Class swarmauri_standard.llms.CohereToolModel.CohereToolModel
CohereToolModel(**data)
              Bases: LLMBase
A language model implementation for interacting with Cohere's API, specifically designed for tool-augmented conversations.
This class provides both synchronous and asynchronous methods for generating responses, handling tool calls, and managing conversations with the Cohere API. It supports streaming responses and batch processing of multiple conversations.
| ATTRIBUTE | DESCRIPTION | 
|---|---|
| api_key | The API key for authenticating with Cohere's API 
                  
                    TYPE:
                       | 
| allowed_models | List of supported Cohere model names 
                  
                    TYPE:
                       | 
| name | The default model name to use 
                  
                    TYPE:
                       | 
| type | The type identifier for this model 
                  
                    TYPE:
                       | 
| resource | The resource type identifier 
                  
                    TYPE:
                       | 
| timeout | Maximum timeout for API requests in seconds 
                  
                    TYPE:
                       | 
Link to Allowed Models: https://docs.cohere.com/docs/models#command Link to API Key: https://dashboard.cohere.com/api-keys
Initialize the CohereToolModel with the provided configuration.
| PARAMETER | DESCRIPTION | 
|---|---|
| **data | Keyword arguments for configuring the model, including api_key 
                  
                    TYPE:
                       | 
Source code in swarmauri_standard/llms/CohereToolModel.py
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instance-attribute
  
api_key
class-attribute
      instance-attribute
  
allowed_models = [
    "command-a-03-2025",
    "command-r7b-12-2024",
    "command-a-translate-08-2025",
    "command-a-reasoning-08-2025",
    "command-a-vision-07-2025",
    "command-r-plus-04-2024",
    "command-r-plus",
    "command-r-08-2024",
    "command-r-03-2024",
    "command-r",
    "command",
    "command-nightly",
    "command-light",
    "command-light-nightly",
]
class-attribute
      instance-attribute
  
name = 'command-a-03-2025'
class-attribute
      instance-attribute
  
type = 'CohereToolModel'
class-attribute
      instance-attribute
  
timeout = 600.0
class-attribute
      instance-attribute
  
model_config = ConfigDict(
    extra="allow", arbitrary_types_allowed=True
)
class-attribute
      instance-attribute
  
id = Field(default_factory=generate_id)
class-attribute
      instance-attribute
  
members = None
class-attribute
      instance-attribute
  
owners = None
class-attribute
      instance-attribute
  
host = None
class-attribute
  
default_logger = None
class-attribute
      instance-attribute
  
logger = None
class-attribute
      instance-attribute
  
version = '0.1.0'
class-attribute
      instance-attribute
  
include_usage = True
class-attribute
      instance-attribute
  
BASE_URL = None
predict(
    conversation,
    toolkit=None,
    temperature=0.3,
    max_tokens=1024,
)
Generate a response for a conversation synchronously.
| PARAMETER | DESCRIPTION | 
|---|---|
| conversation | The conversation to generate a response for 
                  
                    TYPE:
                       | 
| toolkit | Optional toolkit containing available tools 
                  
                    TYPE:
                       | 
| temperature | Sampling temperature 
                  
                    TYPE:
                       | 
| max_tokens | Maximum number of tokens to generate 
                  
                    TYPE:
                       | 
| RETURNS | DESCRIPTION | 
|---|---|
| Conversation | The updated conversation with the model's response 
                  
                    TYPE:
                       | 
Source code in swarmauri_standard/llms/CohereToolModel.py
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stream(
    conversation,
    toolkit=None,
    temperature=0.3,
    max_tokens=1024,
)
Stream a response for a conversation synchronously.
| PARAMETER | DESCRIPTION | 
|---|---|
| conversation | The conversation to generate a response for 
                  
                    TYPE:
                       | 
| toolkit | Optional toolkit containing available tools 
                  
                    TYPE:
                       | 
| temperature | Sampling temperature 
                  
                    TYPE:
                       | 
| max_tokens | Maximum number of tokens to generate 
                  
                    TYPE:
                       | 
| RETURNS | DESCRIPTION | 
|---|---|
| Iterator[str] | Iterator[str]: An iterator yielding response chunks | 
Source code in swarmauri_standard/llms/CohereToolModel.py
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async
  
apredict(
    conversation,
    toolkit=None,
    temperature=0.3,
    max_tokens=1024,
)
Generate a response for a conversation asynchronously.
| PARAMETER | DESCRIPTION | 
|---|---|
| conversation | The conversation to generate a response for 
                  
                    TYPE:
                       | 
| toolkit | Optional toolkit containing available tools 
                  
                    TYPE:
                       | 
| temperature | Sampling temperature 
                  
                    TYPE:
                       | 
| max_tokens | Maximum number of tokens to generate 
                  
                    TYPE:
                       | 
| RETURNS | DESCRIPTION | 
|---|---|
| Conversation | The updated conversation with the model's response 
                  
                    TYPE:
                       | 
Source code in swarmauri_standard/llms/CohereToolModel.py
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async
  
astream(
    conversation,
    toolkit=None,
    temperature=0.3,
    max_tokens=1024,
)
Stream a response for a conversation asynchronously.
| PARAMETER | DESCRIPTION | 
|---|---|
| conversation | The conversation to generate a response for 
                  
                    TYPE:
                       | 
| toolkit | Optional toolkit containing available tools 
                  
                    TYPE:
                       | 
| temperature | Sampling temperature 
                  
                    TYPE:
                       | 
| max_tokens | Maximum number of tokens to generate 
                  
                    TYPE:
                       | 
| RETURNS | DESCRIPTION | 
|---|---|
| AsyncIterator[str] | AsyncIterator[str]: An async iterator yielding response chunks | 
Source code in swarmauri_standard/llms/CohereToolModel.py
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batch(
    conversations,
    toolkit=None,
    temperature=0.3,
    max_tokens=1024,
)
Process multiple conversations in batch mode synchronously.
This method takes a list of conversations and processes them sequentially using the predict method. Each conversation is processed independently with the same parameters.
| PARAMETER | DESCRIPTION | 
|---|---|
| conversations | A list of conversation objects to process 
                  
                    TYPE:
                       | 
| toolkit | The toolkit containing available tools for the model 
                  
                    TYPE:
                       | 
| temperature | The sampling temperature for response generation. Defaults to 0.3 
                  
                    TYPE:
                       | 
| max_tokens | The maximum number of tokens to generate for each response. Defaults to 1024 
                  
                    TYPE:
                       | 
| RETURNS | DESCRIPTION | 
|---|---|
| List[Conversation] | List[Conversation]: A list of processed conversations with their respective responses | 
Source code in swarmauri_standard/llms/CohereToolModel.py
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async
  
abatch(
    conversations,
    toolkit=None,
    temperature=0.3,
    max_tokens=1024,
    max_concurrent=5,
)
Process multiple conversations in batch mode asynchronously.
This method processes multiple conversations concurrently while limiting the maximum number of simultaneous requests using a semaphore. This helps prevent overwhelming the API service while still maintaining efficient processing.
| PARAMETER | DESCRIPTION | 
|---|---|
| conversations | A list of conversation objects to process 
                  
                    TYPE:
                       | 
| toolkit | The toolkit containing available tools for the model 
                  
                    TYPE:
                       | 
| temperature | The sampling temperature for response generation. Defaults to 0.3 
                  
                    TYPE:
                       | 
| max_tokens | The maximum number of tokens to generate for each response. Defaults to 1024 
                  
                    TYPE:
                       | 
| max_concurrent | The maximum number of conversations to process simultaneously. Defaults to 5 
                  
                    TYPE:
                       | 
| RETURNS | DESCRIPTION | 
|---|---|
| List[Conversation] | List[Conversation]: A list of processed conversations with their respective responses | 
Note
The max_concurrent parameter helps control API usage and prevent rate limiting while still allowing for parallel processing of multiple conversations.
Source code in swarmauri_standard/llms/CohereToolModel.py
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get_allowed_models()
Query the LLMProvider API endpoint to get the list of allowed models.
| RETURNS | DESCRIPTION | 
|---|---|
| List[str] | List[str]: List of allowed model names from the API | 
Source code in swarmauri_standard/llms/CohereToolModel.py
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classmethod
  
register_model()
Decorator to register a base model in the unified registry.
| RETURNS | DESCRIPTION | 
|---|---|
| Callable | A decorator function that registers the model class. 
                  
                    TYPE:
                       | 
Source code in swarmauri_base/DynamicBase.py
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classmethod
  
register_type(resource_type=None, type_name=None)
Decorator to register a subtype under one or more base models in the unified registry.
| PARAMETER | DESCRIPTION | 
|---|---|
| resource_type | The base model(s) under which to register the subtype. If None, all direct base classes (except DynamicBase) are used. 
                  
                    TYPE:
                       | 
| type_name | An optional custom type name for the subtype. 
                  
                    TYPE:
                       | 
| RETURNS | DESCRIPTION | 
|---|---|
| Callable | A decorator function that registers the subtype. 
                  
                    TYPE:
                       | 
Source code in swarmauri_base/DynamicBase.py
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classmethod
  
model_validate_toml(toml_data)
Validate a model from a TOML string.
Source code in swarmauri_base/TomlMixin.py
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model_dump_toml(
    fields_to_exclude=None, api_key_placeholder=None
)
Return a TOML representation of the model.
Source code in swarmauri_base/TomlMixin.py
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classmethod
  
model_validate_yaml(yaml_data)
Validate a model from a YAML string.
Source code in swarmauri_base/YamlMixin.py
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model_dump_yaml(
    fields_to_exclude=None, api_key_placeholder=None
)
Return a YAML representation of the model.
Source code in swarmauri_base/YamlMixin.py
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model_post_init(logger=None)
Assign a logger instance after model initialization.
Source code in swarmauri_base/LoggerMixin.py
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add_allowed_model(model)
Add a new model to the list of allowed models.
| RAISES | DESCRIPTION | 
|---|---|
| ValueError | If the model is already in the allowed models list. | 
Source code in swarmauri_base/llms/LLMBase.py
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remove_allowed_model(model)
Remove a model from the list of allowed models.
| RAISES | DESCRIPTION | 
|---|---|
| ValueError | If the model is not in the allowed models list. | 
Source code in swarmauri_base/llms/LLMBase.py
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