core[patch]: update structured output tracing (#30123)

- Trace JSON schema in `options`
- Rename to `ls_structured_output_format`
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ccurme 2025-03-07 13:05:25 -05:00 committed by GitHub
parent d0f5bcda29
commit 806211475a
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13 changed files with 184 additions and 714 deletions

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@ -48,4 +48,5 @@ _e2e_test:
poetry run pip install -e ../../../standard-tests && \
make format lint tests && \
poetry install --with test_integration && \
poetry run pip install -e ../../../core && \
make integration_test

View File

@ -31,11 +31,12 @@ langchain-cli = "langchain_cli.cli:app"
[dependency-groups]
dev = ["pytest<8.0.0,>=7.4.2", "pytest-watch<5.0.0,>=4.2.0"]
lint = ["ruff<1.0,>=0.5", "mypy<2.0.0,>=1.13.0"]
test = ["langchain"]
test = ["langchain-core", "langchain"]
typing = ["langchain"]
test_integration = []
[tool.uv.sources]
langchain-core = { path = "../core", editable = true }
langchain = { path = "../langchain", editable = true }
[tool.ruff.lint]

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@ -6,120 +6,6 @@ resolution-markers = [
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[[package]]
name = "zstandard"
version = "0.23.0"

View File

@ -65,7 +65,10 @@ from langchain_core.rate_limiters import BaseRateLimiter
from langchain_core.runnables import RunnableMap, RunnablePassthrough
from langchain_core.runnables.config import ensure_config, run_in_executor
from langchain_core.tracers._streaming import _StreamingCallbackHandler
from langchain_core.utils.function_calling import convert_to_openai_tool
from langchain_core.utils.function_calling import (
convert_to_json_schema,
convert_to_openai_tool,
)
from langchain_core.utils.pydantic import TypeBaseModel, is_basemodel_subclass
if TYPE_CHECKING:
@ -116,6 +119,25 @@ async def agenerate_from_stream(
return await run_in_executor(None, generate_from_stream, iter(chunks))
def _format_ls_structured_output(ls_structured_output_format: Optional[dict]) -> dict:
if ls_structured_output_format:
try:
ls_structured_output_format_dict = {
"ls_structured_output_format": {
"kwargs": ls_structured_output_format.get("kwargs", {}),
"schema": convert_to_json_schema(
ls_structured_output_format["schema"]
),
}
}
except ValueError:
ls_structured_output_format_dict = {}
else:
ls_structured_output_format_dict = {}
return ls_structured_output_format_dict
class BaseChatModel(BaseLanguageModel[BaseMessage], ABC):
"""Base class for chat models.
@ -366,28 +388,18 @@ class BaseChatModel(BaseLanguageModel[BaseMessage], ABC):
else:
config = ensure_config(config)
messages = self._convert_input(input).to_messages()
structured_output_format = kwargs.pop("structured_output_format", None)
if structured_output_format:
try:
structured_output_format_dict = {
"structured_output_format": {
"kwargs": structured_output_format.get("kwargs", {}),
"schema": convert_to_openai_tool(
structured_output_format["schema"]
),
}
}
except ValueError:
structured_output_format_dict = {}
else:
structured_output_format_dict = {}
ls_structured_output_format = kwargs.pop(
"ls_structured_output_format", None
)
ls_structured_output_format_dict = _format_ls_structured_output(
ls_structured_output_format
)
params = self._get_invocation_params(stop=stop, **kwargs)
options = {"stop": stop, **kwargs}
options = {"stop": stop, **kwargs, **ls_structured_output_format_dict}
inheritable_metadata = {
**(config.get("metadata") or {}),
**self._get_ls_params(stop=stop, **kwargs),
**structured_output_format_dict,
}
callback_manager = CallbackManager.configure(
config.get("callbacks"),
@ -460,28 +472,16 @@ class BaseChatModel(BaseLanguageModel[BaseMessage], ABC):
config = ensure_config(config)
messages = self._convert_input(input).to_messages()
structured_output_format = kwargs.pop("structured_output_format", None)
if structured_output_format:
try:
structured_output_format_dict = {
"structured_output_format": {
"kwargs": structured_output_format.get("kwargs", {}),
"schema": convert_to_openai_tool(
structured_output_format["schema"]
),
}
}
except ValueError:
structured_output_format_dict = {}
else:
structured_output_format_dict = {}
ls_structured_output_format = kwargs.pop("ls_structured_output_format", None)
ls_structured_output_format_dict = _format_ls_structured_output(
ls_structured_output_format
)
params = self._get_invocation_params(stop=stop, **kwargs)
options = {"stop": stop, **kwargs}
options = {"stop": stop, **kwargs, **ls_structured_output_format_dict}
inheritable_metadata = {
**(config.get("metadata") or {}),
**self._get_ls_params(stop=stop, **kwargs),
**structured_output_format_dict,
}
callback_manager = AsyncCallbackManager.configure(
config.get("callbacks"),
@ -642,28 +642,16 @@ class BaseChatModel(BaseLanguageModel[BaseMessage], ABC):
An LLMResult, which contains a list of candidate Generations for each input
prompt and additional model provider-specific output.
"""
structured_output_format = kwargs.pop("structured_output_format", None)
if structured_output_format:
try:
structured_output_format_dict = {
"structured_output_format": {
"kwargs": structured_output_format.get("kwargs", {}),
"schema": convert_to_openai_tool(
structured_output_format["schema"]
),
}
}
except ValueError:
structured_output_format_dict = {}
else:
structured_output_format_dict = {}
ls_structured_output_format = kwargs.pop("ls_structured_output_format", None)
ls_structured_output_format_dict = _format_ls_structured_output(
ls_structured_output_format
)
params = self._get_invocation_params(stop=stop, **kwargs)
options = {"stop": stop}
options = {"stop": stop, **ls_structured_output_format_dict}
inheritable_metadata = {
**(metadata or {}),
**self._get_ls_params(stop=stop, **kwargs),
**structured_output_format_dict,
}
callback_manager = CallbackManager.configure(
@ -750,28 +738,16 @@ class BaseChatModel(BaseLanguageModel[BaseMessage], ABC):
An LLMResult, which contains a list of candidate Generations for each input
prompt and additional model provider-specific output.
"""
structured_output_format = kwargs.pop("structured_output_format", None)
if structured_output_format:
try:
structured_output_format_dict = {
"structured_output_format": {
"kwargs": structured_output_format.get("kwargs", {}),
"schema": convert_to_openai_tool(
structured_output_format["schema"]
),
}
}
except ValueError:
structured_output_format_dict = {}
else:
structured_output_format_dict = {}
ls_structured_output_format = kwargs.pop("ls_structured_output_format", None)
ls_structured_output_format_dict = _format_ls_structured_output(
ls_structured_output_format
)
params = self._get_invocation_params(stop=stop, **kwargs)
options = {"stop": stop}
options = {"stop": stop, **ls_structured_output_format_dict}
inheritable_metadata = {
**(metadata or {}),
**self._get_ls_params(stop=stop, **kwargs),
**structured_output_format_dict,
}
callback_manager = AsyncCallbackManager.configure(
@ -1314,7 +1290,10 @@ class BaseChatModel(BaseLanguageModel[BaseMessage], ABC):
llm = self.bind_tools(
[schema],
tool_choice="any",
structured_output_format={"kwargs": {}, "schema": schema},
ls_structured_output_format={
"kwargs": {"method": "function_calling"},
"schema": schema,
},
)
if isinstance(schema, type) and is_basemodel_subclass(schema):
output_parser: OutputParserLike = PydanticToolsParser(

View File

@ -538,6 +538,35 @@ def convert_to_openai_tool(
return {"type": "function", "function": oai_function}
def convert_to_json_schema(
schema: Union[dict[str, Any], type[BaseModel], Callable, BaseTool],
*,
strict: Optional[bool] = None,
) -> dict[str, Any]:
"""Convert a schema representation to a JSON schema."""
openai_tool = convert_to_openai_tool(schema, strict=strict)
if (
not isinstance(openai_tool, dict)
or "function" not in openai_tool
or "name" not in openai_tool["function"]
):
error_message = "Input must be a valid OpenAI-format tool."
raise ValueError(error_message)
openai_function = openai_tool["function"]
json_schema = {}
json_schema["title"] = openai_function["name"]
if "description" in openai_function:
json_schema["description"] = openai_function["description"]
if "parameters" in openai_function:
parameters = openai_function["parameters"].copy()
json_schema.update(parameters)
return json_schema
@beta()
def tool_example_to_messages(
input: str,

View File

@ -31,6 +31,7 @@ from langchain_core.runnables import Runnable, RunnableLambda
from langchain_core.tools import BaseTool, StructuredTool, Tool, tool
from langchain_core.utils.function_calling import (
_convert_typed_dict_to_openai_function,
convert_to_json_schema,
convert_to_openai_function,
tool_example_to_messages,
)
@ -1019,3 +1020,46 @@ def test_convert_to_openai_function_no_args() -> None:
},
"strict": True,
}
def test_convert_to_json_schema(
pydantic: type[BaseModel],
function: Callable,
function_docstring_annotations: Callable,
dummy_structured_tool: StructuredTool,
dummy_structured_tool_args_schema_dict: StructuredTool,
dummy_tool: BaseTool,
json_schema: dict,
anthropic_tool: dict,
bedrock_converse_tool: dict,
annotated_function: Callable,
dummy_pydantic: type[BaseModel],
dummy_typing_typed_dict: type,
dummy_typing_typed_dict_docstring: type,
dummy_extensions_typed_dict: type,
dummy_extensions_typed_dict_docstring: type,
) -> None:
expected = json_schema
for fn in (
pydantic,
function,
function_docstring_annotations,
dummy_structured_tool,
dummy_structured_tool_args_schema_dict,
dummy_tool,
json_schema,
anthropic_tool,
bedrock_converse_tool,
expected,
Dummy.dummy_function,
DummyWithClassMethod.dummy_function,
annotated_function,
dummy_pydantic,
dummy_typing_typed_dict,
dummy_typing_typed_dict_docstring,
dummy_extensions_typed_dict,
dummy_extensions_typed_dict_docstring,
):
actual = convert_to_json_schema(fn) # type: ignore
assert actual == expected

View File

@ -1014,7 +1014,10 @@ class ChatAnthropic(BaseChatModel):
warnings.warn(thinking_admonition)
llm = self.bind_tools(
[schema],
structured_output_format={"kwargs": {}, "schema": formatted_tool},
ls_structured_output_format={
"kwargs": {"method": "function_calling"},
"schema": formatted_tool,
},
)
def _raise_if_no_tool_calls(message: AIMessage) -> AIMessage:
@ -1329,7 +1332,10 @@ class ChatAnthropic(BaseChatModel):
llm = self.bind_tools(
[schema],
tool_choice=tool_name,
structured_output_format={"kwargs": {}, "schema": formatted_tool},
ls_structured_output_format={
"kwargs": {"method": "function_calling"},
"schema": formatted_tool,
},
)
if isinstance(schema, type) and is_basemodel_subclass(schema):

View File

@ -970,7 +970,7 @@ class ChatFireworks(BaseChatModel):
llm = self.bind_tools(
[schema],
tool_choice=tool_name,
structured_output_format={
ls_structured_output_format={
"kwargs": {"method": "function_calling"},
"schema": formatted_tool,
},
@ -987,7 +987,7 @@ class ChatFireworks(BaseChatModel):
elif method == "json_mode":
llm = self.bind(
response_format={"type": "json_object"},
structured_output_format={
ls_structured_output_format={
"kwargs": {"method": "json_mode"},
"schema": schema,
},

View File

@ -1001,7 +1001,7 @@ class ChatGroq(BaseChatModel):
llm = self.bind_tools(
[schema],
tool_choice=tool_name,
structured_output_format={
ls_structured_output_format={
"kwargs": {"method": "function_calling"},
"schema": formatted_tool,
},
@ -1018,7 +1018,7 @@ class ChatGroq(BaseChatModel):
elif method == "json_mode":
llm = self.bind(
response_format={"type": "json_object"},
structured_output_format={
ls_structured_output_format={
"kwargs": {"method": "json_mode"},
"schema": schema,
},

View File

@ -950,7 +950,7 @@ class ChatMistralAI(BaseChatModel):
llm = self.bind_tools(
[schema],
tool_choice="any",
structured_output_format={
ls_structured_output_format={
"kwargs": {"method": "function_calling"},
"schema": schema,
},
@ -968,7 +968,7 @@ class ChatMistralAI(BaseChatModel):
elif method == "json_mode":
llm = self.bind(
response_format={"type": "json_object"},
structured_output_format={
ls_structured_output_format={
"kwargs": {
# this is correct - name difference with mistral api
"method": "json_mode"
@ -990,7 +990,7 @@ class ChatMistralAI(BaseChatModel):
response_format = _convert_to_openai_response_format(schema, strict=True)
llm = self.bind(
response_format=response_format,
structured_output_format={
ls_structured_output_format={
"kwargs": {"method": "json_schema"},
"schema": schema,
},

View File

@ -1090,7 +1090,7 @@ class ChatOllama(BaseChatModel):
llm = self.bind_tools(
[schema],
tool_choice=tool_name,
structured_output_format={
ls_structured_output_format={
"kwargs": {"method": method},
"schema": formatted_tool,
},
@ -1107,7 +1107,7 @@ class ChatOllama(BaseChatModel):
elif method == "json_mode":
llm = self.bind(
format="json",
structured_output_format={
ls_structured_output_format={
"kwargs": {"method": method},
"schema": schema,
},
@ -1127,7 +1127,7 @@ class ChatOllama(BaseChatModel):
schema = cast(TypeBaseModel, schema)
llm = self.bind(
format=schema.model_json_schema(),
structured_output_format={
ls_structured_output_format={
"kwargs": {"method": method},
"schema": schema,
},
@ -1148,7 +1148,7 @@ class ChatOllama(BaseChatModel):
response_format = schema
llm = self.bind(
format=response_format,
structured_output_format={
ls_structured_output_format={
"kwargs": {"method": method},
"schema": response_format,
},

View File

@ -1431,7 +1431,7 @@ class BaseChatOpenAI(BaseChatModel):
tool_choice=tool_name,
parallel_tool_calls=False,
strict=strict,
structured_output_format={
ls_structured_output_format={
"kwargs": {"method": method},
"schema": schema,
},
@ -1450,7 +1450,7 @@ class BaseChatOpenAI(BaseChatModel):
elif method == "json_mode":
llm = self.bind(
response_format={"type": "json_object"},
structured_output_format={
ls_structured_output_format={
"kwargs": {"method": method},
"schema": schema,
},
@ -1469,7 +1469,7 @@ class BaseChatOpenAI(BaseChatModel):
response_format = _convert_to_openai_response_format(schema, strict=strict)
llm = self.bind(
response_format=response_format,
structured_output_format={
ls_structured_output_format={
"kwargs": {"method": method},
"schema": convert_to_openai_tool(schema),
},

View File

@ -20,7 +20,7 @@ from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.tools import BaseTool, tool
from langchain_core.utils.function_calling import (
convert_to_openai_tool,
convert_to_json_schema,
tool_example_to_messages,
)
from pydantic import BaseModel, Field
@ -72,21 +72,21 @@ def _get_joke_class(
class _TestCallbackHandler(BaseCallbackHandler):
metadatas: list[Optional[dict]]
options: list[Optional[dict]]
def __init__(self) -> None:
super().__init__()
self.metadatas = []
self.options = []
def on_chat_model_start(
self,
serialized: Any,
messages: Any,
*,
metadata: Optional[dict[str, Any]] = None,
options: Optional[dict[str, Any]] = None,
**kwargs: Any,
) -> None:
self.metadatas.append(metadata)
self.options.append(options)
class _MagicFunctionSchema(BaseModel):
@ -1243,16 +1243,16 @@ class ChatModelIntegrationTests(ChatModelTests):
)
validation_function(result)
assert len(invoke_callback.metadatas) == 1, (
assert len(invoke_callback.options) == 1, (
"Expected on_chat_model_start to be called once"
)
assert isinstance(invoke_callback.metadatas[0], dict)
assert isinstance(invoke_callback.options[0], dict)
assert isinstance(
invoke_callback.metadatas[0]["structured_output_format"]["schema"], dict
invoke_callback.options[0]["ls_structured_output_format"]["schema"], dict
)
assert invoke_callback.metadatas[0]["structured_output_format"][
assert invoke_callback.options[0]["ls_structured_output_format"][
"schema"
] == convert_to_openai_tool(schema)
] == convert_to_json_schema(schema)
stream_callback = _TestCallbackHandler()
@ -1262,16 +1262,16 @@ class ChatModelIntegrationTests(ChatModelTests):
validation_function(chunk)
assert chunk
assert len(stream_callback.metadatas) == 1, (
assert len(stream_callback.options) == 1, (
"Expected on_chat_model_start to be called once"
)
assert isinstance(stream_callback.metadatas[0], dict)
assert isinstance(stream_callback.options[0], dict)
assert isinstance(
stream_callback.metadatas[0]["structured_output_format"]["schema"], dict
stream_callback.options[0]["ls_structured_output_format"]["schema"], dict
)
assert stream_callback.metadatas[0]["structured_output_format"][
assert stream_callback.options[0]["ls_structured_output_format"][
"schema"
] == convert_to_openai_tool(schema)
] == convert_to_json_schema(schema)
@pytest.mark.parametrize("schema_type", ["pydantic", "typeddict", "json_schema"])
async def test_structured_output_async(
@ -1319,16 +1319,16 @@ class ChatModelIntegrationTests(ChatModelTests):
)
validation_function(result)
assert len(ainvoke_callback.metadatas) == 1, (
assert len(ainvoke_callback.options) == 1, (
"Expected on_chat_model_start to be called once"
)
assert isinstance(ainvoke_callback.metadatas[0], dict)
assert isinstance(ainvoke_callback.options[0], dict)
assert isinstance(
ainvoke_callback.metadatas[0]["structured_output_format"]["schema"], dict
ainvoke_callback.options[0]["ls_structured_output_format"]["schema"], dict
)
assert ainvoke_callback.metadatas[0]["structured_output_format"][
assert ainvoke_callback.options[0]["ls_structured_output_format"][
"schema"
] == convert_to_openai_tool(schema)
] == convert_to_json_schema(schema)
astream_callback = _TestCallbackHandler()
@ -1338,17 +1338,17 @@ class ChatModelIntegrationTests(ChatModelTests):
validation_function(chunk)
assert chunk
assert len(astream_callback.metadatas) == 1, (
assert len(astream_callback.options) == 1, (
"Expected on_chat_model_start to be called once"
)
assert isinstance(astream_callback.metadatas[0], dict)
assert isinstance(astream_callback.options[0], dict)
assert isinstance(
astream_callback.metadatas[0]["structured_output_format"]["schema"], dict
astream_callback.options[0]["ls_structured_output_format"]["schema"], dict
)
assert astream_callback.metadatas[0]["structured_output_format"][
assert astream_callback.options[0]["ls_structured_output_format"][
"schema"
] == convert_to_openai_tool(schema)
] == convert_to_json_schema(schema)
@pytest.mark.skipif(PYDANTIC_MAJOR_VERSION != 2, reason="Test requires pydantic 2.")
def test_structured_output_pydantic_2_v1(self, model: BaseChatModel) -> None: