"""Azure OpenAI chat wrapper.""" from __future__ import annotations import logging import os from collections.abc import AsyncIterator, Awaitable, Callable, Iterator from typing import Any, Literal, TypeAlias, TypeVar import openai from langchain_core.language_models import LanguageModelInput from langchain_core.language_models.chat_models import LangSmithParams from langchain_core.outputs import ChatGenerationChunk, ChatResult from langchain_core.runnables import Runnable from langchain_core.utils import from_env, secret_from_env from langchain_core.utils.pydantic import is_basemodel_subclass from pydantic import BaseModel, Field, SecretStr, model_validator from typing_extensions import Self from langchain_openai.chat_models.base import BaseChatOpenAI, _get_default_model_profile logger = logging.getLogger(__name__) _BM = TypeVar("_BM", bound=BaseModel) _DictOrPydanticClass: TypeAlias = dict[str, Any] | type[_BM] | type _DictOrPydantic: TypeAlias = dict | _BM def _is_pydantic_class(obj: Any) -> bool: return isinstance(obj, type) and is_basemodel_subclass(obj) class AzureChatOpenAI(BaseChatOpenAI): r"""Azure OpenAI chat model integration. Setup: Head to the Azure [OpenAI quickstart guide](https://learn.microsoft.com/en-us/azure/ai-foundry/openai/chatgpt-quickstart?tabs=keyless%2Ctypescript-keyless%2Cpython-new%2Ccommand-line&pivots=programming-language-python) to create your Azure OpenAI deployment. Then install `langchain-openai` and set environment variables `AZURE_OPENAI_API_KEY` and `AZURE_OPENAI_ENDPOINT`: ```bash 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.](https://learn.microsoft.com/en-us/azure/ai-services/openai/reference#rest-api-versioning) 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: ```python 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: ```python 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: ```python messages = [ ( "system", "You are a helpful translator. Translate the user sentence to French.", ), ("human", "I love programming."), ] model.invoke(messages) ``` ```python 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: ```python for chunk in model.stream(messages): print(chunk.text, end="") ``` ```python 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", ) ``` ```python stream = model.stream(messages) full = next(stream) for chunk in stream: full += chunk full ``` ```python 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: ```python await model.ainvoke(messages) # stream: # async for chunk in (await model.astream(messages)) # batch: # await model.abatch([messages]) ``` Tool calling: ```python 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 ``` ```python [ { "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: ```python 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") ``` ```python 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: ```python 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 ``` ```python '\\n{\\n "random_ints": [23, 87, 45, 12, 78, 34, 56, 90, 11, 67]\\n}' ``` Image input: ```python 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 ``` ```python "The weather in the image appears to be quite pleasant. The sky is mostly clear" ``` Token usage: ```python ai_msg = model.invoke(messages) ai_msg.usage_metadata ``` ```python {"input_tokens": 28, "output_tokens": 5, "total_tokens": 33} ``` Logprobs: ```python 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 ```python 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"}, }, } ``` """ # noqa: E501 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: 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: 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.""" # Check OPENAI_API_KEY for backwards compatibility. # TODO: Remove OPENAI_API_KEY support to avoid possible conflict when using # other forms of azure credentials. 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: 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](https://www.microsoft.com/en-us/security/business/identity-access/microsoft-entra-id). """ 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: Callable[[], Awaitable[str]] | None = None """A function that returns an Azure Active Directory token. Will be invoked on every async request. """ 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: str | None = Field( default_factory=from_env("OPENAI_API_TYPE", default="azure") ) """Legacy, for `openai<1.0.0` support.""" 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: str | None = Field(default=None, alias="model") # type: ignore[assignment] """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: 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: int | None = Field(default=None, alias="max_completion_tokens") # type: ignore[assignment] """Maximum number of tokens to generate.""" @classmethod def get_lc_namespace(cls) -> list[str]: """Get the namespace of the LangChain object. Returns: `["langchain", "chat_models", "azure_openai"]` """ return ["langchain", "chat_models", "azure_openai"] @property def lc_secrets(self) -> dict[str, str]: """Get the mapping of secret environment variables.""" return { "openai_api_key": "AZURE_OPENAI_API_KEY", "azure_ad_token": "AZURE_OPENAI_AD_TOKEN", } @classmethod def is_lc_serializable(cls) -> bool: """Check if the class is serializable in langchain.""" return True @model_validator(mode="after") def validate_environment(self) -> Self: """Validate that api key and python package exists in environment.""" if self.n is not None and self.n < 1: msg = "n must be at least 1." raise ValueError(msg) if self.n is not None and self.n > 1 and self.streaming: msg = "n must be 1 when streaming." raise ValueError(msg) if self.disabled_params is None: # As of 09-17-2024 'parallel_tool_calls' param is only supported for gpt-4o. if self.model_name and self.model_name == "gpt-4o": pass else: self.disabled_params = {"parallel_tool_calls": None} # Check OPENAI_ORGANIZATION for backwards compatibility. self.openai_organization = ( self.openai_organization or os.getenv("OPENAI_ORG_ID") or os.getenv("OPENAI_ORGANIZATION") ) # Enable stream_usage by default if using default base URL and client if all( getattr(self, key, None) is None for key in ( "stream_usage", "openai_proxy", "openai_api_base", "base_url", "client", "root_client", "async_client", "root_async_client", "http_client", "http_async_client", ) ): self.stream_usage = True # For backwards compatibility. Before openai v1, no distinction was made # between azure_endpoint and base_url (openai_api_base). openai_api_base = self.openai_api_base if openai_api_base and self.validate_base_url: if "/openai" not in openai_api_base: msg = ( "As of openai>=1.0.0, Azure endpoints should be specified via " "the `azure_endpoint` param not `openai_api_base` " "(or alias `base_url`)." ) raise ValueError(msg) if self.deployment_name: msg = ( "As of openai>=1.0.0, if `azure_deployment` (or alias " "`deployment_name`) is specified then " "`base_url` (or alias `openai_api_base`) should not be. " "If specifying `azure_deployment`/`deployment_name` then use " "`azure_endpoint` instead of `base_url`.\n\n" "For example, you could specify:\n\n" 'azure_endpoint="https://xxx.openai.azure.com/", ' 'azure_deployment="my-deployment"\n\n' "Or you can equivalently specify:\n\n" 'base_url="https://xxx.openai.azure.com/openai/deployments/my-deployment"' ) raise ValueError(msg) client_params: dict = { "api_version": self.openai_api_version, "azure_endpoint": self.azure_endpoint, "azure_deployment": self.deployment_name, "api_key": ( self.openai_api_key.get_secret_value() if self.openai_api_key else None ), "azure_ad_token": ( self.azure_ad_token.get_secret_value() if self.azure_ad_token else None ), "azure_ad_token_provider": self.azure_ad_token_provider, "organization": self.openai_organization, "base_url": self.openai_api_base, "timeout": self.request_timeout, "default_headers": { **(self.default_headers or {}), "User-Agent": "langchain-partner-python-azure-openai", }, "default_query": self.default_query, } if self.max_retries is not None: client_params["max_retries"] = self.max_retries if not self.client: sync_specific = {"http_client": self.http_client} self.root_client = openai.AzureOpenAI(**client_params, **sync_specific) # type: ignore[arg-type] self.client = self.root_client.chat.completions if not self.async_client: async_specific = {"http_client": self.http_async_client} if self.azure_ad_async_token_provider: client_params["azure_ad_token_provider"] = ( self.azure_ad_async_token_provider ) self.root_async_client = openai.AsyncAzureOpenAI( **client_params, **async_specific, # type: ignore[arg-type] ) self.async_client = self.root_async_client.chat.completions return self @model_validator(mode="after") def _set_model_profile(self) -> Self: """Set model profile if not overridden.""" if self.profile is None and self.deployment_name is not None: self.profile = _get_default_model_profile(self.deployment_name) return self @property def _identifying_params(self) -> dict[str, Any]: """Get the identifying parameters.""" return { "azure_deployment": self.deployment_name, **super()._identifying_params, } @property def _llm_type(self) -> str: return "azure-openai-chat" @property def lc_attributes(self) -> dict[str, Any]: """Get the attributes relevant to tracing.""" return { "openai_api_type": self.openai_api_type, "openai_api_version": self.openai_api_version, } @property def _default_params(self) -> dict[str, Any]: """Get the default parameters for calling Azure OpenAI API.""" params = super()._default_params if "max_tokens" in params: params["max_completion_tokens"] = params.pop("max_tokens") return params def _get_ls_params( self, stop: list[str] | None = None, **kwargs: Any ) -> LangSmithParams: """Get the parameters used to invoke the model.""" params = super()._get_ls_params(stop=stop, **kwargs) params["ls_provider"] = "azure" if self.model_name: if self.model_version and self.model_version not in self.model_name: params["ls_model_name"] = ( self.model_name + "-" + self.model_version.lstrip("-") ) else: params["ls_model_name"] = self.model_name elif self.deployment_name: params["ls_model_name"] = self.deployment_name return params def _create_chat_result( self, response: dict | openai.BaseModel, generation_info: dict | None = None, ) -> ChatResult: chat_result = super()._create_chat_result(response, generation_info) if not isinstance(response, dict): response = response.model_dump() for res in response["choices"]: if res.get("finish_reason", None) == "content_filter": msg = ( "Azure has not provided the response due to a content filter " "being triggered" ) raise ValueError(msg) if "model" in response: model = response["model"] if self.model_version: model = f"{model}-{self.model_version}" chat_result.llm_output = chat_result.llm_output or {} chat_result.llm_output["model_name"] = model if "prompt_filter_results" in response: chat_result.llm_output = chat_result.llm_output or {} chat_result.llm_output["prompt_filter_results"] = response[ "prompt_filter_results" ] for chat_gen, response_choice in zip( chat_result.generations, response["choices"], strict=False ): chat_gen.generation_info = chat_gen.generation_info or {} chat_gen.generation_info["content_filter_results"] = response_choice.get( "content_filter_results", {} ) return chat_result def _get_request_payload( self, input_: LanguageModelInput, *, stop: list[str] | None = None, **kwargs: Any, ) -> dict: """Get the request payload, using deployment name for Azure Responses API.""" payload = super()._get_request_payload(input_, stop=stop, **kwargs) # For Azure Responses API, use deployment name instead of model name if ( self._use_responses_api(payload) and not payload.get("model") and self.deployment_name ): payload["model"] = self.deployment_name return payload def _stream(self, *args: Any, **kwargs: Any) -> Iterator[ChatGenerationChunk]: """Route to Chat Completions or Responses API.""" if self._use_responses_api({**kwargs, **self.model_kwargs}): return super()._stream_responses(*args, **kwargs) return super()._stream(*args, **kwargs) async def _astream( self, *args: Any, **kwargs: Any ) -> AsyncIterator[ChatGenerationChunk]: """Route to Chat Completions or Responses API.""" if self._use_responses_api({**kwargs, **self.model_kwargs}): async for chunk in super()._astream_responses(*args, **kwargs): yield chunk else: async for chunk in super()._astream(*args, **kwargs): yield chunk def with_structured_output( self, 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]: r"""Model wrapper that returns outputs formatted to match the given schema. Args: 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. method: The method for steering model generation, one of: - `'json_schema'`: Uses OpenAI's [Structured Output API](https://platform.openai.com/docs/guides/structured-outputs). 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](https://platform.openai.com/docs/guides/function-calling) - `'json_mode'`: Uses OpenAI's [JSON mode](https://platform.openai.com/docs/guides/structured-outputs/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](https://platform.openai.com/docs/guides/structured-outputs/function-calling-vs-response-format). 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'`. strict: - True: Model output is guaranteed to exactly match the schema. The input schema will also be validated according to the [supported schemas](https://platform.openai.com/docs/guides/structured-outputs/supported-schemas?api-mode=responses#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'`. kwargs: Additional keyword args are passed through to the model. Returns: 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` !!! warning "Behavior changed in `langchain-openai` 0.3.0" `method` default changed from "function_calling" to "json_schema". !!! warning "Behavior changed in `langchain-openai` 0.3.12" Support for `tools` added. !!! warning "Behavior changed in `langchain-openai` 0.3.21" Pass `kwargs` through to the model. ??? note "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](https://platform.openai.com/docs/guides/structured-outputs/supported-schemas). ```python 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.' # ) ``` ??? note "Example: `schema=Pydantic` class, `method='function_calling'`, `include_raw=False`, `strict=False`" ```python 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.' # ) ``` ??? note "Example: `schema=Pydantic` class, `method='json_schema'`, `include_raw=True`" ```python 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 # } ``` ??? note "Example: `schema=TypedDict` class, `method='json_schema'`, `include_raw=False`, `strict=False`" ```python 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.' # } ``` ??? note "Example: `schema=OpenAI` function schema, `method='json_schema'`, `include_raw=False`" ```python 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.' # } ``` ??? note "Example: `schema=Pydantic` class, `method='json_mode'`, `include_raw=True`" ```python 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 # } ``` ??? note "Example: `schema=None`, `method='json_mode'`, `include_raw=True`" ```python 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 # } ``` """ # noqa: E501 return super().with_structured_output( schema, method=method, include_raw=include_raw, strict=strict, **kwargs )