U hy@sdZddlmZddlZddlZddlmZddlmZm Z m Z m Z m Z m Z mZmZmZmZmZmZddlZddlmZddlmZddlmZdd lmZmZdd lmZdd l m!Z!m"Z"dd l#m$Z$dd l%m&Z&m'Z'm(Z(m)Z)ddl*m+Z+m,Z,m-Z-ddl.m/Z/ddl0m1Z1m2Z2ddl3m4Z4ddl5m6Z6ddl7m8Z8e9e:Z;ede&dZee ed?d6d@dAdBdCZed[d;d-d<d=d>dDd6dEdAdFdCZd\d;d-d<d=d>d d6dEdAdGdCZedHd%fdIdJ Zedd%dKdLZedHd%dMdNZd]dOd6dPdQfdRdS Zd^dTdUdVdWfdXdY Z Z!S)_AzureChatOpenAIu :Azure 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: 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 langchain_core.pydantic_v1 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 langchain_core.pydantic_v1 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)r>r;openai_api_versionapi_keyAZURE_OPENAI_API_KEYZOPENAI_API_KEYzOptional[SecretStr]openai_api_keyAZURE_OPENAI_AD_TOKENazure_ad_tokenzUnion[Callable[[], str], None]azure_ad_token_providerstr model_versionZOPENAI_API_TYPEazureopenai_api_typeTr2validate_base_urlmodel model_namez List[str])r4cCs dddgS)z*Get the namespace of the langchain object.Z langchainZ chat_modelsZ azure_openair0clsr0r0r1get_lc_namespace(sz AzureChatOpenAI.get_lc_namespacezDict[str, str]cCs dddS)NrCrE)rDrFr0selfr0r0r1 lc_secrets-szAzureChatOpenAI.lc_secretscCsdS)NTr0rPr0r0r1is_lc_serializable4sz"AzureChatOpenAI.is_lc_serializableF)preZskip_on_failurer)valuesr4c Csn|ddkrtd|ddkr0|dr0td|dpJtdpJtd|d<|d }|r|d rd |krttd |d rtd|d|d|d |dr|dnd|dr|dnd|d|d|d |d|d|d|dd }|ds.d|di}tjf|||d<|djj|d<|dsjd|di}tj f|||d<|djj|d<|S) z?Validate that api key and python package exists in environment.nzn must be at least 1.Z streamingzn must be 1 when streaming.Zopenai_organizationZ OPENAI_ORG_IDZOPENAI_ORGANIZATIONopenai_api_baserMz/openaizAs of openai>=1.0.0, Azure endpoints should be specified via the `azure_endpoint` param not `openai_api_base` (or alias `base_url`).r?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. 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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"rAr<rDNrFrGrequest_timeout max_retriesdefault_headers default_query) r@r<r=rBrFrGZ organizationbase_urltimeoutr]r^r_client http_clientZ root_clientZ async_clientZhttp_async_clientZroot_async_client) ValueErrorosgetenvZget_secret_valuegetopenaiZ AzureOpenAIZchatZ completionsZAsyncAzureOpenAI)rQrXr[Z client_paramsZ sync_specificZasync_specificr0r0r1validate_environment8s`        z$AzureChatOpenAI.validate_environment tool_choicez9Sequence[Union[Dict[str, Any], Type, Callable, BaseTool]]zNOptional[Union[dict, str, Literal[('auto', 'none', 'required', 'any')], bool]]rz)Runnable[LanguageModelInput, BaseMessage])toolsrkkwargsr4c sR|dkr:t|dkr&td|dnt|ddd}tj|fd|i|S) N)anyrequiredTrZz4Azure OpenAI does not currently support tool_choice=zC. 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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, either "function_calling" or "json_mode". If "function_calling" then the schema will be converted to an OpenAI function and the returned model will make use of the function-calling API. If "json_mode" then OpenAI's JSON mode will be used. Note that if using "json_mode" then you must include instructions for formatting the output into the desired schema into the model call. 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". 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] Example: schema=Pydantic class, method="function_calling", include_raw=False: .. code-block:: python from typing import Optional from langchain_openai import AzureChatOpenAI from langchain_core.pydantic_v1 import BaseModel, Field class AnswerWithJustification(BaseModel): '''An answer to the user question along with justification for the answer.''' answer: str # If we provide default values and/or descriptions for fields, these will be passed # to the model. This is an important part of improving a model's ability to # correctly return structured outputs. justification: Optional[str] = Field( default=None, description="A justification for the answer." ) llm = AzureChatOpenAI(model="gpt-3.5-turbo-0125", 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.' # ) Example: schema=Pydantic class, method="function_calling", include_raw=True: .. code-block:: python from langchain_openai import AzureChatOpenAI from langchain_core.pydantic_v1 import BaseModel class AnswerWithJustification(BaseModel): '''An answer to the user question along with justification for the answer.''' answer: str justification: str llm = AzureChatOpenAI(model="gpt-3.5-turbo-0125", 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 # } Example: schema=TypedDict class, method="function_calling", include_raw=False: .. code-block:: python # IMPORTANT: If you are using Python <=3.8, you need to import Annotated # from typing_extensions, not from typing. 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(model="gpt-3.5-turbo-0125", 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.' # } Example: schema=OpenAI function schema, method="function_calling", 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(model="gpt-3.5-turbo-0125", 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.' # } Example: schema=Pydantic class, method="json_mode", include_raw=True: .. code-block:: from langchain_openai import AzureChatOpenAI from langchain_core.pydantic_v1 import BaseModel class AnswerWithJustification(BaseModel): answer: str justification: str llm = AzureChatOpenAI(model="gpt-3.5-turbo-0125", 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'. " "What's heavier a pound of bricks or a pound of feathers?" ) # -> { # 'raw': AIMessage(content='{ "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." }'), # '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: .. 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'. " "What's heavier a pound of bricks or a pound of feathers?" ) # -> { # 'raw': AIMessage(content='{ "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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