langchain_community.llms.minimax.Minimax¶

Note

Minimax implements the standard Runnable Interface. 🏃

class langchain_community.llms.minimax.Minimax[source]¶

Bases: MinimaxCommon, LLM

Minimax large language models.

To use, you should have the environment variable MINIMAX_API_KEY and MINIMAX_GROUP_ID set with your API key, or pass them as a named parameter to the constructor. .. rubric:: Example

. code-block:: python

from langchain_community.llms.minimax import Minimax minimax = Minimax(model=”<model_name>”, minimax_api_key=”my-api-key”,

minimax_group_id=”my-group-id”)

Create a new model by parsing and validating input data from keyword arguments.

Raises ValidationError if the input data cannot be parsed to form a valid model.

param cache: Union[BaseCache, bool, None] = None¶

Whether to cache the response.

  • If true, will use the global cache.

  • If false, will not use a cache

  • If None, will use the global cache if it’s set, otherwise no cache.

  • If instance of BaseCache, will use the provided cache.

Caching is not currently supported for streaming methods of models.

param callback_manager: Optional[BaseCallbackManager] = None¶

[DEPRECATED]

param callbacks: Callbacks = None¶

Callbacks to add to the run trace.

param custom_get_token_ids: Optional[Callable[[str], List[int]]] = None¶

Optional encoder to use for counting tokens.

param max_tokens: int = 256¶

Denotes the number of tokens to predict per generation.

param metadata: Optional[Dict[str, Any]] = None¶

Metadata to add to the run trace.

param minimax_api_host: Optional[str] = None¶
param minimax_api_key: Optional[SecretStr] = None¶
Constraints
  • type = string

  • writeOnly = True

  • format = password

param minimax_group_id: Optional[str] = None¶
param model: str = 'abab5.5-chat'¶

Model name to use.

param model_kwargs: Dict[str, Any] [Optional]¶

Holds any model parameters valid for create call not explicitly specified.

param tags: Optional[List[str]] = None¶

Tags to add to the run trace.

param temperature: float = 0.7¶

A non-negative float that tunes the degree of randomness in generation.

param top_p: float = 0.95¶

Total probability mass of tokens to consider at each step.

param verbose: bool [Optional]¶

Whether to print out response text.

__call__(prompt: str, stop: Optional[List[str]] = None, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, *, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) str¶

[Deprecated] Check Cache and run the LLM on the given prompt and input.

Notes

Deprecated since version langchain-core==0.1.7: Use invoke instead.

Parameters
  • prompt (str) –

  • stop (Optional[List[str]]) –

  • callbacks (Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]) –

  • tags (Optional[List[str]]) –

  • metadata (Optional[Dict[str, Any]]) –

  • kwargs (Any) –

Return type

str

async agenerate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Union[List[BaseCallbackHandler], BaseCallbackManager, None, List[Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]]] = None, *, tags: Optional[Union[List[str], List[List[str]]]] = None, metadata: Optional[Union[Dict[str, Any], List[Dict[str, Any]]]] = None, run_name: Optional[Union[str, List[str]]] = None, run_id: Optional[Union[UUID, List[Optional[UUID]]]] = None, **kwargs: Any) LLMResult¶

Asynchronously pass a sequence of prompts to a model and return generations.

This method should make use of batched calls for models that expose a batched API.

Use this method when you want to:
  1. take advantage of batched calls,

  2. need more output from the model than just the top generated value,

  3. are building chains that are agnostic to the underlying language model

    type (e.g., pure text completion models vs chat models).

Parameters
  • prompts (List[str]) – List of string prompts.

  • stop (Optional[List[str]]) – Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings.

  • callbacks (Union[List[BaseCallbackHandler], BaseCallbackManager, None, List[Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]]]) – Callbacks to pass through. Used for executing additional functionality, such as logging or streaming, throughout generation.

  • **kwargs (Any) – Arbitrary additional keyword arguments. These are usually passed to the model provider API call.

  • tags (Optional[Union[List[str], List[List[str]]]]) –

  • metadata (Optional[Union[Dict[str, Any], List[Dict[str, Any]]]]) –

  • run_name (Optional[Union[str, List[str]]]) –

  • run_id (Optional[Union[UUID, List[Optional[UUID]]]]) –

  • **kwargs –

Returns

An LLMResult, which contains a list of candidate Generations for each input

prompt and additional model provider-specific output.

Return type

LLMResult

async agenerate_prompt(prompts: List[PromptValue], stop: Optional[List[str]] = None, callbacks: Union[List[BaseCallbackHandler], BaseCallbackManager, None, List[Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]]] = None, **kwargs: Any) LLMResult¶

Asynchronously pass a sequence of prompts and return model generations.

This method should make use of batched calls for models that expose a batched API.

Use this method when you want to:
  1. take advantage of batched calls,

  2. need more output from the model than just the top generated value,

  3. are building chains that are agnostic to the underlying language model

    type (e.g., pure text completion models vs chat models).

Parameters
  • prompts (List[PromptValue]) – List of PromptValues. A PromptValue is an object that can be converted to match the format of any language model (string for pure text generation models and BaseMessages for chat models).

  • stop (Optional[List[str]]) – Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings.

  • callbacks (Union[List[BaseCallbackHandler], BaseCallbackManager, None, List[Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]]]) – Callbacks to pass through. Used for executing additional functionality, such as logging or streaming, throughout generation.

  • **kwargs (Any) – Arbitrary additional keyword arguments. These are usually passed to the model provider API call.

Returns

An LLMResult, which contains a list of candidate Generations for each input

prompt and additional model provider-specific output.

Return type

LLMResult

async apredict(text: str, *, stop: Optional[Sequence[str]] = None, **kwargs: Any) str¶

[Deprecated]

Notes

Deprecated since version langchain-core==0.1.7: Use ainvoke instead.

Parameters
  • text (str) –

  • stop (Optional[Sequence[str]]) –

  • kwargs (Any) –

Return type

str

async apredict_messages(messages: List[BaseMessage], *, stop: Optional[Sequence[str]] = None, **kwargs: Any) BaseMessage¶

[Deprecated]

Notes

Deprecated since version langchain-core==0.1.7: Use ainvoke instead.

Parameters
  • messages (List[BaseMessage]) –

  • stop (Optional[Sequence[str]]) –

  • kwargs (Any) –

Return type

BaseMessage

generate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Union[List[BaseCallbackHandler], BaseCallbackManager, None, List[Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]]] = None, *, tags: Optional[Union[List[str], List[List[str]]]] = None, metadata: Optional[Union[Dict[str, Any], List[Dict[str, Any]]]] = None, run_name: Optional[Union[str, List[str]]] = None, run_id: Optional[Union[UUID, List[Optional[UUID]]]] = None, **kwargs: Any) LLMResult¶

Pass a sequence of prompts to a model and return generations.

This method should make use of batched calls for models that expose a batched API.

Use this method when you want to:
  1. take advantage of batched calls,

  2. need more output from the model than just the top generated value,

  3. are building chains that are agnostic to the underlying language model

    type (e.g., pure text completion models vs chat models).

Parameters
  • prompts (List[str]) – List of string prompts.

  • stop (Optional[List[str]]) – Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings.

  • callbacks (Union[List[BaseCallbackHandler], BaseCallbackManager, None, List[Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]]]) – Callbacks to pass through. Used for executing additional functionality, such as logging or streaming, throughout generation.

  • **kwargs (Any) – Arbitrary additional keyword arguments. These are usually passed to the model provider API call.

  • tags (Optional[Union[List[str], List[List[str]]]]) –

  • metadata (Optional[Union[Dict[str, Any], List[Dict[str, Any]]]]) –

  • run_name (Optional[Union[str, List[str]]]) –

  • run_id (Optional[Union[UUID, List[Optional[UUID]]]]) –

  • **kwargs –

Returns

An LLMResult, which contains a list of candidate Generations for each input

prompt and additional model provider-specific output.

Return type

LLMResult

generate_prompt(prompts: List[PromptValue], stop: Optional[List[str]] = None, callbacks: Union[List[BaseCallbackHandler], BaseCallbackManager, None, List[Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]]] = None, **kwargs: Any) LLMResult¶

Pass a sequence of prompts to the model and return model generations.

This method should make use of batched calls for models that expose a batched API.

Use this method when you want to:
  1. take advantage of batched calls,

  2. need more output from the model than just the top generated value,

  3. are building chains that are agnostic to the underlying language model

    type (e.g., pure text completion models vs chat models).

Parameters
  • prompts (List[PromptValue]) – List of PromptValues. A PromptValue is an object that can be converted to match the format of any language model (string for pure text generation models and BaseMessages for chat models).

  • stop (Optional[List[str]]) – Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings.

  • callbacks (Union[List[BaseCallbackHandler], BaseCallbackManager, None, List[Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]]]) – Callbacks to pass through. Used for executing additional functionality, such as logging or streaming, throughout generation.

  • **kwargs (Any) – Arbitrary additional keyword arguments. These are usually passed to the model provider API call.

Returns

An LLMResult, which contains a list of candidate Generations for each input

prompt and additional model provider-specific output.

Return type

LLMResult

get_num_tokens(text: str) int¶

Get the number of tokens present in the text.

Useful for checking if an input will fit in a model’s context window.

Parameters

text (str) – The string input to tokenize.

Returns

The integer number of tokens in the text.

Return type

int

get_num_tokens_from_messages(messages: List[BaseMessage]) int¶

Get the number of tokens in the messages.

Useful for checking if an input will fit in a model’s context window.

Parameters

messages (List[BaseMessage]) – The message inputs to tokenize.

Returns

The sum of the number of tokens across the messages.

Return type

int

get_token_ids(text: str) List[int]¶

Return the ordered ids of the tokens in a text.

Parameters

text (str) – The string input to tokenize.

Returns

A list of ids corresponding to the tokens in the text, in order they occur

in the text.

Return type

List[int]

predict(text: str, *, stop: Optional[Sequence[str]] = None, **kwargs: Any) str¶

[Deprecated]

Notes

Deprecated since version langchain-core==0.1.7: Use invoke instead.

Parameters
  • text (str) –

  • stop (Optional[Sequence[str]]) –

  • kwargs (Any) –

Return type

str

predict_messages(messages: List[BaseMessage], *, stop: Optional[Sequence[str]] = None, **kwargs: Any) BaseMessage¶

[Deprecated]

Notes

Deprecated since version langchain-core==0.1.7: Use invoke instead.

Parameters
  • messages (List[BaseMessage]) –

  • stop (Optional[Sequence[str]]) –

  • kwargs (Any) –

Return type

BaseMessage

save(file_path: Union[Path, str]) None¶

Save the LLM.

Parameters

file_path (Union[Path, str]) – Path to file to save the LLM to.

Return type

None

Example: .. code-block:: python

llm.save(file_path=”path/llm.yaml”)

with_structured_output(schema: Union[Dict, Type[BaseModel]], **kwargs: Any) Runnable[Union[PromptValue, str, Sequence[Union[BaseMessage, List[str], Tuple[str, str], str, Dict[str, Any]]]], Union[Dict, BaseModel]]¶

Not implemented on this class.

Parameters
  • schema (Union[Dict, Type[BaseModel]]) –

  • kwargs (Any) –

Return type

Runnable[Union[PromptValue, str, Sequence[Union[BaseMessage, List[str], Tuple[str, str], str, Dict[str, Any]]]], Union[Dict, BaseModel]]

Examples using Minimax¶