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core[patch]: make oai tool description optional (#27756)

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Bagatur 2024-11-06 10:06:47 -08:00 committed by GitHub
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2 changed files with 174 additions and 23 deletions
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73
libs/core/langchain_core/utils/function_calling.py
View File

@ -341,7 +341,7 @@ def convert_to_openai_function(
A dictionary, Pydantic BaseModel class, TypedDict class, a LangChain
Tool object, or a Python function. If a dictionary is passed in, it is
assumed to already be a valid OpenAI function, a JSON schema with
top-level 'title' and 'description' keys specified, an Anthropic format
top-level 'title' key specified, an Anthropic format
tool, or an Amazon Bedrock Converse format tool.
strict:
If True, model output is guaranteed to exactly match the JSON Schema
@ -366,40 +366,47 @@ def convert_to_openai_function(
.. versionchanged:: 0.3.14
Support for Amazon Bedrock Converse format tools added.
.. versionchanged:: 0.3.16
'description' and 'parameters' keys are now optional. Only 'name' is
required and guaranteed to be part of the output.
"""
from langchain_core.tools import BaseTool
# already in OpenAI function format
if isinstance(function, dict) and all(
k in function for k in ("name", "description", "parameters")
):
oai_function = function
# a JSON schema with title and description
elif isinstance(function, dict) and all(
k in function for k in ("title", "description", "properties")
):
function = function.copy()
oai_function = {
"name": function.pop("title"),
"description": function.pop("description"),
"parameters": function,
}
# an Anthropic format tool
elif isinstance(function, dict) and all(
k in function for k in ("name", "description", "input_schema")
if isinstance(function, dict) and all(
k in function for k in ("name", "input_schema")
):
oai_function = {
"name": function["name"],
"description": function["description"],
"parameters": function["input_schema"],
}
if "description" in function:
oai_function["description"] = function["description"]
# an Amazon Bedrock Converse format tool
elif isinstance(function, dict) and "toolSpec" in function:
oai_function = {
"name": function["toolSpec"]["name"],
"description": function["toolSpec"]["description"],
"parameters": function["toolSpec"]["inputSchema"]["json"],
}
if "description" in function["toolSpec"]:
oai_function["description"] = function["toolSpec"]["description"]
# already in OpenAI function format
elif isinstance(function, dict) and "name" in function:
oai_function = {
k: v
for k, v in function.items()
if k in ("name", "description", "parameters", "strict")
}
# a JSON schema with title and description
elif isinstance(function, dict) and "title" in function:
function_copy = function.copy()
oai_function = {"name": function_copy.pop("title")}
if "description" in function_copy:
oai_function["description"] = function_copy.pop("description")
if function_copy and "properties" in function_copy:
oai_function["parameters"] = function_copy
elif isinstance(function, type) and is_basemodel_subclass(function):
oai_function = cast(dict, convert_pydantic_to_openai_function(function))
elif is_typeddict(function):
@ -420,6 +427,13 @@ def convert_to_openai_function(
raise ValueError(msg)
if strict is not None:
if "strict" in oai_function and oai_function["strict"] != strict:
msg = (
f"Tool/function already has a 'strict' key wth value "
f"{oai_function['strict']} which is different from the explicit "
f"`strict` arg received {strict=}."
)
raise ValueError(msg)
oai_function["strict"] = strict
if strict:
# As of 08/06/24, OpenAI requires that additionalProperties be supplied and
@ -438,12 +452,16 @@ def convert_to_openai_tool(
) -> dict[str, Any]:
"""Convert a tool-like object to an OpenAI tool schema.
OpenAI tool schema reference:
https://platform.openai.com/docs/api-reference/chat/create#chat-create-tools
Args:
tool:
Either a dictionary, a pydantic.BaseModel class, Python function, or
BaseTool. If a dictionary is passed in, it is assumed to already be a valid
OpenAI tool, OpenAI function, a JSON schema with top-level 'title' and
'description' keys specified, or an Anthropic format tool.
BaseTool. If a dictionary is passed in, it is
assumed to already be a valid OpenAI function, a JSON schema with
top-level 'title' key specified, an Anthropic format
tool, or an Amazon Bedrock Converse format tool.
strict:
If True, model output is guaranteed to exactly match the JSON Schema
provided in the function definition. If None, ``strict`` argument will not
@ -460,6 +478,15 @@ def convert_to_openai_tool(
.. versionchanged:: 0.3.13
Support for Anthropic format tools added.
.. versionchanged:: 0.3.14
Support for Amazon Bedrock Converse format tools added.
.. versionchanged:: 0.3.16
'description' and 'parameters' keys are now optional. Only 'name' is
required and guaranteed to be part of the output.
"""
if isinstance(tool, dict) and tool.get("type") == "function" and "function" in tool:
return tool

124
libs/core/tests/unit_tests/utils/test_function_calling.py
View File

@ -459,6 +459,130 @@ def test_convert_to_openai_function_nested_strict() -> None:
assert actual == expected
json_schema_no_description_no_params = {
"title": "dummy_function",
}
json_schema_no_description = {
"title": "dummy_function",
"type": "object",
"properties": {
"arg1": {"description": "foo", "type": "integer"},
"arg2": {
"description": "one of 'bar', 'baz'",
"enum": ["bar", "baz"],
"type": "string",
},
},
"required": ["arg1", "arg2"],
}
anthropic_tool_no_description = {
"name": "dummy_function",
"input_schema": {
"type": "object",
"properties": {
"arg1": {"description": "foo", "type": "integer"},
"arg2": {
"description": "one of 'bar', 'baz'",
"enum": ["bar", "baz"],
"type": "string",
},
},
"required": ["arg1", "arg2"],
},
}
bedrock_converse_tool_no_description = {
"toolSpec": {
"name": "dummy_function",
"inputSchema": {
"json": {
"type": "object",
"properties": {
"arg1": {"description": "foo", "type": "integer"},
"arg2": {
"description": "one of 'bar', 'baz'",
"enum": ["bar", "baz"],
"type": "string",
},
},
"required": ["arg1", "arg2"],
}
},
}
}
openai_function_no_description = {
"name": "dummy_function",
"parameters": {
"type": "object",
"properties": {
"arg1": {"description": "foo", "type": "integer"},
"arg2": {
"description": "one of 'bar', 'baz'",
"enum": ["bar", "baz"],
"type": "string",
},
},
"required": ["arg1", "arg2"],
},
}
openai_function_no_description_no_params = {
"name": "dummy_function",
}
@pytest.mark.parametrize(
"func",
[
anthropic_tool_no_description,
json_schema_no_description,
bedrock_converse_tool_no_description,
openai_function_no_description,
],
)
def test_convert_to_openai_function_no_description(func: dict) -> None:
expected = {
"name": "dummy_function",
"parameters": {
"type": "object",
"properties": {
"arg1": {"description": "foo", "type": "integer"},
"arg2": {
"description": "one of 'bar', 'baz'",
"enum": ["bar", "baz"],
"type": "string",
},
},
"required": ["arg1", "arg2"],
},
}
actual = convert_to_openai_function(func)
assert actual == expected
@pytest.mark.parametrize(
"func",
[
json_schema_no_description_no_params,
openai_function_no_description_no_params,
],
)
def test_convert_to_openai_function_no_description_no_params(func: dict) -> None:
expected = {
"name": "dummy_function",
}
actual = convert_to_openai_function(func)
assert actual == expected
@pytest.mark.xfail(
reason="Pydantic converts Optional[str] to str in .model_json_schema()"
)