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Lm Invoker

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

AnthropicLMInvoker(model_name, api_key=None, model_kwargs=None, default_hyperparameters=None, tools=None, response_schema=None, output_analytics=False, retry_config=None, thinking=False, input_transformers=None, output_transformers=None, input_transformer=None, output_transformer=None)

Bases: BaseLMInvoker

A language model invoker to interact with Anthropic language models.

Examples:

lm_invoker = AnthropicLMInvoker(model_name="claude-sonnet-4-6")
result = await lm_invoker.invoke("Hi there!")
Supported features
  1. Basic invocation
  2. Streaming output
  3. Message roles
  4. Multimodal input a. Text b. Document c. Image
  5. Structured output
  6. Tool calling
  7. Native tools a. Code interpreter b. Skill c. Web search
  8. Thinking
  9. Output analytics
  10. Retry and timeout
  11. Extra capabilities a. Input transformer b. Output transformer c. Batch invocation d. File management e. Skill management

For full documentation and examples, please refer to the LM Invoker tutorial: https://gdplabs.gitbook.io/sdk/gen-ai-sdk/tutorials/inference/lm-invoker

Attributes:

Name Type Description
model_id str

The model ID of the language model.
model_provider str

The provider of the language model.
model_name str

The name of the language model.
client AsyncAnthropic

The Anthropic client instance.
default_config dict[str, Any]

Default config for invoking the model.
tools list[Tool]

Tools provided to the model to enable tool calling.
response_schema ResponseSchema | None

The schema of the response. If provided, the model will output a structured response as defined by the schema. Supports both Pydantic BaseModel and JSON schema dictionary.
output_analytics bool

Whether to output the invocation analytics.
retry_config RetryConfig

The retry configuration for the language model.
thinking ThinkingConfig

The thinking configuration for the language model.
input_transformers list[InputTransformerConfig]

The list of input transformer configs to use.
output_transformers list[OutputTransformerConfig]

The list of output transformer configs to use.

Initializes the AnthropicLmInvoker instance.

Parameters:

Name Type Description Default
model_name str

The name of the Anthropic language model.

 required
api_key str | None

The Anthropic API key. Defaults to None, in which case the ANTHROPIC_API_KEY environment variable will be used.

 None
model_kwargs dict[str, Any] | None

Additional keyword arguments for the Anthropic client.

 None
default_hyperparameters dict[str, Any] | None

Default hyperparameters for invoking the model. Defaults to None.

 None
tools list[LMTool] | None

Tools provided to the model to enable tool calling. Defaults to None, in which case an empty list is used.

 None
response_schema ResponseSchema | None

The schema of the response. If provided, the model will output a structured response as defined by the schema. Supports both Pydantic BaseModel and JSON schema dictionary. Defaults to None.

 None
output_analytics bool

Whether to output the invocation analytics. Defaults to False.

 False
retry_config RetryConfig | None

The retry configuration for the language model. Defaults to None, in which case a default config with no retry and 30.0 seconds timeout will be used.

 None
thinking bool | ThinkingConfig

A boolean or ThinkingConfig object to configure thinking. Defaults to False.

 False
input_transformers list[InputTransformer] | None

The list of input transformers to use. Defaults to None, in which case no input transformers will be used.

 None
output_transformers list[OutputTransformer] | None

The list of output transformers to use. Defaults to None, in which case no output transformers will be used.

 None
input_transformer InputTransformerType | None

Deprecated param to define input transformer. Will be removed in v0.7. Defaults to None.

 None
output_transformer OutputTransformerType | None

Deprecated param to define output transformer. Will be removed in v0.7. Defaults to None.

 None

Raises:

Type Description
ValueError

if response_schema is provided, but tools or thinking are also provided.

batch cached property

The batch operations for the language model.

Returns:

Name Type Description
AnthropicBatchOperations AnthropicBatchOperations

The batch operations for the language model.

file cached property

The file operations for the language model.

Returns:

Name Type Description
AnthropicFileOperations AnthropicFileOperations

The file operations for the language model.

skill cached property

The skill operations for the language model.

Returns:

Name Type Description
AnthropicSkillOperations AnthropicSkillOperations

The skill operations for the language model.

AzureOpenAILMInvoker(azure_endpoint, azure_deployment, api_key=None, model_kwargs=None, default_hyperparameters=None, tools=None, response_schema=None, output_analytics=False, retry_config=None, thinking=False, input_transformers=None, output_transformers=None, input_transformer=None, output_transformer=None)

Bases: OpenAILMInvoker

A language model invoker to interact with Azure OpenAI language models.

Examples:

lm_invoker = AzureOpenAILMInvoker(
    azure_endpoint="https://<your-azure-openai-endpoint>.openai.azure.com/openai/v1",
    azure_deployment="<your-azure-openai-deployment>",
)
result = await lm_invoker.invoke("Hi there!")
Supported features
  1. Basic invocation
  2. Streaming output
  3. Message roles
  4. Multimodal input a. Text b. Document c. Image
  5. Structured output
  6. Tool calling
  7. Thinking
  8. Output analytics
  9. Retry and timeout
  10. Extra capabilities a. Input transformer b. Output transformer

For full documentation and examples, please refer to the LM Invoker tutorial: https://gdplabs.gitbook.io/sdk/gen-ai-sdk/tutorials/inference/lm-invoker

Attributes:

Name Type Description
model_id str

The model ID of the language model.
model_provider str

The provider of the language model.
model_name str

The name of the Azure OpenAI language model deployment.
client_kwargs dict[str, Any]

The keyword arguments for the Azure OpenAI client.
default_config dict[str, Any]

Default config for invoking the model.
tools list[Tool]

The list of tools provided to the model to enable tool calling.
response_schema ResponseSchema | None

The schema of the response. If provided, the model will output a structured response as defined by the schema. Supports both Pydantic BaseModel and JSON schema dictionary.
output_analytics bool

Whether to output the invocation analytics.
retry_config RetryConfig

The retry configuration for the language model.
thinking ThinkingConfig

The thinking configuration for the language model.
data_stores list[AttachmentStore]

The data stores to retrieve internal knowledge from.
input_transformers list[InputTransformerConfig]

The list of input transformer configs to use.
output_transformers list[OutputTransformerConfig]

The list of output transformer configs to use.

Initializes a new instance of the AzureOpenAILMInvoker 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, in which case an empty dictionary is used.

 None
tools list[LMTool] | None

Tools provided to the model to enable tool calling. Defaults to None, in which case an empty list is used.

 None
response_schema ResponseSchema | None

The schema of the response. If provided, the model will output a structured response as defined by the schema. Supports both Pydantic BaseModel and JSON schema dictionary. Defaults to None.

 None
output_analytics bool

Whether to output the invocation analytics. Defaults to False.

 False
retry_config RetryConfig | None

The retry configuration for the language model. Defaults to None, in which case a default config with no retry and 30.0 seconds timeout will be used.

 None
thinking bool | ThinkingConfig

A boolean or ThinkingConfig object to configure thinking. Defaults to False.

 False
input_transformers list[InputTransformer] | None

The list of input transformers to use. Defaults to None, in which case no input transformers will be used.

 None
output_transformers list[OutputTransformer] | None

The list of output transformers to use. Defaults to None, in which case no output transformers will be used.

 None
input_transformer InputTransformerType | None

Deprecated param to define input transformer. Will be removed in v0.7. Defaults to None.

 None
output_transformer OutputTransformerType | None

Deprecated param to define output transformer. Will be removed in v0.7. Defaults to None.

 None

BedrockLMInvoker(model_name, access_key_id=None, secret_access_key=None, region_name='us-east-1', model_kwargs=None, default_hyperparameters=None, tools=None, response_schema=None, output_analytics=False, retry_config=None, input_transformers=None, output_transformers=None, input_transformer=None, output_transformer=None)

Bases: BaseLMInvoker

A language model invoker to interact with AWS Bedrock language models.

Examples:

lm_invoker = BedrockLMInvoker(
    model_name="us.anthropic.claude-sonnet-4-20250514-v1:0",
    aws_access_key_id="<your-aws-access-key-id>",
    aws_secret_access_key="<your-aws-secret-access-key>",
)
result = await lm_invoker.invoke("Hi there!")
Supported features
  1. Basic invocation
  2. Streaming output
  3. Message roles
  4. Multimodal input a. Text b. Document c. Image d. Video
  5. Structured output
  6. Tool calling
  7. Output analytics
  8. Retry and timeout
  9. Extra capabilities a. Input transformer b. Output transformer

For full documentation and examples, please refer to the LM Invoker tutorial: https://gdplabs.gitbook.io/sdk/gen-ai-sdk/tutorials/inference/lm-invoker

Attributes:

Name Type Description
model_id str

The model ID of the language model.
model_provider str

The provider of the language model.
model_name str

The name of the language model.
session Session

The Bedrock client session.
client_kwargs dict[str, Any]

The Bedrock client kwargs.
default_config dict[str, Any]

Default config for invoking the model.
tools list[Tool]

Tools provided to the model to enable tool calling.
response_schema ResponseSchema | None

The schema of the response. If provided, the model will output a structured response as defined by the schema. Supports both Pydantic BaseModel and JSON schema dictionary.
output_analytics bool

Whether to output the invocation analytics.
retry_config RetryConfig

The retry configuration for the language model.
input_transformers list[InputTransformerConfig]

The list of input transformer configs to use.
output_transformers list[OutputTransformerConfig]

The list of output transformer configs to use.

Initializes the BedrockLMInvoker instance.

Parameters:

Name Type Description Default
model_name str

The name of the Bedrock language model.

 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.

 None
default_hyperparameters dict[str, Any] | None

Default hyperparameters for invoking the model. Defaults to None.

 None
tools list[LMTool] | None

Tools provided to the model to enable tool calling. Defaults to None, in which case an empty list is used.

 None
response_schema ResponseSchema | None

The schema of the response. If provided, the model will output a structured response as defined by the schema. Supports both Pydantic BaseModel and JSON schema dictionary. Defaults to None.

 None
output_analytics bool

Whether to output the invocation analytics. Defaults to False.

 False
retry_config RetryConfig | None

The retry configuration for the language model. Defaults to None, in which case a default config with no retry and 30.0 seconds timeout will be used.

 None
input_transformers list[InputTransformer] | None

The list of input transformers to use. Defaults to None, in which case no input transformers will be used.

 None
output_transformers list[OutputTransformer] | None

The list of output transformers to use. Defaults to None, in which case no output transformers will be used.

 None
input_transformer InputTransformerType | None

Deprecated param to define input transformer. Will be removed in v0.7. Defaults to None.

 None
output_transformer OutputTransformerType | None

Deprecated param to define output transformer. Will be removed in v0.7. Defaults to None.

 None

Raises:

Type Description
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.

DatasaurLMInvoker(base_url, api_key=None, jwe_secret_or_config=None, model_kwargs=None, default_hyperparameters=None, output_analytics=False, retry_config=None, citations=False, input_transformers=None, output_transformers=None, input_transformer=None, output_transformer=None)

Bases: OpenAIChatCompletionsLMInvoker

A language model invoker to interact with Datasaur LLM Projects Deployment API.

Examples:

lm_invoker = DatasaurLMInvoker(base_url="https://deployment.datasaur.ai/api/deployment/teamId/deploymentId/")
result = await lm_invoker.invoke("Hi there!")
Authentication

The DatasaurLMInvoker can use either Deployment API or AgentKit API.

Deployment API requires an API key for authentication:

lm_invoker = DatasaurLMInvoker(
    base_url="https://deployment.datasaur.ai/api/deployment/teamId/deploymentId/",
    api_key="your_api_key"
)

AgentKit API requires a JWE secret or configuration for authentication:

lm_invoker = DatasaurLMInvoker(
    base_url="https://my-agentkit-url/v1/",
    jwe_secret_or_config="your_jwe_secret"
)

If neither api_key nor jwe_secret_or_config is provided, Deployment API will be used by default. The DATASAUR_API_KEY environment variable will be used for Deployment API authentication.

Supported features
  1. Basic invocation
  2. Streaming output
  3. Message roles
  4. Multimodal input a. Text b. Audio c. Document d. Image
  5. Output analytics
  6. Retry and timeout
  7. Extra capabilities a. Input transformer b. Output transformer

For full documentation and examples, please refer to the LM Invoker tutorial: https://gdplabs.gitbook.io/sdk/gen-ai-sdk/tutorials/inference/lm-invoker

Citations

The DatasaurLMInvoker can be configured to output the citations used to generate the response. This feature can be enabled by setting the citations parameter to True.

Citations outputs are stored in the outputs attribute of the LMOutput object and can be accessed via the citations property.

lm_invoker = DatasaurLMInvoker(..., citations=True)

Attributes:

Name Type Description
model_id str

The model ID of the language model.
model_provider str

The provider of the language model.
model_name str

The name of the language model.
client_kwargs dict[str, Any]

The keyword arguments for the OpenAI client.
default_config dict[str, Any]

Default config for invoking the model.
tools list[Any]

The list of tools provided to the model to enable tool calling. Currently not supported.
response_schema ResponseSchema | None

The schema of the response. Currently not supported.
output_analytics bool

Whether to output the invocation analytics.
retry_config RetryConfig | None

The retry configuration for the language model.
citations bool

Whether to output the citations.
input_transformers list[InputTransformerConfig]

The list of input transformer configs to use.
output_transformers list[OutputTransformerConfig]

The list of output transformer configs to use.

Initializes a new instance of the DatasaurLMInvoker class.

Parameters:

Name Type Description Default
base_url str

The base URL of the Datasaur LLM Projects Deployment API.

 required
api_key str | None

The API key for authenticating with Datasaur LLM Projects Deployment API. Defaults to None, in which case the DATASAUR_API_KEY environment variable will be used.

 None
jwe_secret_or_config str | dict[str, Any] | DatasaurJWEConfig | None

The shared secret for JWE token generation or the JWE configuration. Cannot be used together with api_key. Defaults to None.

 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
output_analytics bool

Whether to output the invocation analytics. Defaults to False.

 False
retry_config RetryConfig | None

The retry configuration for the language model. Defaults to None, in which case a default config with no retry and 30.0 seconds timeout will be used.

 None
citations bool

Whether to output the citations. Defaults to False.

 False
input_transformers list[InputTransformer] | None

The list of input transformers to use. Defaults to None, in which case no input transformers will be used.

 None
output_transformers list[OutputTransformer] | None

The list of output transformers to use. Defaults to None, in which case no output transformers will be used.

 None
input_transformer InputTransformerType | None

Deprecated param to define input transformer. Will be removed in v0.7. Defaults to None.

 None
output_transformer OutputTransformerType | None

Deprecated param to define output transformer. Will be removed in v0.7. Defaults to None.

 None

set_response_schema(response_schema)

Sets the response schema for the Datasaur LLM Projects Deployment API.

This method raises a NotImplementedError because the Datasaur LLM Projects Deployment API does not support response schema.

Parameters:

Name Type Description Default
response_schema ResponseSchema | None

The response schema to be used.

 required

Raises:

Type Description
NotImplementedError

This method is not supported for the Datasaur LLM Projects Deployment API.

set_tools(tools)

Sets the tools for the Datasaur LLM Projects Deployment API.

This method is raises a NotImplementedError because the Datasaur LLM Projects Deployment API does not support tools.

Parameters:

Name Type Description Default
tools list[LMTool]

The list of tools to be used.

 required

Raises:

Type Description
NotImplementedError

This method is not supported for the Datasaur LLM Projects Deployment API.

DeepSeekLMInvoker(model_name, api_key=None, model_kwargs=None, default_hyperparameters=None, tools=None, output_analytics=False, retry_config=None, input_transformers=None, output_transformers=None)

Bases: OpenAIChatCompletionsLMInvoker

A language model invoker to interact with DeepSeek API.

Examples:

lm_invoker = DeepSeekLMInvoker(model_name="deepseek-v4-flash")
result = await lm_invoker.invoke("Hi there!")
Supported features
  1. Basic invocation
  2. Streaming output
  3. Message roles
  4. Tool calling
  5. Output analytics
  6. Retry and timeout
  7. Extra capabilities a. Input transformer b. Output transformer

For full documentation and examples, please refer to the LM Invoker tutorial: https://gdplabs.gitbook.io/sdk/gen-ai-sdk/tutorials/inference/lm-invoker

Initializes a new instance of the DeepSeekLMInvoker class.

Parameters:

Name Type Description Default
model_name str

The name of the DeepSeek language model.

 required
api_key str | None

The API key for authenticating with the DeepSeek API. Defaults to None, in which case the DEEPSEEK_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
tools list[LMTool] | None

Tools provided to the model to enable tool calling. Defaults to None.

 None
output_analytics bool

Whether to output the invocation analytics. Defaults to False.

 False
retry_config RetryConfig | None

The retry configuration for the language model. Defaults to None, in which case a default config with no retry and 30.0 seconds timeout will be used.

 None
input_transformers list[InputTransformer] | None

The list of input transformers to use. Defaults to None, in which case no input transformers will be used.

 None
output_transformers list[OutputTransformer] | None

The list of output transformers to use. Defaults to None, in which case no output transformers will be used.

 None

set_response_schema(response_schema)

Sets the response schema for the DeepSeek API.

This method raises a NotImplementedError because the DeepSeek API does not support response schema.

Parameters:

Name Type Description Default
response_schema ResponseSchema | None

The response schema to be used.

 required

Raises:

Type Description
NotImplementedError

This method is not supported for the DeepSeek API.

GoogleLMInvoker(model_name, api_key=None, credentials_path=None, project_id=None, location='us-central1', model_kwargs=None, default_hyperparameters=None, tools=None, response_schema=None, output_analytics=False, retry_config=None, thinking=None, data_stores=None, auto_upload=True, input_transformers=None, output_transformers=None, input_transformer=None, output_transformer=None)

Bases: BaseLMInvoker

A language model invoker to interact with Google language models.

Examples:

lm_invoker = GoogleLMInvoker(model_name="gemini-2.5-flash")
result = await lm_invoker.invoke("Hi there!")
Authentication

The GoogleLMInvoker 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:

lm_invoker = GoogleLMInvoker(
    model_name="gemini-2.5-flash",
    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:

lm_invoker = GoogleLMInvoker(
    model_name="gemini-2.5-flash",
    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
  1. Basic invocation
  2. Streaming output
  3. Message roles
  4. Multimodal input a. Text b. Audio c. Document d. Image e. Video
  5. Structured output
  6. Tool calling
  7. Native tools a. Code interpreter b. Data store c. Image generation d. Web search
  8. Thinking
  9. Output analytics
  10. Retry and timeout
  11. Extra capabilities a. Input transformer b. Output transformer c. Batch invocation d. File management e. Data store management

For full documentation and examples, please refer to the LM Invoker tutorial: https://gdplabs.gitbook.io/sdk/gen-ai-sdk/tutorials/inference/lm-invoker

Attributes:

Name Type Description
model_id str

The model ID of the language model.
model_provider str

The provider of the language model.
model_name str

The name of the language model.
client_params dict[str, Any]

The Google client instance init parameters.
default_config dict[str, Any]

Default config for invoking the model.
tools list[Tool]

The list of tools provided to the model to enable tool calling.
response_schema ResponseSchema | None

The schema of the response. If provided, the model will output a structured response as defined by the schema. Supports both Pydantic BaseModel and JSON schema dictionary.
output_analytics bool

Whether to output the invocation analytics.
retry_config RetryConfig | None

The retry configuration for the language model.
thinking ThinkingConfig

The thinking configuration for the language model.
image_generation bool

Whether to generate image. Only allowed for image generation models.
data_stores list[AttachmentStore]

The data stores to retrieve internal knowledge from.
auto_upload bool

Whether to automatically upload attachments to files API if the inputs total size exceeds the threshold of 20MB.
input_transformers list[InputTransformerConfig]

The list of input transformer configs to use.
output_transformers list[OutputTransformerConfig]

The list of output transformer configs to use.

Initializes a new instance of the GoogleLMInvoker 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 Vertex AI client.

 None
default_hyperparameters dict[str, Any] | None

Default hyperparameters for invoking the model. Defaults to None.

 None
tools list[LMTool] | None

Tools provided to the model to enable tool calling. Defaults to None.

 None
response_schema ResponseSchema | None

The schema of the response. If provided, the model will output a structured response as defined by the schema. Supports both Pydantic BaseModel and JSON schema dictionary. Defaults to None.

 None
output_analytics bool

Whether to output the invocation analytics. Defaults to False.

 False
retry_config RetryConfig | None

The retry configuration for the language model. Defaults to None, in which case a default config with no retry and 30.0 seconds timeout will be used.

 None
thinking bool | ThinkingConfig | None

A boolean or ThinkingConfig object to configure thinking. Defaults to None.

 None
data_stores list[AttachmentStore] | None

The data stores to retrieve internal knowledge from. Defaults to None, in which case no data stores will be used.

 None
auto_upload bool

Whether to automatically upload attachments to files API if the inputs total size exceeds the threshold of 20MB. Defaults to True.

 True
input_transformers list[InputTransformer] | None

The list of input transformers to use. Defaults to None, in which case no input transformers will be used.

 None
output_transformers list[OutputTransformer] | None

The list of output transformers to use. Defaults to None, in which case no output transformers will be used.

 None
input_transformer InputTransformerType | None

Deprecated param to define input transformer. Will be removed in v0.7. Defaults to None.

 None
output_transformer OutputTransformerType | None

Deprecated param to define output transformer. Will be removed in v0.7. Defaults to None.

 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.

batch cached property

The batch operations for the language model.

Returns:

Name Type Description
GoogleBatchOperations GoogleBatchOperations

The batch operations for the language model.

data_store cached property

The data store operations for the language model.

Returns:

Name Type Description
GoogleDataStoreOperations GoogleDataStoreOperations

The data store operations for the language model.

file cached property

The file operations for the language model.

Returns:

Name Type Description
GoogleFileOperations GoogleFileOperations

The file operations for the language model.

set_data_stores(data_stores)

Sets the data stores for the Google language model.

This method sets the data stores for the Google language model. Any existing data stores will be replaced.

Parameters:

Name Type Description Default
data_stores list[AttachmentStore]

The list of data stores to be used.

 required

LangChainLMInvoker(model=None, model_class_path=None, model_name=None, model_kwargs=None, default_hyperparameters=None, tools=None, response_schema=None, output_analytics=False, retry_config=None, input_transformers=None, output_transformers=None, input_transformer=None, output_transformer=None)

Bases: BaseLMInvoker

A language model invoker to interact with LangChain's BaseChatModel.

Examples:

lm_invoker = LangChainLMInvoker(
    model_class_path="langchain_openai.ChatOpenAI",
    model_name="gpt-5-nano",
)
result = await lm_invoker.invoke("Hi there!")
Initialization

The LangChainLMInvoker can be initialized by either passing:

  1. A LangChain's BaseChatModel instance: Usage example:
from langchain_openai import ChatOpenAI

model = ChatOpenAI(model="gpt-5-nano", api_key="your_api_key")
lm_invoker = LangChainLMInvoker(model=model)
  1. A model path in the format of ".": Usage example:
lm_invoker = LangChainLMInvoker(
    model_class_path="langchain_openai.ChatOpenAI",
    model_name="gpt-5-nano",
    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
  1. Basic invocation
  2. Streaming output
  3. Message roles
  4. Multimodal input a. Text b. Image
  5. Structured output
  6. Tool calling
  7. Output analytics
  8. Retry and timeout
  9. Extra capabilities a. Input transformer b. Output transformer

For full documentation and examples, please refer to the LM Invoker tutorial: https://gdplabs.gitbook.io/sdk/gen-ai-sdk/tutorials/inference/lm-invoker

Attributes:

Name Type Description
model_id str

The model ID of the language model.
model_provider str

The provider of the language model.
model_name str

The name of the language model.
model BaseChatModel

The LangChain's BaseChatModel instance.
default_config dict[str, Any]

Default config for invoking the model.
tools list[Any]

The list of tools provided to the model to enable tool calling.
response_schema ResponseSchema | None

The schema of the response. If provided, the model will output a structured response as defined by the schema. Supports both Pydantic BaseModel and JSON schema dictionary.
output_analytics bool

Whether to output the invocation analytics.
retry_config RetryConfig | None

The retry configuration for the language model.
input_transformers list[InputTransformerConfig]

The list of input transformer configs to use.
output_transformers list[OutputTransformerConfig]

The list of output transformer configs to use.

Initializes a new instance of the LangChainLMInvoker class.

Parameters:

Name Type Description Default
model BaseChatModel | None

The LangChain's BaseChatModel instance. If provided, will take precedence over the model_class_path parameter. Defaults to None.

 None
model_class_path str | None

The LangChain's BaseChatModel class path. Must be formatted as "." (e.g. "langchain_openai.ChatOpenAI"). 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
tools list[LMTool] | None

Tools provided to the model to enable tool calling. Defaults to None.

 None
response_schema ResponseSchema | None

The schema of the response. If provided, the model will output a structured response as defined by the schema. Supports both Pydantic BaseModel and JSON schema dictionary. Defaults to None.

 None
output_analytics bool

Whether to output the invocation analytics. Defaults to False.

 False
retry_config RetryConfig | None

The retry configuration for the language model. Defaults to None, in which case a default config with no retry and 30.0 seconds timeout will be used.

 None
input_transformers list[InputTransformer] | None

The list of input transformers to use. Defaults to None, in which case no input transformers will be used.

 None
output_transformers list[OutputTransformer] | None

The list of output transformers to use. Defaults to None, in which case no output transformers will be used.

 None
input_transformer InputTransformerType | None

Deprecated param to define input transformer. Will be removed in v0.7. Defaults to None.

 None
output_transformer OutputTransformerType | None

Deprecated param to define output transformer. Will be removed in v0.7. Defaults to None.

 None

Raises:

Type Description
ValueError

If response_schema is provided, but tools are also provided.

LiteLLMLMInvoker(model_id, default_hyperparameters=None, tools=None, response_schema=None, output_analytics=False, retry_config=None, thinking=False, input_transformers=None, output_transformers=None, input_transformer=None, output_transformer=None)

Bases: OpenAIChatCompletionsLMInvoker

A language model invoker to interact with language models using LiteLLM.

Examples:

lm_invoker = LiteLLMLMInvoker(model_id="openai/gpt-5-nano")
result = await lm_invoker.invoke("Hi there!")
Initialization

The LiteLLMLMInvoker provides an interface to interact with multiple language model providers. In order to use this class: 1. The model_id parameter must be in the format of provider/model_name. e.g. openai/gpt-4o-mini. 2. The required credentials must be provided via the environment variables.

Usage example:

os.environ["OPENAI_API_KEY"] = "your_openai_api_key"
lm_invoker = LiteLLMLMInvoker(model_id="openai/gpt-4o-mini")

For the complete list of supported providers and their required credentials, please refer to the LiteLLM documentation: https://docs.litellm.ai/docs/providers/

Supported features
  1. Basic invocation
  2. Streaming output
  3. Message roles
  4. Multimodal input a. Text b. Audio c. Image
  5. Structured output
  6. Tool calling
  7. Thinking
  8. Output analytics
  9. Retry and timeout
  10. Extra capabilities a. Input transformer b. Output transformer

For full documentation and examples, please refer to the LM Invoker tutorial: https://gdplabs.gitbook.io/sdk/gen-ai-sdk/tutorials/inference/lm-invoker

Attributes:

Name Type Description
model_id str

The model ID of the language model.
model_provider str

The provider of the language model.
model_name str

The name of the language model.
default_config dict[str, Any]

Default config for invoking the model.
tools list[Tool]

The list of tools provided to the model to enable tool calling.
response_schema ResponseSchema | None

The schema of the response. If provided, the model will output a structured response as defined by the schema. Supports both Pydantic BaseModel and JSON schema dictionary.
output_analytics bool

Whether to output the invocation analytics.
retry_config RetryConfig | None

The retry configuration for the language model.
thinking ThinkingConfig

The thinking configuration for the language model.
input_transformers list[InputTransformerConfig]

The list of input transformer configs to use.
output_transformers list[OutputTransformerConfig]

The list of output transformer configs to use.

Initializes a new instance of the LiteLLMLMInvoker class.

Parameters:

Name Type Description Default
model_id str

The ID of the model to use. Must be in the format of provider/model_name.

 required
default_hyperparameters dict[str, Any] | None

Default hyperparameters for invoking the model. Defaults to None.

 None
tools list[LMTool] | None

Tools provided to the model to enable tool calling. Defaults to None.

 None
response_schema ResponseSchema | None

The schema of the response. If provided, the model will output a structured response as defined by the schema. Supports both Pydantic BaseModel and JSON schema dictionary. Defaults to None.

 None
output_analytics bool

Whether to output the invocation analytics. Defaults to False.

 False
retry_config RetryConfig | None

The retry configuration for the language model. Defaults to None, in which case a default config with no retry and 30.0 seconds timeout will be used.

 None
thinking bool | ThinkingConfig

A boolean or ThinkingConfig object to configure thinking. Defaults to False.

 False
input_transformers list[InputTransformer] | None

The list of input transformers to use. Defaults to None, in which case no input transformers will be used.

 None
output_transformers list[OutputTransformer] | None

The list of output transformers to use. Defaults to None, in which case no output transformers will be used.

 None
input_transformer InputTransformerType | None

Deprecated param to define input transformer. Will be removed in v0.7. Defaults to None.

 None
output_transformer OutputTransformerType | None

Deprecated param to define output transformer. Will be removed in v0.7. Defaults to None.

 None

OpenAIChatCompletionsLMInvoker(model_name, api_key=None, base_url=OPENAI_DEFAULT_URL, model_kwargs=None, default_hyperparameters=None, tools=None, response_schema=None, output_analytics=False, retry_config=None, thinking=False, input_transformers=None, output_transformers=None, input_transformer=None, output_transformer=None)

Bases: StreamingBufferMixin, BaseLMInvoker

A language model invoker to interact with OpenAI language models using the Chat Completions API.

This class provides support for OpenAI's Chat Completions API schema. Use this class only when you have a specific reason to use the Chat Completions API over the Responses API, as OpenAI recommends using the Responses API whenever possible. The Responses API schema is supported through the OpenAILMInvoker class.

Examples:

lm_invoker = OpenAIChatCompletionsLMInvoker(model_name="gpt-5-nano")
result = await lm_invoker.invoke("Hi there!")
Supported features
  1. Basic invocation
  2. Streaming output
  3. Message roles
  4. Multimodal input a. Text b. Audio c. Document d. Image e. Video Note: Video is only supported by compatible third-party endpoints such as OpenRouter and vLLM, but not natively supported by OpenAI's Chat Completions API.
  5. Structured output
  6. Tool calling
  7. Thinking
  8. Output analytics
  9. Retry and timeout
  10. Extra capabilities a. Input transformer b. Output transformer

For full documentation and examples, please refer to the LM Invoker tutorial: https://gdplabs.gitbook.io/sdk/gen-ai-sdk/tutorials/inference/lm-invoker

OpenAI compatible endpoints

This class can interact with endpoints compatible with OpenAI's Chat Completions API schema. This includes but are not limited to: 1. DeepInfra (https://deepinfra.com/) 2. DeepSeek (https://deepseek.com/) 3. Groq (https://groq.com/) 4. OpenRouter (https://openrouter.ai/) 5. Text Generation Inference (https://github.com/huggingface/text-generation-inference) 6. Together.ai (https://together.ai/) 7. 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 language model.
model_provider str

The provider of the language model.
model_name str

The name of the language model.
client_kwargs dict[str, Any]

The keyword arguments for the OpenAI client.
default_config dict[str, Any]

Default config for invoking the model.
tools list[Tool]

The list of tools provided to the model to enable tool calling.
response_schema ResponseSchema | None

The schema of the response. If provided, the model will output a structured response as defined by the schema. Supports both Pydantic BaseModel and JSON schema dictionary.
thinking ThinkingConfig

The thinking configuration for the language model.
output_analytics bool

Whether to output the invocation analytics.
retry_config RetryConfig | None

The retry configuration for the language model.
input_transformers list[InputTransformerConfig]

The list of input transformer configs to use.
output_transformers list[OutputTransformerConfig]

The list of output transformer configs to use.

Initializes a new instance of the OpenAIChatCompletionsLMInvoker class.

Parameters:

Name Type Description Default
model_name str

The name of the OpenAI model.

 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 Chat Completions API schema. Defaults to OpenAI's default URL.

 OPENAI_DEFAULT_URL
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
tools list[LMTool] | None

Tools provided to the model to enable tool calling. Defaults to None.

 None
response_schema ResponseSchema | None

The schema of the response. If provided, the model will output a structured response as defined by the schema. Supports both Pydantic BaseModel and JSON schema dictionary. Defaults to None.

 None
output_analytics bool

Whether to output the invocation analytics. Defaults to False.

 False
retry_config RetryConfig | None

The retry configuration for the language model. Defaults to None, in which case a default config with no retry and 30.0 seconds timeout will be used.

 None
thinking bool | ThinkingConfig

A boolean or ThinkingConfig object to configure thinking. Defaults to False.

 False
input_transformers list[InputTransformer] | None

The list of input transformers to use. Defaults to None, in which case no input transformers will be used.

 None
output_transformers list[OutputTransformer] | None

The list of output transformers to use. Defaults to None, in which case no output transformers will be used.

 None
input_transformer InputTransformerType | None

Deprecated param to define input transformer. Will be removed in v0.7. Defaults to None.

 None
output_transformer OutputTransformerType | None

Deprecated param to define output transformer. Will be removed in v0.7. Defaults to None.

 None

OpenAILMInvoker(model_name, api_key=None, base_url=OPENAI_DEFAULT_URL, model_kwargs=None, default_hyperparameters=None, tools=None, response_schema=None, output_analytics=False, retry_config=None, thinking=False, image_generation=False, data_stores=None, input_transformers=None, output_transformers=None, input_transformer=None, output_transformer=None, output_hooks=None, streaming_hooks=None)

Bases: BaseLMInvoker

A language model invoker to interact with OpenAI language models.

This class provides support for OpenAI's Responses API schema, which is recommended by OpenAI as the preferred API to use whenever possible. Use this class unless you have a specific reason to use the Chat Completions API instead. The Chat Completions API schema is supported through the OpenAIChatCompletionsLMInvoker class.

Examples:

lm_invoker = OpenAILMInvoker(model_name="gpt-5-nano")
result = await lm_invoker.invoke("Hi there!")
Supported features
  1. Basic invocation
  2. Streaming output
  3. Message roles
  4. Multimodal input a. Text b. Document c. Image
  5. Structured output
  6. Tool calling
  7. Native tools a. Code interpreter b. Data store c. Image generation d. MCP connector e. MCP server f. Web search
  8. Thinking
  9. Output analytics
  10. Retry and timeout
  11. Extra capabilities a. Input transformer b. Output transformer c. Batch invocation d. File management e. Data store management f. [BETA] Custom output hooks & streaming hooks

For full documentation and examples, please refer to the LM Invoker tutorial: https://gdplabs.gitbook.io/sdk/gen-ai-sdk/tutorials/inference/lm-invoker

OpenAI compatible endpoints

This class can interact with endpoints compatible with OpenAI's Responses API schema (e.g. SGLang: https://github.com/sgl-project/sglang). 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 language model.
model_provider str

The provider of the language model.
model_name str

The name of the language model.
client_kwargs dict[str, Any]

The keyword arguments for the OpenAI client.
default_config dict[str, Any]

Default config for invoking the model.
tools list[Tool]

The list of tools provided to the model to enable tool calling.
response_schema ResponseSchema | None

The schema of the response. If provided, the model will output a structured response as defined by the schema. Supports both Pydantic BaseModel and JSON schema dictionary.
output_analytics bool

Whether to output the invocation analytics.
retry_config RetryConfig

The retry configuration for the language model.
thinking ThinkingConfig

The thinking configuration for the language model.
image_generation bool

Whether to enable image generation.
data_stores list[AttachmentStore]

The data stores to retrieve internal knowledge from.
input_transformers list[InputTransformerConfig]

The list of input transformer configs to use.
output_transformers list[OutputTransformerConfig]

The list of output transformer configs to use.

Initializes a new instance of the OpenAILMInvoker class.

Parameters:

Name Type Description Default
model_name str

The name of the OpenAI model.

 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 Responses API schema. Defaults to OpenAI's default URL.

 OPENAI_DEFAULT_URL
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
tools list[LMTool] | None

Tools provided to the model to enable tool calling. Defaults to None, in which case an empty list is used.

 None
response_schema ResponseSchema | None

The schema of the response. If provided, the model will output a structured response as defined by the schema. Supports both Pydantic BaseModel and JSON schema dictionary. Defaults to None.

 None
output_analytics bool

Whether to output the invocation analytics. Defaults to False.

 False
retry_config RetryConfig | None

The retry configuration for the language model. Defaults to None, in which case a default config with no retry and 30.0 seconds timeout will be used.

 None
thinking bool | ThinkingConfig

A boolean or ThinkingConfig object to configure thinking. Defaults to False.

 False
image_generation bool

Whether to enable image generation. Defaults to False.

 False
data_stores list[AttachmentStore] | None

The data stores to retrieve internal knowledge from. Defaults to None, in which case no data stores will be used.

 None
input_transformers list[InputTransformer] | None

The list of input transformers to use. Defaults to None, in which case no input transformers will be used.

 None
output_transformers list[OutputTransformer] | None

The list of output transformers to use. Defaults to None, in which case no output transformers will be used.

 None
input_transformer InputTransformerType | None

Deprecated param to define input transformer. Will be removed in v0.7. Defaults to None.

 None
output_transformer OutputTransformerType | None

Deprecated param to define output transformer. Will be removed in v0.7. Defaults to None.

 None
output_hooks list[OutputHooks] | None

[BETA] Custom standard output processing hooks. Defaults to None, in which case no output hooks will be used.

 None
streaming_hooks list[StreamingHooks] | None

[BETA] Custom streaming output processing hooks. Defaults to None, in which case no streaming hooks will be used.

 None

batch cached property

The batch operations for the language model.

Returns:

Name Type Description
OpenAIBatchOperations OpenAIBatchOperations

The batch operations for the language model.

data_store cached property

The data store operations for the language model.

Returns:

Name Type Description
OpenAIDataStoreOperations OpenAIDataStoreOperations

The data store operations for the language model.

file cached property

The file operations for the language model.

Returns:

Name Type Description
OpenAIFileOperations OpenAIFileOperations

The file operations for the language model.

set_data_stores(data_stores)

Sets the data stores for the OpenAI language model.

This method sets the data stores for the OpenAI language model. Any existing data stores will be replaced.

Parameters:

Name Type Description Default
data_stores list[AttachmentStore]

The list of data stores to be used.

 required

PortkeyLMInvoker(model_name=None, portkey_api_key=None, provider=None, api_key=None, config=None, provider_api_key=None, custom_host=None, model_kwargs=None, default_hyperparameters=None, tools=None, response_schema=None, output_analytics=False, retry_config=None, thinking=False, input_transformers=None, output_transformers=None, input_transformer=None, output_transformer=None)

Bases: OpenAIChatCompletionsLMInvoker

A language model invoker to interact with Portkey's Universal API.

This class provides support for Portkey's Universal AI Gateway, which enables unified access to multiple providers (e.g., OpenAI, Anthropic, Google, Cohere, Bedrock) via a single API key. The PortkeyLMInvoker is compatible with all Portkey model routing configurations, including model catalog entries, direct providers, and pre-defined configs.

Examples:

lm_invoker = PortkeyLMInvoker(model_name="@openai-custom/gpt-5-nano")
result = await lm_invoker.invoke("Hi there!")
Authentication

The PortkeyLMInvoker supports multiple mutually exclusive authentication methods. These methods are checked in the following precedence order, in which the first matching method is used:

  1. Config ID Use a pre-configured routing setup from Portkey's dashboard. Usage example:
lm_invoker = PortkeyLMInvoker(
    config="<your-config-id>",
    api_key="<your-portkey-api-key>",
)

For more information, please refer to the following page: https://portkey.ai/docs/product/ai-gateway/configs

  1. Model catalog Provider name must match the provider name set in the model catalog. Usage example (Combined format):
lm_invoker = PortkeyLMInvoker(
    model_name="@openai-custom/gpt-5-nano",
    api_key="<your-portkey-api-key>",
)

Usage example (Separated format):

lm_invoker = PortkeyLMInvoker(
    provider="@openai-custom",
    model_name="gpt-5-nano",
    api_key="<your-portkey-api-key>",
)

For the list of supported providers, please refer to the following page: https://portkey.ai/docs/product/model-catalog.

  1. Direct provider Usage example:
lm_invoker = PortkeyLMInvoker(
    provider="openai",
    model_name="gpt-5-nano",
    api_key="<your-portkey-api-key>",
    provider_api_key="sk-...",
)

For more information, please refer to the following page: https://portkey.ai/docs/product/ai-gateway/configs

Custom host

The default host can be overridden using the custom_host parameter. This is available for all authentication methods except for the config ID authentication.

lm_invoker = PortkeyLMInvoker(..., custom_host="https://your-custom-host.com/v1")

For more information, please refer to the following page: https://portkey.ai/docs/product/ai-gateway/custom-hosts

Supported features
  1. Basic invocation
  2. Streaming output
  3. Message roles
  4. Multimodal input a. Text b. Audio c. Document d. Image
  5. Structured output
  6. Tool calling
  7. Thinking
  8. Output analytics
  9. Retry and timeout
  10. Extra capabilities a. Input transformer b. Output transformer

For full documentation and examples, please refer to the LM Invoker tutorial: https://gdplabs.gitbook.io/sdk/gen-ai-sdk/tutorials/inference/lm-invoker

Attributes:

Name Type Description
model_id str

The model ID of the language model.
model_provider str

The provider of the language model.
model_name str

The catalog name of the language model.
client_kwargs dict[str, Any]

The keyword arguments for the Portkey client.
default_config dict[str, Any]

Default config for invoking the model.
tools list[Tool]

The list of tools provided to the model to enable tool calling.
response_schema ResponseSchema | None

The schema of the response. If provided, the model will output a structured response as defined by the schema. Supports both Pydantic BaseModel and JSON schema dictionary.
output_analytics bool

Whether to output the invocation analytics.
retry_config RetryConfig

The retry configuration for the language model.
thinking ThinkingConfig

The thinking configuration for the language model.
input_transformers list[InputTransformerConfig]

The list of input transformer configs to use.
output_transformers list[OutputTransformerConfig]

The list of output transformer configs to use.

Initializes a new instance of the PortkeyLMInvoker class.

Parameters:

Name Type Description Default
model_name str | None

The name of the model to use. Acceptable formats: 1. 'model' for direct authentication, 2. '@provider-slug/model' for model catalog authentication. Defaults to None.

 None
portkey_api_key str | None

Deprecated. Use api_key instead. The Portkey API key. Defaults to None, in which case the PORTKEY_API_KEY environment variable will be used.

 None
provider str | None

Provider name or catalog slug. Acceptable formats: 1. '@provider-slug' for model catalog authentication (no provider_api_key needed), 2. 'provider' for direct authentication (requires provider_api_key). Will be combined with model_name if model name is not in the format '@provider-slug/model'. Defaults to None.

 None
api_key str | None

The Portkey API key. Defaults to None, in which case the PORTKEY_API_KEY environment variable will be used.

 None
config str | None

Portkey config ID for complex routing configurations, load balancing, or fallback scenarios. Defaults to None.

 None
provider_api_key str | None

Provider's API key for direct authentication. Must be used with 'provider' parameter (without '@' prefix). Not needed for catalog providers. Defaults to None.

 None
custom_host str | None

Custom host URL for self-hosted or custom endpoints. Can be combined with catalog providers. Defaults to None.

 None
model_kwargs dict[str, Any] | None

Additional model parameters and authentication. Defaults to None.

 None
default_hyperparameters dict[str, Any] | None

Default hyperparameters for model invocation (temperature, max_tokens, etc.). Defaults to None.

 None
tools list[LMTool] | None

Tools for enabling tool calling functionality. Defaults to None.

 None
response_schema ResponseSchema | None

Schema for structured output generation. Defaults to None.

 None
output_analytics bool

Whether to output detailed invocation analytics including token usage and timing. Defaults to False.

 False
retry_config RetryConfig | None

Configuration for retry behavior on failures. Defaults to None.

 None
thinking bool | ThinkingConfig

A boolean or ThinkingConfig object to configure thinking. Defaults to False.

 False
input_transformers list[InputTransformer] | None

The list of input transformers to use. Defaults to None, in which case no input transformers will be used.

 None
output_transformers list[OutputTransformer] | None

The list of output transformers to use. Defaults to None, in which case no output transformers will be used.

 None
input_transformer InputTransformerType | None

Deprecated param to define input transformer. Will be removed in v0.7. Defaults to None.

 None
output_transformer OutputTransformerType | None

Deprecated param to define output transformer. Will be removed in v0.7. Defaults to None.

 None

SeaLionLMInvoker(model_name, api_key=None, model_kwargs=None, default_hyperparameters=None, tools=None, response_schema=None, output_analytics=False, retry_config=None, input_transformers=None, output_transformers=None, input_transformer=None, output_transformer=None)

Bases: OpenAIChatCompletionsLMInvoker

A language model invoker to interact with SEA-LION API.

Examples:

lm_invoker = SeaLionLMInvoker(model_id="aisingapore/Qwen-SEA-LION-v4-32B-IT")
result = await lm_invoker.invoke("Hi there!")
Supported features
  1. Basic invocation
  2. Streaming output
  3. Message roles
  4. Structured output
  5. Tool calling
  6. Output analytics
  7. Retry and timeout
  8. Extra capabilities a. Input transformer b. Output transformer

For full documentation and examples, please refer to the LM Invoker tutorial: https://gdplabs.gitbook.io/sdk/gen-ai-sdk/tutorials/inference/lm-invoker

Attributes:

Name Type Description
model_id str

The model ID of the language model.
model_provider str

The provider of the language model.
model_name str

The name of the language model.
client_kwargs dict[str, Any]

The keyword arguments for the OpenAI client.
default_config dict[str, Any]

Default config for invoking the model.
tools list[Tool]

The list of tools provided to the model to enable tool calling.
response_schema ResponseSchema | None

The schema of the response. If provided, the model will output a structured response as defined by the schema. Supports both Pydantic BaseModel and JSON schema dictionary.
output_analytics bool

Whether to output the invocation analytics.
retry_config RetryConfig | None

The retry configuration for the language model.
input_transformers list[InputTransformerConfig]

The list of input transformer configs to use.
output_transformers list[OutputTransformerConfig]

The list of output transformer configs to use.

Initializes a new instance of the SeaLionLMInvoker class.

Parameters:

Name Type Description Default
model_name str

The name of the SEA-LION language model.

 required
api_key str | None

The API key for authenticating with the SEA-LION API. Defaults to None, in which case the SEA_LION_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
tools list[LMTool] | None

Tools provided to the model to enable tool calling. Defaults to None.

 None
response_schema ResponseSchema | None

The schema of the response. If provided, the model will output a structured response as defined by the schema. Supports both Pydantic BaseModel and JSON schema dictionary. Defaults to None.

 None
output_analytics bool

Whether to output the invocation analytics. Defaults to False.

 False
retry_config RetryConfig | None

The retry configuration for the language model. Defaults to None, in which case a default config with no retry and 30.0 seconds timeout will be used.

 None
input_transformers list[InputTransformer] | None

The list of input transformers to use. Defaults to None, in which case no input transformers will be used.

 None
output_transformers list[OutputTransformer] | None

The list of output transformers to use. Defaults to None, in which case no output transformers will be used.

 None
input_transformer InputTransformerType | None

Deprecated param to define input transformer. Will be removed in v0.7. Defaults to None.

 None
output_transformer OutputTransformerType | None

Deprecated param to define output transformer. Will be removed in v0.7. Defaults to None.

 None

XAILMInvoker(model_name, api_key=None, model_kwargs=None, default_hyperparameters=None, tools=None, response_schema=None, output_analytics=False, retry_config=None, thinking=False, input_transformers=None, output_transformers=None, input_transformer=None, output_transformer=None)

Bases: StreamingBufferMixin, BaseLMInvoker

A language model invoker to interact with xAI language models.

Examples:

lm_invoker = XAILMInvoker(model_name="grok-4-fast-non-reasoning")
result = await lm_invoker.invoke("Hi there!")
Supported features
  1. Basic invocation
  2. Streaming output
  3. Message roles
  4. Multimodal input a. Text b. Image
  5. Structured output
  6. Tool calling
  7. Native tools a. Code interpreter b. Web search i. "web_search": Standard web search. ii. "x_search": X/Twitter platform search. c. Image generation
  8. Thinking
  9. Output analytics
  10. Retry and timeout
  11. Extra capabilities a. Input transformer b. Output transformer c. File management

For full documentation and examples, please refer to the LM Invoker tutorial: https://gdplabs.gitbook.io/sdk/gen-ai-sdk/tutorials/inference/lm-invoker

Attributes:

Name Type Description
model_id str

The model ID of the language model.
model_provider str

The provider of the language model.
model_name str

The name of the language model.
client_params dict[str, Any]

The xAI client initialization parameters.
default_config dict[str, Any]

Default config for invoking the model.
tools list[Tool]

The list of tools provided to the model to enable tool calling.
response_schema ResponseSchema | None

The schema of the response. If provided, the model will output a structured response as defined by the schema. Supports both Pydantic BaseModel and JSON schema dictionary.
output_analytics bool

Whether to output the invocation analytics.
retry_config RetryConfig | None

The retry configuration for the language model.
thinking ThinkingConfig

The thinking configuration for the language model.
image_generation bool

Whether to enable image generation.
input_transformers list[InputTransformerConfig]

The list of input transformer configs to use.
output_transformers list[OutputTransformerConfig]

The list of output transformer configs to use.

Initializes a new instance of the XAILMInvoker class.

Parameters:

Name Type Description Default
model_name str

The name of the xAI model.

 required
api_key str | None

The API key for authenticating with xAI. Defaults to None, in which case the XAI_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
tools list[LMTool] | None

Tools provided to the language model to enable tool calling. Defaults to None.

 None
response_schema ResponseSchema | None

The schema of the response. If provided, the model will output a structured response as defined by the schema. Supports both Pydantic BaseModel and JSON schema dictionary. Defaults to None.

 None
output_analytics bool

Whether to output the invocation analytics. Defaults to False.

 False
retry_config RetryConfig | None

The retry configuration for the language model. Defaults to None, in which case a default config with no retry and 30.0 seconds timeout is used.

 None
thinking bool | ThinkingConfig

A boolean or ThinkingConfig object to configure thinking. Defaults to False.

 False
input_transformers list[InputTransformer] | None

The list of input transformers to use. Defaults to None, in which case no input transformers will be used.

 None
output_transformers list[OutputTransformer] | None

The list of output transformers to use. Defaults to None, in which case no output transformers will be used.

 None
input_transformer InputTransformerType | None

Deprecated param to define input transformer. Will be removed in v0.7. Defaults to None.

 None
output_transformer OutputTransformerType | None

Deprecated param to define output transformer. Will be removed in v0.7. Defaults to None.

 None

file cached property

The file operations for the language model.

Returns:

Name Type Description
XAIFileOperations XAIFileOperations

The file operations for the language model.