Optional
fields: ChatMistralAIInputThe API key to use.
{process.env.MISTRAL_API_KEY}
The maximum number of tokens to generate in the completion. The token count of your prompt plus max_tokens cannot exceed the model's context length.
The name of the model to use.
{"mistral-small-latest"}
use safePrompt instead
This feature is deprecated and will be removed in the future.
It is not recommended for use.
Whether to inject a safety prompt before all conversations.
{false}
Whether or not to stream the response.
{false}
What sampling temperature to use, between 0.0 and 2.0. Higher values like 0.8 will make the output more random, while lower values like 0.2 will make it more focused and deterministic.
{0.7}
Nucleus sampling, where the model considers the results of the tokens with top_p
probability mass.
So 0.1 means only the tokens comprising the top 10% probability mass are considered.
Should be between 0 and 1.
{1}
Optional
endpointOverride the default endpoint.
Optional
randomThe seed to use for random sampling. If set, different calls will generate deterministic results.
Calls the MistralAI API with retry logic in case of failures.
The input to send to the MistralAI API.
The response from the MistralAI API.
Model wrapper that returns outputs formatted to match the given schema.
The input type for the Runnable, expected to be the same input for the LLM.
The output type for the Runnable, expected to be a Zod schema object for structured output validation.
Optional
method?: "functionCalling" | "jsonMode"A new runnable that calls the LLM with structured output.
Model wrapper that returns outputs formatted to match the given schema.
The input type for the Runnable, expected to be the same input for the LLM.
The output type for the Runnable, expected to be a Zod schema object for structured output validation.
Optional
includeOptional
method?: "functionCalling" | "jsonMode"A new runnable that calls the LLM with structured output.
Generated using TypeDoc
Integration with a chat model.