mirror of
https://github.com/hwchase17/langchain.git
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4fa3ef0d55
- This pull request includes various changes to add a `user_agent` parameter to Azure OpenAI, Azure Search and Whisper in the Community and Partner packages. This helps in identifying the source of API requests so we can better track usage and help support the community better. I will also be adding the user_agent to the new `langchain-azure` repo as well. - No issue connected or updated dependencies. - Utilises existing tests and docs --------- Co-authored-by: Erick Friis <erick@langchain.dev>
1067 lines
42 KiB
Python
1067 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 typing import (
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Any,
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Awaitable,
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Callable,
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Dict,
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List,
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Optional,
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Type,
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TypedDict,
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TypeVar,
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Union,
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)
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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)
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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 = ""
|
|
"""The version of the model (e.g. "0125" for gpt-3.5-0125).
|
|
|
|
Azure OpenAI doesn't return model version with the response by default so it must
|
|
be manually specified if you want to use this information downstream, e.g. when
|
|
calculating costs.
|
|
|
|
When you specify the version, it will be appended to the model name in the
|
|
response. Setting correct version will help you to calculate the cost properly.
|
|
Model version is not validated, so make sure you set it correctly to get the
|
|
correct cost.
|
|
"""
|
|
|
|
openai_api_type: 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"
|
|
)
|
|
# -> {
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|
# 'answer': 'They weigh the same',
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# '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.'
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|
# }
|
|
|
|
.. dropdown:: Example: schema=Pydantic class, method="json_mode", include_raw=True
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|
|
|
.. 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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|
)
|
|
structured_llm = llm.with_structured_output(
|
|
AnswerWithJustification,
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|
method="json_mode",
|
|
include_raw=True
|
|
)
|
|
|
|
structured_llm.invoke(
|
|
"Answer the following question. "
|
|
"Make sure to return a JSON blob with keys 'answer' and 'justification'.\\n\\n"
|
|
"What's heavier a pound of bricks or a pound of feathers?"
|
|
)
|
|
# -> {
|
|
# 'raw': AIMessage(content='{\\n "answer": "They are both the same weight.",\\n "justification": "Both a pound of bricks and a pound of feathers weigh one pound. The difference lies in the volume and density of the materials, not the weight." \\n}'),
|
|
# 'parsed': AnswerWithJustification(answer='They are both the same weight.', justification='Both a pound of bricks and a pound of feathers weigh one pound. The difference lies in the volume and density of the materials, not the weight.'),
|
|
# 'parsing_error': None
|
|
# }
|
|
|
|
.. dropdown:: Example: schema=None, method="json_mode", include_raw=True
|
|
|
|
.. code-block::
|
|
|
|
structured_llm = llm.with_structured_output(method="json_mode", include_raw=True)
|
|
|
|
structured_llm.invoke(
|
|
"Answer the following question. "
|
|
"Make sure to return a JSON blob with keys 'answer' and 'justification'.\\n\\n"
|
|
"What's heavier a pound of bricks or a pound of feathers?"
|
|
)
|
|
# -> {
|
|
# 'raw': AIMessage(content='{\\n "answer": "They are both the same weight.",\\n "justification": "Both a pound of bricks and a pound of feathers weigh one pound. The difference lies in the volume and density of the materials, not the weight." \\n}'),
|
|
# 'parsed': {
|
|
# 'answer': 'They are both the same weight.',
|
|
# 'justification': 'Both a pound of bricks and a pound of feathers weigh one pound. The difference lies in the volume and density of the materials, not the weight.'
|
|
# },
|
|
# 'parsing_error': None
|
|
# }
|
|
""" # noqa: E501
|
|
return super().with_structured_output(
|
|
schema, method=method, include_raw=include_raw, strict=strict, **kwargs
|
|
)
|