ChatGoogleGenerativeAI

Google API key to use

The async caller should be used by subclasses to make any async calls, which will thus benefit from the concurrency and retry logic.

Whether or not model supports system instructions. The following models support system instructions:

• All Gemini 1.5 Pro model versions
• All Gemini 1.5 Flash model versions
• Gemini 1.0 Pro version gemini-1.0-pro-002

Whether to disable streaming.

If streaming is bypassed, then stream() will defer to invoke().

• If true, will always bypass streaming case.
• If false (default), will always use streaming case if available.

Whether or not to force the model to respond with JSON. Available for gemini-1.5 models and later.

Maximum number of tokens to generate in the completion.

Model Name to use

Note: The format must follow the pattern - {model}

Version of AIMessage output format to store in message content.

AIMessage.contentBlocks will lazily parse the contents of content into a standard format. This flag can be used to additionally store the standard format as the message content, e.g., for serialization purposes.

• "v0": provider-specific format in content (can lazily parse with .contentBlocks)
• "v1": standardized format in content (consistent with .contentBlocks)

You can also set LC_OUTPUT_VERSION as an environment variable to "v1" to enable this by default.

A list of unique SafetySetting instances for blocking unsafe content. The API will block any prompts and responses that fail to meet the thresholds set by these settings. If there is no SafetySetting for a given SafetyCategory provided in the list, the API will use the default safety setting for that category.

The set of character sequences (up to 5) that will stop output generation. If specified, the API will stop at the first appearance of a stop sequence.

Note: The stop sequence will not be included as part of the response. Note: stopSequences is only supported for Gemini models

Whether to stream the results or not

Whether or not to include usage data, like token counts in the streamed response chunks.

Controls the randomness of the output.

Values can range from [0.0,2.0], inclusive. A value closer to 2.0 will produce responses that are more varied and creative, while a value closer to 0.0 will typically result in less surprising responses from the model.

Note: The default value varies by model

Optional. Config for thinking features. An error will be returned if this field is set for models that don't support thinking.

Top-k changes how the model selects tokens for output.

A top-k of 1 means the selected token is the most probable among all tokens in the model's vocabulary (also called greedy decoding), while a top-k of 3 means that the next token is selected from among the 3 most probable tokens (using temperature).

Note: The default value varies by model

Top-p changes how the model selects tokens for output.

Tokens are selected from most probable to least until the sum of their probabilities equals the top-p value.

For example, if tokens A, B, and C have a probability of .3, .2, and .1 and the top-p value is .5, then the model will select either A or B as the next token (using temperature).

Note: The default value varies by model

Whether to print out response text.

Internal method that handles batching and configuration for a runnable It takes a function, input values, and optional configuration, and returns a promise that resolves to the output values.

Create a unique cache key for a specific call to a specific language model.

Get the identifying parameters of the LLM.

Default streaming implementation. Subclasses should override this method if they support streaming output.

Helper method to transform an Iterator of Input values into an Iterator of Output values, with callbacks. Use this to implement stream() or transform() in Runnable subclasses.

Assigns new fields to the dict output of this runnable. Returns a new runnable.

Convert a runnable to a tool. Return a new instance of RunnableToolLike which contains the runnable, name, description and schema.

Default implementation of batch, which calls invoke N times. Subclasses should override this method if they can batch more efficiently.

Bind tool-like objects to this chat model.

Generates chat based on the input messages.

Generates a prompt based on the input prompt values.

Get the number of tokens in the content.

Get the parameters used to invoke the model

Invokes the chat model with a single 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.

Stream output in chunks.

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.

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.

Create a new runnable from the current one that will try invoking other passed fallback runnables if the initial invocation fails.

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.

Add retry logic to an existing runnable.

The name of the serializable. Override to provide an alias or to preserve the serialized module name in minified environments.

Implemented as a static method to support loading logic.

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);
 }
}