Optional
fields: MistralAIInputThe API key to use.
Batch size to use when passing multiple documents to generate
Optional
beforeA list of custom hooks that must follow (req: Request) => Awaitable<Request | void> They are automatically added when a ChatMistralAI instance is created
Optional
cacheOptional
callbacksThe async caller should be used by subclasses to make any async calls, which will thus benefit from the concurrency and retry logic.
Optional
httpOptional custom HTTP client to manage API requests Allows users to add custom fetch implementations, hooks, as well as error and response processing.
Optional
maxThe maximum number of concurrent calls that can be made.
Defaults to Infinity
, which means no limit.
Optional
maxThe maximum number of retries that can be made for a single call, with an exponential backoff between each attempt. Defaults to 6.
Optional
maxThe maximum number of tokens to generate in the completion. The token count of your prompt plus maxTokens cannot exceed the model's context length.
Optional
metadataThe name of the model to use.
Optional
nameOptional
randomThe seed to use for random sampling. If set, different calls will generate deterministic results.
Alias for seed
Optional
requestA list of custom hooks that must follow (err: unknown, req: Request) => Awaitable
Optional
responseA list of custom hooks that must follow (res: Response, req: Request) => Awaitable
Optional
serverURLOverride the default server URL used by the Mistral SDK.
Whether or not to stream the response.
Optional
tagsWhat 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.
Optional
topPNucleus sampling, where the model considers the results of the tokens with topP
probability mass.
So 0.1 means only the tokens comprising the top 10% probability mass are considered.
Should be between 0 and 1.
Whether to print out response text.
Keys that the language model accepts as call options.
Convert a runnable to a tool. Return a new instance of RunnableToolLike
which contains the runnable, name, description and schema.
Optional
description?: stringThe description of the tool. Falls back to the description on the Zod schema if not provided, or undefined if neither are provided.
Optional
name?: stringThe name of the tool. If not provided, it will default to the name of the runnable.
The Zod schema for the input of the tool. Infers the Zod type from the input type of the runnable.
An instance of RunnableToolLike
which is a runnable that can be used as a tool.
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<MistralAICallOptions> | Partial<MistralAICallOptions>[]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<MistralAICallOptions> | Partial<MistralAICallOptions>[]Optional
batchOptions: RunnableBatchOptions & { Optional
options: Partial<MistralAICallOptions> | Partial<MistralAICallOptions>[]Optional
batchOptions: RunnableBatchOptionsBind arguments to a Runnable, returning a new Runnable.
A new RunnableBinding that, when invoked, will apply the bound args.
Optional
options: string[] | MistralAICallOptionsOptional
callbacks: CallbacksUse .invoke() instead. Will be removed in 0.2.0. Convenience wrapper for generate that takes in a single string prompt and returns a single string output.
Run the LLM on the given prompts and input, handling caching.
Optional
options: string[] | MistralAICallOptionsOptional
callbacks: CallbacksThis method takes prompt values, options, and callbacks, and generates a result based on the prompts.
Prompt values for the LLM.
Optional
options: string[] | MistralAICallOptionsOptions for the LLM call.
Optional
callbacks: CallbacksCallbacks for the LLM call.
An LLMResult based on the prompts.
Get the parameters used to invoke the model
This method takes an input and options, and returns a string. It converts the input to a prompt value and generates a result based on the prompt.
Input for the LLM.
Optional
options: MistralAICallOptionsOptions for the LLM call.
A string result based on the prompt.
Return a new Runnable that maps a list of inputs to a list of outputs, by calling invoke() with each input.
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.
Input text for the prediction.
Optional
options: string[] | MistralAICallOptionsOptions for the LLM call.
Optional
callbacks: CallbacksCallbacks for the LLM call.
A prediction based on the input text.
A list of messages for the prediction.
Optional
options: string[] | MistralAICallOptionsOptions for the LLM call.
Optional
callbacks: CallbacksCallbacks for the LLM call.
A predicted message based on the list of messages.
Stream output in chunks.
Optional
options: Partial<MistralAICallOptions>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.
ATTENTION This reference table is for the V2 version of the schema.
+----------------------+-----------------------------+------------------------------------------+
| event | input | output/chunk |
+======================+=============================+==========================================+
| on_chat_model_start | {"messages": BaseMessage[]} | |
+----------------------+-----------------------------+------------------------------------------+
| on_chat_model_stream | | AIMessageChunk("hello") |
+----------------------+-----------------------------+------------------------------------------+
| on_chat_model_end | {"messages": BaseMessage[]} | AIMessageChunk("hello world") |
+----------------------+-----------------------------+------------------------------------------+
| on_llm_start | {'input': 'hello'} | |
+----------------------+-----------------------------+------------------------------------------+
| on_llm_stream | | 'Hello' |
+----------------------+-----------------------------+------------------------------------------+
| on_llm_end | 'Hello human!' | |
+----------------------+-----------------------------+------------------------------------------+
| on_chain_start | | |
+----------------------+-----------------------------+------------------------------------------+
| on_chain_stream | | "hello world!" |
+----------------------+-----------------------------+------------------------------------------+
| on_chain_end | [Document(...)] | "hello world!, goodbye world!" |
+----------------------+-----------------------------+------------------------------------------+
| on_tool_start | {"x": 1, "y": "2"} | |
+----------------------+-----------------------------+------------------------------------------+
| on_tool_end | | {"x": 1, "y": "2"} |
+----------------------+-----------------------------+------------------------------------------+
| on_retriever_start | {"query": "hello"} | |
+----------------------+-----------------------------+------------------------------------------+
| on_retriever_end | {"query": "hello"} | [Document(...), ..] |
+----------------------+-----------------------------+------------------------------------------+
| on_prompt_start | {"question": "hello"} | |
+----------------------+-----------------------------+------------------------------------------+
| on_prompt_end | {"question": "hello"} | ChatPromptValue(messages: BaseMessage[]) |
+----------------------+-----------------------------+------------------------------------------+
The "on_chain_*" events are the default for Runnables that don't fit one of the above categories.
In addition to the standard events above, users can also dispatch custom events.
Custom events will be only be surfaced with in the v2
version of the API!
A custom event has following format:
+-----------+------+------------------------------------------------------------+
| Attribute | Type | Description |
+===========+======+============================================================+
| name | str | A user defined name for the event. |
+-----------+------+------------------------------------------------------------+
| data | Any | The data associated with the event. This can be anything. |
+-----------+------+------------------------------------------------------------+
Here's an example:
import { RunnableLambda } from "@langchain/core/runnables";
import { dispatchCustomEvent } from "@langchain/core/callbacks/dispatch";
// Use this import for web environments that don't support "async_hooks"
// and manually pass config to child runs.
// import { dispatchCustomEvent } from "@langchain/core/callbacks/dispatch/web";
const slowThing = RunnableLambda.from(async (someInput: string) => {
// Placeholder for some slow operation
await new Promise((resolve) => setTimeout(resolve, 100));
await dispatchCustomEvent("progress_event", {
message: "Finished step 1 of 2",
});
await new Promise((resolve) => setTimeout(resolve, 100));
return "Done";
});
const eventStream = await slowThing.streamEvents("hello world", {
version: "v2",
});
for await (const event of eventStream) {
if (event.event === "on_custom_event") {
console.log(event);
}
}
Optional
streamOptions: Omit<EventStreamCallbackHandlerInput, "autoClose">Optional
streamOptions: Omit<EventStreamCallbackHandlerInput, "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<MistralAICallOptions>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.
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: RunnableConfig<Record<string, any>>Optional
onCalled if the runnable throws an error, with the Run object.
Optional
config: RunnableConfig<Record<string, any>>Optional
onCalled before the runnable starts running, with the Run object.
Optional
config: RunnableConfig<Record<string, any>>Add retry logic to an existing runnable.
Optional
fields: { Optional
onOptional
stopA new RunnableRetry that, when invoked, will retry according to the parameters.
Optional
withModel wrapper that returns outputs formatted to match the given schema.
The schema for the structured output. Either as a Zod schema or a valid JSON schema object. If a Zod schema is passed, the returned attributes will be validated, whereas with JSON schema they will not be.
Optional
config: StructuredOutputMethodOptions<boolean>A new runnable that calls the LLM with structured output.
Static
deserializeStatic
is
MistralAI completions LLM.