github/langchain
1
0
Fork 0
mirror of https://github.com/hwchase17/langchain.git synced 2025-08-02 01:23:07 +00:00
Code Issues Packages Projects Releases Wiki Activity

openai[patch]: enable Azure structured output, parallel_tool_calls=Fa… (#26599)

Browse Source 
…lse, tool_choice=required

response_format=json_schema, tool_choice=required, parallel_tool_calls
are all supported for gpt-4o on azure.
This commit is contained in:
Bagatur 2024-09-22 22:25:22 -07:00 committed by GitHub
parent bb40a0fb32
commit e1e4f88b3e
No known key found for this signature in database
GPG Key ID: B5690EEEBB952194
4 changed files with 98 additions and 350 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

364
libs/partners/openai/langchain_openai/chat_models/azure.py
View File

@ -4,37 +4,13 @@ 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,
)
from typing import Any, Callable, Dict, List, Optional, Type, TypedDict, TypeVar, Union
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.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 pydantic import BaseModel, Field, SecretStr, model_validator
from typing_extensions import Self
@ -538,6 +514,7 @@ class AzureChatOpenAI(BaseChatOpenAI):
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.
@ -550,6 +527,28 @@ class AzureChatOpenAI(BaseChatOpenAI):
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."""
@ -574,6 +573,13 @@ class AzureChatOpenAI(BaseChatOpenAI):
if 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
@ -634,311 +640,6 @@ class AzureChatOpenAI(BaseChatOpenAI):
self.async_client = self.root_async_client.chat.completions
return self
def bind_tools(
self,
tools: Sequence[Union[Dict[str, Any], Type, Callable, BaseTool]],
*,
tool_choice: Optional[
Union[dict, str, Literal["auto", "none", "required", "any"], bool]
] = None,
**kwargs: Any,
) -> Runnable[LanguageModelInput, BaseMessage]:
# As of 05/2024 Azure OpenAI doesn't support tool_choice="required".
# TODO: Update this condition once tool_choice="required" is supported.
if tool_choice in ("any", "required", True):
if len(tools) > 1:
raise ValueError(
f"Azure OpenAI does not currently support {tool_choice=}. Should "
f"be one of 'auto', 'none', or the name of the tool to call."
)
else:
tool_choice = convert_to_openai_tool(tools[0])["function"]["name"]
return super().bind_tools(tools, tool_choice=tool_choice, **kwargs)
# 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]: ...
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 pydantic 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 pydantic 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 pydantic 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."""
@ -980,6 +681,8 @@ class AzureChatOpenAI(BaseChatOpenAI):
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"]:
@ -988,7 +691,6 @@ class AzureChatOpenAI(BaseChatOpenAI):
"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"]

43
libs/partners/openai/langchain_openai/chat_models/base.py
View File

@ -454,6 +454,23 @@ class BaseChatOpenAI(BaseChatModel):
making requests to OpenAI compatible APIs, such as vLLM."""
include_response_headers: bool = False
"""Whether to include response headers in the output message response_metadata."""
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.base.ChatOpenAI.with_structured_output`.
However this does not prevent a user from directly passed in the parameter during
invocation.
"""
model_config = ConfigDict(populate_by_name=True)
@ -1401,12 +1418,11 @@ class BaseChatOpenAI(BaseChatModel):
"Received None."
)
tool_name = convert_to_openai_tool(schema)["function"]["name"]
llm = self.bind_tools(
[schema],
tool_choice=tool_name,
parallel_tool_calls=False,
strict=strict,
bind_kwargs = self._filter_disabled_params(
tool_choice=tool_name, parallel_tool_calls=False, strict=strict
)
llm = self.bind_tools([schema], **bind_kwargs)
if is_pydantic_schema:
output_parser: Runnable = PydanticToolsParser(
tools=[schema], # type: ignore[list-item]
@ -1456,6 +1472,21 @@ class BaseChatOpenAI(BaseChatModel):
else:
return llm | output_parser
def _filter_disabled_params(self, **kwargs: Any) -> Dict[str, Any]:
if not self.disabled_params:
return kwargs
filtered = {}
for k, v in kwargs.items():
# Skip param
if k in self.disabled_params and (
self.disabled_params[k] is None or v in self.disabled_params[k]
):
continue
# Keep param
else:
filtered[k] = v
return filtered
class ChatOpenAI(BaseChatOpenAI):
"""OpenAI chat model integration.
@ -2114,7 +2145,7 @@ def _oai_structured_outputs_parser(ai_msg: AIMessage) -> PydanticBaseModel:
else:
raise ValueError(
"Structured Output response does not have a 'parsed' field nor a 'refusal' "
"field."
"field. Received message:\n\n{ai_msg}"
)

38
libs/partners/openai/tests/integration_tests/chat_models/test_azure_standard.py
View File

@ -9,16 +9,8 @@ from langchain_standard_tests.integration_tests import ChatModelIntegrationTests
from langchain_openai import AzureChatOpenAI
OPENAI_API_VERSION = os.environ.get("AZURE_OPENAI_API_VERSION", "")
OPENAI_API_BASE = os.environ.get("AZURE_OPENAI_API_BASE", "")
OPENAI_API_KEY = os.environ.get("AZURE_OPENAI_API_KEY", "")
DEPLOYMENT_NAME = os.environ.get(
"AZURE_OPENAI_DEPLOYMENT_NAME",
os.environ.get("AZURE_OPENAI_CHAT_DEPLOYMENT_NAME", ""),
)
class TestOpenAIStandard(ChatModelIntegrationTests):
class TestAzureOpenAIStandard(ChatModelIntegrationTests):
@property
def chat_model_class(self) -> Type[BaseChatModel]:
return AzureChatOpenAI
@ -26,10 +18,30 @@ class TestOpenAIStandard(ChatModelIntegrationTests):
@property
def chat_model_params(self) -> dict:
return {
"deployment_name": DEPLOYMENT_NAME,
"openai_api_version": OPENAI_API_VERSION,
"azure_endpoint": OPENAI_API_BASE,
"api_key": OPENAI_API_KEY,
"deployment_name": os.environ["AZURE_OPENAI_CHAT_DEPLOYMENT_NAME"],
"model": "gpt-4o",
}
@property
def supports_image_inputs(self) -> bool:
return True
@pytest.mark.xfail(reason="Not yet supported.")
def test_usage_metadata_streaming(self, model: BaseChatModel) -> None:
super().test_usage_metadata_streaming(model)
class TestAzureOpenAIStandardLegacy(ChatModelIntegrationTests):
"""Test a legacy model."""
@property
def chat_model_class(self) -> Type[BaseChatModel]:
return AzureChatOpenAI
@property
def chat_model_params(self) -> dict:
return {
"deployment_name": os.environ["AZURE_OPENAI_LEGACY_CHAT_DEPLOYMENT_NAME"]
}
@pytest.mark.xfail(reason="Not yet supported.")

3
libs/partners/openai/tests/unit_tests/chat_models/__snapshots__/test_azure_standard.ambr
View File

@ -10,6 +10,9 @@
'kwargs': dict({
'azure_endpoint': 'https://test.azure.com',
'deployment_name': 'test',
'disabled_params': dict({
'parallel_tool_calls': None,
}),
'max_retries': 2,
'max_tokens': 100,
'n': 1,