mirror of
https://github.com/hwchase17/langchain.git
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8c6734325b
Using `pyupgrade` to get all `partners` code up to 3.9 standards (mostly, fixing old `typing` imports).
1057 lines
42 KiB
Python
1057 lines
42 KiB
Python
"""Azure OpenAI chat wrapper."""
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from __future__ import annotations
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import logging
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import os
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from collections.abc import Awaitable
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from typing import Any, Callable, Optional, TypedDict, TypeVar, Union
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import openai
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from langchain_core.language_models import LanguageModelInput
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from langchain_core.language_models.chat_models import LangSmithParams
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from langchain_core.messages import BaseMessage
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from langchain_core.outputs import ChatResult
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from langchain_core.runnables import Runnable
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from langchain_core.utils import from_env, secret_from_env
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from langchain_core.utils.pydantic import is_basemodel_subclass
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from pydantic import BaseModel, Field, SecretStr, model_validator
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from typing_extensions import Literal, Self
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from langchain_openai.chat_models.base import BaseChatOpenAI
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logger = logging.getLogger(__name__)
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_BM = TypeVar("_BM", bound=BaseModel)
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_DictOrPydanticClass = Union[dict[str, Any], type[_BM]]
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_DictOrPydantic = Union[dict, _BM]
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class _AllReturnType(TypedDict):
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raw: BaseMessage
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parsed: Optional[_DictOrPydantic]
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parsing_error: Optional[BaseException]
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def _is_pydantic_class(obj: Any) -> bool:
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return isinstance(obj, type) and is_basemodel_subclass(obj)
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class AzureChatOpenAI(BaseChatOpenAI):
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"""Azure OpenAI chat model integration.
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Setup:
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Head to the https://learn.microsoft.com/en-us/azure/ai-services/openai/chatgpt-quickstart?tabs=command-line%2Cpython-new&pivots=programming-language-python
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to create your Azure OpenAI deployment.
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Then install ``langchain-openai`` and set environment variables
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``AZURE_OPENAI_API_KEY`` and ``AZURE_OPENAI_ENDPOINT``:
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.. code-block:: bash
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pip install -U langchain-openai
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export AZURE_OPENAI_API_KEY="your-api-key"
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export AZURE_OPENAI_ENDPOINT="https://your-endpoint.openai.azure.com/"
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Key init args — completion params:
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azure_deployment: str
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Name of Azure OpenAI deployment to use.
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temperature: float
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Sampling temperature.
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max_tokens: Optional[int]
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Max number of tokens to generate.
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logprobs: Optional[bool]
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Whether to return logprobs.
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Key init args — client params:
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api_version: str
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Azure OpenAI API version to use. See more on the different versions here:
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https://learn.microsoft.com/en-us/azure/ai-services/openai/reference#rest-api-versioning
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timeout: Union[float, Tuple[float, float], Any, None]
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Timeout for requests.
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max_retries: Optional[int]
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Max number of retries.
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organization: Optional[str]
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OpenAI organization ID. If not passed in will be read from env
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var OPENAI_ORG_ID.
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model: Optional[str]
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The name of the underlying OpenAI model. Used for tracing and token
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counting. Does not affect completion. E.g. "gpt-4", "gpt-35-turbo", etc.
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model_version: Optional[str]
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The version of the underlying OpenAI model. Used for tracing and token
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counting. Does not affect completion. E.g., "0125", "0125-preview", etc.
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See full list of supported init args and their descriptions in the params section.
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Instantiate:
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.. code-block:: python
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from langchain_openai import AzureChatOpenAI
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llm = AzureChatOpenAI(
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azure_deployment="your-deployment",
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api_version="2024-05-01-preview",
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temperature=0,
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max_tokens=None,
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timeout=None,
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max_retries=2,
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# organization="...",
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# model="gpt-35-turbo",
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# model_version="0125",
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# other params...
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)
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**NOTE**: Any param which is not explicitly supported will be passed directly to the
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``openai.AzureOpenAI.chat.completions.create(...)`` API every time to the model is
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invoked. For example:
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.. code-block:: python
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from langchain_openai import AzureChatOpenAI
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import openai
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AzureChatOpenAI(..., logprobs=True).invoke(...)
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# results in underlying API call of:
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openai.AzureOpenAI(..).chat.completions.create(..., logprobs=True)
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# which is also equivalent to:
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AzureChatOpenAI(...).invoke(..., logprobs=True)
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Invoke:
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.. code-block:: python
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messages = [
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(
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"system",
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"You are a helpful translator. Translate the user sentence to French.",
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),
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("human", "I love programming."),
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]
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llm.invoke(messages)
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.. code-block:: python
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AIMessage(
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content="J'adore programmer.",
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usage_metadata={"input_tokens": 28, "output_tokens": 6, "total_tokens": 34},
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response_metadata={
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"token_usage": {
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"completion_tokens": 6,
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"prompt_tokens": 28,
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"total_tokens": 34,
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},
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"model_name": "gpt-4",
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"system_fingerprint": "fp_7ec89fabc6",
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"prompt_filter_results": [
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{
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"prompt_index": 0,
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"content_filter_results": {
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"hate": {"filtered": False, "severity": "safe"},
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"self_harm": {"filtered": False, "severity": "safe"},
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"sexual": {"filtered": False, "severity": "safe"},
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"violence": {"filtered": False, "severity": "safe"},
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},
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}
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],
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"finish_reason": "stop",
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"logprobs": None,
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"content_filter_results": {
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"hate": {"filtered": False, "severity": "safe"},
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"self_harm": {"filtered": False, "severity": "safe"},
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"sexual": {"filtered": False, "severity": "safe"},
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"violence": {"filtered": False, "severity": "safe"},
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},
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},
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id="run-6d7a5282-0de0-4f27-9cc0-82a9db9a3ce9-0",
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)
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Stream:
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.. code-block:: python
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for chunk in llm.stream(messages):
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print(chunk.text(), end="")
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.. code-block:: python
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AIMessageChunk(content="", id="run-a6f294d3-0700-4f6a-abc2-c6ef1178c37f")
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AIMessageChunk(content="J", id="run-a6f294d3-0700-4f6a-abc2-c6ef1178c37f")
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AIMessageChunk(content="'", id="run-a6f294d3-0700-4f6a-abc2-c6ef1178c37f")
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AIMessageChunk(content="ad", id="run-a6f294d3-0700-4f6a-abc2-c6ef1178c37f")
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AIMessageChunk(content="ore", id="run-a6f294d3-0700-4f6a-abc2-c6ef1178c37f")
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AIMessageChunk(content=" la", id="run-a6f294d3-0700-4f6a-abc2-c6ef1178c37f")
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AIMessageChunk(content=" programm", id="run-a6f294d3-0700-4f6a-abc2-c6ef1178c37f")
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AIMessageChunk(content="ation", id="run-a6f294d3-0700-4f6a-abc2-c6ef1178c37f")
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AIMessageChunk(content=".", id="run-a6f294d3-0700-4f6a-abc2-c6ef1178c37f")
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AIMessageChunk(
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content="",
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response_metadata={
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"finish_reason": "stop",
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"model_name": "gpt-4",
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"system_fingerprint": "fp_811936bd4f",
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},
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id="run-a6f294d3-0700-4f6a-abc2-c6ef1178c37f",
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)
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.. code-block:: python
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stream = llm.stream(messages)
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full = next(stream)
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for chunk in stream:
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full += chunk
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full
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.. code-block:: python
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AIMessageChunk(
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content="J'adore la programmation.",
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response_metadata={
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"finish_reason": "stop",
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"model_name": "gpt-4",
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"system_fingerprint": "fp_811936bd4f",
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},
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id="run-ba60e41c-9258-44b8-8f3a-2f10599643b3",
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)
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Async:
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.. code-block:: python
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await llm.ainvoke(messages)
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# stream:
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# async for chunk in (await llm.astream(messages))
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# batch:
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# await llm.abatch([messages])
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Tool calling:
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.. code-block:: python
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from pydantic import BaseModel, Field
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class GetWeather(BaseModel):
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'''Get the current weather in a given location'''
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location: str = Field(
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..., description="The city and state, e.g. San Francisco, CA"
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)
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class GetPopulation(BaseModel):
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'''Get the current population in a given location'''
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location: str = Field(
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..., description="The city and state, e.g. San Francisco, CA"
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)
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llm_with_tools = llm.bind_tools([GetWeather, GetPopulation])
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ai_msg = llm_with_tools.invoke(
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"Which city is hotter today and which is bigger: LA or NY?"
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)
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ai_msg.tool_calls
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.. code-block:: python
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[
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{
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"name": "GetWeather",
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"args": {"location": "Los Angeles, CA"},
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"id": "call_6XswGD5Pqk8Tt5atYr7tfenU",
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},
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{
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"name": "GetWeather",
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"args": {"location": "New York, NY"},
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"id": "call_ZVL15vA8Y7kXqOy3dtmQgeCi",
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},
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{
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"name": "GetPopulation",
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"args": {"location": "Los Angeles, CA"},
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"id": "call_49CFW8zqC9W7mh7hbMLSIrXw",
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},
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{
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"name": "GetPopulation",
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"args": {"location": "New York, NY"},
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"id": "call_6ghfKxV264jEfe1mRIkS3PE7",
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},
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]
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Structured output:
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.. code-block:: python
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from typing import Optional
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from pydantic import BaseModel, Field
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class Joke(BaseModel):
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'''Joke to tell user.'''
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setup: str = Field(description="The setup of the joke")
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punchline: str = Field(description="The punchline to the joke")
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rating: Optional[int] = Field(description="How funny the joke is, from 1 to 10")
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structured_llm = llm.with_structured_output(Joke)
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structured_llm.invoke("Tell me a joke about cats")
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.. code-block:: python
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Joke(
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setup="Why was the cat sitting on the computer?",
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punchline="To keep an eye on the mouse!",
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rating=None,
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)
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See ``AzureChatOpenAI.with_structured_output()`` for more.
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JSON mode:
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.. code-block:: python
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json_llm = llm.bind(response_format={"type": "json_object"})
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ai_msg = json_llm.invoke(
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"Return a JSON object with key 'random_ints' and a value of 10 random ints in [0-99]"
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)
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ai_msg.content
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.. code-block:: python
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'\\n{\\n "random_ints": [23, 87, 45, 12, 78, 34, 56, 90, 11, 67]\\n}'
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Image input:
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.. code-block:: python
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import base64
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import httpx
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from langchain_core.messages import HumanMessage
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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"
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image_data = base64.b64encode(httpx.get(image_url).content).decode("utf-8")
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message = HumanMessage(
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content=[
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{"type": "text", "text": "describe the weather in this image"},
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{
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"type": "image_url",
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"image_url": {"url": f"data:image/jpeg;base64,{image_data}"},
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},
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]
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)
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ai_msg = llm.invoke([message])
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ai_msg.content
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.. code-block:: python
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"The weather in the image appears to be quite pleasant. The sky is mostly clear"
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Token usage:
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.. code-block:: python
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ai_msg = llm.invoke(messages)
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ai_msg.usage_metadata
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.. code-block:: python
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{"input_tokens": 28, "output_tokens": 5, "total_tokens": 33}
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Logprobs:
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.. code-block:: python
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logprobs_llm = llm.bind(logprobs=True)
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ai_msg = logprobs_llm.invoke(messages)
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ai_msg.response_metadata["logprobs"]
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.. code-block:: python
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{
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"content": [
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{
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"token": "J",
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"bytes": [74],
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"logprob": -4.9617593e-06,
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"top_logprobs": [],
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},
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{
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"token": "'adore",
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"bytes": [39, 97, 100, 111, 114, 101],
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"logprob": -0.25202933,
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"top_logprobs": [],
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},
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{
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"token": " la",
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"bytes": [32, 108, 97],
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"logprob": -0.20141791,
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"top_logprobs": [],
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},
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{
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"token": " programmation",
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"bytes": [
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32,
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112,
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114,
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111,
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103,
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114,
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97,
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109,
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109,
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97,
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116,
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105,
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111,
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110,
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],
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"logprob": -1.9361265e-07,
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"top_logprobs": [],
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},
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{
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"token": ".",
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"bytes": [46],
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"logprob": -1.2233183e-05,
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"top_logprobs": [],
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},
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]
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}
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Response metadata
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.. code-block:: python
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ai_msg = llm.invoke(messages)
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ai_msg.response_metadata
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.. code-block:: python
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{
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"token_usage": {
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"completion_tokens": 6,
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"prompt_tokens": 28,
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"total_tokens": 34,
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},
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"model_name": "gpt-35-turbo",
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"system_fingerprint": None,
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"prompt_filter_results": [
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{
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"prompt_index": 0,
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"content_filter_results": {
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"hate": {"filtered": False, "severity": "safe"},
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"self_harm": {"filtered": False, "severity": "safe"},
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"sexual": {"filtered": False, "severity": "safe"},
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"violence": {"filtered": False, "severity": "safe"},
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},
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}
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],
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"finish_reason": "stop",
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"logprobs": None,
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"content_filter_results": {
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"hate": {"filtered": False, "severity": "safe"},
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"self_harm": {"filtered": False, "severity": "safe"},
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"sexual": {"filtered": False, "severity": "safe"},
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"violence": {"filtered": False, "severity": "safe"},
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},
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}
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""" # noqa: E501
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azure_endpoint: Optional[str] = Field(
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default_factory=from_env("AZURE_OPENAI_ENDPOINT", default=None)
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)
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"""Your Azure endpoint, including the resource.
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Automatically inferred from env var `AZURE_OPENAI_ENDPOINT` if not provided.
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Example: `https://example-resource.azure.openai.com/`
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"""
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deployment_name: Union[str, None] = Field(default=None, alias="azure_deployment")
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"""A model deployment.
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If given sets the base client URL to include `/deployments/{azure_deployment}`.
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Note: this means you won't be able to use non-deployment endpoints.
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"""
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openai_api_version: Optional[str] = Field(
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alias="api_version",
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default_factory=from_env("OPENAI_API_VERSION", default=None),
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)
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"""Automatically inferred from env var `OPENAI_API_VERSION` if not provided."""
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# Check OPENAI_API_KEY for backwards compatibility.
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# TODO: Remove OPENAI_API_KEY support to avoid possible conflict when using
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# other forms of azure credentials.
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openai_api_key: Optional[SecretStr] = Field(
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alias="api_key",
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default_factory=secret_from_env(
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["AZURE_OPENAI_API_KEY", "OPENAI_API_KEY"], default=None
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),
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)
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"""Automatically inferred from env var `AZURE_OPENAI_API_KEY` if not provided."""
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azure_ad_token: Optional[SecretStr] = Field(
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default_factory=secret_from_env("AZURE_OPENAI_AD_TOKEN", default=None)
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)
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"""Your Azure Active Directory token.
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Automatically inferred from env var `AZURE_OPENAI_AD_TOKEN` if not provided.
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For more:
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https://www.microsoft.com/en-us/security/business/identity-access/microsoft-entra-id.
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"""
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azure_ad_token_provider: Union[Callable[[], str], None] = None
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"""A function that returns an Azure Active Directory token.
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Will be invoked on every sync request. For async requests,
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will be invoked if `azure_ad_async_token_provider` is not provided.
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"""
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azure_ad_async_token_provider: Union[Callable[[], Awaitable[str]], None] = None
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"""A function that returns an Azure Active Directory token.
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Will be invoked on every async request.
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"""
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model_version: str = ""
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"""The version of the model (e.g. "0125" for gpt-3.5-0125).
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Azure OpenAI doesn't return model version with the response by default so it must
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be manually specified if you want to use this information downstream, e.g. when
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calculating costs.
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When you specify the version, it will be appended to the model name in the
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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: Optional[str] = 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: Optional[str] = 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. Does NOT affect completion.
|
|
"""
|
|
|
|
disabled_params: Optional[dict[str, Any]] = 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
|
|
:meth:`~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.
|
|
"""
|
|
|
|
@classmethod
|
|
def get_lc_namespace(cls) -> list[str]:
|
|
"""Get the namespace of the langchain object."""
|
|
return ["langchain", "chat_models", "azure_openai"]
|
|
|
|
@property
|
|
def lc_secrets(self) -> dict[str, str]:
|
|
return {
|
|
"openai_api_key": "AZURE_OPENAI_API_KEY",
|
|
"azure_ad_token": "AZURE_OPENAI_AD_TOKEN",
|
|
}
|
|
|
|
@classmethod
|
|
def is_lc_serializable(cls) -> bool:
|
|
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:
|
|
raise ValueError("n must be at least 1.")
|
|
elif self.n is not None and self.n > 1 and self.streaming:
|
|
raise ValueError("n must be 1 when streaming.")
|
|
|
|
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")
|
|
)
|
|
# 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:
|
|
raise ValueError(
|
|
"As of openai>=1.0.0, Azure endpoints should be specified via "
|
|
"the `azure_endpoint` param not `openai_api_base` "
|
|
"(or alias `base_url`)."
|
|
)
|
|
if self.deployment_name:
|
|
raise ValueError(
|
|
"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"'
|
|
)
|
|
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
|
|
|
|
@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]:
|
|
return {
|
|
"openai_api_type": self.openai_api_type,
|
|
"openai_api_version": self.openai_api_version,
|
|
}
|
|
|
|
def _get_ls_params(
|
|
self, stop: Optional[list[str]] = 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: Union[dict, openai.BaseModel],
|
|
generation_info: Optional[dict] = 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":
|
|
raise ValueError(
|
|
"Azure has not provided the response due to a content filter "
|
|
"being triggered"
|
|
)
|
|
|
|
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"]
|
|
):
|
|
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 with_structured_output(
|
|
self,
|
|
schema: Optional[_DictOrPydanticClass] = None,
|
|
*,
|
|
method: Literal["function_calling", "json_mode", "json_schema"] = "json_schema",
|
|
include_raw: bool = False,
|
|
strict: Optional[bool] = None,
|
|
**kwargs: Any,
|
|
) -> Runnable[LanguageModelInput, _DictOrPydantic]:
|
|
"""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,
|
|
- 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::
|
|
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from langchain_openai import AzureChatOpenAI
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from pydantic import BaseModel
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class AnswerWithJustification(BaseModel):
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answer: str
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justification: str
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llm = AzureChatOpenAI(
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azure_deployment="...",
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model="gpt-4o",
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temperature=0,
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)
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structured_llm = llm.with_structured_output(
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AnswerWithJustification,
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method="json_mode",
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include_raw=True
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)
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structured_llm.invoke(
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"Answer the following question. "
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"Make sure to return a JSON blob with keys 'answer' and 'justification'.\\n\\n"
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"What's heavier a pound of bricks or a pound of feathers?"
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)
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# -> {
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# '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}'),
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# '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.'),
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# 'parsing_error': None
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# }
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.. dropdown:: Example: schema=None, method="json_mode", include_raw=True
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.. code-block::
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structured_llm = llm.with_structured_output(method="json_mode", include_raw=True)
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structured_llm.invoke(
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"Answer the following question. "
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"Make sure to return a JSON blob with keys 'answer' and 'justification'.\\n\\n"
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"What's heavier a pound of bricks or a pound of feathers?"
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)
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# -> {
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# '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}'),
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# 'parsed': {
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# 'answer': 'They are both the same weight.',
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# '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.'
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# },
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# 'parsing_error': None
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# }
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""" # noqa: E501
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return super().with_structured_output(
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schema, method=method, include_raw=include_raw, strict=strict, **kwargs
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)
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