"""Azure OpenAI chat wrapper."""
from __future__ import annotations
import logging
import os
from operator import itemgetter
from typing import (
Any,
Callable,
Dict,
List,
Literal,
Optional,
Sequence,
Type,
TypedDict,
TypeVar,
Union,
overload,
)
import openai
from langchain_core.language_models import LanguageModelInput
from langchain_core.language_models.chat_models import LangSmithParams
from langchain_core.messages import BaseMessage
from langchain_core.output_parsers import JsonOutputParser, PydanticOutputParser
from langchain_core.output_parsers.base import OutputParserLike
from langchain_core.output_parsers.openai_tools import (
JsonOutputKeyToolsParser,
PydanticToolsParser,
)
from langchain_core.outputs import ChatResult
from langchain_core.pydantic_v1 import BaseModel, Field, SecretStr, root_validator
from langchain_core.runnables import Runnable, RunnableMap, RunnablePassthrough
from langchain_core.tools import BaseTool
from langchain_core.utils import from_env, secret_from_env
from langchain_core.utils.function_calling import convert_to_openai_tool
from langchain_core.utils.pydantic import is_basemodel_subclass
from langchain_openai.chat_models.base import BaseChatOpenAI
logger = logging.getLogger(__name__)
_BM = TypeVar("_BM", bound=BaseModel)
_DictOrPydanticClass = Union[Dict[str, Any], Type[_BM]]
_DictOrPydantic = Union[Dict, _BM]
class _AllReturnType(TypedDict):
raw: BaseMessage
parsed: Optional[_DictOrPydantic]
parsing_error: Optional[BaseException]
def _is_pydantic_class(obj: Any) -> bool:
return isinstance(obj, type) and is_basemodel_subclass(obj)
[docs]class AzureChatOpenAI(BaseChatOpenAI):
"""Azure OpenAI chat model integration.
Setup:
Head to the https://learn.microsoft.com/en-us/azure/ai-services/openai/chatgpt-quickstart?tabs=command-line%2Cpython-new&pivots=programming-language-python
to create your Azure OpenAI deployment.
Then install ``langchain-openai`` and set environment variables
``AZURE_OPENAI_API_KEY`` and ``AZURE_OPENAI_ENDPOINT``:
.. code-block:: bash
pip install -U langchain-openai
export AZURE_OPENAI_API_KEY="your-api-key"
export AZURE_OPENAI_ENDPOINT="https://your-endpoint.openai.azure.com/"
Key init args — completion params:
azure_deployment: str
Name of Azure OpenAI deployment to use.
temperature: float
Sampling temperature.
max_tokens: Optional[int]
Max number of tokens to generate.
logprobs: Optional[bool]
Whether to return logprobs.
Key init args — client params:
api_version: str
Azure OpenAI API version to use. See more on the different versions here:
https://learn.microsoft.com/en-us/azure/ai-services/openai/reference#rest-api-versioning
timeout: Union[float, Tuple[float, float], Any, None]
Timeout for requests.
max_retries: int
Max number of retries.
organization: Optional[str]
OpenAI organization ID. If not passed in will be read from env
var OPENAI_ORG_ID.
model: Optional[str]
The name of the underlying OpenAI model. Used for tracing and token
counting. Does not affect completion. E.g. "gpt-4", "gpt-35-turbo", etc.
model_version: Optional[str]
The version of the underlying OpenAI model. Used for tracing and token
counting. Does not affect completion. E.g., "0125", "0125-preview", etc.
See full list of supported init args and their descriptions in the params section.
Instantiate:
.. code-block:: python
from langchain_openai import AzureChatOpenAI
llm = AzureChatOpenAI(
azure_deployment="your-deployment",
api_version="2024-05-01-preview",
temperature=0,
max_tokens=None,
timeout=None,
max_retries=2,
# organization="...",
# model="gpt-35-turbo",
# model_version="0125",
# other params...
)
**NOTE**: Any param which is not explicitly supported will be passed directly to the
``openai.AzureOpenAI.chat.completions.create(...)`` API every time to the model is
invoked. For example:
.. code-block:: python
from langchain_openai import AzureChatOpenAI
import openai
AzureChatOpenAI(..., logprobs=True).invoke(...)
# results in underlying API call of:
openai.AzureOpenAI(..).chat.completions.create(..., logprobs=True)
# which is also equivalent to:
AzureChatOpenAI(...).invoke(..., logprobs=True)
Invoke:
.. code-block:: python
messages = [
(
"system",
"You are a helpful translator. Translate the user sentence to French.",
),
("human", "I love programming."),
]
llm.invoke(messages)
.. code-block:: python
AIMessage(
content="J'adore programmer.",
usage_metadata={"input_tokens": 28, "output_tokens": 6, "total_tokens": 34},
response_metadata={
"token_usage": {
"completion_tokens": 6,
"prompt_tokens": 28,
"total_tokens": 34,
},
"model_name": "gpt-4",
"system_fingerprint": "fp_7ec89fabc6",
"prompt_filter_results": [
{
"prompt_index": 0,
"content_filter_results": {
"hate": {"filtered": False, "severity": "safe"},
"self_harm": {"filtered": False, "severity": "safe"},
"sexual": {"filtered": False, "severity": "safe"},
"violence": {"filtered": False, "severity": "safe"},
},
}
],
"finish_reason": "stop",
"logprobs": None,
"content_filter_results": {
"hate": {"filtered": False, "severity": "safe"},
"self_harm": {"filtered": False, "severity": "safe"},
"sexual": {"filtered": False, "severity": "safe"},
"violence": {"filtered": False, "severity": "safe"},
},
},
id="run-6d7a5282-0de0-4f27-9cc0-82a9db9a3ce9-0",
)
Stream:
.. code-block:: python
for chunk in llm.stream(messages):
print(chunk)
.. code-block:: python
AIMessageChunk(content="", id="run-a6f294d3-0700-4f6a-abc2-c6ef1178c37f")
AIMessageChunk(content="J", id="run-a6f294d3-0700-4f6a-abc2-c6ef1178c37f")
AIMessageChunk(content="'", id="run-a6f294d3-0700-4f6a-abc2-c6ef1178c37f")
AIMessageChunk(content="ad", id="run-a6f294d3-0700-4f6a-abc2-c6ef1178c37f")
AIMessageChunk(content="ore", id="run-a6f294d3-0700-4f6a-abc2-c6ef1178c37f")
AIMessageChunk(content=" la", id="run-a6f294d3-0700-4f6a-abc2-c6ef1178c37f")
AIMessageChunk(content=" programm", id="run-a6f294d3-0700-4f6a-abc2-c6ef1178c37f")
AIMessageChunk(content="ation", id="run-a6f294d3-0700-4f6a-abc2-c6ef1178c37f")
AIMessageChunk(content=".", id="run-a6f294d3-0700-4f6a-abc2-c6ef1178c37f")
AIMessageChunk(
content="",
response_metadata={
"finish_reason": "stop",
"model_name": "gpt-4",
"system_fingerprint": "fp_811936bd4f",
},
id="run-a6f294d3-0700-4f6a-abc2-c6ef1178c37f",
)
.. code-block:: python
stream = llm.stream(messages)
full = next(stream)
for chunk in stream:
full += chunk
full
.. code-block:: python
AIMessageChunk(
content="J'adore la programmation.",
response_metadata={
"finish_reason": "stop",
"model_name": "gpt-4",
"system_fingerprint": "fp_811936bd4f",
},
id="run-ba60e41c-9258-44b8-8f3a-2f10599643b3",
)
Async:
.. code-block:: python
await llm.ainvoke(messages)
# stream:
# async for chunk in (await llm.astream(messages))
# batch:
# await llm.abatch([messages])
Tool calling:
.. code-block:: python
from langchain_core.pydantic_v1 import BaseModel, Field
class GetWeather(BaseModel):
'''Get the current weather in a given location'''
location: str = Field(
..., description="The city and state, e.g. San Francisco, CA"
)
class GetPopulation(BaseModel):
'''Get the current population in a given location'''
location: str = Field(
..., description="The city and state, e.g. San Francisco, CA"
)
llm_with_tools = llm.bind_tools([GetWeather, GetPopulation])
ai_msg = llm_with_tools.invoke(
"Which city is hotter today and which is bigger: LA or NY?"
)
ai_msg.tool_calls
.. code-block:: python
[
{
"name": "GetWeather",
"args": {"location": "Los Angeles, CA"},
"id": "call_6XswGD5Pqk8Tt5atYr7tfenU",
},
{
"name": "GetWeather",
"args": {"location": "New York, NY"},
"id": "call_ZVL15vA8Y7kXqOy3dtmQgeCi",
},
{
"name": "GetPopulation",
"args": {"location": "Los Angeles, CA"},
"id": "call_49CFW8zqC9W7mh7hbMLSIrXw",
},
{
"name": "GetPopulation",
"args": {"location": "New York, NY"},
"id": "call_6ghfKxV264jEfe1mRIkS3PE7",
},
]
Structured output:
.. code-block:: python
from typing import Optional
from langchain_core.pydantic_v1 import BaseModel, Field
class Joke(BaseModel):
'''Joke to tell user.'''
setup: str = Field(description="The setup of the joke")
punchline: str = Field(description="The punchline to the joke")
rating: Optional[int] = Field(description="How funny the joke is, from 1 to 10")
structured_llm = llm.with_structured_output(Joke)
structured_llm.invoke("Tell me a joke about cats")
.. code-block:: python
Joke(
setup="Why was the cat sitting on the computer?",
punchline="To keep an eye on the mouse!",
rating=None,
)
See ``AzureChatOpenAI.with_structured_output()`` for more.
JSON mode:
.. code-block:: python
json_llm = llm.bind(response_format={"type": "json_object"})
ai_msg = json_llm.invoke(
"Return a JSON object with key 'random_ints' and a value of 10 random ints in [0-99]"
)
ai_msg.content
.. code-block:: python
'\\n{\\n "random_ints": [23, 87, 45, 12, 78, 34, 56, 90, 11, 67]\\n}'
Image input:
.. code-block:: python
import base64
import httpx
from langchain_core.messages import HumanMessage
image_url = "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg"
image_data = base64.b64encode(httpx.get(image_url).content).decode("utf-8")
message = HumanMessage(
content=[
{"type": "text", "text": "describe the weather in this image"},
{
"type": "image_url",
"image_url": {"url": f"data:image/jpeg;base64,{image_data}"},
},
]
)
ai_msg = llm.invoke([message])
ai_msg.content
.. code-block:: python
"The weather in the image appears to be quite pleasant. The sky is mostly clear"
Token usage:
.. code-block:: python
ai_msg = llm.invoke(messages)
ai_msg.usage_metadata
.. code-block:: python
{"input_tokens": 28, "output_tokens": 5, "total_tokens": 33}
Logprobs:
.. code-block:: python
logprobs_llm = llm.bind(logprobs=True)
ai_msg = logprobs_llm.invoke(messages)
ai_msg.response_metadata["logprobs"]
.. code-block:: python
{
"content": [
{
"token": "J",
"bytes": [74],
"logprob": -4.9617593e-06,
"top_logprobs": [],
},
{
"token": "'adore",
"bytes": [39, 97, 100, 111, 114, 101],
"logprob": -0.25202933,
"top_logprobs": [],
},
{
"token": " la",
"bytes": [32, 108, 97],
"logprob": -0.20141791,
"top_logprobs": [],
},
{
"token": " programmation",
"bytes": [
32,
112,
114,
111,
103,
114,
97,
109,
109,
97,
116,
105,
111,
110,
],
"logprob": -1.9361265e-07,
"top_logprobs": [],
},
{
"token": ".",
"bytes": [46],
"logprob": -1.2233183e-05,
"top_logprobs": [],
},
]
}
Response metadata
.. code-block:: python
ai_msg = llm.invoke(messages)
ai_msg.response_metadata
.. code-block:: python
{
"token_usage": {
"completion_tokens": 6,
"prompt_tokens": 28,
"total_tokens": 34,
},
"model_name": "gpt-35-turbo",
"system_fingerprint": None,
"prompt_filter_results": [
{
"prompt_index": 0,
"content_filter_results": {
"hate": {"filtered": False, "severity": "safe"},
"self_harm": {"filtered": False, "severity": "safe"},
"sexual": {"filtered": False, "severity": "safe"},
"violence": {"filtered": False, "severity": "safe"},
},
}
],
"finish_reason": "stop",
"logprobs": None,
"content_filter_results": {
"hate": {"filtered": False, "severity": "safe"},
"self_harm": {"filtered": False, "severity": "safe"},
"sexual": {"filtered": False, "severity": "safe"},
"violence": {"filtered": False, "severity": "safe"},
},
}
""" # noqa: E501
azure_endpoint: Optional[str] = Field(
default_factory=from_env("AZURE_OPENAI_ENDPOINT", default=None)
)
"""Your Azure endpoint, including the resource.
Automatically inferred from env var `AZURE_OPENAI_ENDPOINT` if not provided.
Example: `https://example-resource.azure.openai.com/`
"""
deployment_name: Union[str, None] = Field(default=None, alias="azure_deployment")
"""A model deployment.
If given sets the base client URL to include `/deployments/{azure_deployment}`.
Note: this means you won't be able to use non-deployment endpoints.
"""
openai_api_version: Optional[str] = Field(
alias="api_version",
default_factory=from_env("OPENAI_API_VERSION", default=None),
)
"""Automatically inferred from env var `OPENAI_API_VERSION` if not provided."""
# Check OPENAI_KEY for backwards compatibility.
# TODO: Remove OPENAI_API_KEY support to avoid possible conflict when using
# other forms of azure credentials.
openai_api_key: Optional[SecretStr] = Field(
alias="api_key",
default_factory=secret_from_env(
["AZURE_OPENAI_API_KEY", "OPENAI_API_KEY"], default=None
),
)
"""Automatically inferred from env var `AZURE_OPENAI_API_KEY` if not provided."""
azure_ad_token: Optional[SecretStr] = Field(
default_factory=secret_from_env("AZURE_OPENAI_AD_TOKEN", default=None)
)
"""Your Azure Active Directory token.
Automatically inferred from env var `AZURE_OPENAI_AD_TOKEN` if not provided.
For more:
https://www.microsoft.com/en-us/security/business/identity-access/microsoft-entra-id.
"""
azure_ad_token_provider: Union[Callable[[], str], None] = None
"""A function that returns an Azure Active Directory token.
Will be invoked on every request.
"""
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.
"""
@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
@root_validator(pre=False, skip_on_failure=True)
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that api key and python package exists in environment."""
if values["n"] < 1:
raise ValueError("n must be at least 1.")
if values["n"] > 1 and values["streaming"]:
raise ValueError("n must be 1 when streaming.")
# Check OPENAI_ORGANIZATION for backwards compatibility.
values["openai_organization"] = (
values["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 = values["openai_api_base"]
if openai_api_base and values["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 values["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 = {
"api_version": values["openai_api_version"],
"azure_endpoint": values["azure_endpoint"],
"azure_deployment": values["deployment_name"],
"api_key": (
values["openai_api_key"].get_secret_value()
if values["openai_api_key"]
else None
),
"azure_ad_token": (
values["azure_ad_token"].get_secret_value()
if values["azure_ad_token"]
else None
),
"azure_ad_token_provider": values["azure_ad_token_provider"],
"organization": values["openai_organization"],
"base_url": values["openai_api_base"],
"timeout": values["request_timeout"],
"max_retries": values["max_retries"],
"default_headers": values["default_headers"],
"default_query": values["default_query"],
}
if not values.get("client"):
sync_specific = {"http_client": values["http_client"]}
values["root_client"] = openai.AzureOpenAI(**client_params, **sync_specific)
values["client"] = values["root_client"].chat.completions
if not values.get("async_client"):
async_specific = {"http_client": values["http_async_client"]}
values["root_async_client"] = openai.AsyncAzureOpenAI(
**client_params, **async_specific
)
values["async_client"] = values["root_async_client"].chat.completions
return values
# TODO: Fix typing.
@overload # type: ignore[override]
def with_structured_output(
self,
schema: Optional[_DictOrPydanticClass] = None,
*,
method: Literal["function_calling", "json_mode"] = "function_calling",
include_raw: Literal[True] = True,
**kwargs: Any,
) -> Runnable[LanguageModelInput, _AllReturnType]: ...
@overload
def with_structured_output(
self,
schema: Optional[_DictOrPydanticClass] = None,
*,
method: Literal["function_calling", "json_mode"] = "function_calling",
include_raw: Literal[False] = False,
**kwargs: Any,
) -> Runnable[LanguageModelInput, _DictOrPydantic]: ...
[docs] def with_structured_output(
self,
schema: Optional[_DictOrPydanticClass] = None,
*,
method: Literal["function_calling", "json_mode"] = "function_calling",
include_raw: bool = False,
**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:
- an OpenAI function/tool schema,
- a JSON Schema,
- a TypedDict class,
- or a Pydantic class.
If ``schema`` is a Pydantic class then the model output will be a
Pydantic instance of that class, and the model-generated fields will be
validated by the Pydantic class. Otherwise the model output will be a
dict and will not be validated. See :meth:`langchain_core.utils.function_calling.convert_to_openai_tool`
for more on how to properly specify types and descriptions of
schema fields when specifying a Pydantic or TypedDict class.
method:
The method for steering model generation, either "function_calling"
or "json_mode". If "function_calling" then the schema will be converted
to an OpenAI function and the returned model will make use of the
function-calling API. If "json_mode" then OpenAI's JSON mode will be
used. Note that if using "json_mode" then you must include instructions
for formatting the output into the desired schema into the model call.
include_raw:
If False then only the parsed structured output is returned. If
an error occurs during model output parsing it will be raised. If True
then both the raw model response (a BaseMessage) and the parsed model
response will be returned. If an error occurs during output parsing it
will be caught and returned as well. The final output is always a dict
with keys "raw", "parsed", and "parsing_error".
Returns:
A Runnable that takes same inputs as a :class:`langchain_core.language_models.chat.BaseChatModel`.
If ``include_raw`` is False and ``schema`` is a Pydantic class, Runnable outputs
an instance of ``schema`` (i.e., a Pydantic object).
Otherwise, if ``include_raw`` is False then Runnable outputs a dict.
If ``include_raw`` is True, then Runnable outputs a dict with keys:
- ``"raw"``: BaseMessage
- ``"parsed"``: None if there was a parsing error, otherwise the type depends on the ``schema`` as described above.
- ``"parsing_error"``: Optional[BaseException]
Example: schema=Pydantic class, method="function_calling", include_raw=False:
.. code-block:: python
from typing import Optional
from langchain_openai import AzureChatOpenAI
from langchain_core.pydantic_v1 import BaseModel, Field
class AnswerWithJustification(BaseModel):
'''An answer to the user question along with justification for the answer.'''
answer: str
# If we provide default values and/or descriptions for fields, these will be passed
# to the model. This is an important part of improving a model's ability to
# correctly return structured outputs.
justification: Optional[str] = Field(
default=None, description="A justification for the answer."
)
llm = AzureChatOpenAI(model="gpt-3.5-turbo-0125", temperature=0)
structured_llm = llm.with_structured_output(AnswerWithJustification)
structured_llm.invoke(
"What weighs more a pound of bricks or a pound of feathers"
)
# -> AnswerWithJustification(
# answer='They weigh the same',
# justification='Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume or density of the objects may differ.'
# )
Example: schema=Pydantic class, method="function_calling", include_raw=True:
.. code-block:: python
from langchain_openai import AzureChatOpenAI
from langchain_core.pydantic_v1 import BaseModel
class AnswerWithJustification(BaseModel):
'''An answer to the user question along with justification for the answer.'''
answer: str
justification: str
llm = AzureChatOpenAI(model="gpt-3.5-turbo-0125", temperature=0)
structured_llm = llm.with_structured_output(
AnswerWithJustification, include_raw=True
)
structured_llm.invoke(
"What weighs more a pound of bricks or a pound of feathers"
)
# -> {
# 'raw': AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_Ao02pnFYXD6GN1yzc0uXPsvF', 'function': {'arguments': '{"answer":"They weigh the same.","justification":"Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume or density of the objects may differ."}', 'name': 'AnswerWithJustification'}, 'type': 'function'}]}),
# 'parsed': AnswerWithJustification(answer='They weigh the same.', justification='Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume or density of the objects may differ.'),
# 'parsing_error': None
# }
Example: schema=TypedDict class, method="function_calling", include_raw=False:
.. code-block:: python
# IMPORTANT: If you are using Python <=3.8, you need to import Annotated
# from typing_extensions, not from typing.
from typing_extensions import Annotated, TypedDict
from langchain_openai import AzureChatOpenAI
class AnswerWithJustification(TypedDict):
'''An answer to the user question along with justification for the answer.'''
answer: str
justification: Annotated[
Optional[str], None, "A justification for the answer."
]
llm = AzureChatOpenAI(model="gpt-3.5-turbo-0125", temperature=0)
structured_llm = llm.with_structured_output(AnswerWithJustification)
structured_llm.invoke(
"What weighs more a pound of bricks or a pound of feathers"
)
# -> {
# 'answer': 'They weigh the same',
# 'justification': 'Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume and density of the two substances differ.'
# }
Example: schema=OpenAI function schema, method="function_calling", include_raw=False:
.. code-block:: python
from langchain_openai import AzureChatOpenAI
oai_schema = {
'name': 'AnswerWithJustification',
'description': 'An answer to the user question along with justification for the answer.',
'parameters': {
'type': 'object',
'properties': {
'answer': {'type': 'string'},
'justification': {'description': 'A justification for the answer.', 'type': 'string'}
},
'required': ['answer']
}
}
llm = AzureChatOpenAI(model="gpt-3.5-turbo-0125", temperature=0)
structured_llm = llm.with_structured_output(oai_schema)
structured_llm.invoke(
"What weighs more a pound of bricks or a pound of feathers"
)
# -> {
# 'answer': 'They weigh the same',
# 'justification': 'Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume and density of the two substances differ.'
# }
Example: schema=Pydantic class, method="json_mode", include_raw=True:
.. code-block::
from langchain_openai import AzureChatOpenAI
from langchain_core.pydantic_v1 import BaseModel
class AnswerWithJustification(BaseModel):
answer: str
justification: str
llm = AzureChatOpenAI(model="gpt-3.5-turbo-0125", temperature=0)
structured_llm = llm.with_structured_output(
AnswerWithJustification,
method="json_mode",
include_raw=True
)
structured_llm.invoke(
"Answer the following question. "
"Make sure to return a JSON blob with keys 'answer' and 'justification'.\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
# }
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
if kwargs:
raise ValueError(f"Received unsupported arguments {kwargs}")
is_pydantic_schema = _is_pydantic_class(schema)
if method == "function_calling":
if schema is None:
raise ValueError(
"schema must be specified when method is 'function_calling'. "
"Received None."
)
tool_name = convert_to_openai_tool(schema)["function"]["name"]
llm = self.bind_tools([schema], tool_choice=tool_name)
if is_pydantic_schema:
output_parser: OutputParserLike = PydanticToolsParser(
tools=[schema], # type: ignore[list-item]
first_tool_only=True, # type: ignore[list-item]
)
else:
output_parser = JsonOutputKeyToolsParser(
key_name=tool_name, first_tool_only=True
)
elif method == "json_mode":
llm = self.bind(response_format={"type": "json_object"})
output_parser = (
PydanticOutputParser(pydantic_object=schema) # type: ignore[arg-type]
if is_pydantic_schema
else JsonOutputParser()
)
else:
raise ValueError(
f"Unrecognized method argument. Expected one of 'function_calling' or "
f"'json_mode'. Received: '{method}'"
)
if include_raw:
parser_assign = RunnablePassthrough.assign(
parsed=itemgetter("raw") | output_parser, parsing_error=lambda _: None
)
parser_none = RunnablePassthrough.assign(parsed=lambda _: None)
parser_with_fallback = parser_assign.with_fallbacks(
[parser_none], exception_key="parsing_error"
)
return RunnableMap(raw=llm) | parser_with_fallback
else:
return llm | output_parser
@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:
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"
)
chat_result = super()._create_chat_result(response, generation_info)
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
