a `g@sRdZddlmZddlZddlZddlmZmZmZm Z m Z m Z m Z m Z mZmZddlZddlmZddlmZddlmZddlmZdd lmZdd lmZmZdd lmZdd l m!Z!m"Z"m#Z#m$Z$dd l%m&Z&m'Z'ddl(m)Z)e*e+Z,ede!dZ-ee e.efe e-fZ/ee e-fZ0Gddde Z1dddddZ2Gddde)Z3dS)zAzure OpenAI chat wrapper.) annotationsN) Any AwaitableCallableDictListOptionalType TypedDictTypeVarUnion)LanguageModelInput)LangSmithParams) BaseMessage) ChatResult)Runnable)from_envsecret_from_env)is_basemodel_subclass) BaseModelField SecretStrmodel_validator)LiteralSelf)BaseChatOpenAI_BM)boundc@s&eZdZUded<ded<ded<dS)_AllReturnTyperrawzOptional[_DictOrPydantic]parsedzOptional[BaseException]Z parsing_errorN)__name__ __module__ __qualname____annotations__r%r%p/var/www/html/cobodadashboardai.evdpl.com/venv/lib/python3.9/site-packages/langchain_openai/chat_models/azure.pyr)s rrbool)objreturncCst|tot|S)N) isinstancetyper)r(r%r%r&_is_pydantic_class/sr,c seZdZUdZeeddddZded<eddd Zd ed <ed ed dddZ ded<ede ddgdddZ ded<ee ddddZ ded<dZ ded<dZded<dZded<eeddddZded <d!Zd"ed#<edd$d Zded%<eddZd&ed'<ed(d)d*d+Zed,d)d-d.Zed"d)d/d0Zed1d2d3d)d4d5Zed6d)fd7d8 Zedd)d9d:Zed6d)d;d<ZdSd=d>d?d@fdAdB ZdTdCdDdEdFfdGdH ZdUdIdJddKdLdMd"dNd>dOdPfdQdRZ Z!S)VAzureChatOpenAIu9Azure OpenAI chat model integration. Setup: Head to the https://learn.microsoft.com/en-us/azure/ai-services/openai/chatgpt-quickstart?tabs=command-line%2Cpython-new&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``: .. code-block:: 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: str Name of Azure OpenAI deployment to use. temperature: float Sampling temperature. max_tokens: Optional[int] Max number of tokens to generate. logprobs: Optional[bool] Whether to return logprobs. Key init args — client params: api_version: str Azure OpenAI API version to use. See more on the different versions here: https://learn.microsoft.com/en-us/azure/ai-services/openai/reference#rest-api-versioning timeout: Union[float, Tuple[float, float], Any, None] Timeout for requests. max_retries: Optional[int] Max number of retries. organization: Optional[str] OpenAI organization ID. If not passed in will be read from env var OPENAI_ORG_ID. model: Optional[str] 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: Optional[str] 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: .. code-block:: python from langchain_openai import AzureChatOpenAI llm = 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: .. code-block:: 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: .. code-block:: python messages = [ ( "system", "You are a helpful translator. Translate the user sentence to French.", ), ("human", "I love programming."), ] llm.invoke(messages) .. code-block:: 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: .. code-block:: python for chunk in llm.stream(messages): print(chunk) .. code-block:: 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", ) .. code-block:: python stream = llm.stream(messages) full = next(stream) for chunk in stream: full += chunk full .. code-block:: 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: .. code-block:: python await llm.ainvoke(messages) # stream: # async for chunk in (await llm.astream(messages)) # batch: # await llm.abatch([messages]) Tool calling: .. code-block:: 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" ) llm_with_tools = llm.bind_tools([GetWeather, GetPopulation]) ai_msg = llm_with_tools.invoke( "Which city is hotter today and which is bigger: LA or NY?" ) ai_msg.tool_calls .. code-block:: 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: .. code-block:: 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: Optional[int] = Field(description="How funny the joke is, from 1 to 10") structured_llm = llm.with_structured_output(Joke) structured_llm.invoke("Tell me a joke about cats") .. code-block:: 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: .. code-block:: python json_llm = llm.bind(response_format={"type": "json_object"}) ai_msg = json_llm.invoke( "Return a JSON object with key 'random_ints' and a value of 10 random ints in [0-99]" ) ai_msg.content .. code-block:: python '\n{\n "random_ints": [23, 87, 45, 12, 78, 34, 56, 90, 11, 67]\n}' Image input: .. code-block:: 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 = llm.invoke([message]) ai_msg.content .. code-block:: python "The weather in the image appears to be quite pleasant. The sky is mostly clear" Token usage: .. code-block:: python ai_msg = llm.invoke(messages) ai_msg.usage_metadata .. code-block:: python {"input_tokens": 28, "output_tokens": 5, "total_tokens": 33} Logprobs: .. code-block:: python logprobs_llm = llm.bind(logprobs=True) ai_msg = logprobs_llm.invoke(messages) ai_msg.response_metadata["logprobs"] .. code-block:: 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 .. code-block:: python ai_msg = llm.invoke(messages) ai_msg.response_metadata .. code-block:: 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"}, }, } ZAZURE_OPENAI_ENDPOINTN)default)default_factoryz Optional[str]azure_endpointazure_deployment)r.aliaszUnion[str, None]deployment_name api_versionZOPENAI_API_VERSION)r2r/openai_api_versionapi_keyAZURE_OPENAI_API_KEYZOPENAI_API_KEYzOptional[SecretStr]openai_api_keyAZURE_OPENAI_AD_TOKENazure_ad_tokenzUnion[Callable[[], str], None]azure_ad_token_providerz)Union[Callable[[], Awaitable[str]], None]azure_ad_async_token_providerstr model_versionZOPENAI_API_TYPEazureopenai_api_typeTr'validate_base_urlmodel model_namezOptional[Dict[str, Any]]disabled_paramsz List[str])r)cCsgdS)z*Get the namespace of the langchain object.)Z langchainZ chat_modelsZ azure_openair%clsr%r%r&get_lc_namespace<sz AzureChatOpenAI.get_lc_namespacezDict[str, str]cCs dddS)Nr7r9)r8r:r%selfr%r%r& lc_secretsAszAzureChatOpenAI.lc_secretscCsdS)NTr%rFr%r%r&is_lc_serializableHsz"AzureChatOpenAI.is_lc_serializableafter)moderc Cs|jdur|jdkrtdn"|jdur@|jdkr@|jr@td|jdurf|jr\|jdkr\n ddi|_|jp~tdp~td|_|j}|r|j rd |vrtd |j rtd |j |j |j |j r|j nd|jr|jnd|j|j|j|ji|jpid d i|jd }|jdur$|j|d<|jsXd|ji}tjfi|||_|jjj|_|jsd|ji}|jr||j|d<tjfi|||_ |j jj|_|S)z?Validate that api key and python package exists in environment.Nzn must be at least 1.zn must be 1 when streaming.zgpt-4oZparallel_tool_callsZ OPENAI_ORG_IDZOPENAI_ORGANIZATIONz/openaizAs of openai>=1.0.0, Azure endpoints should be specified via the `azure_endpoint` param not `openai_api_base` (or alias `base_url`).aAs 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`. For example, you could specify: azure_endpoint="https://xxx.openai.azure.com/", azure_deployment="my-deployment" Or you can equivalently specify: base_url="https://xxx.openai.azure.com/openai/deployments/my-deployment"z User-Agentz%langchain-partner-python-azure-openai) r4r0r1r6r:r;Z organizationbase_urltimeoutdefault_headers default_query max_retries http_clientr;)!n ValueErrorZ streamingrErDZopenai_organizationosgetenvopenai_api_baserBr3r5r0r8Zget_secret_valuer:r;request_timeoutrRrSrTclientrUopenaiZ AzureOpenAIZ root_clientZchatZ completionsZ async_clientZhttp_async_clientr<ZAsyncAzureOpenAIZroot_async_client)rJrZZ client_paramsZ sync_specificZasync_specificr%r%r&validate_environmentLsr           z$AzureChatOpenAI.validate_environmentzDict[str, Any]csid|jitjS)zGet the identifying parameters.r1)r3super_identifying_paramsrI __class__r%r&r`s z#AzureChatOpenAI._identifying_paramscCsdS)Nzazure-openai-chatr%rIr%r%r& _llm_typeszAzureChatOpenAI._llm_typecCs|j|jdS)NrAr5rdrIr%r%r& lc_attributesszAzureChatOpenAI.lc_attributeszOptional[List[str]]rr)stopkwargsr)c srtjfd|i|}d|d<|jr^|jrR|j|jvrR|jd|jd|d<qn|j|d<n|jrn|j|d<|S)z,Get the parameters used to invoke the model.rfr@Z ls_provider-Z ls_model_name)r__get_ls_paramsrDr?lstripr3)rJrfrgparamsrar%r&ris  zAzureChatOpenAI._get_ls_paramszUnion[dict, openai.BaseModel]zOptional[Dict]r)responsegeneration_infor)cst||}t|ts |}|dD]}|dddkr(tdq(d|vr|d}|jrl|d|j}|jpti|_||jd<d|vr|jpi|_|d|jd<t |j |dD]&\}}|j pi|_ |d i|j d <q|S) NchoicesZ finish_reasonZcontent_filterzKAzure has not provided the response due to a content filter being triggeredrCrhrDZprompt_filter_resultsZcontent_filter_results) r__create_chat_resultr*dictZ model_dumpgetrWr?Z llm_outputzipZ generationsrm)rJrlrmZ chat_resultresrCZchat_genZresponse_choicerar%r&ros6         z#AzureChatOpenAI._create_chat_resultZ json_schemaFmethod include_rawstrictzOptional[_DictOrPydanticClass]z7Literal['function_calling', 'json_mode', 'json_schema']zOptional[bool]z-Runnable[LanguageModelInput, _DictOrPydantic])schemarurvrwrgr)c stj|f|||d|S)a9Model 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, - or a Pydantic class, - 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 :meth:`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. Note that if using JSON mode then you must include instructions for formatting the output into the desired schema into the model call: https://platform.openai.com/docs/guides/structured-outputs/json-mode Learn more about the differences between the methods and which models support which methods here: - https://platform.openai.com/docs/guides/structured-outputs/structured-outputs-vs-json-mode - 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 https://platform.openai.com/docs/guides/structured-outputs/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 aren't supported. Returns: A Runnable that takes same inputs as a :class:`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": Optional[BaseException] .. versionchanged:: 0.1.20 Added support for TypedDict class ``schema``. .. versionchanged:: 0.1.21 Support for ``strict`` argument added. Support for ``method="json_schema"`` added. .. versionchanged:: 0.3.0 ``method`` default changed from "function_calling" to "json_schema". .. dropdown:: 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 .. code-block:: 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: Optional[str] = Field( default=..., description="A justification for the answer." ) llm = AzureChatOpenAI(azure_deployment="...", model="gpt-4o", temperature=0) structured_llm = llm.with_structured_output(AnswerWithJustification) structured_llm.invoke( "What weighs more a pound of bricks or a pound of feathers" ) # -> AnswerWithJustification( # answer='They weigh the same', # justification='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.' # ) .. dropdown:: Example: schema=Pydantic class, method="function_calling", include_raw=False, strict=False .. code-block:: 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: Optional[str] = Field( default=..., description="A justification for the answer." ) llm = AzureChatOpenAI(azure_deployment="...", model="gpt-4o", temperature=0) structured_llm = llm.with_structured_output( AnswerWithJustification, method="function_calling" ) structured_llm.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.' # ) .. dropdown:: Example: schema=Pydantic class, method="json_schema", include_raw=True .. code-block:: 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 llm = AzureChatOpenAI(azure_deployment="...", model="gpt-4o", temperature=0) structured_llm = llm.with_structured_output( AnswerWithJustification, include_raw=True ) structured_llm.invoke( "What weighs more a pound of bricks or a pound of feathers" ) # -> { # 'raw': AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_Ao02pnFYXD6GN1yzc0uXPsvF', 'function': {'arguments': '{"answer":"They weigh the same.","justification":"Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume or density of the objects may differ."}', 'name': 'AnswerWithJustification'}, 'type': 'function'}]}), # 'parsed': AnswerWithJustification(answer='They weigh the same.', justification='Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume or density of the objects may differ.'), # 'parsing_error': None # } .. dropdown:: Example: schema=TypedDict class, method="json_schema", include_raw=False, strict=False .. code-block:: 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[ Optional[str], None, "A justification for the answer." ] llm = AzureChatOpenAI(azure_deployment="...", model="gpt-4o", temperature=0) structured_llm = llm.with_structured_output(AnswerWithJustification) structured_llm.invoke( "What weighs more a pound of bricks or a pound of feathers" ) # -> { # 'answer': 'They weigh the same', # 'justification': 'Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume and density of the two substances differ.' # } .. dropdown:: Example: schema=OpenAI function schema, method="json_schema", include_raw=False .. code-block:: 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'] } } llm = AzureChatOpenAI( azure_deployment="...", model="gpt-4o", temperature=0, ) structured_llm = llm.with_structured_output(oai_schema) structured_llm.invoke( "What weighs more a pound of bricks or a pound of feathers" ) # -> { # 'answer': 'They weigh the same', # 'justification': 'Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume and density of the two substances differ.' # } .. dropdown:: Example: schema=Pydantic class, method="json_mode", include_raw=True .. code-block:: from langchain_openai import AzureChatOpenAI from pydantic import BaseModel class AnswerWithJustification(BaseModel): answer: str justification: str llm = AzureChatOpenAI( azure_deployment="...", model="gpt-4o", temperature=0, ) structured_llm = llm.with_structured_output( AnswerWithJustification, method="json_mode", include_raw=True ) structured_llm.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 # } .. dropdown:: Example: schema=None, method="json_mode", include_raw=True .. code-block:: structured_llm = llm.with_structured_output(method="json_mode", include_raw=True) structured_llm.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 # } rt)r_with_structured_output)rJrxrurvrwrgrar%r&rys>z&AzureChatOpenAI.with_structured_output)N)N)N)"r!r"r#__doc__rrr0r$r3r5rr8r:r;r<r?rArBrDrE classmethodrHpropertyrKrLrr^r`rcrerirory __classcell__r%r%rar&r-3sl "         U(r-)4rz __future__rloggingrXtypingrrrrrrr r r r r]Zlangchain_core.language_modelsr Z*langchain_core.language_models.chat_modelsrZlangchain_core.messagesrZlangchain_core.outputsrZlangchain_core.runnablesrZlangchain_core.utilsrrZlangchain_core.utils.pydanticrZpydanticrrrrZtyping_extensionsrrZ!langchain_openai.chat_models.baser getLoggerr!loggerrr>Z_DictOrPydanticClassZ_DictOrPydanticrr,r-r%r%r%r&s, 0