters for invoking the embedding model.
`retry_config`	`RetryConfig`	The retry configuration for the embedding model.
`truncation_config`	`TruncationConfig \| None`	The truncation configuration for the embedding model.
`vector_fuser`	`BaseVectorFuser \| None`	The vector fuser to handle mixed content.

Initializes a new instance of the HuggingFaceEMInvoker class.

Parameters:

Name	Type	Description	Default
`model_name`	`str`	The name of the HuggingFace embedding model to be used. This should be the full model ID (e.g. "sentence-transformers/all-MiniLM-L6-v2").	required
`api_key`	`str \| None`	The API key (HF token) for authenticating with HuggingFace. Defaults to None, in which case the `HF_TOKEN` environment variable will be used.	`None`
`model_kwargs`	`dict[str, Any] \| None`	Additional keyword arguments for the HF client. Defaults to None.	`None`
`default_hyperparameters`	`dict[str, Any] \| None`	Default hyperparameters for invoking the model. Defaults to None.	`None`
`retry_config`	`RetryConfig \| dict[str, Any] \| None`	The retry config for the embedding model. Defaults to None, in which case a default config with no retry and 30.0 seconds timeout will be used.	`None`
`truncation_config`	`TruncationConfig \| dict[str, Any] \| None`	Config for text truncation behavior. Defaults to None, in which case no truncation is applied.	`None`
`vector_fuser`	`BaseVectorFuser \| VectorFuserType \| dict[str, Any] \| None`	The vector fuser to handle mixed content. Defaults to None, in which case handling the mixed modality content depends on the EM's capabilities.	`None`

Raises:

Type	Description
`ValueError`	If neither `api_key` nor `HF_TOKEN` environment variable is provided.

`release_resources()` `async`

Release the underlying AsyncInferenceClient session.

`JinaEMInvoker(model_name, api_key=None, base_url=JINA_DEFAULT_URL, model_kwargs=None, default_hyperparameters=None, retry_config=None, truncation_config=None, vector_fuser=None)`

Bases: BaseEMInvoker

An embedding model invoker to interact with Jina AI embedding models.

Examples:

em_invoker = JinaEMInvoker(model_name="jina-embeddings-v2-large")
result = await em_invoker.invoke("Hi there!")

Supported features

Basic invocation
Batch invocation
Multimodal input a. Text b. Image
Text truncation
Vector fusion
Retry and timeout

Attributes:

Name	Type	Description
`model_id`	`str`	The model ID of the embedding model.
`model_provider`	`str`	The provider of the embedding model.
`model_name`	`str`	The name of the embedding model.
`client`	`AsyncClient`	The client for the Jina AI API.
`default_hyperparameters`	`dict[str, Any]`	Default hyperparameters for invoking the embedding model.
`retry_config`	`RetryConfig`	The retry configuration for the embedding model.
`truncation_config`	`TruncationConfig \| None`	The truncation configuration for the embedding model.
`vector_fuser`	`BaseVectorFuser \| None`	The vector fuser to handle mixed content.

Initializes a new instance of the JinaEMInvoker class.

Parameters:

Name	Type	Description	Default
`model_name`	`str`	The name of the Jina embedding model to be used.	required
`api_key`	`str \| None`	The API key for authenticating with Jina AI. Defaults to None, in which case the `JINA_API_KEY` environment variable will be used.	`None`
`base_url`	`str`	The base URL for the Jina AI API. Defaults to "https://api.jina.ai/v1".	`JINA_DEFAULT_URL`
`model_kwargs`	`dict[str, Any] \| None`	Additional keyword arguments for the HTTP client. Defaults to None.	`None`
`default_hyperparameters`	`dict[str, Any] \| None`	Default hyperparameters for invoking the model. Defaults to None.	`None`
`retry_config`	`RetryConfig \| dict[str, Any] \| None`	The retry config for the embedding model. Defaults to None, in which case a default config with no retry and 30.0 seconds timeout will be used.	`None`
`truncation_config`	`TruncationConfig \| dict[str, Any] \| None`	Config for text truncation behavior. Defaults to None, in which case no truncation is applied.	`None`
`vector_fuser`	`BaseVectorFuser \| VectorFuserType \| dict[str, Any] \| None`	The vector fuser to handle mixed content. Defaults to None, in which case handling the mixed modality content depends on the EM's capabilities.	`None`

Raises:

Type	Description
`ValueError`	If neither `api_key` nor `JINA_API_KEY` environment variable is provided.

`LangChainEMInvoker(model=None, model_class_path=None, model_name=None, model_kwargs=None, default_hyperparameters=None, retry_config=None, truncation_config=None, vector_fuser=None)`

Bases: BaseEMInvoker

An embedding model invoker to interact with LangChain's Embeddings.

Examples:

em_invoker = LangChainEMInvoker(
    model_class_path="langchain_openai.OpenAIEmbeddings",
    model_name="text-embedding-3-small",
)
result = await em_invoker.invoke("Hi there!")

Initialization

The LangChainEMInvoker can be initialized by either passing:

A LangChain's Embeddings instance: Usage example:

from langchain_openai import OpenAIEmbeddings

model = OpenAIEmbeddings(model="text-embedding-3-small", api_key="your_api_key")
em_invoker = LangChainEMInvoker(model=model)

A model path in the format of ".": Usage example:

em_invoker = LangChainEMInvoker(
    model_class_path="langchain_openai.OpenAIEmbeddings",
    model_name="text-embedding-3-small",
    model_kwargs={"api_key": "your_api_key"}
)

For the list of supported providers, please refer to the following table: https://docs.langchain.com/oss/python/integrations/providers/overview#featured-providers

Supported features

Basic invocation
Batch invocation
Text input only
Text truncation
Vector fusion
Retry and timeout

Attributes:

Name	Type	Description
`model_id`	`str`	The model ID of the embedding model.
`model_provider`	`str`	The provider of the embedding model.
`model_name`	`str`	The name of the embedding model.
`em`	`Embeddings`	The instance to interact with an embedding model defined using LangChain's Embeddings.
`retry_config`	`RetryConfig`	The retry configuration for the embedding model.
`truncation_config`	`TruncationConfig \| None`	The truncation configuration for the embedding model.
`vector_fuser`	`BaseVectorFuser \| None`	The vector fuser to handle mixed content.

Initializes a new instance of the LangChainEMInvoker class.

Parameters:

Name	Type	Description	Default
`model`	`Embeddings \| None`	The LangChain's Embeddings instance. If provided, will take precedence over the `model_class_path` parameter. Defaults to None.	`None`
`model_class_path`	`str \| None`	The LangChain's Embeddings class path. Must be formatted as "." (e.g. "langchain_openai.OpenAIEmbeddings"). Ignored if `model` is provided. Defaults to None.	`None`
`model_name`	`str \| None`	The model name. Only used if `model_class_path` is provided. Defaults to None.	`None`
`model_kwargs`	`dict[str, Any] \| None`	The additional keyword arguments. Only used if `model_class_path` is provided. Defaults to None.	`None`
`default_hyperparameters`	`dict[str, Any] \| None`	Default hyperparameters for invoking the model. Defaults to None.	`None`
`retry_config`	`RetryConfig \| dict[str, Any] \| None`	The retry config for the embedding model. Defaults to None, in which case a default config with no retry and 30.0 seconds timeout will be used.	`None`
`truncation_config`	`TruncationConfig \| dict[str, Any] \| None`	Config for text truncation behavior. Defaults to None, in which case no truncation is applied.	`None`
`vector_fuser`	`BaseVectorFuser \| VectorFuserType \| dict[str, Any] \| None`	The vector fuser to handle mixed content. Defaults to None, in which case handling the mixed modality content depends on the EM's capabilities.	`None`

`OpenAIEMInvoker(model_name, api_key=None, base_url=OPENAI_DEFAULT_URL, model_kwargs=None, default_hyperparameters=None, retry_config=None, truncation_config=None, vector_fuser=None)`

Bases: BaseEMInvoker, PersistentClientMixin

An embedding model invoker to interact with OpenAI embedding models.

Examples:

em_invoker = OpenAIEMInvoker(model_name="text-embedding-3-small")
result = await em_invoker.invoke("Hi there!")

Supported features

Basic invocation
Batch invocation
Text input only
Text truncation
Vector fusion
Retry and timeout

OpenAI compatible endpoints

This class can interact with endpoints that are compatible with OpenAI's Embeddings API schema. This includes but are not limited to: 1. Text Embeddings Inference (https://github.com/huggingface/text-embeddings-inference) 2. vLLM (https://vllm.ai/) To do this, simply set base_url to the endpoint URL. Supported features may vary between endpoints. Unsupported features will result in an error.

Attributes:

Name	Type	Description
`model_id`	`str`	The model ID of the embedding model.
`model_provider`	`str`	The provider of the embedding model.
`model_name`	`str`	The name of the embedding model.
`client_kwargs`	`dict[str, Any]`	The keyword arguments for the OpenAI client.
`default_hyperparameters`	`dict[str, Any]`	Default hyperparameters for invoking the embedding model.
`retry_config`	`RetryConfig`	The retry configuration for the embedding model.
`truncation_config`	`TruncationConfig \| None`	The truncation configuration for the embedding model.
`vector_fuser`	`BaseVectorFuser \| None`	The vector fuser to handle mixed content.

Initializes a new instance of the OpenAIEMInvoker class.

Parameters:

Name	Type	Description	Default
`model_name`	`str`	The name of the OpenAI embedding model to be used.	required
`api_key`	`str \| None`	The API key for authenticating with OpenAI. Defaults to None, in which case the `OPENAI_API_KEY` environment variable will be used. If the endpoint does not require an API key, a dummy value can be passed (e.g. "").	`None`
`base_url`	`str`	The base URL of a custom endpoint that is compatible with OpenAI's Embeddings API schema. Defaults to OpenAI's default URL.	`OPENAI_DEFAULT_URL`
`model_kwargs`	`dict[str, Any] \| None`	Additional keyword arguments for the OpenAI client. Defaults to None.	`None`
`default_hyperparameters`	`dict[str, Any] \| None`	Default hyperparameters for invoking the model. Defaults to None.	`None`
`retry_config`	`RetryConfig \| dict[str, Any] \| None`	The retry config for the embedding model. Defaults to None, in which case a default config with no retry and 30.0 seconds timeout will be used.	`None`
`truncation_config`	`TruncationConfig \| dict[str, Any] \| None`	Config for text truncation behavior. Defaults to None, in which case no truncation is applied.	`None`
`vector_fuser`	`BaseVectorFuser \| VectorFuserType \| dict[str, Any] \| None`	The vector fuser to handle mixed content. Defaults to None, in which case handling the mixed modality content depends on the EM's capabilities.	`None`

`SnowflakeEMInvoker(model_name, connection_config, default_hyperparameters=None, retry_config=None, truncation_config=None, vector_fuser=None)`

Bases: BaseEMInvoker

An embedding model invoker that calls AI_EMBED via the Snowflake Python connector.

Examples:

em_invoker = SnowflakeEMInvoker(
    model_name="snowflake-arctic-embed-m-v1.5",
    connection_config={...},
)
result = await em_invoker.invoke("Hi there!")

Initialization

Authentication options (passed through connection_config): 1. Password: json { "user": "<user>", "password": "<password>", "account": "<account>", "warehouse": "<warehouse>", "database": "<database>", "schema": "<schema>", } 2. Programmatic access token (PAT): json { "user": "...", "account": "...", "authenticator": "programmatic_access_token", "token": "<PAT>", "warehouse": "..." } More authentication options can be found in https://docs.snowflake.com/en/developer-guide/snowflake-rest-api/authentication.

Supported features

Basic invocation
Batch invocation
Text input only
Retry and timeout

Attributes:

Name	Type	Description
`model_id`	`str`	The model ID of the embedding model.
`model_provider`	`str`	The provider of the embedding model.
`model_name`	`str`	The name of the embedding model.
`default_hyperparameters`	`dict[str, Any]`	Default hyperparameters for invoking the embedding model.
`retry_config`	`RetryConfig`	The retry configuration for the embedding model.
`truncation_config`	`TruncationConfig \| None`	The truncation configuration for the embedding model.
`vector_fuser`	`BaseVectorFuser \| None`	The vector fuser to handle mixed content.

Initializes a new instance of the SnowflakeEMInvoker class.

Parameters:

Name	Type	Description	Default
`model_name`	`str`	The name of the Snowflake Cortex embedding model to be used. Supported models: `e5-base-v2`, `snowflake-arctic-embed-m-v1.5`, `snowflake-arctic-embed-l-v2.0`.	required
`connection_config`	`dict[str, Any]`	Connection parameters passed to `snowflake.connector.connect()` (e.g. user, password, account, warehouse, database, schema).	required
`default_hyperparameters`	`dict[str, Any] \| None`	Default hyperparameters for invoking the model. Defaults to None.	`None`
`retry_config`	`RetryConfig \| dict[str, Any] \| None`	The retry config for the embedding model. Defaults to None, in which case a default config with no retry and 30.0 seconds timeout will be used.	`None`
`truncation_config`	`TruncationConfig \| dict[str, Any] \| None`	Config for text truncation behavior. Defaults to None, in which case no truncation is applied.	`None`
`vector_fuser`	`BaseVectorFuser \| VectorFuserType \| dict[str, Any] \| None`	The vector fuser to handle mixed content. Defaults to None, in which case handling the mixed modality content depends on the EM's capabilities.	`None`

`TwelveLabsEMInvoker(model_name, api_key=None, model_kwargs=None, default_hyperparameters=None, retry_config=None, truncation_config=None, vector_fuser=None)`

Bases: BaseEMInvoker

An embedding model invoker to interact with TwelveLabs embedding models.

Examples:

em_invoker = TwelveLabsEMInvoker(model_name="Marengo-retrieval-2.7")
result = await em_invoker.invoke("Hi there!")

Supported features

Basic invocation
Batch invocation
Multimodal input a. Text b. Audio c. Image d. Video
Text truncation
Vector fusion
Retry and timeout

Video embedding

For advanced video embedding customization, you can pass hyperparameters to control the embedding process:

hyperparameters={
    "video_embed_create_params": {}, # optional parameters for EmbedClient.TasksClient.create(),
    "video_embed_retrieve_params": {}, # optional parameters for EmbedClient.TasksClient.retrieve()
    "wait_for_done_params": {}, # optional parameters for EmbedClient.TasksClient.wait_for_done()
}

video = Attachment.from_path("path/to/local/video.mp4")
result = await em_invoker.invoke(video, hyperparameters)

For video related hyperparameters, see https://docs.twelvelabs.io/sdk-reference/python/create-embeddings-v-1/create-video-embeddings.

Furthermore, when embedding video: 1. The video format should meet the requirements of TwelveLabs embedding model. For more details, see Marengo requirements 2. VectorFuser is required to embed video content.

Attributes:

Name	Type	Description
`model_id`	`str`	The model ID of the embedding model.
`model_provider`	`str`	The provider of the embedding model.
`model_name`	`str`	The name of the embedding model.
`client`	`Client`	The client for the TwelveLabs API.
`default_hyperparameters`	`dict[str, Any]`	Default hyperparameters for invoking the embedding model.
`retry_config`	`RetryConfig`	The retry configuration for the embedding model.
`truncation_config`	`TruncationConfig \| None`	The truncation configuration for the embedding model.
`vector_fuser`	`BaseVectorFuser \| None`	The vector fuser to handle mixed content.

Initializes a new instance of the TwelveLabsEMInvoker class.

Parameters:

Name	Type	Description	Default
`model_name`	`str`	The name of the TwelveLabs embedding model to be used.	required
`api_key`	`str \| None`	The API key for the TwelveLabs API. Defaults to None, in which case the `TWELVELABS_API_KEY` environment variable will be used.	`None`
`model_kwargs`	`dict[str, Any] \| None`	Additional keyword arguments for the TwelveLabs client. Defaults to None.	`None`
`default_hyperparameters`	`dict[str, Any] \| None`	Default hyperparameters for invoking the model. Defaults to None.	`None`
`retry_config`	`RetryConfig \| dict[str, Any] \| None`	The retry config for the embedding model. Defaults to None, in which case a default config with no retry and 30.0 seconds timeout will be used.	`None`
`truncation_config`	`TruncationConfig \| dict[str, Any] \| None`	Config for text truncation behavior. Defaults to None, in which case no truncation is applied.	`None`
`vector_fuser`	`BaseVectorFuser \| VectorFuserType \| dict[str, Any] \| None`	The vector fuser to handle mixed content. Defaults to None, in which case handling the mixed modality content depends on the EM's capabilities.	`None`

`VLLMEMInvoker(model_name, base_url, api_key=None, model_kwargs=None, default_hyperparameters=None, retry_config=None, truncation_config=None, vector_fuser=None)`

Bases: BaseEMInvoker

An embedding model invoker to interact with vLLM embedding models.

Examples:

em_invoker = VLLMEMInvoker(model_name="TIGER-Lab/VLM2Vec-Full", base_url="http://localhost:8000/v1")
result = await em_invoker.invoke("Hi there!")

Supported features

Basic invocation
Batch invocation
Multimodal input a. Text b. Image c. Audio
Text truncation
Vector fusion
Retry and timeout

Attributes:

Name	Type	Description
`model_id`	`str`	The model ID of the embedding model.
`model_provider`	`str`	The provider of the embedding model.
`model_name`	`str`	The name of the embedding model.
`client`	`AsyncClient`	The HTTP client for the vLLM API.
`default_hyperparameters`	`dict[str, Any]`	Default hyperparameters for invoking the model.
`retry_config`	`RetryConfig`	The retry configuration for the embedding model.
`truncation_config`	`TruncationConfig \| None`	The truncation configuration for the embedding model.
`vector_fuser`	`BaseVectorFuser \| None`	The vector fuser to handle mixed content.

Initializes a new instance of the VLLMEMInvoker class.

Parameters:

Name	Type	Description	Default
`model_name`	`str`	The name of the vLLM embedding model to be used.	required
`base_url`	`str`	The base URL of the vLLM OpenAI-compatible API.	required
`api_key`	`str \| None`	The API key for authentication. Defaults to None.	`None`
`model_kwargs`	`dict[str, Any] \| None`	Additional keyword arguments for the HTTP client. Defaults to None.	`None`
`default_hyperparameters`	`dict[str, Any] \| None`	Default hyperparameters for invoking the model. Defaults to None.	`None`
`retry_config`	`RetryConfig \| dict[str, Any] \| None`	The retry config for the embedding model. Defaults to None, in which case a default config with no retry and 30.0 seconds timeout will be used.	`None`
`truncation_config`	`TruncationConfig \| dict[str, Any] \| None`	Config for text truncation behavior. Defaults to None, in which case no truncation is applied.	`None`
`vector_fuser`	`BaseVectorFuser \| VectorFuserType \| dict[str, Any] \| None`	The vector fuser to handle mixed content. Defaults to None, in which case handling the mixed modality content depends on the EM's capabilities.	`None`

`VoyageEMInvoker(model_name, api_key=None, model_kwargs=None, default_hyperparameters=None, retry_config=None, truncation_config=None, vector_fuser=None, input_type=VoyageInputType.QUERY)`

Bases: BaseEMInvoker

An embedding model invoker to interact with Voyage embedding models.

Examples:

em_invoker = VoyageEMInvoker(model_name="voyage-3.5-lite")
result = await em_invoker.invoke("Hi there!")

Supported features

Basic invocation
Batch invocation
Multimodal input a. Text b. Image
Text truncation
Vector fusion
Retry and timeout

Attributes:

Name	Type	Description
`model_id`	`str`	The model ID of the embedding model.
`model_provider`	`str`	The provider of the embedding model.
`model_name`	`str`	The name of the embedding model.
`client`	`Client`	The client for the Voyage API.
`default_hyperparameters`	`dict[str, Any]`	Default hyperparameters for invoking the embedding model.
`retry_config`	`RetryConfig`	The retry configuration for the embedding model.
`truncation_config`	`TruncationConfig \| None`	The truncation configuration for the embedding model.
`vector_fuser`	`BaseVectorFuser \| None`	The vector fuser to handle mixed content.
`input_type`	`VoyageInputType`	The input type for the embedding model.

Initializes a new instance of the VoyageEMInvoker class.

Parameters:

Name	Type	Description	Default
`model_name`	`str`	The name of the Voyage embedding model to be used.	required
`api_key`	`str \| None`	The API key for the Voyage API. Defaults to None, in which case the `VOYAGE_API_KEY` environment variable will be used.	`None`
`model_kwargs`	`dict[str, Any] \| None`	Additional keyword arguments for the Voyage client. Defaults to None.	`None`
`default_hyperparameters`	`dict[str, Any] \| None`	Default hyperparameters for invoking the model. Defaults to None.	`None`
`retry_config`	`RetryConfig \| dict[str, Any] \| None`	The retry config for the embedding model. Defaults to None, in which case a default config with no retry and 30.0 seconds timeout will be used.	`None`
`truncation_config`	`TruncationConfig \| dict[str, Any] \| None`	Config for text truncation behavior. Defaults to None, in which case no truncation is applied.	`None`
`vector_fuser`	`BaseVectorFuser \| VectorFuserType \| dict[str, Any] \| None`	The vector fuser to handle mixed content. Defaults to None, in which case handling the mixed modality content depends on the EM's capabilities.	`None`
`input_type`	`VoyageInputType \| None`	The input type for the embedding model. Defaults to VoyageInputType.QUERY.	`QUERY`

Em Invoker

AzureOpenAIEMInvoker(azure_endpoint, azure_deployment, api_key=None, model_kwargs=None, default_hyperparameters=None, retry_config=None, truncation_config=None, vector_fuser=None)

BedrockEMInvoker(model_name, access_key_id=None, secret_access_key=None, region_name='us-east-1', model_kwargs=None, default_hyperparameters=None, retry_config=None, truncation_config=None, vector_fuser=None)

CohereEMInvoker(model_name, api_key=None, base_url=None, model_kwargs=None, default_hyperparameters=None, retry_config=None, truncation_config=None, vector_fuser=None, input_type=CohereInputType.SEARCH_DOCUMENT)

GoogleEMInvoker(model_name, api_key=None, credentials_path=None, project_id=None, location='us-central1', model_kwargs=None, default_hyperparameters=None, retry_config=None, truncation_config=None, vector_fuser=None)

HuggingFaceEMInvoker(model_name, api_key=None, model_kwargs=None, default_hyperparameters=None, retry_config=None, truncation_config=None, vector_fuser=None)

release_resources() async

JinaEMInvoker(model_name, api_key=None, base_url=JINA_DEFAULT_URL, model_kwargs=None, default_hyperparameters=None, retry_config=None, truncation_config=None, vector_fuser=None)

LangChainEMInvoker(model=None, model_class_path=None, model_name=None, model_kwargs=None, default_hyperparameters=None, retry_config=None, truncation_config=None, vector_fuser=None)

OpenAIEMInvoker(model_name, api_key=None, base_url=OPENAI_DEFAULT_URL, model_kwargs=None, default_hyperparameters=None, retry_config=None, truncation_config=None, vector_fuser=None)

SnowflakeEMInvoker(model_name, connection_config, default_hyperparameters=None, retry_config=None, truncation_config=None, vector_fuser=None)

TwelveLabsEMInvoker(model_name, api_key=None, model_kwargs=None, default_hyperparameters=None, retry_config=None, truncation_config=None, vector_fuser=None)

VLLMEMInvoker(model_name, base_url, api_key=None, model_kwargs=None, default_hyperparameters=None, retry_config=None, truncation_config=None, vector_fuser=None)

VoyageEMInvoker(model_name, api_key=None, model_kwargs=None, default_hyperparameters=None, retry_config=None, truncation_config=None, vector_fuser=None, input_type=VoyageInputType.QUERY)

`AzureOpenAIEMInvoker(azure_endpoint, azure_deployment, api_key=None, model_kwargs=None, default_hyperparameters=None, retry_config=None, truncation_config=None, vector_fuser=None)`

`BedrockEMInvoker(model_name, access_key_id=None, secret_access_key=None, region_name='us-east-1', model_kwargs=None, default_hyperparameters=None, retry_config=None, truncation_config=None, vector_fuser=None)`

`CohereEMInvoker(model_name, api_key=None, base_url=None, model_kwargs=None, default_hyperparameters=None, retry_config=None, truncation_config=None, vector_fuser=None, input_type=CohereInputType.SEARCH_DOCUMENT)`

`GoogleEMInvoker(model_name, api_key=None, credentials_path=None, project_id=None, location='us-central1', model_kwargs=None, default_hyperparameters=None, retry_config=None, truncation_config=None, vector_fuser=None)`

`HuggingFaceEMInvoker(model_name, api_key=None, model_kwargs=None, default_hyperparameters=None, retry_config=None, truncation_config=None, vector_fuser=None)`

`release_resources()` `async`

`JinaEMInvoker(model_name, api_key=None, base_url=JINA_DEFAULT_URL, model_kwargs=None, default_hyperparameters=None, retry_config=None, truncation_config=None, vector_fuser=None)`

`LangChainEMInvoker(model=None, model_class_path=None, model_name=None, model_kwargs=None, default_hyperparameters=None, retry_config=None, truncation_config=None, vector_fuser=None)`

`OpenAIEMInvoker(model_name, api_key=None, base_url=OPENAI_DEFAULT_URL, model_kwargs=None, default_hyperparameters=None, retry_config=None, truncation_config=None, vector_fuser=None)`

`SnowflakeEMInvoker(model_name, connection_config, default_hyperparameters=None, retry_config=None, truncation_config=None, vector_fuser=None)`

`TwelveLabsEMInvoker(model_name, api_key=None, model_kwargs=None, default_hyperparameters=None, retry_config=None, truncation_config=None, vector_fuser=None)`

`VLLMEMInvoker(model_name, base_url, api_key=None, model_kwargs=None, default_hyperparameters=None, retry_config=None, truncation_config=None, vector_fuser=None)`

`VoyageEMInvoker(model_name, api_key=None, model_kwargs=None, default_hyperparameters=None, retry_config=None, truncation_config=None, vector_fuser=None, input_type=VoyageInputType.QUERY)`