ChatVertexAI

Reference docs

This page contains reference documentation for ChatVertexAI. See the docs for conceptual guides, tutorials, and examples on using ChatVertexAI.

ChatVertexAI deprecated

Bases: _VertexAICommon, BaseChatModel

Deprecated

Use ChatGoogleGenerativeAI instead.

Google Cloud Vertex AI chat model integration.

Setup

You must either:

• Have credentials configured for your environment (gcloud, workload identity, etc...)
• Store the path to a service account JSON file as the GOOGLE_APPLICATION_CREDENTIALS environment variable

This codebase uses the google.auth library which first looks for the application credentials variable mentioned above, and then looks for system-level auth.

More information:

• Credential types
• google.auth API reference

Key init args — completion params: model: str Name of ChatVertexAI model to use. e.g. 'gemini-2.0-flash-001', 'gemini-2.5-pro', etc. temperature: Optional[float] Sampling temperature. seed: Optional[int] Sampling integer to use. max_tokens: Optional[int] Max number of tokens to generate. stop: Optional[List[str]] Default stop sequences. safety_settings: Optional[Dict[vertexai.generative_models.HarmCategory, vertexai.generative_models.HarmBlockThreshold]] The default safety settings to use for all generations.

Key init args — client params: max_retries: int Max number of retries. wait_exponential_kwargs: Optional[dict[str, float]] Optional dictionary with parameters for wait_exponential: - multiplier: Initial wait time multiplier (default: 1.0) - min: Minimum wait time in seconds (default: 4.0) - max: Maximum wait time in seconds (default: 10.0) - exp_base: Exponent base to use (default: 2.0) credentials: Optional[google.auth.credentials.Credentials] The default custom credentials to use when making API calls. If not provided, credentials will be ascertained from the environment. project: Optional[str] The default GCP project to use when making Vertex API calls. location: str = "us-central1" The default location to use when making API calls. request_parallelism: int = 5 The amount of parallelism allowed for requests issued to VertexAI models. base_url: Optional[str] Base URL for API requests.

See full list of supported init args and their descriptions in the params section.

Instantiate

from langchain_google_vertexai import ChatVertexAI

llm = ChatVertexAI(
    model="gemini-2.5-flash",
    temperature=0,
    max_tokens=None,
    max_retries=6,
    stop=None,
    # other params...
)

Thinking

For thinking models, you have the option to adjust the number of internal thinking tokens used (thinking_budget) or to disable thinking altogether. Note that not all models allow disabling thinking.

See the Gemini API docs for more details on thinking models.

To see a thinking model's thoughts, set include_thoughts=True to have the model's reasoning summaries included in the response.

llm = ChatVertexAI(
    model="gemini-2.5-flash",
    include_thoughts=True,
)
ai_msg = llm.invoke("How many 'r's are in the word 'strawberry'?")

Invoke

messages = [
    (
        "system",
        "You are a helpful translator. Translate the user sentence to French.",
    ),
    ("human", "I love programming."),
]
llm.invoke(messages)

AIMessage(
    content="J'adore programmer. ",
    response_metadata={
        "is_blocked": False,
        "safety_ratings": [
            {
                "category": "HARM_CATEGORY_HATE_SPEECH",
                "probability_label": "NEGLIGIBLE",
                "probability_score": 0.1,
                "blocked": False,
                "severity": "HARM_SEVERITY_NEGLIGIBLE",
                "severity_score": 0.1,
            },
            {
                "category": "HARM_CATEGORY_DANGEROUS_CONTENT",
                "probability_label": "NEGLIGIBLE",
                "probability_score": 0.1,
                "blocked": False,
                "severity": "HARM_SEVERITY_NEGLIGIBLE",
                "severity_score": 0.1,
            },
            {
                "category": "HARM_CATEGORY_HARASSMENT",
                "probability_label": "NEGLIGIBLE",
                "probability_score": 0.1,
                "blocked": False,
                "severity": "HARM_SEVERITY_NEGLIGIBLE",
                "severity_score": 0.1,
            },
            {
                "category": "HARM_CATEGORY_SEXUALLY_EXPLICIT",
                "probability_label": "NEGLIGIBLE",
                "probability_score": 0.1,
                "blocked": False,
                "severity": "HARM_SEVERITY_NEGLIGIBLE",
                "severity_score": 0.1,
            },
        ],
        "citation_metadata": None,
        "usage_metadata": {
            "prompt_token_count": 17,
            "candidates_token_count": 7,
            "total_token_count": 24,
        },
    },
    id="run-925ce305-2268-44c4-875f-dde9128520ad-0",
)

Stream

for chunk in llm.stream(messages):
    print(chunk)

AIMessageChunk(
    content="J",
    response_metadata={
        "is_blocked": False,
        "safety_ratings": [],
        "citation_metadata": None,
    },
    id="run-9df01d73-84d9-42db-9d6b-b1466a019e89",
)
AIMessageChunk(
    content="'adore programmer. ",
    response_metadata={
        "is_blocked": False,
        "safety_ratings": [
            {
                "category": "HARM_CATEGORY_HATE_SPEECH",
                "probability_label": "NEGLIGIBLE",
                "probability_score": 0.1,
                "blocked": False,
                "severity": "HARM_SEVERITY_NEGLIGIBLE",
                "severity_score": 0.1,
            },
            {
                "category": "HARM_CATEGORY_DANGEROUS_CONTENT",
                "probability_label": "NEGLIGIBLE",
                "probability_score": 0.1,
                "blocked": False,
                "severity": "HARM_SEVERITY_NEGLIGIBLE",
                "severity_score": 0.1,
            },
            {
                "category": "HARM_CATEGORY_HARASSMENT",
                "probability_label": "NEGLIGIBLE",
                "probability_score": 0.1,
                "blocked": False,
                "severity": "HARM_SEVERITY_NEGLIGIBLE",
                "severity_score": 0.1,
            },
            {
                "category": "HARM_CATEGORY_SEXUALLY_EXPLICIT",
                "probability_label": "NEGLIGIBLE",
                "probability_score": 0.1,
                "blocked": False,
                "severity": "HARM_SEVERITY_NEGLIGIBLE",
                "severity_score": 0.1,
            },
        ],
        "citation_metadata": None,
    },
    id="run-9df01d73-84d9-42db-9d6b-b1466a019e89",
)
AIMessageChunk(
    content="",
    response_metadata={
        "is_blocked": False,
        "safety_ratings": [],
        "citation_metadata": None,
        "usage_metadata": {
            "prompt_token_count": 17,
            "candidates_token_count": 7,
            "total_token_count": 24,
        },
    },
    id="run-9df01d73-84d9-42db-9d6b-b1466a019e89",
)

stream = llm.stream(messages)
full = next(stream)
for chunk in stream:
    full += chunk
full

AIMessageChunk(
    content="J'adore programmer. ",
    response_metadata={
        "is_blocked": False,
        "safety_ratings": [
            {
                "category": "HARM_CATEGORY_HATE_SPEECH",
                "probability_label": "NEGLIGIBLE",
                "probability_score": 0.1,
                "blocked": False,
                "severity": "HARM_SEVERITY_NEGLIGIBLE",
                "severity_score": 0.1,
            },
            {
                "category": "HARM_CATEGORY_DANGEROUS_CONTENT",
                "probability_label": "NEGLIGIBLE",
                "probability_score": 0.1,
                "blocked": False,
                "severity": "HARM_SEVERITY_NEGLIGIBLE",
                "severity_score": 0.1,
            },
            {
                "category": "HARM_CATEGORY_HARASSMENT",
                "probability_label": "NEGLIGIBLE",
                "probability_score": 0.1,
                "blocked": False,
                "severity": "HARM_SEVERITY_NEGLIGIBLE",
                "severity_score": 0.1,
            },
            {
                "category": "HARM_CATEGORY_SEXUALLY_EXPLICIT",
                "probability_label": "NEGLIGIBLE",
                "probability_score": 0.1,
                "blocked": False,
                "severity": "HARM_SEVERITY_NEGLIGIBLE",
                "severity_score": 0.1,
            },
        ],
        "citation_metadata": None,
        "usage_metadata": {
            "prompt_token_count": 17,
            "candidates_token_count": 7,
            "total_token_count": 24,
        },
    },
    id="run-b7f7492c-4cb5-42d0-8fc3-dce9b293b0fb",
)

Async invocation

await llm.ainvoke(messages)

# stream
async for chunk in (await llm.astream(messages))

# batch
await llm.abatch([messages])

Context Caching

Context caching allows you to store and reuse content (e.g., PDFs, images) for faster processing.

The cached_content parameter accepts a cache name created via the Google Generative AI API with Vertex AI.

Content caching

This caches content from GCS and queries it.

from google import genai
from google.genai.types import (
    Content,
    CreateCachedContentConfig,
    HttpOptions,
    Part,
)
from langchain_google_vertexai import ChatVertexAI
from langchain_core.messages import HumanMessage

client = genai.Client(http_options=HttpOptions(api_version="v1beta1"))

contents = [
    Content(
        role="user",
        parts=[
            Part.from_uri(
                file_uri="gs://your-bucket/file1",
                mime_type="application/pdf",
            ),
            Part.from_uri(
                file_uri="gs://your-bucket/file2",
                mime_type="image/jpeg",
            ),
        ],
    )
]

cache = client.caches.create(
    model="gemini-2.5-flash",
    config=CreateCachedContentConfig(
        contents=contents,
        system_instruction="You are an expert content analyzer.",
        display_name="content-cache",
        ttl="300s",
    ),
)

llm = ChatVertexAI(
    model_name="gemini-2.5-flash",
    cached_content=cache.name,
)
message = HumanMessage(
    content="Provide a summary of the key information across the content."
)
llm.invoke([message])

Tool calling

from pydantic import BaseModel, Field


class GetWeather(BaseModel):
    '''Get the current weather in a given location'''

    location: str = Field(
        ..., description="The city and state, e.g. San Francisco, CA"
    )


class GetPopulation(BaseModel):
    '''Get the current population in a given location'''

    location: str = Field(
        ..., description="The city and state, e.g. San Francisco, CA"
    )


llm_with_tools = llm.bind_tools([GetWeather, GetPopulation])
ai_msg = llm_with_tools.invoke(
    "Which city is hotter today and which is bigger: LA or NY?"
)
ai_msg.tool_calls

[
    {
        "name": "GetWeather",
        "args": {"location": "Los Angeles, CA"},
        "id": "2a2401fa-40db-470d-83ce-4e52de910d9e",
    },
    {
        "name": "GetWeather",
        "args": {"location": "New York City, NY"},
        "id": "96761deb-ab7f-4ef9-b4b4-6d44562fc46e",
    },
    {
        "name": "GetPopulation",
        "args": {"location": "Los Angeles, CA"},
        "id": "9147d532-abee-43a2-adb5-12f164300484",
    },
    {
        "name": "GetPopulation",
        "args": {"location": "New York City, NY"},
        "id": "c43374ea-bde5-49ca-8487-5b83ebeea1e6",
    },
]

See bind_tools for more.

Built-in search

from google.cloud.aiplatform_v1beta1.types import Tool as VertexTool
from langchain_google_vertexai import ChatVertexAI

llm = ChatVertexAI(model="gemini-2.5-flash")
resp = llm.invoke(
    "When is the next total solar eclipse in US?",
    tools=[VertexTool(google_search={})],
)

Built-in code execution

from google.cloud.aiplatform_v1beta1.types import Tool as VertexTool
from langchain_google_vertexai import ChatVertexAI

llm = ChatVertexAI(model="gemini-2.5-flash")
resp = llm.invoke(
    "What is 3^3?",
    tools=[VertexTool(code_execution={})],
)

Structured output

from typing import Optional

from pydantic import BaseModel, Field


class Joke(BaseModel):
    '''Joke to tell user.'''

    setup: str = Field(description="The setup of the joke")
    punchline: str = Field(description="The punchline to the joke")
    rating: Optional[int] = Field(
        default=None, description="How funny the joke is, from 1 to 10"
    )


structured_llm = llm.with_structured_output(Joke)
structured_llm.invoke("Tell me a joke about cats")

Joke(
    setup="What do you call a cat that loves to bowl?",
    punchline="An alley cat!",
    rating=None,
)

See with_structured_output for more.

Image input

import base64
import httpx
from langchain_core.messages import HumanMessage

image_url = "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg"
image_data = base64.b64encode(httpx.get(image_url).content).decode("utf-8")
message = HumanMessage(
    content=[
        {"type": "text", "text": "describe the weather in this image"},
        {
            "type": "image_url",
            "image_url": {"url": f"data:image/jpeg;base64,{image_data}"},
        },
    ],
)
ai_msg = llm.invoke([message])
ai_msg.content

The weather in this image appears to be sunny and pleasant. The sky is a bright
blue with scattered white clouds, suggesting a clear and mild day. The lush
green grass indicates recent rainfall or sufficient moisture. The absence of...

You can also point to GCS files which is faster / more efficient because bytes are transferred back and forth.

llm.invoke(
    [
        HumanMessage(
            [
                "What's in the image?",
                {
                    "type": "media",
                    "file_uri": "gs://cloud-samples-data/generative-ai/image/scones.jpg",
                    "mime_type": "image/jpeg",
                },
            ]
        )
    ]
).content

The image is of five blueberry scones arranged on a piece of baking paper. Here
is a list of what is in the picture:* **Five blueberry scones:** They are
scattered across the parchment paper, dusted with powdered sugar.  * **Two...

PDF input

import base64
from langchain_core.messages import HumanMessage

pdf_bytes = open("/path/to/your/test.pdf", "rb").read()
pdf_base64 = base64.b64encode(pdf_bytes).decode("utf-8")
message = HumanMessage(
    content=[
        {"type": "text", "text": "describe the document in a sentence"},
        {
            "type": "file",
            "mime_type": "application/pdf",
            "base64": pdf_base64,
        },
    ]
)
ai_msg = llm.invoke([message])
ai_msg.content

This research paper describes a system developed for SemEval-2025 Task 9, which
aims to automate the detection of food hazards from recall reports, addressing
the class imbalance problem by leveraging LLM-based data augmentation...

You can also point to GCS files.

llm.invoke(
    [
        HumanMessage(
            [
                "describe the document in a sentence",
                {
                    "type": "media",
                    "file_uri": "gs://cloud-samples-data/generative-ai/pdf/1706.03762v7.pdf",
                    "mime_type": "application/pdf",
                },
            ]
        )
    ]
).content

The article introduces Transformer, a new model architecture for sequence
transduction based solely on attention mechanisms, outperforming previous models
in machine translation tasks and demonstrating good generalization to English...

Video input

import base64
from langchain_core.messages import HumanMessage

video_bytes = open("/path/to/your/video.mp4", "rb").read()
video_base64 = base64.b64encode(video_bytes).decode("utf-8")

message = HumanMessage(
    content=[
        {
            "type": "text",
            "text": "describe what's in this video in a sentence",
        },
        {
            "type": "file",
            "mime_type": "video/mp4",
            "base64": video_base64,
        },
    ]
)
ai_msg = llm.invoke([message])
ai_msg.content

Tom and Jerry, along with a turkey, engage in a chaotic Thanksgiving-themed
adventure involving a corn-on-the-cob chase, maze antics, and a disastrous
attempt to prepare a turkey dinner.

You can also pass YouTube URLs directly:

from langchain_core.messages import HumanMessage

message = HumanMessage(
    content=[
        {"type": "text", "text": "summarize the video in 3 sentences."},
        {
            "type": "media",
            "file_uri": "https://www.youtube.com/watch?v=9hE5-98ZeCg",
            "mime_type": "video/mp4",
        },
    ]
)
ai_msg = llm.invoke([message])
ai_msg.content

The video is a demo of multimodal live streaming in Gemini 2.0. The narrator is
sharing his screen in AI Studio and asks if the AI can see it. The AI then reads
text that is highlighted on the screen, defines the word “multimodal,” and...

You can also point to GCS files.

llm = ChatVertexAI(model="gemini-2.5-pro")

llm.invoke(
    [
        HumanMessage(
            [
                "What's in the video?",
                {
                    "type": "media",
                    "file_uri": "gs://cloud-samples-data/video/animals.mp4",
                    "mime_type": "video/mp4",
                },
            ]
        )
    ]
).content

The video is about a new feature in Google Photos called "Zoomable Selfies". The
feature allows users to take selfies with animals at the zoo. The video shows
several examples of people taking selfies with animals, including a tiger,...

Audio input

import base64
from langchain_core.messages import HumanMessage

audio_bytes = open("/path/to/your/audio.mp3", "rb").read()
audio_base64 = base64.b64encode(audio_bytes).decode("utf-8")

message = HumanMessage(
    content=[
        {"type": "text", "text": "summarize this audio in a sentence"},
        {
            "type": "file",
            "mime_type": "audio/mp3",
            "base64": audio_base64,
        },
    ]
)
ai_msg = llm.invoke([message])
ai_msg.content

"In this episode of the Made by Google podcast, Stephen Johnson and Simon Tokumine discuss NotebookLM, a tool designed to help users understand complex material in various modalities, with a focus on its unexpected uses, the development of audio overviews, and the implementation of new features like mind maps and source discovery."

You can also point to GCS files.

from langchain_core.messages import HumanMessage

llm = ChatVertexAI(model="gemini-2.5-flash")

llm.invoke(
    [
        HumanMessage(
            [
                "What's this audio about?",
                {
                    "type": "media",
                    "file_uri": "gs://cloud-samples-data/generative-ai/audio/pixel.mp3",
                    "mime_type": "audio/mpeg",
                },
            ]
        )
    ]
).content

"This audio is an interview with two product managers from Google who work on Pixel feature drops. They discuss how feature drops are important for showcasing how Google devices are constantly improving and getting better. They also discuss some of the highlights of the January feature drop and the new features coming in the March drop for Pixel phones and Pixel watches. The interview concludes with discussion of how user feedback is extremely important to them in deciding which features to include in the feature drops."

Token usage

ai_msg = llm.invoke(messages)
ai_msg.usage_metadata

{"input_tokens": 17, "output_tokens": 7, "total_tokens": 24}

Logprobs

llm = ChatVertexAI(model="gemini-2.5-flash", logprobs=True)
ai_msg = llm.invoke(messages)
ai_msg.response_metadata["logprobs_result"]

[
    {"token": "J", "logprob": -1.549651415189146e-06, "top_logprobs": []},
    {"token": "'", "logprob": -1.549651415189146e-06, "top_logprobs": []},
    {"token": "adore", "logprob": 0.0, "top_logprobs": []},
    {
        "token": " programmer",
        "logprob": -1.1922384146600962e-07,
        "top_logprobs": [],
    },
    {"token": ".", "logprob": -4.827636439586058e-05, "top_logprobs": []},
    {"token": " ", "logprob": -0.018011733889579773, "top_logprobs": []},
    {"token": "\\n", "logprob": -0.0008687592926435173, "top_logprobs": []},
]

Response metadata

ai_msg = llm.invoke(messages)
ai_msg.response_metadata

{
    "is_blocked": False,
    "safety_ratings": [
        {
            "category": "HARM_CATEGORY_HATE_SPEECH",
            "probability_label": "NEGLIGIBLE",
            "probability_score": 0.1,
            "blocked": False,
            "severity": "HARM_SEVERITY_NEGLIGIBLE",
            "severity_score": 0.1,
        },
        {
            "category": "HARM_CATEGORY_DANGEROUS_CONTENT",
            "probability_label": "NEGLIGIBLE",
            "probability_score": 0.1,
            "blocked": False,
            "severity": "HARM_SEVERITY_NEGLIGIBLE",
            "severity_score": 0.1,
        },
        {
            "category": "HARM_CATEGORY_HARASSMENT",
            "probability_label": "NEGLIGIBLE",
            "probability_score": 0.1,
            "blocked": False,
            "severity": "HARM_SEVERITY_NEGLIGIBLE",
            "severity_score": 0.1,
        },
        {
            "category": "HARM_CATEGORY_SEXUALLY_EXPLICIT",
            "probability_label": "NEGLIGIBLE",
            "probability_score": 0.1,
            "blocked": False,
            "severity": "HARM_SEVERITY_NEGLIGIBLE",
            "severity_score": 0.1,
        },
    ],
    "usage_metadata": {
        "prompt_token_count": 17,
        "candidates_token_count": 7,
        "total_token_count": 24,
    },
}

Safety settings

from langchain_google_vertexai import HarmBlockThreshold, HarmCategory

llm = ChatVertexAI(
    model="gemini-2.5-pro",
    safety_settings={
        HarmCategory.HARM_CATEGORY_HATE_SPEECH: HarmBlockThreshold.BLOCK_LOW_AND_ABOVE,
        HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: HarmBlockThreshold.BLOCK_MEDIUM_AND_ABOVE,
        HarmCategory.HARM_CATEGORY_HARASSMENT: HarmBlockThreshold.BLOCK_LOW_AND_ABOVE,
        HarmCategory.HARM_CATEGORY_SEXUALLY_EXPLICIT: HarmBlockThreshold.BLOCK_ONLY_HIGH,
    },
)

llm.invoke(messages).response_metadata

{
    "is_blocked": False,
    "safety_ratings": [
        {
            "category": "HARM_CATEGORY_HATE_SPEECH",
            "probability_label": "NEGLIGIBLE",
            "probability_score": 0.1,
            "blocked": False,
            "severity": "HARM_SEVERITY_NEGLIGIBLE",
            "severity_score": 0.1,
        },
        {
            "category": "HARM_CATEGORY_DANGEROUS_CONTENT",
            "probability_label": "NEGLIGIBLE",
            "probability_score": 0.1,
            "blocked": False,
            "severity": "HARM_SEVERITY_NEGLIGIBLE",
            "severity_score": 0.1,
        },
        {
            "category": "HARM_CATEGORY_HARASSMENT",
            "probability_label": "NEGLIGIBLE",
            "probability_score": 0.1,
            "blocked": False,
            "severity": "HARM_SEVERITY_NEGLIGIBLE",
            "severity_score": 0.1,
        },
        {
            "category": "HARM_CATEGORY_SEXUALLY_EXPLICIT",
            "probability_label": "NEGLIGIBLE",
            "probability_score": 0.1,
            "blocked": False,
            "severity": "HARM_SEVERITY_NEGLIGIBLE",
            "severity_score": 0.1,
        },
    ],
    "usage_metadata": {
        "prompt_token_count": 17,
        "candidates_token_count": 7,
        "total_token_count": 24,
    },
}

METHOD	DESCRIPTION
get_name	Get the name of the Runnable.
get_input_schema	Get a Pydantic model that can be used to validate input to the Runnable.
get_input_jsonschema	Get a JSON schema that represents the input to the Runnable.
get_output_schema	Get a Pydantic model that can be used to validate output to the Runnable.
get_output_jsonschema	Get a JSON schema that represents the output of the Runnable.
config_schema	The type of config this Runnable accepts specified as a Pydantic model.
get_config_jsonschema	Get a JSON schema that represents the config of the Runnable.
get_graph	Return a graph representation of this Runnable.
get_prompts	Return a list of prompts used by this Runnable.
__or__	Runnable "or" operator.
__ror__	Runnable "reverse-or" operator.
pipe	Pipe Runnable objects.
pick	Pick keys from the output dict of this Runnable.
assign	Assigns new fields to the dict output of this Runnable.
invoke	Transform a single input into an output.
ainvoke	Transform a single input into an output.
batch	Default implementation runs invoke in parallel using a thread pool executor.
batch_as_completed	Run invoke in parallel on a list of inputs.
abatch	Default implementation runs ainvoke in parallel using asyncio.gather.
abatch_as_completed	Run ainvoke in parallel on a list of inputs.
stream	Default implementation of stream, which calls invoke.
astream	Default implementation of astream, which calls ainvoke.
astream_log	Stream all output from a Runnable, as reported to the callback system.
astream_events	Generate a stream of events.
transform	Transform inputs to outputs.
atransform	Transform inputs to outputs.
bind	Bind arguments to a Runnable, returning a new Runnable.
with_config	Bind config to a Runnable, returning a new Runnable.
with_listeners	Bind lifecycle listeners to a Runnable, returning a new Runnable.
with_alisteners	Bind async lifecycle listeners to a Runnable.
with_types	Bind input and output types to a Runnable, returning a new Runnable.
with_retry	Create a new Runnable that retries the original Runnable on exceptions.
map	Return a new Runnable that maps a list of inputs to a list of outputs.
with_fallbacks	Add fallbacks to a Runnable, returning a new Runnable.
as_tool	Create a BaseTool from a Runnable.
lc_id	Return a unique identifier for this class for serialization purposes.
to_json	Serialize the Runnable to JSON.
to_json_not_implemented	Serialize a "not implemented" object.
configurable_fields	Configure particular Runnable fields at runtime.
configurable_alternatives	Configure alternatives for Runnable objects that can be set at runtime.
set_verbose	If verbose is None, set it.
generate_prompt	Pass a sequence of prompts to the model and return model generations.
agenerate_prompt	Asynchronously pass a sequence of prompts and return model generations.
get_token_ids	Return the ordered IDs of the tokens in a text.
get_num_tokens_from_messages	Get the number of tokens in the messages.
generate	Pass a sequence of prompts to the model and return model generations.
agenerate	Asynchronously pass a sequence of prompts to a model and return generations.
dict	Return a dictionary of the LLM.
__init__	Needed for mypy typing to recognize model_name as a valid arg and for arg
is_lc_serializable	Is this class serializable?
get_lc_namespace	Get the namespace of the langchain object.
build_extra	Build extra kwargs from additional params that were passed in.
validate_environment	Validate that the python package exists in environment.
get_num_tokens	Get the number of tokens present in the text.
with_structured_output	Model wrapper that returns outputs formatted to match the given schema.
bind_tools	Bind tool-like objects to this chat model.

name class-attribute instance-attribute

name: str | None = None

The name of the Runnable. Used for debugging and tracing.

InputType property

InputType: TypeAlias

Get the input type for this Runnable.

OutputType property

OutputType: Any

Get the output type for this Runnable.

input_schema property

input_schema: type[BaseModel]

The type of input this Runnable accepts specified as a Pydantic model.

output_schema property

output_schema: type[BaseModel]

Output schema.

The type of output this Runnable produces specified as a Pydantic model.

config_specs property

config_specs: list[ConfigurableFieldSpec]

List configurable fields for this Runnable.

lc_secrets property

lc_secrets: dict[str, str]

A map of constructor argument names to secret ids.

For example, {"openai_api_key": "OPENAI_API_KEY"}

lc_attributes property

lc_attributes: dict

List of attribute names that should be included in the serialized kwargs.

These attributes must be accepted by the constructor.

Default is an empty dictionary.

cache class-attribute instance-attribute

cache: BaseCache | bool | None = Field(default=None, exclude=True)

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.

verbose class-attribute instance-attribute

verbose: bool = Field(default_factory=_get_verbosity, exclude=True, repr=False)

Whether to print out response text.

callbacks class-attribute instance-attribute

callbacks: Callbacks = Field(default=None, exclude=True)

Callbacks to add to the run trace.

tags class-attribute instance-attribute

tags: list[str] | None = Field(default=None, exclude=True)

Tags to add to the run trace.

metadata class-attribute instance-attribute

metadata: dict[str, Any] | None = Field(default=None, exclude=True)

Metadata to add to the run trace.

custom_get_token_ids class-attribute instance-attribute

custom_get_token_ids: Callable[[str], list[int]] | None = Field(
    default=None, exclude=True
)

Optional encoder to use for counting tokens.

rate_limiter class-attribute instance-attribute

rate_limiter: BaseRateLimiter | None = Field(default=None, exclude=True)

An optional rate limiter to use for limiting the number of requests.

disable_streaming class-attribute instance-attribute

disable_streaming: bool | Literal['tool_calling'] = False

Whether to disable streaming for this model.

If streaming is bypassed, then stream/astream/astream_events will defer to invoke/ainvoke.

• If True, will always bypass streaming case.
• If 'tool_calling', will bypass streaming case only when the model is called with a tools keyword argument. In other words, LangChain will automatically switch to non-streaming behavior (invoke) only when the tools argument is provided. This offers the best of both worlds.
• If False (Default), will always use streaming case if available.

The main reason for this flag is that code might be written using stream and a user may want to swap out a given model for another model whose the implementation does not properly support streaming.

output_version class-attribute instance-attribute

output_version: str | None = Field(
    default_factory=from_env("LC_OUTPUT_VERSION", default=None)
)

Version of AIMessage output format to store in message content.

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

Supported values:

• 'v0': provider-specific format in content (can lazily-parse with content_blocks)
• 'v1': standardized format in content (consistent with content_blocks)

Partner packages (e.g., langchain-openai) can also use this field to roll out new content formats in a backward-compatible way.

Added in langchain-core 1.0.0

profile class-attribute instance-attribute

profile: ModelProfile | None = Field(default=None, exclude=True)

Profile detailing model capabilities.

Beta feature

This is a beta feature. The format of model profiles is subject to change.

If not specified, automatically loaded from the provider package on initialization if data is available.

Example profile data includes context window sizes, supported modalities, or support for tool calling, structured output, and other features.

Added in langchain-core 1.1.0

project class-attribute instance-attribute

project: str | None = None

The default GCP project to use when making Vertex API calls.

location class-attribute instance-attribute

location: str = Field(default=_DEFAULT_LOCATION)

The default location to use when making API calls.

request_parallelism class-attribute instance-attribute

request_parallelism: int = 5

The amount of parallelism allowed for requests issued to VertexAI models.

max_retries class-attribute instance-attribute

max_retries: int = 6

The maximum number of retries to make when generating.

stop class-attribute instance-attribute

stop: list[str] | None = Field(default=None, alias='stop_sequences')

Optional list of stop words to use when generating.

full_model_name class-attribute instance-attribute

full_model_name: str | None = Field(default=None, exclude=True)

The full name of the model's endpoint.

api_endpoint class-attribute instance-attribute

api_endpoint: str | None = Field(default=None, alias='base_url')

Desired API endpoint, e.g., us-central1-aiplatform.googleapis.com.

api_transport class-attribute instance-attribute

api_transport: str | None = Field(default=None, alias='transport')

The desired API transport method, can be either 'grpc' or 'rest'.

Uses the default parameter from vertexai.init if defined, otherwise uses the Google client library default (typically 'grpc').

additional_headers class-attribute instance-attribute

additional_headers: dict[str, str] | None = Field(default=None)

Key-value dictionary representing additional headers for the model call.

client_cert_source class-attribute instance-attribute

client_cert_source: Callable[[], tuple[bytes, bytes]] | None = None

A callback which returns client certificate bytes and private key bytes.

Both should be in PEM format.

credentials class-attribute instance-attribute

credentials: Any = Field(default=None, exclude=True)

The default custom credentials to use when making API calls.

(google.auth.credentials.Credentials)

If not provided, credentials will be ascertained from the environment.

endpoint_version class-attribute instance-attribute

endpoint_version: Literal['v1', 'v1beta1'] = 'v1beta1'

Whether to use v1 or v1beta1 endpoint.

prediction_client property

prediction_client: PredictionServiceClient | PredictionServiceClient

Returns PredictionServiceClient.

async_prediction_client property

async_prediction_client: PredictionServiceAsyncClient | PredictionServiceAsyncClient

Returns PredictionServiceClient.

temperature class-attribute instance-attribute

temperature: float | None = None

Sampling temperature, it controls the degree of randomness in token selection.

frequency_penalty class-attribute instance-attribute

frequency_penalty: float | None = None

Positive values penalize tokens that repeatedly appear in the generated text, decreasing the probability of repeating content.

presence_penalty class-attribute instance-attribute

presence_penalty: float | None = None

Positive values penalize tokens that already appear in the generated text, increasing the probability of generating more diverse content.

max_output_tokens class-attribute instance-attribute

max_output_tokens: int | None = Field(default=None, alias='max_tokens')

Token limit determines the maximum amount of text output from one prompt.

top_p class-attribute instance-attribute

top_p: float | None = None

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

top_k class-attribute instance-attribute

top_k: int | None = None

How the model selects tokens for output, the next token is selected from among the top-k most probable tokens.

n class-attribute instance-attribute

n: int = 1

How many completions to generate for each prompt.

seed class-attribute instance-attribute

seed: int | None = None

Random seed for the generation.

streaming class-attribute instance-attribute

streaming: bool = False

Whether to stream the results or not.

safety_settings class-attribute instance-attribute

safety_settings: SafetySettingsType | None = None

The default safety settings to use for all generations.

Example

from langchain_google_vertexai import HarmBlockThreshold, HarmCategory

safety_settings = {
    HarmCategory.HARM_CATEGORY_UNSPECIFIED: HarmBlockThreshold.BLOCK_NONE,
    HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: HarmBlockThreshold.BLOCK_MEDIUM_AND_ABOVE,
    HarmCategory.HARM_CATEGORY_HATE_SPEECH: HarmBlockThreshold.BLOCK_ONLY_HIGH,
    HarmCategory.HARM_CATEGORY_HARASSMENT: HarmBlockThreshold.BLOCK_LOW_AND_ABOVE,
    HarmCategory.HARM_CATEGORY_SEXUALLY_EXPLICIT: HarmBlockThreshold.BLOCK_NONE,
}

tuned_model_name class-attribute instance-attribute

tuned_model_name: str | None = None

The name of a tuned model.

response_modalities class-attribute instance-attribute

response_modalities: list[Modality] | None = Field(default=None)

A list of modalities of the response.

thinking_budget class-attribute instance-attribute

thinking_budget: int | None = Field(default=None)

Indicates the thinking budget in tokens.

Used to disable thinking for supported models (when set to 0) or to constrain the number of tokens used for thinking.

Dynamic thinking (allowing the model to decide how many tokens to use) is enabled when set to -1.

More information, including per-model limits, can be found in the Gemini API docs.

include_thoughts class-attribute instance-attribute

include_thoughts: bool | None = Field(default=None)

Indicates whether to include thoughts in the response.

Note

This parameter is only applicable for models that support thinking.

This does not disable thinking; to disable thinking, set thinking_budget to 0. for supported models. See the thinking_budget parameter for more details.

audio_timestamp class-attribute instance-attribute

audio_timestamp: bool | None = Field(default=None)

Enable timestamp understanding of audio-only files.

timeout class-attribute instance-attribute

timeout: float | Timeout | None = Field(
    default=None, description="Timeout for API requests."
)

The timeout for requests to the Vertex AI API, in seconds.

model_name class-attribute instance-attribute

model_name: str = Field(alias='model')

Underlying model name.

response_mime_type class-attribute instance-attribute

response_mime_type: str | None = None

Output response MIME type of the generated candidate text.

Supported MIME types:

• 'text/plain': (default) Text output.
• 'application/json': JSON response in the candidates.
• 'text/x.enum': Enum in plain text.

The model also needs to be prompted to output the appropriate response type, otherwise the behavior is undefined.

This is a preview feature.

response_schema class-attribute instance-attribute

response_schema: dict[str, Any] | None = None

Enforce a schema to the output.

The format of the dictionary should follow Open API schema.

cached_content class-attribute instance-attribute

cached_content: str | None = None

Whether to use the model in cache mode.

Must be a string containing the cache name (A sequence of numbers)

logprobs class-attribute instance-attribute

logprobs: bool | int = False

Whether to return logprobs as part of AIMessage.response_metadata.

If False, don't return logprobs. If True, return logprobs for top candidate. If int, return logprobs for top logprobs candidates.

labels class-attribute instance-attribute

labels: dict[str, str] | None = None

Optional tag llm calls with metadata to help in tracebility and biling.

perform_literal_eval_on_string_raw_content class-attribute instance-attribute

perform_literal_eval_on_string_raw_content: bool = False

Whether to perform literal eval on string raw content.

wait_exponential_kwargs class-attribute instance-attribute

wait_exponential_kwargs: dict[str, float] | None = None

Optional dictionary with parameters for wait_exponential:

• multiplier: Initial wait time multiplier (Default: 1.0)
• min: Minimum wait time in seconds (Default: 4.0)
• max: Maximum wait time in seconds (Default: 10.0)
• exp_base: Exponent base to use (Default: 2.0)

model_kwargs class-attribute instance-attribute

model_kwargs: dict[str, Any] = Field(default_factory=dict)

Holds any unexpected initialization parameters.

get_name

get_name(suffix: str | None = None, *, name: str | None = None) -> str

Get the name of the Runnable.

PARAMETER	DESCRIPTION
suffix	An optional suffix to append to the name. TYPE: str | None DEFAULT: None
name	An optional name to use instead of the Runnable's name. TYPE: str | None DEFAULT: None

RETURNS	DESCRIPTION
str	The name of the Runnable.

get_input_schema

get_input_schema(config: RunnableConfig | None = None) -> type[BaseModel]

Get a Pydantic model that can be used to validate input to the Runnable.

Runnable objects that leverage the configurable_fields and configurable_alternatives methods will have a dynamic input schema that depends on which configuration the Runnable is invoked with.

This method allows to get an input schema for a specific configuration.

PARAMETER	DESCRIPTION
config	A config to use when generating the schema. TYPE: RunnableConfig | None DEFAULT: None

RETURNS	DESCRIPTION
type[BaseModel]	A Pydantic model that can be used to validate input.

get_input_jsonschema

get_input_jsonschema(config: RunnableConfig | None = None) -> dict[str, Any]

Get a JSON schema that represents the input to the Runnable.

PARAMETER	DESCRIPTION
config	A config to use when generating the schema. TYPE: RunnableConfig | None DEFAULT: None

RETURNS	DESCRIPTION
dict[str, Any]	A JSON schema that represents the input to the Runnable.

Example

from langchain_core.runnables import RunnableLambda


def add_one(x: int) -> int:
    return x + 1


runnable = RunnableLambda(add_one)

print(runnable.get_input_jsonschema())

Added in langchain-core 0.3.0

get_output_schema

get_output_schema(config: RunnableConfig | None = None) -> type[BaseModel]

Get a Pydantic model that can be used to validate output to the Runnable.

Runnable objects that leverage the configurable_fields and configurable_alternatives methods will have a dynamic output schema that depends on which configuration the Runnable is invoked with.

This method allows to get an output schema for a specific configuration.

PARAMETER	DESCRIPTION
config	A config to use when generating the schema. TYPE: RunnableConfig | None DEFAULT: None

RETURNS	DESCRIPTION
type[BaseModel]	A Pydantic model that can be used to validate output.

get_output_jsonschema

get_output_jsonschema(config: RunnableConfig | None = None) -> dict[str, Any]

Get a JSON schema that represents the output of the Runnable.

PARAMETER	DESCRIPTION
config	A config to use when generating the schema. TYPE: RunnableConfig | None DEFAULT: None

RETURNS	DESCRIPTION
dict[str, Any]	A JSON schema that represents the output of the Runnable.

Example

from langchain_core.runnables import RunnableLambda


def add_one(x: int) -> int:
    return x + 1


runnable = RunnableLambda(add_one)

print(runnable.get_output_jsonschema())

Added in langchain-core 0.3.0

config_schema

config_schema(*, include: Sequence[str] | None = None) -> type[BaseModel]

The type of config this Runnable accepts specified as a Pydantic model.

To mark a field as configurable, see the configurable_fields and configurable_alternatives methods.

PARAMETER	DESCRIPTION
include	A list of fields to include in the config schema. TYPE: Sequence[str] | None DEFAULT: None

RETURNS	DESCRIPTION
type[BaseModel]	A Pydantic model that can be used to validate config.

get_config_jsonschema

get_config_jsonschema(*, include: Sequence[str] | None = None) -> dict[str, Any]

Get a JSON schema that represents the config of the Runnable.

PARAMETER	DESCRIPTION
include	A list of fields to include in the config schema. TYPE: Sequence[str] | None DEFAULT: None

RETURNS	DESCRIPTION
dict[str, Any]	A JSON schema that represents the config of the Runnable.

Added in langchain-core 0.3.0

get_graph

get_graph(config: RunnableConfig | None = None) -> Graph

Return a graph representation of this Runnable.

get_prompts

get_prompts(config: RunnableConfig | None = None) -> list[BasePromptTemplate]

Return a list of prompts used by this Runnable.

__or__

__or__(
    other: Runnable[Any, Other]
    | Callable[[Iterator[Any]], Iterator[Other]]
    | Callable[[AsyncIterator[Any]], AsyncIterator[Other]]
    | Callable[[Any], Other]
    | Mapping[str, Runnable[Any, Other] | Callable[[Any], Other] | Any],
) -> RunnableSerializable[Input, Other]

Runnable "or" operator.

Compose this Runnable with another object to create a RunnableSequence.

PARAMETER	DESCRIPTION
other	Another Runnable or a Runnable-like object. TYPE: Runnable[Any, Other] | Callable[[Iterator[Any]], Iterator[Other]] | Callable[[AsyncIterator[Any]], AsyncIterator[Other]] | Callable[[Any], Other] | Mapping[str, Runnable[Any, Other] | Callable[[Any], Other] | Any]

RETURNS	DESCRIPTION
RunnableSerializable[Input, Other]	A new Runnable.

__ror__

__ror__(
    other: Runnable[Other, Any]
    | Callable[[Iterator[Other]], Iterator[Any]]
    | Callable[[AsyncIterator[Other]], AsyncIterator[Any]]
    | Callable[[Other], Any]
    | Mapping[str, Runnable[Other, Any] | Callable[[Other], Any] | Any],
) -> RunnableSerializable[Other, Output]

Runnable "reverse-or" operator.

Compose this Runnable with another object to create a RunnableSequence.

PARAMETER	DESCRIPTION
other	Another Runnable or a Runnable-like object. TYPE: Runnable[Other, Any] | Callable[[Iterator[Other]], Iterator[Any]] | Callable[[AsyncIterator[Other]], AsyncIterator[Any]] | Callable[[Other], Any] | Mapping[str, Runnable[Other, Any] | Callable[[Other], Any] | Any]

RETURNS	DESCRIPTION
RunnableSerializable[Other, Output]	A new Runnable.

pipe

pipe(
    *others: Runnable[Any, Other] | Callable[[Any], Other], name: str | None = None
) -> RunnableSerializable[Input, Other]

Pipe Runnable objects.

Compose this Runnable with Runnable-like objects to make a RunnableSequence.

Equivalent to RunnableSequence(self, *others) or self | others[0] | ...

Example

from langchain_core.runnables import RunnableLambda


def add_one(x: int) -> int:
    return x + 1


def mul_two(x: int) -> int:
    return x * 2


runnable_1 = RunnableLambda(add_one)
runnable_2 = RunnableLambda(mul_two)
sequence = runnable_1.pipe(runnable_2)
# Or equivalently:
# sequence = runnable_1 | runnable_2
# sequence = RunnableSequence(first=runnable_1, last=runnable_2)
sequence.invoke(1)
await sequence.ainvoke(1)
# -> 4

sequence.batch([1, 2, 3])
await sequence.abatch([1, 2, 3])
# -> [4, 6, 8]

PARAMETER	DESCRIPTION
*others	Other Runnable or Runnable-like objects to compose TYPE: Runnable[Any, Other] | Callable[[Any], Other] DEFAULT: ()
name	An optional name for the resulting RunnableSequence. TYPE: str | None DEFAULT: None

RETURNS	DESCRIPTION
RunnableSerializable[Input, Other]	A new Runnable.

pick

pick(keys: str | list[str]) -> RunnableSerializable[Any, Any]

Pick keys from the output dict of this Runnable.

Pick a single key

import json

from langchain_core.runnables import RunnableLambda, RunnableMap

as_str = RunnableLambda(str)
as_json = RunnableLambda(json.loads)
chain = RunnableMap(str=as_str, json=as_json)

chain.invoke("[1, 2, 3]")
# -> {"str": "[1, 2, 3]", "json": [1, 2, 3]}

json_only_chain = chain.pick("json")
json_only_chain.invoke("[1, 2, 3]")
# -> [1, 2, 3]

Pick a list of keys

from typing import Any

import json

from langchain_core.runnables import RunnableLambda, RunnableMap

as_str = RunnableLambda(str)
as_json = RunnableLambda(json.loads)


def as_bytes(x: Any) -> bytes:
    return bytes(x, "utf-8")


chain = RunnableMap(
    str=as_str, json=as_json, bytes=RunnableLambda(as_bytes)
)

chain.invoke("[1, 2, 3]")
# -> {"str": "[1, 2, 3]", "json": [1, 2, 3], "bytes": b"[1, 2, 3]"}

json_and_bytes_chain = chain.pick(["json", "bytes"])
json_and_bytes_chain.invoke("[1, 2, 3]")
# -> {"json": [1, 2, 3], "bytes": b"[1, 2, 3]"}

PARAMETER	DESCRIPTION
keys	A key or list of keys to pick from the output dict. TYPE: str | list[str]

RETURNS	DESCRIPTION
RunnableSerializable[Any, Any]	a new Runnable.

assign

assign(
    **kwargs: Runnable[dict[str, Any], Any]
    | Callable[[dict[str, Any]], Any]
    | Mapping[str, Runnable[dict[str, Any], Any] | Callable[[dict[str, Any]], Any]],
) -> RunnableSerializable[Any, Any]

Assigns new fields to the dict output of this Runnable.

from langchain_core.language_models.fake import FakeStreamingListLLM
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import SystemMessagePromptTemplate
from langchain_core.runnables import Runnable
from operator import itemgetter

prompt = (
    SystemMessagePromptTemplate.from_template("You are a nice assistant.")
    + "{question}"
)
model = FakeStreamingListLLM(responses=["foo-lish"])

chain: Runnable = prompt | model | {"str": StrOutputParser()}

chain_with_assign = chain.assign(hello=itemgetter("str") | model)

print(chain_with_assign.input_schema.model_json_schema())
# {'title': 'PromptInput', 'type': 'object', 'properties':
{'question': {'title': 'Question', 'type': 'string'}}}
print(chain_with_assign.output_schema.model_json_schema())
# {'title': 'RunnableSequenceOutput', 'type': 'object', 'properties':
{'str': {'title': 'Str',
'type': 'string'}, 'hello': {'title': 'Hello', 'type': 'string'}}}

PARAMETER	DESCRIPTION
**kwargs	A mapping of keys to Runnable or Runnable-like objects that will be invoked with the entire output dict of this Runnable. TYPE: Runnable[dict[str, Any], Any] | Callable[[dict[str, Any]], Any] | Mapping[str, Runnable[dict[str, Any], Any] | Callable[[dict[str, Any]], Any]] DEFAULT: {}

RETURNS	DESCRIPTION
RunnableSerializable[Any, Any]	A new Runnable.

invoke

invoke(
    input: LanguageModelInput,
    config: RunnableConfig | None = None,
    *,
    stop: list[str] | None = None,
    **kwargs: Any,
) -> AIMessage

Transform a single input into an output.

PARAMETER	DESCRIPTION
input	The input to the Runnable. TYPE: Input
config	A config to use when invoking the Runnable. The config supports standard keys like 'tags', 'metadata' for tracing purposes, 'max_concurrency' for controlling how much work to do in parallel, and other keys. Please refer to RunnableConfig for more details. TYPE: RunnableConfig | None DEFAULT: None

RETURNS	DESCRIPTION
Output	The output of the Runnable.

ainvoke async

ainvoke(
    input: LanguageModelInput,
    config: RunnableConfig | None = None,
    *,
    stop: list[str] | None = None,
    **kwargs: Any,
) -> AIMessage

Transform a single input into an output.

PARAMETER	DESCRIPTION
input	The input to the Runnable. TYPE: Input
config	A config to use when invoking the Runnable. The config supports standard keys like 'tags', 'metadata' for tracing purposes, 'max_concurrency' for controlling how much work to do in parallel, and other keys. Please refer to RunnableConfig for more details. TYPE: RunnableConfig | None DEFAULT: None

RETURNS	DESCRIPTION
Output	The output of the Runnable.

batch

batch(
    inputs: list[Input],
    config: RunnableConfig | list[RunnableConfig] | None = None,
    *,
    return_exceptions: bool = False,
    **kwargs: Any | None,
) -> list[Output]

Default implementation runs invoke in parallel using a thread pool executor.

The default implementation of batch works well for IO bound runnables.

Subclasses must override this method if they can batch more efficiently; e.g., if the underlying Runnable uses an API which supports a batch mode.

PARAMETER	DESCRIPTION
inputs	A list of inputs to the Runnable. TYPE: list[Input]
config	A config to use when invoking the Runnable. The config supports standard keys like 'tags', 'metadata' for tracing purposes, 'max_concurrency' for controlling how much work to do in parallel, and other keys. Please refer to RunnableConfig for more details. TYPE: RunnableConfig | list[RunnableConfig] | None DEFAULT: None
return_exceptions	Whether to return exceptions instead of raising them. TYPE: bool DEFAULT: False
**kwargs	Additional keyword arguments to pass to the Runnable. TYPE: Any | None DEFAULT: {}

RETURNS	DESCRIPTION
list[Output]	A list of outputs from the Runnable.

batch_as_completed

batch_as_completed(
    inputs: Sequence[Input],
    config: RunnableConfig | Sequence[RunnableConfig] | None = None,
    *,
    return_exceptions: bool = False,
    **kwargs: Any | None,
) -> Iterator[tuple[int, Output | Exception]]

Run invoke in parallel on a list of inputs.

Yields results as they complete.

PARAMETER	DESCRIPTION
inputs	A list of inputs to the Runnable. TYPE: Sequence[Input]
config	A config to use when invoking the Runnable. The config supports standard keys like 'tags', 'metadata' for tracing purposes, 'max_concurrency' for controlling how much work to do in parallel, and other keys. Please refer to RunnableConfig for more details. TYPE: RunnableConfig | Sequence[RunnableConfig] | None DEFAULT: None
return_exceptions	Whether to return exceptions instead of raising them. TYPE: bool DEFAULT: False
**kwargs	Additional keyword arguments to pass to the Runnable. TYPE: Any | None DEFAULT: {}

YIELDS	DESCRIPTION
tuple[int, Output | Exception]	Tuples of the index of the input and the output from the Runnable.

abatch async

abatch(
    inputs: list[Input],
    config: RunnableConfig | list[RunnableConfig] | None = None,
    *,
    return_exceptions: bool = False,
    **kwargs: Any | None,
) -> list[Output]

Default implementation runs ainvoke in parallel using asyncio.gather.

The default implementation of batch works well for IO bound runnables.

Subclasses must override this method if they can batch more efficiently; e.g., if the underlying Runnable uses an API which supports a batch mode.

PARAMETER	DESCRIPTION
inputs	A list of inputs to the Runnable. TYPE: list[Input]
config	A config to use when invoking the Runnable. The config supports standard keys like 'tags', 'metadata' for tracing purposes, 'max_concurrency' for controlling how much work to do in parallel, and other keys. Please refer to RunnableConfig for more details. TYPE: RunnableConfig | list[RunnableConfig] | None DEFAULT: None
return_exceptions	Whether to return exceptions instead of raising them. TYPE: bool DEFAULT: False
**kwargs	Additional keyword arguments to pass to the Runnable. TYPE: Any | None DEFAULT: {}

RETURNS	DESCRIPTION
list[Output]	A list of outputs from the Runnable.

abatch_as_completed async

abatch_as_completed(
    inputs: Sequence[Input],
    config: RunnableConfig | Sequence[RunnableConfig] | None = None,
    *,
    return_exceptions: bool = False,
    **kwargs: Any | None,
) -> AsyncIterator[tuple[int, Output | Exception]]

Run ainvoke in parallel on a list of inputs.

Yields results as they complete.

PARAMETER	DESCRIPTION
inputs	A list of inputs to the Runnable. TYPE: Sequence[Input]
config	A config to use when invoking the Runnable. The config supports standard keys like 'tags', 'metadata' for tracing purposes, 'max_concurrency' for controlling how much work to do in parallel, and other keys. Please refer to RunnableConfig for more details. TYPE: RunnableConfig | Sequence[RunnableConfig] | None DEFAULT: None
return_exceptions	Whether to return exceptions instead of raising them. TYPE: bool DEFAULT: False
**kwargs	Additional keyword arguments to pass to the Runnable. TYPE: Any | None DEFAULT: {}

YIELDS	DESCRIPTION
AsyncIterator[tuple[int, Output | Exception]]	A tuple of the index of the input and the output from the Runnable.

stream

stream(
    input: LanguageModelInput,
    config: RunnableConfig | None = None,
    *,
    stop: list[str] | None = None,
    **kwargs: Any,
) -> Iterator[AIMessageChunk]

Default implementation of stream, which calls invoke.

Subclasses must override this method if they support streaming output.

PARAMETER	DESCRIPTION
input	The input to the Runnable. TYPE: Input
config	The config to use for the Runnable. TYPE: RunnableConfig | None DEFAULT: None
**kwargs	Additional keyword arguments to pass to the Runnable. TYPE: Any | None DEFAULT: {}

YIELDS	DESCRIPTION
Output	The output of the Runnable.

astream async

astream(
    input: LanguageModelInput,
    config: RunnableConfig | None = None,
    *,
    stop: list[str] | None = None,
    **kwargs: Any,
) -> AsyncIterator[AIMessageChunk]

Default implementation of astream, which calls ainvoke.

Subclasses must override this method if they support streaming output.

PARAMETER	DESCRIPTION
input	The input to the Runnable. TYPE: Input
config	The config to use for the Runnable. TYPE: RunnableConfig | None DEFAULT: None
**kwargs	Additional keyword arguments to pass to the Runnable. TYPE: Any | None DEFAULT: {}

YIELDS	DESCRIPTION
AsyncIterator[Output]	The output of the Runnable.

astream_log async

astream_log(
    input: Any,
    config: RunnableConfig | None = None,
    *,
    diff: bool = True,
    with_streamed_output_list: bool = True,
    include_names: Sequence[str] | None = None,
    include_types: Sequence[str] | None = None,
    include_tags: Sequence[str] | None = None,
    exclude_names: Sequence[str] | None = None,
    exclude_types: Sequence[str] | None = None,
    exclude_tags: Sequence[str] | None = None,
    **kwargs: Any,
) -> AsyncIterator[RunLogPatch] | AsyncIterator[RunLog]

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.

PARAMETER	DESCRIPTION
input	The input to the Runnable. TYPE: Any
config	The config to use for the Runnable. TYPE: RunnableConfig | None DEFAULT: None
diff	Whether to yield diffs between each step or the current state. TYPE: bool DEFAULT: True
with_streamed_output_list	Whether to yield the streamed_output list. TYPE: bool DEFAULT: True
include_names	Only include logs with these names. TYPE: Sequence[str] | None DEFAULT: None
include_types	Only include logs with these types. TYPE: Sequence[str] | None DEFAULT: None
include_tags	Only include logs with these tags. TYPE: Sequence[str] | None DEFAULT: None
exclude_names	Exclude logs with these names. TYPE: Sequence[str] | None DEFAULT: None
exclude_types	Exclude logs with these types. TYPE: Sequence[str] | None DEFAULT: None
exclude_tags	Exclude logs with these tags. TYPE: Sequence[str] | None DEFAULT: None
**kwargs	Additional keyword arguments to pass to the Runnable. TYPE: Any DEFAULT: {}

YIELDS	DESCRIPTION
AsyncIterator[RunLogPatch] | AsyncIterator[RunLog]	A RunLogPatch or RunLog object.

astream_events async

astream_events(
    input: Any,
    config: RunnableConfig | None = None,
    *,
    version: Literal["v1", "v2"] = "v2",
    include_names: Sequence[str] | None = None,
    include_types: Sequence[str] | None = None,
    include_tags: Sequence[str] | None = None,
    exclude_names: Sequence[str] | None = None,
    exclude_types: Sequence[str] | None = None,
    exclude_tags: Sequence[str] | None = None,
    **kwargs: Any,
) -> AsyncIterator[StreamEvent]

Generate a stream of events.

Use to create an iterator over StreamEvent that provide real-time information about the progress of the Runnable, including StreamEvent from intermediate results.

A StreamEvent is a dictionary with the following schema:

• event: Event names are of the format: on_[runnable_type]_(start|stream|end).
• name: The name of the Runnable that generated the event.
• run_id: 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.
• parent_ids: The IDs of the parent runnables that generated the event. The root Runnable will have an empty list. The order of the parent IDs is from the root to the immediate parent. Only available for v2 version of the API. The v1 version of the API will return an empty list.
• tags: The tags of the Runnable that generated the event.
• metadata: The metadata of the Runnable that generated the event.
• data: The data associated with the event. The contents of this field depend on the type of event. See the table below for more details.

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.

Note

This reference table is for the v2 version of the schema.

event	name	chunk	input	output
on_chat_model_start	'[model name]'		{"messages": [[SystemMessage, HumanMessage]]}
on_chat_model_stream	'[model name]'	AIMessageChunk(content="hello")
on_chat_model_end	'[model name]'		{"messages": [[SystemMessage, HumanMessage]]}	AIMessageChunk(content="hello world")
on_llm_start	'[model name]'		{'input': 'hello'}
on_llm_stream	'[model name]'	'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_end	'some_tool'			{"x": 1, "y": "2"}
on_retriever_start	'[retriever name]'		{"query": "hello"}
on_retriever_end	'[retriever name]'		{"query": "hello"}	[Document(...), ..]
on_prompt_start	'[template_name]'		{"question": "hello"}
on_prompt_end	'[template_name]'		{"question": "hello"}	ChatPromptValue(messages: [SystemMessage, ...])

In addition to the standard events, users can also dispatch custom events (see example below).

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, though we suggest making it JSON serializable.

Here are declarations associated with the standard events shown above:

format_docs:

def format_docs(docs: list[Document]) -> str:
    '''Format the docs.'''
    return ", ".join([doc.page_content for doc in docs])


format_docs = RunnableLambda(format_docs)

some_tool:

@tool
def some_tool(x: int, y: str) -> dict:
    '''Some_tool.'''
    return {"x": x, "y": y}

prompt:

template = ChatPromptTemplate.from_messages(
    [
        ("system", "You are Cat Agent 007"),
        ("human", "{question}"),
    ]
).with_config({"run_name": "my_template", "tags": ["my_template"]})

Example

from langchain_core.runnables import RunnableLambda


async def reverse(s: str) -> str:
    return s[::-1]


chain = RunnableLambda(func=reverse)

events = [
    event async for event in chain.astream_events("hello", version="v2")
]

# Will produce the following events
# (run_id, and parent_ids has been omitted for brevity):
[
    {
        "data": {"input": "hello"},
        "event": "on_chain_start",
        "metadata": {},
        "name": "reverse",
        "tags": [],
    },
    {
        "data": {"chunk": "olleh"},
        "event": "on_chain_stream",
        "metadata": {},
        "name": "reverse",
        "tags": [],
    },
    {
        "data": {"output": "olleh"},
        "event": "on_chain_end",
        "metadata": {},
        "name": "reverse",
        "tags": [],
    },
]

Dispatch custom event

from langchain_core.callbacks.manager import (
    adispatch_custom_event,
)
from langchain_core.runnables import RunnableLambda, RunnableConfig
import asyncio


async def slow_thing(some_input: str, config: RunnableConfig) -> str:
    """Do something that takes a long time."""
    await asyncio.sleep(1) # Placeholder for some slow operation
    await adispatch_custom_event(
        "progress_event",
        {"message": "Finished step 1 of 3"},
        config=config # Must be included for python < 3.10
    )
    await asyncio.sleep(1) # Placeholder for some slow operation
    await adispatch_custom_event(
        "progress_event",
        {"message": "Finished step 2 of 3"},
        config=config # Must be included for python < 3.10
    )
    await asyncio.sleep(1) # Placeholder for some slow operation
    return "Done"

slow_thing = RunnableLambda(slow_thing)

async for event in slow_thing.astream_events("some_input", version="v2"):
    print(event)

PARAMETER	DESCRIPTION
input	The input to the Runnable. TYPE: Any
config	The config to use for the Runnable. TYPE: RunnableConfig | None DEFAULT: None
version	The version of the schema to use, either 'v2' or 'v1'. Users should use 'v2'. 'v1' is for backwards compatibility and will be deprecated in 0.4.0. No default will be assigned until the API is stabilized. custom events will only be surfaced in 'v2'. TYPE: Literal['v1', 'v2'] DEFAULT: 'v2'
include_names	Only include events from Runnable objects with matching names. TYPE: Sequence[str] | None DEFAULT: None
include_types	Only include events from Runnable objects with matching types. TYPE: Sequence[str] | None DEFAULT: None
include_tags	Only include events from Runnable objects with matching tags. TYPE: Sequence[str] | None DEFAULT: None
exclude_names	Exclude events from Runnable objects with matching names. TYPE: Sequence[str] | None DEFAULT: None
exclude_types	Exclude events from Runnable objects with matching types. TYPE: Sequence[str] | None DEFAULT: None
exclude_tags	Exclude events from Runnable objects with matching tags. TYPE: Sequence[str] | None DEFAULT: None
**kwargs	Additional keyword arguments to pass to the Runnable. These will be passed to astream_log as this implementation of astream_events is built on top of astream_log. TYPE: Any DEFAULT: {}

YIELDS	DESCRIPTION
AsyncIterator[StreamEvent]	An async stream of StreamEvent.

RAISES	DESCRIPTION
NotImplementedError	If the version is not 'v1' or 'v2'.

transform

transform(
    input: Iterator[Input], config: RunnableConfig | None = None, **kwargs: Any | None
) -> Iterator[Output]

Transform inputs to outputs.

Default implementation of transform, which buffers input and calls astream.

Subclasses must override this method if they can start producing output while input is still being generated.

PARAMETER	DESCRIPTION
input	An iterator of inputs to the Runnable. TYPE: Iterator[Input]
config	The config to use for the Runnable. TYPE: RunnableConfig | None DEFAULT: None
**kwargs	Additional keyword arguments to pass to the Runnable. TYPE: Any | None DEFAULT: {}

YIELDS	DESCRIPTION
Output	The output of the Runnable.

atransform async

atransform(
    input: AsyncIterator[Input],
    config: RunnableConfig | None = None,
    **kwargs: Any | None,
) -> AsyncIterator[Output]

Transform inputs to outputs.

Default implementation of atransform, which buffers input and calls astream.

Subclasses must override this method if they can start producing output while input is still being generated.

PARAMETER	DESCRIPTION
input	An async iterator of inputs to the Runnable. TYPE: AsyncIterator[Input]
config	The config to use for the Runnable. TYPE: RunnableConfig | None DEFAULT: None
**kwargs	Additional keyword arguments to pass to the Runnable. TYPE: Any | None DEFAULT: {}

YIELDS	DESCRIPTION
AsyncIterator[Output]	The output of the Runnable.

bind

bind(**kwargs: Any) -> Runnable[Input, Output]

Bind arguments to a Runnable, returning a new Runnable.

Useful when a Runnable in a chain requires an argument that is not in the output of the previous Runnable or included in the user input.

PARAMETER	DESCRIPTION
**kwargs	The arguments to bind to the Runnable. TYPE: Any DEFAULT: {}

RETURNS	DESCRIPTION
Runnable[Input, Output]	A new Runnable with the arguments bound.

Example

from langchain_ollama import ChatOllama
from langchain_core.output_parsers import StrOutputParser

model = ChatOllama(model="llama3.1")

# Without bind
chain = model | StrOutputParser()

chain.invoke("Repeat quoted words exactly: 'One two three four five.'")
# Output is 'One two three four five.'

# With bind
chain = model.bind(stop=["three"]) | StrOutputParser()

chain.invoke("Repeat quoted words exactly: 'One two three four five.'")
# Output is 'One two'

with_config

with_config(
    config: RunnableConfig | None = None, **kwargs: Any
) -> Runnable[Input, Output]

Bind config to a Runnable, returning a new Runnable.

PARAMETER	DESCRIPTION
config	The config to bind to the Runnable. TYPE: RunnableConfig | None DEFAULT: None
**kwargs	Additional keyword arguments to pass to the Runnable. TYPE: Any DEFAULT: {}

RETURNS	DESCRIPTION
Runnable[Input, Output]	A new Runnable with the config bound.

with_listeners

with_listeners(
    *,
    on_start: Callable[[Run], None]
    | Callable[[Run, RunnableConfig], None]
    | None = None,
    on_end: Callable[[Run], None] | Callable[[Run, RunnableConfig], None] | None = None,
    on_error: Callable[[Run], None]
    | Callable[[Run, RunnableConfig], None]
    | None = None,
) -> Runnable[Input, Output]

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, start_time, end_time, and any tags or metadata added to the run.

PARAMETER	DESCRIPTION
on_start	Called before the Runnable starts running, with the Run object. TYPE: Callable[[Run], None] | Callable[[Run, RunnableConfig], None] | None DEFAULT: None
on_end	Called after the Runnable finishes running, with the Run object. TYPE: Callable[[Run], None] | Callable[[Run, RunnableConfig], None] | None DEFAULT: None
on_error	Called if the Runnable throws an error, with the Run object. TYPE: Callable[[Run], None] | Callable[[Run, RunnableConfig], None] | None DEFAULT: None

RETURNS	DESCRIPTION
Runnable[Input, Output]	A new Runnable with the listeners bound.

Example

from langchain_core.runnables import RunnableLambda
from langchain_core.tracers.schemas import Run

import time


def test_runnable(time_to_sleep: int):
    time.sleep(time_to_sleep)


def fn_start(run_obj: Run):
    print("start_time:", run_obj.start_time)


def fn_end(run_obj: Run):
    print("end_time:", run_obj.end_time)


chain = RunnableLambda(test_runnable).with_listeners(
    on_start=fn_start, on_end=fn_end
)
chain.invoke(2)

with_alisteners

with_alisteners(
    *,
    on_start: AsyncListener | None = None,
    on_end: AsyncListener | None = None,
    on_error: AsyncListener | None = None,
) -> Runnable[Input, Output]

Bind async lifecycle listeners to a Runnable.

Returns a new Runnable.

The Run object contains information about the run, including its id, type, input, output, error, start_time, end_time, and any tags or metadata added to the run.

PARAMETER	DESCRIPTION
on_start	Called asynchronously before the Runnable starts running, with the Run object. TYPE: AsyncListener | None DEFAULT: None
on_end	Called asynchronously after the Runnable finishes running, with the Run object. TYPE: AsyncListener | None DEFAULT: None
on_error	Called asynchronously if the Runnable throws an error, with the Run object. TYPE: AsyncListener | None DEFAULT: None

RETURNS	DESCRIPTION
Runnable[Input, Output]	A new Runnable with the listeners bound.

Example

from langchain_core.runnables import RunnableLambda, Runnable
from datetime import datetime, timezone
import time
import asyncio


def format_t(timestamp: float) -> str:
    return datetime.fromtimestamp(timestamp, tz=timezone.utc).isoformat()


async def test_runnable(time_to_sleep: int):
    print(f"Runnable[{time_to_sleep}s]: starts at {format_t(time.time())}")
    await asyncio.sleep(time_to_sleep)
    print(f"Runnable[{time_to_sleep}s]: ends at {format_t(time.time())}")


async def fn_start(run_obj: Runnable):
    print(f"on start callback starts at {format_t(time.time())}")
    await asyncio.sleep(3)
    print(f"on start callback ends at {format_t(time.time())}")


async def fn_end(run_obj: Runnable):
    print(f"on end callback starts at {format_t(time.time())}")
    await asyncio.sleep(2)
    print(f"on end callback ends at {format_t(time.time())}")


runnable = RunnableLambda(test_runnable).with_alisteners(
    on_start=fn_start, on_end=fn_end
)


async def concurrent_runs():
    await asyncio.gather(runnable.ainvoke(2), runnable.ainvoke(3))


asyncio.run(concurrent_runs())
# Result:
# on start callback starts at 2025-03-01T07:05:22.875378+00:00
# on start callback starts at 2025-03-01T07:05:22.875495+00:00
# on start callback ends at 2025-03-01T07:05:25.878862+00:00
# on start callback ends at 2025-03-01T07:05:25.878947+00:00
# Runnable[2s]: starts at 2025-03-01T07:05:25.879392+00:00
# Runnable[3s]: starts at 2025-03-01T07:05:25.879804+00:00
# Runnable[2s]: ends at 2025-03-01T07:05:27.881998+00:00
# on end callback starts at 2025-03-01T07:05:27.882360+00:00
# Runnable[3s]: ends at 2025-03-01T07:05:28.881737+00:00
# on end callback starts at 2025-03-01T07:05:28.882428+00:00
# on end callback ends at 2025-03-01T07:05:29.883893+00:00
# on end callback ends at 2025-03-01T07:05:30.884831+00:00

with_types

with_types(
    *, input_type: type[Input] | None = None, output_type: type[Output] | None = None
) -> Runnable[Input, Output]

Bind input and output types to a Runnable, returning a new Runnable.

PARAMETER	DESCRIPTION
input_type	The input type to bind to the Runnable. TYPE: type[Input] | None DEFAULT: None
output_type	The output type to bind to the Runnable. TYPE: type[Output] | None DEFAULT: None

RETURNS	DESCRIPTION
Runnable[Input, Output]	A new Runnable with the types bound.

with_retry

with_retry(
    *,
    retry_if_exception_type: tuple[type[BaseException], ...] = (Exception,),
    wait_exponential_jitter: bool = True,
    exponential_jitter_params: ExponentialJitterParams | None = None,
    stop_after_attempt: int = 3,
) -> Runnable[Input, Output]

Create a new Runnable that retries the original Runnable on exceptions.

PARAMETER	DESCRIPTION
retry_if_exception_type	A tuple of exception types to retry on. TYPE: tuple[type[BaseException], ...] DEFAULT: (Exception,)
wait_exponential_jitter	Whether to add jitter to the wait time between retries. TYPE: bool DEFAULT: True
stop_after_attempt	The maximum number of attempts to make before giving up. TYPE: int DEFAULT: 3
exponential_jitter_params	Parameters for tenacity.wait_exponential_jitter. Namely: initial, max, exp_base, and jitter (all float values). TYPE: ExponentialJitterParams | None DEFAULT: None

RETURNS	DESCRIPTION
Runnable[Input, Output]	A new Runnable that retries the original Runnable on exceptions.

Example

from langchain_core.runnables import RunnableLambda

count = 0


def _lambda(x: int) -> None:
    global count
    count = count + 1
    if x == 1:
        raise ValueError("x is 1")
    else:
        pass


runnable = RunnableLambda(_lambda)
try:
    runnable.with_retry(
        stop_after_attempt=2,
        retry_if_exception_type=(ValueError,),
    ).invoke(1)
except ValueError:
    pass

assert count == 2

map

map() -> Runnable[list[Input], list[Output]]

Return a new Runnable that maps a list of inputs to a list of outputs.

Calls invoke with each input.

RETURNS	DESCRIPTION
Runnable[list[Input], list[Output]]	A new Runnable that maps a list of inputs to a list of outputs.

Example

from langchain_core.runnables import RunnableLambda


def _lambda(x: int) -> int:
    return x + 1


runnable = RunnableLambda(_lambda)
print(runnable.map().invoke([1, 2, 3]))  # [2, 3, 4]

with_fallbacks

with_fallbacks(
    fallbacks: Sequence[Runnable[Input, Output]],
    *,
    exceptions_to_handle: tuple[type[BaseException], ...] = (Exception,),
    exception_key: str | None = None,
) -> RunnableWithFallbacks[Input, Output]

Add fallbacks to a Runnable, returning a new Runnable.

The new Runnable will try the original Runnable, and then each fallback in order, upon failures.

PARAMETER	DESCRIPTION
fallbacks	A sequence of runnables to try if the original Runnable fails. TYPE: Sequence[Runnable[Input, Output]]
exceptions_to_handle	A tuple of exception types to handle. TYPE: tuple[type[BaseException], ...] DEFAULT: (Exception,)
exception_key	If string is specified then handled exceptions will be passed to fallbacks as part of the input under the specified key. If None, exceptions will not be passed to fallbacks. If used, the base Runnable and its fallbacks must accept a dictionary as input. TYPE: str | None DEFAULT: None

RETURNS	DESCRIPTION
RunnableWithFallbacks[Input, Output]	A new Runnable that will try the original Runnable, and then each Fallback in order, upon failures.

Example

from typing import Iterator

from langchain_core.runnables import RunnableGenerator


def _generate_immediate_error(input: Iterator) -> Iterator[str]:
    raise ValueError()
    yield ""


def _generate(input: Iterator) -> Iterator[str]:
    yield from "foo bar"


runnable = RunnableGenerator(_generate_immediate_error).with_fallbacks(
    [RunnableGenerator(_generate)]
)
print("".join(runnable.stream({})))  # foo bar

PARAMETER	DESCRIPTION
fallbacks	A sequence of runnables to try if the original Runnable fails. TYPE: Sequence[Runnable[Input, Output]]
exceptions_to_handle	A tuple of exception types to handle. TYPE: tuple[type[BaseException], ...] DEFAULT: (Exception,)
exception_key	If string is specified then handled exceptions will be passed to fallbacks as part of the input under the specified key. If None, exceptions will not be passed to fallbacks. If used, the base Runnable and its fallbacks must accept a dictionary as input. TYPE: str | None DEFAULT: None

RETURNS	DESCRIPTION
RunnableWithFallbacks[Input, Output]	A new Runnable that will try the original Runnable, and then each Fallback in order, upon failures.

as_tool

as_tool(
    args_schema: type[BaseModel] | None = None,
    *,
    name: str | None = None,
    description: str | None = None,
    arg_types: dict[str, type] | None = None,
) -> BaseTool

Create a BaseTool from a Runnable.

as_tool will instantiate a BaseTool with a name, description, and args_schema from a Runnable. Where possible, schemas are inferred from runnable.get_input_schema.

Alternatively (e.g., if the Runnable takes a dict as input and the specific dict keys are not typed), the schema can be specified directly with args_schema.

You can also pass arg_types to just specify the required arguments and their types.

PARAMETER	DESCRIPTION
args_schema	The schema for the tool. TYPE: type[BaseModel] | None DEFAULT: None
name	The name of the tool. TYPE: str | None DEFAULT: None
description	The description of the tool. TYPE: str | None DEFAULT: None
arg_types	A dictionary of argument names to types. TYPE: dict[str, type] | None DEFAULT: None

RETURNS	DESCRIPTION
BaseTool	A BaseTool instance.

TypedDict input

from typing_extensions import TypedDict
from langchain_core.runnables import RunnableLambda


class Args(TypedDict):
    a: int
    b: list[int]


def f(x: Args) -> str:
    return str(x["a"] * max(x["b"]))


runnable = RunnableLambda(f)
as_tool = runnable.as_tool()
as_tool.invoke({"a": 3, "b": [1, 2]})

dict input, specifying schema via args_schema

from typing import Any
from pydantic import BaseModel, Field
from langchain_core.runnables import RunnableLambda

def f(x: dict[str, Any]) -> str:
    return str(x["a"] * max(x["b"]))

class FSchema(BaseModel):
    """Apply a function to an integer and list of integers."""

    a: int = Field(..., description="Integer")
    b: list[int] = Field(..., description="List of ints")

runnable = RunnableLambda(f)
as_tool = runnable.as_tool(FSchema)
as_tool.invoke({"a": 3, "b": [1, 2]})

dict input, specifying schema via arg_types

from typing import Any
from langchain_core.runnables import RunnableLambda


def f(x: dict[str, Any]) -> str:
    return str(x["a"] * max(x["b"]))


runnable = RunnableLambda(f)
as_tool = runnable.as_tool(arg_types={"a": int, "b": list[int]})
as_tool.invoke({"a": 3, "b": [1, 2]})

str input

from langchain_core.runnables import RunnableLambda


def f(x: str) -> str:
    return x + "a"


def g(x: str) -> str:
    return x + "z"


runnable = RunnableLambda(f) | g
as_tool = runnable.as_tool()
as_tool.invoke("b")

lc_id classmethod

lc_id() -> list[str]

Return a unique identifier for this class for serialization purposes.

The unique identifier is a list of strings that describes the path to the object.

For example, for the class langchain.llms.openai.OpenAI, the id is ["langchain", "llms", "openai", "OpenAI"].

to_json

to_json() -> SerializedConstructor | SerializedNotImplemented

Serialize the Runnable to JSON.

RETURNS	DESCRIPTION
SerializedConstructor | SerializedNotImplemented	A JSON-serializable representation of the Runnable.

to_json_not_implemented

to_json_not_implemented() -> SerializedNotImplemented

Serialize a "not implemented" object.

RETURNS	DESCRIPTION
SerializedNotImplemented	SerializedNotImplemented.

configurable_fields

configurable_fields(
    **kwargs: AnyConfigurableField,
) -> RunnableSerializable[Input, Output]

Configure particular Runnable fields at runtime.

PARAMETER	DESCRIPTION
**kwargs	A dictionary of ConfigurableField instances to configure. TYPE: AnyConfigurableField DEFAULT: {}

RAISES	DESCRIPTION
ValueError	If a configuration key is not found in the Runnable.

RETURNS	DESCRIPTION
RunnableSerializable[Input, Output]	A new Runnable with the fields configured.

Example

from langchain_core.runnables import ConfigurableField
from langchain_openai import ChatOpenAI

model = ChatOpenAI(max_tokens=20).configurable_fields(
    max_tokens=ConfigurableField(
        id="output_token_number",
        name="Max tokens in the output",
        description="The maximum number of tokens in the output",
    )
)

# max_tokens = 20
print(
    "max_tokens_20: ", model.invoke("tell me something about chess").content
)

# max_tokens = 200
print(
    "max_tokens_200: ",
    model.with_config(configurable={"output_token_number": 200})
    .invoke("tell me something about chess")
    .content,
)

configurable_alternatives

configurable_alternatives(
    which: ConfigurableField,
    *,
    default_key: str = "default",
    prefix_keys: bool = False,
    **kwargs: Runnable[Input, Output] | Callable[[], Runnable[Input, Output]],
) -> RunnableSerializable[Input, Output]

Configure alternatives for Runnable objects that can be set at runtime.

PARAMETER	DESCRIPTION
which	The ConfigurableField instance that will be used to select the alternative. TYPE: ConfigurableField
default_key	The default key to use if no alternative is selected. TYPE: str DEFAULT: 'default'
prefix_keys	Whether to prefix the keys with the ConfigurableField id. TYPE: bool DEFAULT: False
**kwargs	A dictionary of keys to Runnable instances or callables that return Runnable instances. TYPE: Runnable[Input, Output] | Callable[[], Runnable[Input, Output]] DEFAULT: {}

RETURNS	DESCRIPTION
RunnableSerializable[Input, Output]	A new Runnable with the alternatives configured.

Example

from langchain_anthropic import ChatAnthropic
from langchain_core.runnables.utils import ConfigurableField
from langchain_openai import ChatOpenAI

model = ChatAnthropic(
    model_name="claude-sonnet-4-5-20250929"
).configurable_alternatives(
    ConfigurableField(id="llm"),
    default_key="anthropic",
    openai=ChatOpenAI(),
)

# uses the default model ChatAnthropic
print(model.invoke("which organization created you?").content)

# uses ChatOpenAI
print(
    model.with_config(configurable={"llm": "openai"})
    .invoke("which organization created you?")
    .content
)

set_verbose

set_verbose(verbose: bool | None) -> bool

If verbose is None, set it.

This allows users to pass in None as verbose to access the global setting.

PARAMETER	DESCRIPTION
verbose	The verbosity setting to use. TYPE: bool | None

RETURNS	DESCRIPTION
bool	The verbosity setting to use.

generate_prompt

generate_prompt(
    prompts: list[PromptValue],
    stop: list[str] | None = None,
    callbacks: Callbacks = 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).

PARAMETER	DESCRIPTION
prompts	List of PromptValue objects. A PromptValue is an object that can be converted to match the format of any language model (string for pure text generation models and BaseMessage objects for chat models). TYPE: list[PromptValue]
stop	Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings. TYPE: list[str] | None DEFAULT: None
callbacks	Callbacks to pass through. Used for executing additional functionality, such as logging or streaming, throughout generation. TYPE: Callbacks DEFAULT: None
**kwargs	Arbitrary additional keyword arguments. These are usually passed to the model provider API call. TYPE: Any DEFAULT: {}

RETURNS	DESCRIPTION
LLMResult	An LLMResult, which contains a list of candidate Generation objects for each input prompt and additional model provider-specific output.

agenerate_prompt async

agenerate_prompt(
    prompts: list[PromptValue],
    stop: list[str] | None = None,
    callbacks: Callbacks = 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).

PARAMETER	DESCRIPTION
prompts	List of PromptValue objects. A PromptValue is an object that can be converted to match the format of any language model (string for pure text generation models and BaseMessage objects for chat models). TYPE: list[PromptValue]
stop	Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings. TYPE: list[str] | None DEFAULT: None
callbacks	Callbacks to pass through. Used for executing additional functionality, such as logging or streaming, throughout generation. TYPE: Callbacks DEFAULT: None
**kwargs	Arbitrary additional keyword arguments. These are usually passed to the model provider API call. TYPE: Any DEFAULT: {}

RETURNS	DESCRIPTION
LLMResult	An LLMResult, which contains a list of candidate Generation objects for each input prompt and additional model provider-specific output.

get_token_ids

get_token_ids(text: str) -> list[int]

Return the ordered IDs of the tokens in a text.

PARAMETER	DESCRIPTION
text	The string input to tokenize. TYPE: str

RETURNS	DESCRIPTION
list[int]	A list of IDs corresponding to the tokens in the text, in order they occur in the text.

get_num_tokens_from_messages

get_num_tokens_from_messages(
    messages: list[BaseMessage], tools: Sequence | None = None
) -> int

Get the number of tokens in the messages.

Useful for checking if an input fits in a model's context window.

This should be overridden by model-specific implementations to provide accurate token counts via model-specific tokenizers.

Note

• The base implementation of get_num_tokens_from_messages ignores tool schemas.
• The base implementation of get_num_tokens_from_messages adds additional prefixes to messages in represent user roles, which will add to the overall token count. Model-specific implementations may choose to handle this differently.

PARAMETER	DESCRIPTION
messages	The message inputs to tokenize. TYPE: list[BaseMessage]
tools	If provided, sequence of dict, BaseModel, function, or BaseTool objects to be converted to tool schemas. TYPE: Sequence | None DEFAULT: None

RETURNS	DESCRIPTION
int	The sum of the number of tokens across the messages.

generate

generate(
    messages: list[list[BaseMessage]],
    stop: list[str] | None = None,
    callbacks: Callbacks = None,
    *,
    tags: list[str] | None = None,
    metadata: dict[str, Any] | None = None,
    run_name: str | None = None,
    run_id: UUID | None = 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).

PARAMETER	DESCRIPTION
messages	List of list of messages. TYPE: list[list[BaseMessage]]
stop	Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings. TYPE: list[str] | None DEFAULT: None
callbacks	Callbacks to pass through. Used for executing additional functionality, such as logging or streaming, throughout generation. TYPE: Callbacks DEFAULT: None
tags	The tags to apply. TYPE: list[str] | None DEFAULT: None
metadata	The metadata to apply. TYPE: dict[str, Any] | None DEFAULT: None
run_name	The name of the run. TYPE: str | None DEFAULT: None
run_id	The ID of the run. TYPE: UUID | None DEFAULT: None
**kwargs	Arbitrary additional keyword arguments. These are usually passed to the model provider API call. TYPE: Any DEFAULT: {}

RETURNS	DESCRIPTION
LLMResult	An LLMResult, which contains a list of candidate Generations for each input prompt and additional model provider-specific output.

agenerate async

agenerate(
    messages: list[list[BaseMessage]],
    stop: list[str] | None = None,
    callbacks: Callbacks = None,
    *,
    tags: list[str] | None = None,
    metadata: dict[str, Any] | None = None,
    run_name: str | None = None,
    run_id: UUID | None = 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).

PARAMETER	DESCRIPTION
messages	List of list of messages. TYPE: list[list[BaseMessage]]
stop	Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings. TYPE: list[str] | None DEFAULT: None
callbacks	Callbacks to pass through. Used for executing additional functionality, such as logging or streaming, throughout generation. TYPE: Callbacks DEFAULT: None
tags	The tags to apply. TYPE: list[str] | None DEFAULT: None
metadata	The metadata to apply. TYPE: dict[str, Any] | None DEFAULT: None
run_name	The name of the run. TYPE: str | None DEFAULT: None
run_id	The ID of the run. TYPE: UUID | None DEFAULT: None
**kwargs	Arbitrary additional keyword arguments. These are usually passed to the model provider API call. TYPE: Any DEFAULT: {}

RETURNS	DESCRIPTION
LLMResult	An LLMResult, which contains a list of candidate Generations for each input prompt and additional model provider-specific output.

dict

dict(**kwargs: Any) -> dict

Return a dictionary of the LLM.

__init__

__init__(*, model_name: str | None = None, **kwargs: Any) -> None

Needed for mypy typing to recognize model_name as a valid arg and for arg validation.

is_lc_serializable classmethod

is_lc_serializable() -> bool

Is this class serializable?

By design, even if a class inherits from Serializable, it is not serializable by default. This is to prevent accidental serialization of objects that should not be serialized.

RETURNS	DESCRIPTION
bool	Whether the class is serializable. Default is False.

get_lc_namespace classmethod

get_lc_namespace() -> list[str]

Get the namespace of the langchain object.

RETURNS	DESCRIPTION
list[str]	["langchain", "chat_models", "vertexai"]

build_extra classmethod

build_extra(values: dict[str, Any]) -> Any

Build extra kwargs from additional params that were passed in.

validate_environment

validate_environment() -> Self

Validate that the python package exists in environment.

get_num_tokens

get_num_tokens(text: str) -> int

Get the number of tokens present in the text.

More info

with_structured_output

with_structured_output(
    schema: dict | type[BaseModel] | type,
    *,
    include_raw: bool = False,
    method: Literal["json_mode"] | None = None,
    **kwargs: Any,
) -> Runnable[LanguageModelInput, dict | BaseModel]

Model wrapper that returns outputs formatted to match the given schema.

Behavior changed in langchain-google-vertexai 1.1.0

Return type corrected in version 1.1.0. Previously if a dict schema was provided then the output had the form [{"args": {}, "name": "schema_name"}] where the output was a list with a single dict and the "args" of the one dict corresponded to the schema.

As of 1.1.0 this has been fixed so that the schema (the value corresponding to the old "args" key) is returned directly.

PARAMETER	DESCRIPTION
schema	The output schema as a dict or a Pydantic class. If a Pydantic class then the model output will be an object of that class. If a dict then the model output will be a dict. With a Pydantic class the returned attributes will be validated, whereas with a dict they will not be. If method is 'function_calling' and schema is a dict, then the dict must match the OpenAI function-calling spec. TYPE: dict | type[BaseModel] | type
include_raw	If False then only the parsed structured output is returned. If an error occurs during model output parsing it will be raised. If True then both the raw model response (a BaseMessage) and the parsed model response will be returned. If an error occurs during output parsing it will be caught and returned as well. The final output is always a dict with keys 'raw', 'parsed', and 'parsing_error'. TYPE: bool DEFAULT: False
method	If set to 'json_schema' it will use controlled generation to generate the response rather than function calling. Does not work with schemas with references or Pydantic models with self-references. TYPE: Literal['json_mode'] | None DEFAULT: None

RETURNS

DESCRIPTION

Runnable[LanguageModelInput, dict | BaseModel]

A Runnable that takes any chat model input. If 'include_raw' is True then a dict with keys: raw: BaseMessage parsed: _DictOrPydantic | None parsing_error: BaseException | None If 'include_raw' is False, then just _DictOrPydantic is returned, where _DictOrPydantic depends on the schema. If schema is a Pydantic class then _DictOrPydantic is the Pydantic class. If schema is a dict then _DictOrPydantic is a dict.

Pydantic schema, exclude raw

from pydantic import BaseModel
from langchain_google_vertexai import ChatVertexAI


class AnswerWithJustification(BaseModel):
    '''An answer to the user question along with justification for the answer.'''

    answer: str
    justification: str


llm = ChatVertexAI(model_name="gemini-2.0-flash-001", temperature=0)
structured_llm = llm.with_structured_output(AnswerWithJustification)

structured_llm.invoke(
    "What weighs more a pound of bricks or a pound of feathers"
)
# -> AnswerWithJustification(
#     answer='They weigh the same.', justification='A pound is a pound.'
# )

Pydantic schema, include raw

from pydantic import BaseModel
from langchain_google_vertexai import ChatVertexAI


class AnswerWithJustification(BaseModel):
    '''An answer to the user question along with justification for the answer.'''

    answer: str
    justification: str


llm = ChatVertexAI(model_name="gemini-2.0-flash-001", temperature=0)
structured_llm = llm.with_structured_output(
    AnswerWithJustification, include_raw=True
)

structured_llm.invoke(
    "What weighs more a pound of bricks or a pound of feathers"
)
# -> {
#     'raw': AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_Ao02pnFYXD6GN1yzc0uXPsvF', 'function': {'arguments': '{"answer":"They weigh the same.","justification":"Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume or density of the objects may differ."}', 'name': 'AnswerWithJustification'}, 'type': 'function'}]}),
#     'parsed': AnswerWithJustification(answer='They weigh the same.', justification='Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume or density of the objects may differ.'),
#     'parsing_error': None
# }

Dict schema, exclude raw

from pydantic import BaseModel
from langchain_core.utils.function_calling import (
    convert_to_openai_function,
)
from langchain_google_vertexai import ChatVertexAI


class AnswerWithJustification(BaseModel):
    '''An answer to the user question along with justification for the answer.'''

    answer: str
    justification: str


dict_schema = convert_to_openai_function(AnswerWithJustification)
llm = ChatVertexAI(model_name="gemini-2.0-flash-001", temperature=0)
structured_llm = llm.with_structured_output(dict_schema)

structured_llm.invoke(
    "What weighs more a pound of bricks or a pound of feathers"
)
# -> {
#     'answer': 'They weigh the same',
#     'justification': 'Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume and density of the two substances differ.'
# }

Pydantic schema, streaming

from pydantic import BaseModel, Field
from langchain_google_vertexai import ChatVertexAI


class Explanation(BaseModel):
    '''A topic explanation with examples.'''

    description: str = Field(
        description="A brief description of the topic."
    )
    examples: str = Field(description="Two examples related to the topic.")


llm = ChatVertexAI(model_name="gemini-2.0-flash", temperature=0)
structured_llm = llm.with_structured_output(Explanation, method="json_mode")

for chunk in structured_llm.stream("Tell me about transformer models"):
    print(chunk)
    print("-------------------------")
# -> description='Transformer models are a type of neural network architecture that have revolutionized the field of natural language processing (NLP) and are also increasingly used in computer vision and other domains. They rely on the self-attention mechanism to weigh the importance of different parts of the input data, allowing them to effectively capture long-range dependencies. Unlike recurrent neural networks (RNNs), transformers can process the entire input sequence in parallel, leading to significantly faster training times. Key components of transformer models include: the self-attention mechanism (calculates attention weights between different parts of the input), multi-head attention (performs self-attention multiple times with different learned parameters), positional encoding (adds information about the position of tokens in the input sequence), feedforward networks (applies a non-linear transformation to each position), and encoder-decoder structure (used for sequence-to-sequence tasks).' examples='1. BERT (Bidirectional Encoder Representations from Transformers): A pre-trained transformer'
#    -------------------------
#    description='Transformer models are a type of neural network architecture that have revolutionized the field of natural language processing (NLP) and are also increasingly used in computer vision and other domains. They rely on the self-attention mechanism to weigh the importance of different parts of the input data, allowing them to effectively capture long-range dependencies. Unlike recurrent neural networks (RNNs), transformers can process the entire input sequence in parallel, leading to significantly faster training times. Key components of transformer models include: the self-attention mechanism (calculates attention weights between different parts of the input), multi-head attention (performs self-attention multiple times with different learned parameters), positional encoding (adds information about the position of tokens in the input sequence), feedforward networks (applies a non-linear transformation to each position), and encoder-decoder structure (used for sequence-to-sequence tasks).' examples='1. BERT (Bidirectional Encoder Representations from Transformers): A pre-trained transformer model that can be fine-tuned for various NLP tasks like text classification, question answering, and named entity recognition. 2. GPT (Generative Pre-trained Transformer): A language model that uses transformers to generate coherent and contextually relevant text. GPT models are used in chatbots, content creation, and code generation.'
#    -------------------------

bind_tools

bind_tools(
    tools: _ToolsType,
    tool_config: _ToolConfigDict | None = None,
    *,
    tool_choice: _ToolChoiceType | bool | None = None,
    **kwargs: Any,
) -> Runnable[LanguageModelInput, AIMessage]

Bind tool-like objects to this chat model.

Assumes model is compatible with Vertex tool-calling API.

PARAMETER	DESCRIPTION
tools	A list of tool definitions to bind to this chat model. Can be a Pydantic model, Callable, or BaseTool. Pydantic models, Callable, and BaseTool will be automatically converted to their schema dictionary representation. Tools with Union types in their arguments are now supported and converted to anyOf schemas. TYPE: _ToolsType
**kwargs	Any additional parameters to pass to the Runnable constructor. TYPE: Any DEFAULT: {}