Optional fields: ChatMistralAIInputThe API key to use.
{process.env.MISTRAL_API_KEY}
The async caller should be used by subclasses to make any async calls, which will thus benefit from the concurrency and retry logic.
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}
Whether to print out response text.
Optional cacheOptional callbacksOptional endpointOverride the default endpoint.
Optional metadataOptional nameOptional randomThe seed to use for random sampling. If set, different calls will generate deterministic results.
Optional tagsKeys that the language model accepts as call options.
Assigns new fields to the dict output of this runnable. Returns a new runnable.
Default implementation of batch, which calls invoke N times. Subclasses should override this method if they can batch more efficiently.
Array of inputs to each batch call.
Optional options: Partial<CallOptions> | Partial<CallOptions>[]Either a single call options object to apply to each batch call or an array for each call.
Optional batchOptions: RunnableBatchOptions & { An array of RunOutputs, or mixed RunOutputs and errors if batchOptions.returnExceptions is set
Optional options: Partial<CallOptions> | Partial<CallOptions>[]Optional batchOptions: RunnableBatchOptions & { Optional options: Partial<CallOptions> | Partial<CallOptions>[]Optional batchOptions: RunnableBatchOptionsBind arguments to a Runnable, returning a new Runnable.
A new RunnableBinding that, when invoked, will apply the bound args.
An array of BaseMessage instances.
Optional options: string[] | CallOptionsThe call options or an array of stop sequences.
Optional callbacks: CallbacksThe callbacks for the language model.
A Promise that resolves to a BaseMessage.
Use .invoke() instead. Will be removed in 0.2.0.
Makes a single call to the chat model.
The value of the prompt.
Optional options: string[] | CallOptionsThe call options or an array of stop sequences.
Optional callbacks: CallbacksThe callbacks for the language model.
A Promise that resolves to a BaseMessage.
Use .invoke() instead. Will be removed in 0.2.0.
Makes a single call to the chat model with a prompt value.
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.
Generates chat based on the input messages.
An array of arrays of BaseMessage instances.
Optional options: string[] | CallOptionsThe call options or an array of stop sequences.
Optional callbacks: CallbacksThe callbacks for the language model.
A Promise that resolves to an LLMResult.
Generates a prompt based on the input prompt values.
An array of BasePromptValue instances.
Optional options: string[] | CallOptionsThe call options or an array of stop sequences.
Optional callbacks: CallbacksThe callbacks for the language model.
A Promise that resolves to an LLMResult.
Invokes the chat model with a single input.
The input for the language model.
Optional options: CallOptionsThe call options.
A Promise that resolves to a BaseMessageChunk.
Return a new Runnable that maps a list of inputs to a list of outputs, by calling invoke() with each input.
Pick keys from the dict output of this runnable. Returns a new runnable.
Create a new runnable sequence that runs each individual runnable in series, piping the output of one runnable into another runnable or runnable-like.
A runnable, function, or object whose values are functions or runnables.
A new runnable sequence.
The text input.
Optional options: string[] | CallOptionsThe call options or an array of stop sequences.
Optional callbacks: CallbacksThe callbacks for the language model.
A Promise that resolves to a string.
Use .invoke() instead. Will be removed in 0.2.0.
Predicts the next message based on a text input.
An array of BaseMessage instances.
Optional options: string[] | CallOptionsThe call options or an array of stop sequences.
Optional callbacks: CallbacksThe callbacks for the language model.
A Promise that resolves to a BaseMessage.
Use .invoke() instead. Will be removed in 0.2.0.
Predicts the next message based on the input messages.
Return a json-like object representing this LLM.
Stream output in chunks.
Optional options: Partial<CallOptions>A readable stream that is also an iterable.
Generate a stream of events emitted by the internal steps of the runnable.
Use to create an iterator over StreamEvents that provide real-time information about the progress of the runnable, including StreamEvents from intermediate results.
A StreamEvent is a dictionary with the following schema:
event: string - Event names are of the format: on_[runnable_type]_(start|stream|end).name: string - The name of the runnable that generated the event.run_id: string - Randomly generated ID associated with the given execution of
the runnable that emitted the event. A child runnable that gets invoked as part of the execution of a
parent runnable is assigned its own unique ID.tags: string[] - The tags of the runnable that generated the event.metadata: Record<string, any> - The metadata of the runnable that generated the event.data: Record<string, any>Below is a table that illustrates some events that might be emitted by various chains. Metadata fields have been omitted from the table for brevity. Chain definitions have been included after the table.
| event | name | chunk | input | output |
|---|---|---|---|---|
| on_llm_start | [model name] | {'input': 'hello'} | ||
| on_llm_stream | [model name] | 'Hello' OR AIMessageChunk("hello") | ||
| on_llm_end | [model name] | 'Hello human!' | ||
| on_chain_start | format_docs | |||
| on_chain_stream | format_docs | "hello world!, goodbye world!" | ||
| on_chain_end | format_docs | [Document(...)] | "hello world!, goodbye world!" | |
| on_tool_start | some_tool | {"x": 1, "y": "2"} | ||
| on_tool_stream | some_tool | {"x": 1, "y": "2"} | ||
| on_tool_end | some_tool | {"x": 1, "y": "2"} | ||
| on_retriever_start | [retriever name] | {"query": "hello"} | ||
| on_retriever_chunk | [retriever name] | {documents: [...]} | ||
| on_retriever_end | [retriever name] | {"query": "hello"} | {documents: [...]} | |
| on_prompt_start | [template_name] | {"question": "hello"} | ||
| on_prompt_end | [template_name] | {"question": "hello"} | ChatPromptValue(messages: [SystemMessage, ...]) |
Optional streamOptions: Omit<LogStreamCallbackHandlerInput, "autoClose">Stream all output from a runnable, as reported to the callback system. This includes all inner runs of LLMs, Retrievers, Tools, etc. Output is streamed as Log objects, which include a list of jsonpatch ops that describe how the state of the run has changed in each step, and the final state of the run. The jsonpatch ops can be applied in order to construct state.
Optional options: Partial<CallOptions>Optional streamOptions: Omit<LogStreamCallbackHandlerInput, "autoClose">Default implementation of transform, which buffers input and then calls stream. Subclasses should override this method if they can start producing output while input is still being generated.
Bind config to a Runnable, returning a new Runnable.
New configuration parameters to attach to the new runnable.
A new RunnableBinding with a config matching what's passed.
Create a new runnable from the current one that will try invoking other passed fallback runnables if the initial invocation fails.
Other runnables to call if the runnable errors.
A new RunnableWithFallbacks.
Bind lifecycle listeners to a Runnable, returning a new Runnable. The Run object contains information about the run, including its id, type, input, output, error, startTime, endTime, and any tags or metadata added to the run.
The object containing the callback functions.
Optional onCalled after the runnable finishes running, with the Run object.
Optional config: RunnableConfigOptional onCalled if the runnable throws an error, with the Run object.
Optional config: RunnableConfigOptional onCalled before the runnable starts running, with the Run object.
Optional config: RunnableConfigAdd retry logic to an existing runnable.
Optional fields: { Optional onOptional stopA new RunnableRetry that, when invoked, will retry according to the parameters.
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.
Static deserializeLoad an LLM from a json-like object describing it.
Static isGenerated using TypeDoc
Integration with a chat model.