$hYSrSSKJr SSKrSSKrSSKJrJrJr SSK J r J r J r J r JrJr SSKrSSKJr SSKJr SSKJr SS KJrJr SS KJr SS KJrJr SS KJ r SS K!J"r"J#r#J$r$J%r% SSK&J'r'J(r( SSK)J*r* \RV"\,5r-\"S\"S9r.\\/\0\ 4\1\.4r2\\/\.4r3"SS\ 5r4SSjr5"SS\*5r6g)zAzure OpenAI chat wrapper.) annotationsN) AsyncIterator AwaitableIterator)AnyCallableOptional TypedDictTypeVarUnion)LanguageModelInput)LangSmithParams) BaseMessage)ChatGenerationChunk ChatResult)Runnable)from_envsecret_from_env)is_basemodel_subclass) BaseModelField SecretStrmodel_validator)LiteralSelf)BaseChatOpenAI_BM)boundc4\rSrSr%S\S'S\S'S\S'Srg ) _AllReturnTyperrawzOptional[_DictOrPydantic]parsedzOptional[BaseException] parsing_errorN)__name__ __module__ __qualname____firstlineno____annotations____static_attributes__r%Z/var/www/html/shao/venv/lib/python3.13/site-packages/langchain_openai/chat_models/azure.pyr r s  %%**r,r cF[U[5=(a [U5$N) isinstancetyper)objs r-_is_pydantic_classr3%s c4 ?%:3%??r,c^\rSrSr%Sr\"\"SSS9S9rS\S'\"SS S 9r S \S '\"S \"SSS9S9r S\S'\"S\ "SS/SS9S9r S\S'\"\ "SSS9S9r S\S'SrS\S'SrS\S'SrS\S'\"\"SS S9S9rS\S!'S"rS#\S$'\"SS%S 9rS\S&'\"SS9rS'\S('\S;S)j5r\SS.j5r\S?U4S/jj5r\S@S0j5r\S?S1j5rSASBU4S2jjjrSASCU4S3jjjr SDU4S4jjr!SEU4S5jjr"SAS6S7SS8.SFU4S9jjjjr#S:r$U=r%$)GAzureChatOpenAI)u5Azure 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``: .. 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 REST API version to use (distinct from the version of the underlying model). `See more on the different versions. `__ 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.text(), end="") .. 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"}, }, } AZURE_OPENAI_ENDPOINTN)default)default_factoryz Optional[str]azure_endpointazure_deployment)r8aliaszUnion[str, None]deployment_name api_versionOPENAI_API_VERSION)r<r9openai_api_versionapi_keyAZURE_OPENAI_API_KEYOPENAI_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_versionOPENAI_API_TYPEazureopenai_api_typeTboolvalidate_base_urlmodel model_namezOptional[dict[str, Any]]disabled_paramsc /SQ$)z*Get the namespace of the langchain object.) langchain chat_models azure_openair%clss r-get_lc_namespace AzureChatOpenAI.get_lc_namespaceEs <;r,c SSS.$)NrBrE)rDrFr%selfs r- lc_secretsAzureChatOpenAI.lc_secretsJs55  r,cg)NTr%rXs r-is_lc_serializable"AzureChatOpenAI.is_lc_serializableQsr,after)modec URbURS:a [S5eURb,URS:aUR(a [S5eURc+UR(aURS:XaO SS0UlUR =(d3 [ R"S5=(d [ R"S5UlURnU(a>UR(a-S U;a [S 5eUR(a [S 5eURURURUR(aURR5OSUR(aURR5OSUR UR URUR"0UR$=(d 0ES S 0EUR&S. nUR(bUR(US'UR*(dPSUR,0n[.R0"S0UDUD6UlUR2R4R6UlUR8(dpSUR:0nUR<(aUR<US'[.R>"S0UDUD6Ul UR@R4R6UlU$)z?Validate that api key and python package exists in environment.Nzn must be at least 1.zn must be 1 when streaming.zgpt-4oparallel_tool_calls OPENAI_ORG_IDOPENAI_ORGANIZATIONz/openaizAs of openai>=1.0.0, Azure endpoints should be specified via the `azure_endpoint` param not `openai_api_base` (or alias `base_url`).aAs of openai>=1.0.0, if `azure_deployment` (or alias `deployment_name`) is specified then `base_url` (or alias `openai_api_base`) should not be. If specifying `azure_deployment`/`deployment_name` then use `azure_endpoint` instead of `base_url`. For example, you could specify: azure_endpoint="https://xxx.openai.azure.com/", azure_deployment="my-deployment" Or you can equivalently specify: base_url="https://xxx.openai.azure.com/openai/deployments/my-deployment"z User-Agentz%langchain-partner-python-azure-openai) r>r:r;rArFrG organizationbase_urltimeoutdefault_headers default_query max_retries http_clientrGr%)!n ValueError streamingrSrRopenai_organizationosgetenvopenai_api_baserPr=r@r:rDget_secret_valuerFrGrequest_timeoutrnrorpclientrqopenai AzureOpenAI root_clientchat completions async_clienthttp_async_clientrHAsyncAzureOpenAIroot_async_client)r^rx client_params sync_specificasync_specifics r-validate_environment$AzureChatOpenAI.validate_environmentUs 66 $&&1*45 5 VV DFFQJ4>>:; ;    '4??h#>(=t'D$  $ $ 0yy) 0yy./  .. t55/ - ##  _   22"11 $ 4 4:>:M:M##446SW;?:M:M##446SW'+'C'C 44,,++ ''-2 E "//% (    '+/+;+;M- ({{*D,<,<=M%11SMS]SD **//;;DK  +T-C-CDN116678&,%<%<&& &D "!% 6 6 ; ; G GD  r,c8>0SUR0E[TU]E$)zGet the identifying parameters.r;)r=super_identifying_params)r^ __class__s r-r#AzureChatOpenAI._identifying_paramss- !4#7#78 g)  r,cg)Nzazure-openai-chatr%r]s r- _llm_typeAzureChatOpenAI._llm_types"r,c4URURS.$)NrNr@rr]s r- lc_attributesAzureChatOpenAI.lc_attributess  $33"&"9"9  r,c r>[TU]"SSU0UD6nSUS'UR(amUR(aJURUR;a0URS-URR S5-US'U$URUS'U$UR (aUR US'U$)z,Get the parameters used to invoke the model.stoprM ls_provider- ls_model_namer%)r_get_ls_paramsrRrKlstripr=)r^rkwargsparamsrs r-rAzureChatOpenAI._get_ls_paramss',>,E,Ec,JJ' +///'  ! !&*&:&:F? # r,c>[TU]X5n[U[5(dUR 5nUSH$nUR SS5S:XdM[ S5e SU;aOUSnUR(aUSUR3nUR=(d 0UlXSRS'SU;a,UR=(d 0UlUSURS'[URUS5H>upgUR=(d 0Ul UR S 05URS 'M@ U$) Nchoices finish_reasoncontent_filterzKAzure has not provided the response due to a content filter being triggeredrQrrRprompt_filter_resultscontent_filter_results) r_create_chat_resultr0dict model_dumpgetrsrK llm_outputzip generationsgeneration_info) r^responser chat_resultresrQchat_genresponse_choicers r-r#AzureChatOpenAI._create_chat_resultsP g1(L (D))**,HI&Cww-1AA &' h W%E!! '4#5#5"67%0%;%;%ArK "38 " "< 0 "h .%0%;%;%ArK ">F'?K " "#: ;*-  # #Xi%8* %H(0'?'?'E2H $APATAT("BH $ $%= > * r,c>UR0UEURE5(a[TU] "U0UD6$[TU]"U0UD6$)+Route to Chat Completions or Responses API.)_use_responses_api model_kwargsr_stream_responses_stream)r^argsrrs r-rAzureChatOpenAI._streamsN  " "#Bf#B0A0A#B C C7,d=f= =7?D3F3 3r,c># UR0UEURE5(a [TU] "U0UD6ShvN nU7v M[TU]"U0UD6ShvN nU7v MN+ gN g7f)rN)rrr_astream_responses_astream)r^rrchunkrs r-rAzureChatOpenAI._astreamsv  " "#Bf#B0A0A#B C C$w94J6J e $w/@@ e  J @sJ5A0A*A(A*A0A.A,A. A0(A**A0,A..A0 json_schemaFmethod include_rawstrictc ,>[TU]"U4X#US.UD6$)a8Model 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 `__. 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 `__. 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 `__. - 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'``. 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. .. dropdown:: Example .. code-block:: python 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 llm = init_chat_model("openai:gpt-4o-mini") structured_llm = llm.with_structured_output( ResponseSchema, tools=[get_weather], strict=True, include_raw=True, ) structured_llm.invoke("What's the weather in Boston?") .. code-block:: python { "raw": AIMessage(content="", tool_calls=[...], ...), "parsing_error": None, "parsed": None, } kwargs: Additional keyword args are passed through to the model. 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". .. versionchanged:: 0.3.12 Support for ``tools`` added. .. versionchanged:: 0.3.21 Pass ``kwargs`` through to the model. .. 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 `__. .. 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. 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