`AzureChatOpenAI`¶

Reference docs

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

langchain_openai.chat_models.AzureChatOpenAI ¶

Bases: BaseChatOpenAI

Azure OpenAI chat model integration.

Setup

Head to the Azure OpenAI quickstart guide to create your Azure OpenAI deployment.

Then install langchain-openai and set environment variables AZURE_OPENAI_API_KEY and AZURE_OPENAI_ENDPOINT:

pip install -U langchain-openai

export AZURE_OPENAI_API_KEY="your-api-key"
export AZURE_OPENAI_ENDPOINT="https://your-endpoint.openai.azure.com/"

Key init args — completion params: azure_deployment: Name of Azure OpenAI deployment to use. temperature: Sampling temperature. max_tokens: Max number of tokens to generate. logprobs: Whether to return logprobs.

Key init args — client params: api_version: Azure OpenAI REST API version to use (distinct from the version of the underlying model). See more on the different versions. timeout: Timeout for requests. max_retries: Max number of retries. organization: OpenAI organization ID. If not passed in will be read from env var OPENAI_ORG_ID. model: The name of the underlying OpenAI model. Used for tracing and token counting. Does not affect completion. E.g. 'gpt-4', 'gpt-35-turbo', etc. model_version: The version of the underlying OpenAI model. Used for tracing and token counting. Does not affect completion. E.g., '0125', '0125-preview', etc.

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

Instantiate

from langchain_openai import AzureChatOpenAI

model = AzureChatOpenAI(
    azure_deployment="your-deployment",
    api_version="2024-05-01-preview",
    temperature=0,
    max_tokens=None,
    timeout=None,
    max_retries=2,
    # organization="...",
    # model="gpt-35-turbo",
    # model_version="0125",
    # other params...
)

Note

Any param which is not explicitly supported will be passed directly to the openai.AzureOpenAI.chat.completions.create(...) API every time to the model is invoked.

For example:

from langchain_openai import AzureChatOpenAI
import openai

AzureChatOpenAI(..., logprobs=True).invoke(...)

# results in underlying API call of:

openai.AzureOpenAI(..).chat.completions.create(..., logprobs=True)

# which is also equivalent to:

AzureChatOpenAI(...).invoke(..., logprobs=True)

Invoke

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

AIMessage(
    content="J'adore programmer.",
    usage_metadata={
        "input_tokens": 28,
        "output_tokens": 6,
        "total_tokens": 34,
    },
    response_metadata={
        "token_usage": {
            "completion_tokens": 6,
            "prompt_tokens": 28,
            "total_tokens": 34,
        },
        "model_name": "gpt-4",
        "system_fingerprint": "fp_7ec89fabc6",
        "prompt_filter_results": [
            {
                "prompt_index": 0,
                "content_filter_results": {
                    "hate": {"filtered": False, "severity": "safe"},
                    "self_harm": {"filtered": False, "severity": "safe"},
                    "sexual": {"filtered": False, "severity": "safe"},
                    "violence": {"filtered": False, "severity": "safe"},
                },
            }
        ],
        "finish_reason": "stop",
        "logprobs": None,
        "content_filter_results": {
            "hate": {"filtered": False, "severity": "safe"},
            "self_harm": {"filtered": False, "severity": "safe"},
            "sexual": {"filtered": False, "severity": "safe"},
            "violence": {"filtered": False, "severity": "safe"},
        },
    },
    id="run-6d7a5282-0de0-4f27-9cc0-82a9db9a3ce9-0",
)

Stream

for chunk in model.stream(messages):
    print(chunk.text, end="")

AIMessageChunk(content="", id="run-a6f294d3-0700-4f6a-abc2-c6ef1178c37f")
AIMessageChunk(content="J", id="run-a6f294d3-0700-4f6a-abc2-c6ef1178c37f")
AIMessageChunk(content="'", id="run-a6f294d3-0700-4f6a-abc2-c6ef1178c37f")
AIMessageChunk(content="ad", id="run-a6f294d3-0700-4f6a-abc2-c6ef1178c37f")
AIMessageChunk(content="ore", id="run-a6f294d3-0700-4f6a-abc2-c6ef1178c37f")
AIMessageChunk(content=" la", id="run-a6f294d3-0700-4f6a-abc2-c6ef1178c37f")
AIMessageChunk(
    content=" programm", id="run-a6f294d3-0700-4f6a-abc2-c6ef1178c37f"
)
AIMessageChunk(content="ation", id="run-a6f294d3-0700-4f6a-abc2-c6ef1178c37f")
AIMessageChunk(content=".", id="run-a6f294d3-0700-4f6a-abc2-c6ef1178c37f")
AIMessageChunk(
    content="",
    response_metadata={
        "finish_reason": "stop",
        "model_name": "gpt-4",
        "system_fingerprint": "fp_811936bd4f",
    },
    id="run-a6f294d3-0700-4f6a-abc2-c6ef1178c37f",
)

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

AIMessageChunk(
    content="J'adore la programmation.",
    response_metadata={
        "finish_reason": "stop",
        "model_name": "gpt-4",
        "system_fingerprint": "fp_811936bd4f",
    },
    id="run-ba60e41c-9258-44b8-8f3a-2f10599643b3",
)

Async

await model.ainvoke(messages)

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

# batch:
# await model.abatch([messages])

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"
    )


model_with_tools = model.bind_tools([GetWeather, GetPopulation])
ai_msg = model_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": "call_6XswGD5Pqk8Tt5atYr7tfenU",
    },
    {
        "name": "GetWeather",
        "args": {"location": "New York, NY"},
        "id": "call_ZVL15vA8Y7kXqOy3dtmQgeCi",
    },
    {
        "name": "GetPopulation",
        "args": {"location": "Los Angeles, CA"},
        "id": "call_49CFW8zqC9W7mh7hbMLSIrXw",
    },
    {
        "name": "GetPopulation",
        "args": {"location": "New York, NY"},
        "id": "call_6ghfKxV264jEfe1mRIkS3PE7",
    },
]

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: int | None = Field(
        description="How funny the joke is, from 1 to 10"
    )


structured_model = model.with_structured_output(Joke)
structured_model.invoke("Tell me a joke about cats")

Joke(
    setup="Why was the cat sitting on the computer?",
    punchline="To keep an eye on the mouse!",
    rating=None,
)

See AzureChatOpenAI.with_structured_output() for more.

JSON mode

json_model = model.bind(response_format={"type": "json_object"})
ai_msg = json_model.invoke(
    "Return a JSON object with key 'random_ints' and a value of 10 random ints in [0-99]"
)
ai_msg.content

'\\n{\\n  "random_ints": [23, 87, 45, 12, 78, 34, 56, 90, 11, 67]\\n}'

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 = model.invoke([message])
ai_msg.content

"The weather in the image appears to be quite pleasant. The sky is mostly clear"

Token usage

ai_msg = model.invoke(messages)
ai_msg.usage_metadata

{"input_tokens": 28, "output_tokens": 5, "total_tokens": 33}

Logprobs:

logprobs_model = model.bind(logprobs=True)
ai_msg = logprobs_model.invoke(messages)
ai_msg.response_metadata["logprobs"]

```python
{
    "content": [
        {
            "token": "J",
            "bytes": [74],
            "logprob": -4.9617593e-06,
            "top_logprobs": [],
        },
        {
            "token": "'adore",
            "bytes": [39, 97, 100, 111, 114, 101],
            "logprob": -0.25202933,
            "top_logprobs": [],
        },
        {
            "token": " la",
            "bytes": [32, 108, 97],
            "logprob": -0.20141791,
            "top_logprobs": [],
        },
        {
            "token": " programmation",
            "bytes": [
                32,
                112,
                114,
                111,
                103,
                114,
                97,
                109,
                109,
                97,
                116,
                105,
                111,
                110,
            ],
            "logprob": -1.9361265e-07,
            "top_logprobs": [],
        },
        {
            "token": ".",
            "bytes": [46],
            "logprob": -1.2233183e-05,
            "top_logprobs": [],
        },
    ]
}
```

Response metadata

ai_msg = model.invoke(messages)
ai_msg.response_metadata

```python
{
    "token_usage": {
        "completion_tokens": 6,
        "prompt_tokens": 28,
        "total_tokens": 34,
    },
    "model_name": "gpt-35-turbo",
    "system_fingerprint": None,
    "prompt_filter_results": [
        {
            "prompt_index": 0,
            "content_filter_results": {
                "hate": {"filtered": False, "severity": "safe"},
                "self_harm": {"filtered": False, "severity": "safe"},
                "sexual": {"filtered": False, "severity": "safe"},
                "violence": {"filtered": False, "severity": "safe"},
            },
        }
    ],
    "finish_reason": "stop",
    "logprobs": None,
    "content_filter_results": {
        "hate": {"filtered": False, "severity": "safe"},
        "self_harm": {"filtered": False, "severity": "safe"},
        "sexual": {"filtered": False, "severity": "safe"},
        "violence": {"filtered": False, "severity": "safe"},
    },
}
```

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`.
`__init__`
`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`	Get the tokens present in the text with tiktoken package.
`get_num_tokens`	Get the number of tokens present in the text.
`get_num_tokens_from_messages`	Calculate num tokens for `gpt-3.5-turbo` and `gpt-4` with `tiktoken` package.
`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.
`bind_tools`	Bind tool-like objects to this chat model.
`build_extra`	Build extra kwargs from additional params that were passed in.
`validate_temperature`	Validate temperature parameter for different models.
`get_lc_namespace`	Get the namespace of the LangChain object.
`is_lc_serializable`	Check if the class is serializable in langchain.
`validate_environment`	Validate that api key and python package exists in environment.
`with_structured_output`	Model wrapper that returns outputs formatted to match the given schema.

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.

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 use.

This field is used to roll-out new output formats for chat model AIMessage responses in a backwards-compatible way.

Supported values:

'v0': AIMessage format as of langchain-openai 0.3.x.
'responses/v1': Formats Responses API output items into AIMessage content blocks (Responses API only)
'v1': v1 of LangChain cross-provider standard.

Behavior changed in 1.0.0

Default updated to "responses/v1".

temperature `class-attribute` `instance-attribute` ¶

temperature: float | None = None

What sampling temperature to use.

model_kwargs `class-attribute` `instance-attribute` ¶

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

Holds any model parameters valid for create call not explicitly specified.

openai_api_base `class-attribute` `instance-attribute` ¶

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

Base URL path for API requests, leave blank if not using a proxy or service emulator.

openai_organization `class-attribute` `instance-attribute` ¶

openai_organization: str | None = Field(default=None, alias='organization')

Automatically inferred from env var OPENAI_ORG_ID if not provided.

request_timeout `class-attribute` `instance-attribute` ¶

request_timeout: float | tuple[float, float] | Any | None = Field(
    default=None, alias="timeout"
)

Timeout for requests to OpenAI completion API. Can be float, httpx.Timeout or None.

stream_usage `class-attribute` `instance-attribute` ¶

stream_usage: bool | None = None

Whether to include usage metadata in streaming output. If enabled, an additional message chunk will be generated during the stream including usage metadata.

This parameter is enabled unless openai_api_base is set or the model is initialized with a custom client, as many chat completions APIs do not support streaming token usage.

Added in version 0.3.9

Behavior changed in 0.3.35

Enabled for default base URL and client.

max_retries `class-attribute` `instance-attribute` ¶

max_retries: int | None = None

Maximum number of retries to make when generating.

presence_penalty `class-attribute` `instance-attribute` ¶

presence_penalty: float | None = None

Penalizes repeated tokens.

frequency_penalty `class-attribute` `instance-attribute` ¶

frequency_penalty: float | None = None

Penalizes repeated tokens according to frequency.

seed `class-attribute` `instance-attribute` ¶

seed: int | None = None

Seed for generation

logprobs `class-attribute` `instance-attribute` ¶

logprobs: bool | None = None

Whether to return logprobs.

top_logprobs `class-attribute` `instance-attribute` ¶

top_logprobs: int | None = None

Number of most likely tokens to return at each token position, each with an associated log probability. logprobs must be set to true if this parameter is used.

logit_bias `class-attribute` `instance-attribute` ¶

logit_bias: dict[int, int] | None = None

Modify the likelihood of specified tokens appearing in the completion.

streaming `class-attribute` `instance-attribute` ¶

streaming: bool = False

Whether to stream the results or not.

n `class-attribute` `instance-attribute` ¶

n: int | None = None

Number of chat completions to generate for each prompt.

top_p `class-attribute` `instance-attribute` ¶

top_p: float | None = None

Total probability mass of tokens to consider at each step.

reasoning_effort `class-attribute` `instance-attribute` ¶

reasoning_effort: str | None = None

Constrains effort on reasoning for reasoning models. For use with the Chat Completions API.

Reasoning models only.

Currently supported values are 'minimal', 'low', 'medium', and 'high'. Reducing reasoning effort can result in faster responses and fewer tokens used on reasoning in a response.

reasoning `class-attribute` `instance-attribute` ¶

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

Reasoning parameters for reasoning models. For use with the Responses API.

reasoning={
    "effort": "medium",  # Can be "low", "medium", or "high"
    "summary": "auto",  # Can be "auto", "concise", or "detailed"
}

Added in version 0.3.24

verbosity `class-attribute` `instance-attribute` ¶

verbosity: str | None = None

Controls the verbosity level of responses for reasoning models. For use with the Responses API.

Currently supported values are 'low', 'medium', and 'high'.

Added in version 0.3.28

tiktoken_model_name `class-attribute` `instance-attribute` ¶

tiktoken_model_name: str | None = None

The model name to pass to tiktoken when using this class. Tiktoken is used to count the number of tokens in documents to constrain them to be under a certain limit. By default, when set to None, this will be the same as the embedding model name. However, there are some cases where you may want to use this Embedding class with a model name not supported by tiktoken. This can include when using Azure embeddings or when using one of the many model providers that expose an OpenAI-like API but with different models. In those cases, in order to avoid erroring when tiktoken is called, you can specify a model name to use here.

http_client `class-attribute` `instance-attribute` ¶

http_client: Any | None = Field(default=None, exclude=True)

Optional httpx.Client. Only used for sync invocations. Must specify http_async_client as well if you'd like a custom client for async invocations.

http_async_client `class-attribute` `instance-attribute` ¶

http_async_client: Any | None = Field(default=None, exclude=True)

Optional httpx.AsyncClient. Only used for async invocations. Must specify http_client as well if you'd like a custom client for sync invocations.

stop `class-attribute` `instance-attribute` ¶

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

Default stop sequences.

extra_body `class-attribute` `instance-attribute` ¶

extra_body: Mapping[str, Any] | None = None

Optional additional JSON properties to include in the request parameters when making requests to OpenAI compatible APIs, such as vLLM, LM Studio, or other providers.

This is the recommended way to pass custom parameters that are specific to your OpenAI-compatible API provider but not part of the standard OpenAI API.

Examples:

LM Studio TTL parameter: extra_body={"ttl": 300}
vLLM custom parameters: extra_body={"use_beam_search": True}
Any other provider-specific parameters

Warning

Do not use model_kwargs for custom parameters that are not part of the standard OpenAI API, as this will cause errors when making API calls. Use extra_body instead.

include_response_headers `class-attribute` `instance-attribute` ¶

include_response_headers: bool = False

Whether to include response headers in the output message response_metadata.

include `class-attribute` `instance-attribute` ¶

include: list[str] | None = None

Additional fields to include in generations from Responses API.

Supported values:

'file_search_call.results'
'message.input_image.image_url'
'computer_call_output.output.image_url'
'reasoning.encrypted_content'
'code_interpreter_call.outputs'

Added in version 0.3.24

service_tier `class-attribute` `instance-attribute` ¶

service_tier: str | None = None

Latency tier for request. Options are 'auto', 'default', or 'flex'. Relevant for users of OpenAI's scale tier service.

store `class-attribute` `instance-attribute` ¶

store: bool | None = None

If True, OpenAI may store response data for future use.

Defaults to True for the Responses API and False for the Chat Completions API.

Added in version 0.3.24

truncation `class-attribute` `instance-attribute` ¶

truncation: str | None = None

Truncation strategy (Responses API). Can be 'auto' or 'disabled' (default). If 'auto', model may drop input items from the middle of the message sequence to fit the context window.

Added in version 0.3.24

use_previous_response_id `class-attribute` `instance-attribute` ¶

use_previous_response_id: bool = False

If True, always pass previous_response_id using the ID of the most recent response. Responses API only.

Input messages up to the most recent response will be dropped from request payloads.

For example, the following two are equivalent:

model = ChatOpenAI(
    model="...",
    use_previous_response_id=True,
)
model.invoke(
    [
        HumanMessage("Hello"),
        AIMessage("Hi there!", response_metadata={"id": "resp_123"}),
        HumanMessage("How are you?"),
    ]
)

model = ChatOpenAI(model="...", use_responses_api=True)
model.invoke([HumanMessage("How are you?")], previous_response_id="resp_123")

Added in version 0.3.26

use_responses_api `class-attribute` `instance-attribute` ¶

use_responses_api: bool | None = None

Whether to use the Responses API instead of the Chat API.

If not specified then will be inferred based on invocation params.

Added in version 0.3.9

azure_endpoint `class-attribute` `instance-attribute` ¶

azure_endpoint: str | None = Field(
    default_factory=from_env("AZURE_OPENAI_ENDPOINT", default=None)
)

Your Azure endpoint, including the resource.

Automatically inferred from env var AZURE_OPENAI_ENDPOINT if not provided.

Example: https://example-resource.azure.openai.com/

deployment_name `class-attribute` `instance-attribute` ¶

deployment_name: str | None = Field(default=None, alias='azure_deployment')

A model deployment.

If given sets the base client URL to include /deployments/{azure_deployment}

Note

This means you won't be able to use non-deployment endpoints.

openai_api_version `class-attribute` `instance-attribute` ¶

openai_api_version: str | None = Field(
    alias="api_version", default_factory=from_env("OPENAI_API_VERSION", default=None)
)

Automatically inferred from env var OPENAI_API_VERSION if not provided.

openai_api_key `class-attribute` `instance-attribute` ¶

openai_api_key: SecretStr | None = Field(
    alias="api_key",
    default_factory=secret_from_env(
        ["AZURE_OPENAI_API_KEY", "OPENAI_API_KEY"], default=None
    ),
)

Automatically inferred from env var AZURE_OPENAI_API_KEY if not provided.

azure_ad_token `class-attribute` `instance-attribute` ¶

azure_ad_token: SecretStr | None = Field(
    default_factory=secret_from_env("AZURE_OPENAI_AD_TOKEN", default=None)
)

Your Azure Active Directory token.

Automatically inferred from env var AZURE_OPENAI_AD_TOKEN if not provided.

For more, see this page.

azure_ad_token_provider `class-attribute` `instance-attribute` ¶

azure_ad_token_provider: Callable[[], str] | None = None

A function that returns an Azure Active Directory token.

Will be invoked on every sync request. For async requests, will be invoked if azure_ad_async_token_provider is not provided.

azure_ad_async_token_provider `class-attribute` `instance-attribute` ¶

azure_ad_async_token_provider: Callable[[], Awaitable[str]] | None = None

A function that returns an Azure Active Directory token.

Will be invoked on every async request.

model_version `class-attribute` `instance-attribute` ¶

model_version: str = ''

The version of the model (e.g. '0125' for 'gpt-3.5-0125').

Azure OpenAI doesn't return model version with the response by default so it must be manually specified if you want to use this information downstream, e.g. when calculating costs.

When you specify the version, it will be appended to the model name in the response. Setting correct version will help you to calculate the cost properly. Model version is not validated, so make sure you set it correctly to get the correct cost.

openai_api_type `class-attribute` `instance-attribute` ¶

openai_api_type: str | None = Field(
    default_factory=from_env("OPENAI_API_TYPE", default="azure")
)

Legacy, for openai<1.0.0 support.

validate_base_url `class-attribute` `instance-attribute` ¶

validate_base_url: bool = True

If legacy arg openai_api_base is passed in, try to infer if it is a base_url or azure_endpoint and update client params accordingly.

model_name `class-attribute` `instance-attribute` ¶

model_name: str | None = Field(default=None, alias='model')

Name of the deployed OpenAI model, e.g. 'gpt-4o', 'gpt-35-turbo', etc.

Distinct from the Azure deployment name, which is set by the Azure user. Used for tracing and token counting.

Warning

Does NOT affect completion.

disabled_params `class-attribute` `instance-attribute` ¶

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

Parameters of the OpenAI client or chat.completions endpoint that should be disabled for the given model.

Should be specified as {"param": None | ['val1', 'val2']} where the key is the parameter and the value is either None, meaning that parameter should never be used, or it's a list of disabled values for the parameter.

For example, older models may not support the 'parallel_tool_calls' parameter at all, in which case disabled_params={"parallel_tool_calls: None} can ben passed in.

If a parameter is disabled then it will not be used by default in any methods, e.g. in langchain_openai.chat_models.azure.AzureChatOpenAI.with_structured_output. However this does not prevent a user from directly passed in the parameter during invocation.

By default, unless model_name="gpt-4o" is specified, then 'parallel_tools_calls' will be disabled.

max_tokens `class-attribute` `instance-attribute` ¶

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

Maximum number of tokens to generate.

lc_secrets `property` ¶

lc_secrets: dict[str, str]

Get the mapping of secret environment variables.

lc_attributes `property` ¶

lc_attributes: dict[str, Any]

Get the attributes relevant to tracing.

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 version 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 version 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 version 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_community.llms.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 the `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 the `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 the `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 the `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 the `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 the `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"]})

For instance:

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": [],
    },
]

Example: 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.

Typed dict 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]})

String 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")

Added in version 0.2.14

init ¶

__init__(*args: Any, **kwargs: Any) -> None

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.

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.

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

model = ChatAnthropic(
    model_name="claude-3-7-sonnet-20250219"
).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:

Take advantage of batched calls,
Need more output from the model than just the top generated value,
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:

Take advantage of batched calls,
Need more output from the model than just the top generated value,
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]

Get the tokens present in the text with tiktoken package.

get_num_tokens ¶

get_num_tokens(text: str) -> int

Get the number of tokens present in the text.

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

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

RETURNS	DESCRIPTION
`int`	The integer number of tokens in the text.

get_num_tokens_from_messages ¶

get_num_tokens_from_messages(
    messages: Sequence[BaseMessage],
    tools: Sequence[dict[str, Any] | type | Callable | BaseTool] | None = None,
) -> int

Calculate num tokens for gpt-3.5-turbo and gpt-4 with tiktoken package.

Warning

You must have the pillow installed if you want to count image tokens if you are specifying the image as a base64 string, and you must have both pillow and httpx installed if you are specifying the image as a URL. If these aren't installed image inputs will be ignored in token counting.

OpenAI reference.

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

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:

Take advantage of batched calls,
Need more output from the model than just the top generated value,
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:

Take advantage of batched calls,
Need more output from the model than just the top generated value,
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.

bind_tools ¶

bind_tools(
    tools: Sequence[dict[str, Any] | type | Callable | BaseTool],
    *,
    tool_choice: dict | str | bool | None = None,
    strict: bool | None = None,
    parallel_tool_calls: bool | None = None,
    **kwargs: Any,
) -> Runnable[LanguageModelInput, AIMessage]

Bind tool-like objects to this chat model.

Assumes model is compatible with OpenAI tool-calling API.

PARAMETER	DESCRIPTION
`tools`	A list of tool definitions to bind to this chat model. Supports any tool definition handled by `langchain_core.utils.function_calling.convert_to_openai_tool`. TYPE: `Sequence[dict[str, Any] \| type \| Callable \| BaseTool]`
`tool_choice`	Which tool to require the model to call. Options are: `str` of the form `'<<tool_name>>'`: calls `<<tool_name>>` tool. `'auto'`: automatically selects a tool (including no tool). `'none'`: does not call a tool. `'any'` or `'required'` or `True`: force at least one tool to be called. `dict` of the form `{"type": "function", "function": {"name": <<tool_name>>}}`: calls `<<tool_name>>` tool. `False` or `None`: no effect, default OpenAI behavior. TYPE: `dict \| str \| bool \| None` DEFAULT: `None`
`strict`	If `True`, model output is guaranteed to exactly match the JSON Schema provided in the tool definition. The input schema will also be validated according to the supported schemas. If `False`, input schema will not be validated and model output will not be validated. If `None`, `strict` argument will not be passed to the model. TYPE: `bool \| None` DEFAULT: `None`
`parallel_tool_calls`	Set to `False` to disable parallel tool use. Defaults to `None` (no specification, which allows parallel tool use). TYPE: `bool \| None` DEFAULT: `None`
`kwargs`	Any additional parameters are passed directly to `bind`. TYPE: `Any` DEFAULT: `{}`

build_extra `classmethod` ¶

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

Build extra kwargs from additional params that were passed in.

validate_temperature `classmethod` ¶

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

Validate temperature parameter for different models.

gpt-5 models (excluding gpt-5-chat) only allow temperature=1 or unset (Defaults to 1)

get_lc_namespace `classmethod` ¶

get_lc_namespace() -> list[str]

Get the namespace of the LangChain object.

is_lc_serializable `classmethod` ¶

is_lc_serializable() -> bool

Check if the class is serializable in langchain.

validate_environment ¶

validate_environment() -> Self

Validate that api key and python package exists in environment.

with_structured_output ¶

with_structured_output(
    schema: _DictOrPydanticClass | None = None,
    *,
    method: Literal["function_calling", "json_mode", "json_schema"] = "json_schema",
    include_raw: bool = False,
    strict: bool | None = None,
    **kwargs: Any,
) -> Runnable[LanguageModelInput, _DictOrPydantic]

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

PARAMETER	DESCRIPTION
`schema`	The output schema. Can be passed in as: a JSON Schema, a `TypedDict` class, a Pydantic class, or an OpenAI function/tool schema. If `schema` is a Pydantic class then the model output will be a Pydantic instance of that class, and the model-generated fields will be validated by the Pydantic class. Otherwise the model output will be a dict and will not be validated. See `langchain_core.utils.function_calling.convert_to_openai_tool` for more on how to properly specify types and descriptions of schema fields when specifying a Pydantic or `TypedDict` class. TYPE: `_DictOrPydanticClass \| None` DEFAULT: `None`
`method`	The method for steering model generation, one of: `'json_schema'`: Uses OpenAI's Structured Output API. Supported for `'gpt-4o-mini'`, `'gpt-4o-2024-08-06'`, `'o1'`, and later models. `'function_calling'`: Uses OpenAI's tool-calling (formerly called function calling) API `'json_mode'`: Uses OpenAI's JSON mode. Note that if using JSON mode then you must include instructions for formatting the output into the desired schema into the model call Learn more about the differences between the methods and which models support which methods here. TYPE: `Literal['function_calling', 'json_mode', 'json_schema']` DEFAULT: `'json_schema'`
`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`
`strict`	True: Model output is guaranteed to exactly match the schema. The input schema will also be validated according to the supported schemas. False: Input schema will not be validated and model output will not be validated. None: `strict` argument will not be passed to the model. If schema is specified via TypedDict or JSON schema, `strict` is not enabled by default. Pass `strict=True` to enable it. Note `strict` can only be non-null if `method` is `'json_schema'` or `'function_calling'`. TYPE: `bool \| None` DEFAULT: `None`
`tools`	A list of tool-like objects to bind to the chat model. Requires that: `method` is `'json_schema'` (default). `strict=True` `include_raw=True` If a model elects to call a tool, the resulting `AIMessage` in `'raw'` will include tool calls. Example `from langchain.chat_models import init_chat_model from pydantic import BaseModel class ResponseSchema(BaseModel): response: str def get_weather(location: str) -> str: \"\"\"Get weather at a location.\"\"\" pass model = init_chat_model("openai:gpt-4o-mini") structured_model = model.with_structured_output( ResponseSchema, tools=[get_weather], strict=True, include_raw=True, ) structured_model.invoke("What's the weather in Boston?")` `{ "raw": AIMessage(content="", tool_calls=[...], ...), "parsing_error": None, "parsed": None, }`
`kwargs`	Additional keyword args are passed through to the model. TYPE: `Any` DEFAULT: `{}`

RETURNS DESCRIPTION

Runnable[LanguageModelInput, _DictOrPydantic]

A Runnable that takes same inputs as a langchain_core.language_models.chat.BaseChatModel. If include_raw is False and schema is a Pydantic class, Runnable outputs an instance of schema (i.e., a Pydantic object). Otherwise, if include_raw is False then Runnable outputs a dict.

If include_raw is True, then Runnable outputs a dict with keys:

'raw': BaseMessage
'parsed': None if there was a parsing error, otherwise the type depends on the schema as described above.
'parsing_error': BaseException | None

Behavior changed in 0.3.0

method default changed from "function_calling" to "json_schema".

Behavior changed in 0.3.12

Support for tools added.

Behavior changed in 0.3.21

Pass kwargs through to the model.

Example: schema=Pydantic class, method='json_schema', include_raw=False, strict=True

Note, OpenAI has a number of restrictions on what types of schemas can be provided if strict = True. When using Pydantic, our model cannot specify any Field metadata (like min/max constraints) and fields cannot have default values.

See all constraints here.

from typing import Optional

from langchain_openai import AzureChatOpenAI
from pydantic import BaseModel, Field


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

    answer: str
    justification: str | None = Field(
        default=..., description="A justification for the answer."
    )


model = AzureChatOpenAI(
    azure_deployment="...", model="gpt-4o", temperature=0
)
structured_model = model.with_structured_output(AnswerWithJustification)

structured_model.invoke(
    "What weighs more a pound of bricks or a pound of feathers"
)

# -> 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.'
# )

Example: schema=Pydantic class, method='function_calling', include_raw=False, strict=False

from typing import Optional

from langchain_openai import AzureChatOpenAI
from pydantic import BaseModel, Field


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

    answer: str
    justification: str | None = Field(
        default=..., description="A justification for the answer."
    )


model = AzureChatOpenAI(
    azure_deployment="...", model="gpt-4o", temperature=0
)
structured_model = model.with_structured_output(
    AnswerWithJustification, method="function_calling"
)

structured_model.invoke(
    "What weighs more a pound of bricks or a pound of feathers"
)

# -> 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.'
# )

Example: schema=Pydantic class, method='json_schema', include_raw=True

from langchain_openai import AzureChatOpenAI
from pydantic import BaseModel


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

    answer: str
    justification: str


model = AzureChatOpenAI(
    azure_deployment="...", model="gpt-4o", temperature=0
)
structured_model = model.with_structured_output(
    AnswerWithJustification, include_raw=True
)

structured_model.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
# }

Example: schema=TypedDict class, method='json_schema', include_raw=False, strict=False

from typing_extensions import Annotated, TypedDict

from langchain_openai import AzureChatOpenAI


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

    answer: str
    justification: Annotated[
        str | None, None, "A justification for the answer."
    ]


model = AzureChatOpenAI(
    azure_deployment="...", model="gpt-4o", temperature=0
)
structured_model = model.with_structured_output(AnswerWithJustification)

structured_model.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.'
# }

Example: schema=OpenAI function schema, method='json_schema', include_raw=False

from langchain_openai import AzureChatOpenAI

oai_schema = {
    'name': 'AnswerWithJustification',
    'description': 'An answer to the user question along with justification for the answer.',
    'parameters': {
        'type': 'object',
        'properties': {
            'answer': {'type': 'string'},
            'justification': {'description': 'A justification for the answer.', 'type': 'string'}
        },
        'required': ['answer']
    }

    model = AzureChatOpenAI(
        azure_deployment="...",
        model="gpt-4o",
        temperature=0,
    )
    structured_model = model.with_structured_output(oai_schema)

    structured_model.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.'
    # }

Example: schema=Pydantic class, method='json_mode', include_raw=True

from langchain_openai import AzureChatOpenAI
from pydantic import BaseModel


class AnswerWithJustification(BaseModel):
    answer: str
    justification: str


model = AzureChatOpenAI(
    azure_deployment="...",
    model="gpt-4o",
    temperature=0,
)
structured_model = model.with_structured_output(
    AnswerWithJustification, method="json_mode", include_raw=True
)

structured_model.invoke(
    "Answer the following question. "
    "Make sure to return a JSON blob with keys 'answer' and 'justification'.\\n\\n"
    "What's heavier a pound of bricks or a pound of feathers?"
)
# -> {
#     'raw': AIMessage(content='{\\n    "answer": "They are both the same weight.",\\n    "justification": "Both a pound of bricks and a pound of feathers weigh one pound. The difference lies in the volume and density of the materials, not the weight." \\n}'),
#     'parsed': AnswerWithJustification(answer='They are both the same weight.', justification='Both a pound of bricks and a pound of feathers weigh one pound. The difference lies in the volume and density of the materials, not the weight.'),
#     'parsing_error': None
# }

Example: schema=None, method='json_mode', include_raw=True

structured_model = model.with_structured_output(
    method="json_mode", include_raw=True
)

structured_model.invoke(
    "Answer the following question. "
    "Make sure to return a JSON blob with keys 'answer' and 'justification'.\\n\\n"
    "What's heavier a pound of bricks or a pound of feathers?"
)
# -> {
#     'raw': AIMessage(content='{\\n    "answer": "They are both the same weight.",\\n    "justification": "Both a pound of bricks and a pound of feathers weigh one pound. The difference lies in the volume and density of the materials, not the weight." \\n}'),
#     'parsed': {
#         'answer': 'They are both the same weight.',
#         'justification': 'Both a pound of bricks and a pound of feathers weigh one pound. The difference lies in the volume and density of the materials, not the weight.'
#     },
#     'parsing_error': None
# }

AzureChatOpenAI¶

langchain_openai.chat_models.AzureChatOpenAI ¶

name class-attribute instance-attribute ¶

InputType property ¶

OutputType property ¶

input_schema property ¶

output_schema property ¶

config_specs property ¶

cache class-attribute instance-attribute ¶

verbose class-attribute instance-attribute ¶

callbacks class-attribute instance-attribute ¶

tags class-attribute instance-attribute ¶

metadata class-attribute instance-attribute ¶

custom_get_token_ids class-attribute instance-attribute ¶

rate_limiter class-attribute instance-attribute ¶

disable_streaming class-attribute instance-attribute ¶

output_version class-attribute instance-attribute ¶

temperature class-attribute instance-attribute ¶

model_kwargs class-attribute instance-attribute ¶

openai_api_base class-attribute instance-attribute ¶

openai_organization class-attribute instance-attribute ¶

request_timeout class-attribute instance-attribute ¶

stream_usage class-attribute instance-attribute ¶

max_retries class-attribute instance-attribute ¶

presence_penalty class-attribute instance-attribute ¶

frequency_penalty class-attribute instance-attribute ¶

seed class-attribute instance-attribute ¶

logprobs class-attribute instance-attribute ¶

top_logprobs class-attribute instance-attribute ¶

logit_bias class-attribute instance-attribute ¶

streaming class-attribute instance-attribute ¶

n class-attribute instance-attribute ¶

top_p class-attribute instance-attribute ¶

reasoning_effort class-attribute instance-attribute ¶

reasoning class-attribute instance-attribute ¶

verbosity class-attribute instance-attribute ¶

tiktoken_model_name class-attribute instance-attribute ¶

http_client class-attribute instance-attribute ¶

http_async_client class-attribute instance-attribute ¶

stop class-attribute instance-attribute ¶

extra_body class-attribute instance-attribute ¶

include_response_headers class-attribute instance-attribute ¶

include class-attribute instance-attribute ¶

service_tier class-attribute instance-attribute ¶

store class-attribute instance-attribute ¶

truncation class-attribute instance-attribute ¶

use_previous_response_id class-attribute instance-attribute ¶

use_responses_api class-attribute instance-attribute ¶

azure_endpoint class-attribute instance-attribute ¶

deployment_name class-attribute instance-attribute ¶

openai_api_version class-attribute instance-attribute ¶

openai_api_key class-attribute instance-attribute ¶

azure_ad_token class-attribute instance-attribute ¶

azure_ad_token_provider class-attribute instance-attribute ¶

azure_ad_async_token_provider class-attribute instance-attribute ¶

model_version class-attribute instance-attribute ¶

openai_api_type class-attribute instance-attribute ¶

validate_base_url class-attribute instance-attribute ¶

model_name class-attribute instance-attribute ¶

disabled_params class-attribute instance-attribute ¶

max_tokens class-attribute instance-attribute ¶

lc_secrets property ¶

lc_attributes property ¶

get_name ¶

get_input_schema ¶

get_input_jsonschema ¶

get_output_schema ¶

get_output_jsonschema ¶

config_schema ¶

get_config_jsonschema ¶

get_graph ¶

get_prompts ¶

__or__ ¶

__ror__ ¶

pipe ¶

pick ¶

assign ¶

invoke ¶

ainvoke async ¶

batch ¶

`AzureChatOpenAI`¶

name `class-attribute` `instance-attribute` ¶

InputType `property` ¶

OutputType `property` ¶

input_schema `property` ¶

output_schema `property` ¶

config_specs `property` ¶

cache `class-attribute` `instance-attribute` ¶

verbose `class-attribute` `instance-attribute` ¶

callbacks `class-attribute` `instance-attribute` ¶

tags `class-attribute` `instance-attribute` ¶

metadata `class-attribute` `instance-attribute` ¶

custom_get_token_ids `class-attribute` `instance-attribute` ¶

rate_limiter `class-attribute` `instance-attribute` ¶

disable_streaming `class-attribute` `instance-attribute` ¶

output_version `class-attribute` `instance-attribute` ¶

temperature `class-attribute` `instance-attribute` ¶

model_kwargs `class-attribute` `instance-attribute` ¶

openai_api_base `class-attribute` `instance-attribute` ¶

openai_organization `class-attribute` `instance-attribute` ¶

request_timeout `class-attribute` `instance-attribute` ¶

stream_usage `class-attribute` `instance-attribute` ¶

max_retries `class-attribute` `instance-attribute` ¶

presence_penalty `class-attribute` `instance-attribute` ¶

frequency_penalty `class-attribute` `instance-attribute` ¶

seed `class-attribute` `instance-attribute` ¶

logprobs `class-attribute` `instance-attribute` ¶

top_logprobs `class-attribute` `instance-attribute` ¶

logit_bias `class-attribute` `instance-attribute` ¶

streaming `class-attribute` `instance-attribute` ¶

n `class-attribute` `instance-attribute` ¶

top_p `class-attribute` `instance-attribute` ¶

reasoning_effort `class-attribute` `instance-attribute` ¶

reasoning `class-attribute` `instance-attribute` ¶

verbosity `class-attribute` `instance-attribute` ¶

tiktoken_model_name `class-attribute` `instance-attribute` ¶

http_client `class-attribute` `instance-attribute` ¶

http_async_client `class-attribute` `instance-attribute` ¶

stop `class-attribute` `instance-attribute` ¶

extra_body `class-attribute` `instance-attribute` ¶

include_response_headers `class-attribute` `instance-attribute` ¶

include `class-attribute` `instance-attribute` ¶

service_tier `class-attribute` `instance-attribute` ¶

store `class-attribute` `instance-attribute` ¶

truncation `class-attribute` `instance-attribute` ¶

use_previous_response_id `class-attribute` `instance-attribute` ¶

use_responses_api `class-attribute` `instance-attribute` ¶

azure_endpoint `class-attribute` `instance-attribute` ¶

deployment_name `class-attribute` `instance-attribute` ¶

openai_api_version `class-attribute` `instance-attribute` ¶

openai_api_key `class-attribute` `instance-attribute` ¶

azure_ad_token `class-attribute` `instance-attribute` ¶

azure_ad_token_provider `class-attribute` `instance-attribute` ¶

azure_ad_async_token_provider `class-attribute` `instance-attribute` ¶

model_version `class-attribute` `instance-attribute` ¶

openai_api_type `class-attribute` `instance-attribute` ¶

validate_base_url `class-attribute` `instance-attribute` ¶

model_name `class-attribute` `instance-attribute` ¶

disabled_params `class-attribute` `instance-attribute` ¶

max_tokens `class-attribute` `instance-attribute` ¶

lc_secrets `property` ¶

lc_attributes `property` ¶

or ¶

ror ¶

ainvoke `async` ¶

abatch `async` ¶

abatch_as_completed `async` ¶

astream `async` ¶

astream_log `async` ¶

astream_events `async` ¶

atransform `async` ¶

init ¶

lc_id `classmethod` ¶

agenerate_prompt `async` ¶

agenerate `async` ¶

build_extra `classmethod` ¶

validate_temperature `classmethod` ¶

get_lc_namespace `classmethod` ¶

is_lc_serializable `classmethod` ¶