mirror of
https://github.com/crewAIInc/crewAI.git
synced 2026-01-07 07:08:31 +00:00
Json Task Output Truncation with Escape Characters (#1009)
* Fixed special character issue when converting json to models. Added numerous tests to ensure thigns work properly. * Fix linting error and cleaned up tests * Fix customer_converter_cls test failure * Fixed tests. Thank you lorenze for pointing that out. added a few more to ensure converter creation works properly * Address lorenze feedback * Fix linting issues
This commit is contained in:
Brandon Hancock (bhancock_ai)
committed by
GitHub
GitHub
parent
7f5c4fded6
commit
5627159419
@@ -1,6 +1,5 @@
|
||||
import json
|
||||
import os
|
||||
import re
|
||||
import threading
|
||||
import uuid
|
||||
from concurrent.futures import Future
|
||||
@@ -8,7 +7,6 @@ from copy import copy
|
||||
from hashlib import md5
|
||||
from typing import Any, Dict, List, Optional, Tuple, Type, Union
|
||||
|
||||
from langchain_openai import ChatOpenAI
|
||||
from opentelemetry.trace import Span
|
||||
from pydantic import UUID4, BaseModel, Field, field_validator, model_validator
|
||||
from pydantic_core import PydanticCustomError
|
||||
@@ -17,10 +15,8 @@ from crewai.agents.agent_builder.base_agent import BaseAgent
|
||||
from crewai.tasks.output_format import OutputFormat
|
||||
from crewai.tasks.task_output import TaskOutput
|
||||
from crewai.telemetry.telemetry import Telemetry
|
||||
from crewai.utilities.converter import Converter, ConverterError
|
||||
from crewai.utilities.converter import Converter, convert_to_model
|
||||
from crewai.utilities.i18n import I18N
|
||||
from crewai.utilities.printer import Printer
|
||||
from crewai.utilities.pydantic_schema_parser import PydanticSchemaParser
|
||||
|
||||
|
||||
class Task(BaseModel):
|
||||
@@ -254,9 +250,7 @@ class Task(BaseModel):
|
||||
content = (
|
||||
json_output
|
||||
if json_output
|
||||
else pydantic_output.model_dump_json()
|
||||
if pydantic_output
|
||||
else result
|
||||
else pydantic_output.model_dump_json() if pydantic_output else result
|
||||
)
|
||||
self._save_file(content)
|
||||
|
||||
@@ -326,18 +320,6 @@ class Task(BaseModel):
|
||||
|
||||
return copied_task
|
||||
|
||||
def _create_converter(self, *args, **kwargs) -> Converter:
|
||||
"""Create a converter instance."""
|
||||
if self.agent and not self.converter_cls:
|
||||
converter = self.agent.get_output_converter(*args, **kwargs)
|
||||
elif self.converter_cls:
|
||||
converter = self.converter_cls(*args, **kwargs)
|
||||
|
||||
if not converter:
|
||||
raise Exception("No output converter found or set.")
|
||||
|
||||
return converter
|
||||
|
||||
def _export_output(
|
||||
self, result: str
|
||||
) -> Tuple[Optional[BaseModel], Optional[Dict[str, Any]]]:
|
||||
@@ -345,75 +327,26 @@ class Task(BaseModel):
|
||||
json_output: Optional[Dict[str, Any]] = None
|
||||
|
||||
if self.output_pydantic or self.output_json:
|
||||
model_output = self._convert_to_model(result)
|
||||
pydantic_output = (
|
||||
model_output if isinstance(model_output, BaseModel) else None
|
||||
model_output = convert_to_model(
|
||||
result,
|
||||
self.output_pydantic,
|
||||
self.output_json,
|
||||
self.agent,
|
||||
self.converter_cls,
|
||||
)
|
||||
if isinstance(model_output, str):
|
||||
|
||||
if isinstance(model_output, BaseModel):
|
||||
pydantic_output = model_output
|
||||
elif isinstance(model_output, dict):
|
||||
json_output = model_output
|
||||
elif isinstance(model_output, str):
|
||||
try:
|
||||
json_output = json.loads(model_output)
|
||||
except json.JSONDecodeError:
|
||||
json_output = None
|
||||
else:
|
||||
json_output = model_output if isinstance(model_output, dict) else None
|
||||
|
||||
return pydantic_output, json_output
|
||||
|
||||
def _convert_to_model(self, result: str) -> Union[dict, BaseModel, str]:
|
||||
model = self.output_pydantic or self.output_json
|
||||
if model is None:
|
||||
return result
|
||||
|
||||
try:
|
||||
return self._validate_model(result, model)
|
||||
except Exception:
|
||||
return self._handle_partial_json(result, model)
|
||||
|
||||
def _validate_model(
|
||||
self, result: str, model: Type[BaseModel]
|
||||
) -> Union[dict, BaseModel]:
|
||||
exported_result = model.model_validate_json(result)
|
||||
if self.output_json:
|
||||
return exported_result.model_dump()
|
||||
return exported_result
|
||||
|
||||
def _handle_partial_json(
|
||||
self, result: str, model: Type[BaseModel]
|
||||
) -> Union[dict, BaseModel, str]:
|
||||
match = re.search(r"({.*})", result, re.DOTALL)
|
||||
if match:
|
||||
try:
|
||||
exported_result = model.model_validate_json(match.group(0))
|
||||
if self.output_json:
|
||||
return exported_result.model_dump()
|
||||
return exported_result
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
return self._convert_with_instructions(result, model)
|
||||
|
||||
def _convert_with_instructions(
|
||||
self, result: str, model: Type[BaseModel]
|
||||
) -> Union[dict, BaseModel, str]:
|
||||
llm = self.agent.function_calling_llm or self.agent.llm # type: ignore # Item "None" of "BaseAgent | None" has no attribute "function_calling_llm"
|
||||
instructions = self._get_conversion_instructions(model, llm)
|
||||
|
||||
converter = self._create_converter(
|
||||
llm=llm, text=result, model=model, instructions=instructions
|
||||
)
|
||||
exported_result = (
|
||||
converter.to_pydantic() if self.output_pydantic else converter.to_json()
|
||||
)
|
||||
|
||||
if isinstance(exported_result, ConverterError):
|
||||
Printer().print(
|
||||
content=f"{exported_result.message} Using raw output instead.",
|
||||
color="red",
|
||||
)
|
||||
return result
|
||||
|
||||
return exported_result
|
||||
|
||||
def _get_output_format(self) -> OutputFormat:
|
||||
if self.output_json:
|
||||
return OutputFormat.JSON
|
||||
@@ -421,26 +354,6 @@ class Task(BaseModel):
|
||||
return OutputFormat.PYDANTIC
|
||||
return OutputFormat.RAW
|
||||
|
||||
def _get_conversion_instructions(self, model: Type[BaseModel], llm: Any) -> str:
|
||||
instructions = "I'm gonna convert this raw text into valid JSON."
|
||||
if not self._is_gpt(llm):
|
||||
model_schema = PydanticSchemaParser(model=model).get_schema()
|
||||
instructions = f"{instructions}\n\nThe json should have the following structure, with the following keys:\n{model_schema}"
|
||||
return instructions
|
||||
|
||||
def _save_output(self, content: str) -> None:
|
||||
if not self.output_file:
|
||||
raise Exception("Output file path is not set.")
|
||||
|
||||
directory = os.path.dirname(self.output_file)
|
||||
if directory and not os.path.exists(directory):
|
||||
os.makedirs(directory)
|
||||
with open(self.output_file, "w", encoding="utf-8") as file:
|
||||
file.write(content)
|
||||
|
||||
def _is_gpt(self, llm) -> bool:
|
||||
return isinstance(llm, ChatOpenAI) and llm.openai_api_base is None
|
||||
|
||||
def _save_file(self, result: Any) -> None:
|
||||
directory = os.path.dirname(self.output_file) # type: ignore # Value of type variable "AnyOrLiteralStr" of "dirname" cannot be "str | None"
|
||||
|
||||
|
||||
@@ -1,9 +1,14 @@
|
||||
import json
|
||||
import re
|
||||
from typing import Any, Optional, Type, Union
|
||||
|
||||
from langchain.schema import HumanMessage, SystemMessage
|
||||
from langchain_openai import ChatOpenAI
|
||||
from pydantic import BaseModel, ValidationError
|
||||
|
||||
from crewai.agents.agent_builder.utilities.base_output_converter import OutputConverter
|
||||
from crewai.utilities.printer import Printer
|
||||
from crewai.utilities.pydantic_schema_parser import PydanticSchemaParser
|
||||
|
||||
|
||||
class ConverterError(Exception):
|
||||
@@ -72,3 +77,153 @@ class Converter(OutputConverter):
|
||||
def is_gpt(self) -> bool:
|
||||
"""Return if llm provided is of gpt from openai."""
|
||||
return isinstance(self.llm, ChatOpenAI) and self.llm.openai_api_base is None
|
||||
|
||||
|
||||
def convert_to_model(
|
||||
result: str,
|
||||
output_pydantic: Optional[Type[BaseModel]],
|
||||
output_json: Optional[Type[BaseModel]],
|
||||
agent: Any,
|
||||
converter_cls: Optional[Type[Converter]] = None,
|
||||
) -> Union[dict, BaseModel, str]:
|
||||
model = output_pydantic or output_json
|
||||
if model is None:
|
||||
return result
|
||||
|
||||
try:
|
||||
escaped_result = json.dumps(json.loads(result, strict=False))
|
||||
return validate_model(escaped_result, model, bool(output_json))
|
||||
except json.JSONDecodeError as e:
|
||||
Printer().print(
|
||||
content=f"Error parsing JSON: {e}. Attempting to handle partial JSON.",
|
||||
color="yellow",
|
||||
)
|
||||
return handle_partial_json(
|
||||
result, model, bool(output_json), agent, converter_cls
|
||||
)
|
||||
except ValidationError as e:
|
||||
Printer().print(
|
||||
content=f"Pydantic validation error: {e}. Attempting to handle partial JSON.",
|
||||
color="yellow",
|
||||
)
|
||||
return handle_partial_json(
|
||||
result, model, bool(output_json), agent, converter_cls
|
||||
)
|
||||
except Exception as e:
|
||||
Printer().print(
|
||||
content=f"Unexpected error during model conversion: {type(e).__name__}: {e}. Returning original result.",
|
||||
color="red",
|
||||
)
|
||||
return result
|
||||
|
||||
|
||||
def validate_model(
|
||||
result: str, model: Type[BaseModel], is_json_output: bool
|
||||
) -> Union[dict, BaseModel]:
|
||||
exported_result = model.model_validate_json(result)
|
||||
if is_json_output:
|
||||
return exported_result.model_dump()
|
||||
return exported_result
|
||||
|
||||
|
||||
def handle_partial_json(
|
||||
result: str,
|
||||
model: Type[BaseModel],
|
||||
is_json_output: bool,
|
||||
agent: Any,
|
||||
converter_cls: Optional[Type[Converter]] = None,
|
||||
) -> Union[dict, BaseModel, str]:
|
||||
match = re.search(r"({.*})", result, re.DOTALL)
|
||||
if match:
|
||||
try:
|
||||
exported_result = model.model_validate_json(match.group(0))
|
||||
if is_json_output:
|
||||
return exported_result.model_dump()
|
||||
return exported_result
|
||||
except json.JSONDecodeError as e:
|
||||
Printer().print(
|
||||
content=f"Error parsing JSON: {e}. The extracted JSON-like string is not valid JSON. Attempting alternative conversion method.",
|
||||
color="yellow",
|
||||
)
|
||||
except ValidationError as e:
|
||||
Printer().print(
|
||||
content=f"Pydantic validation error: {e}. The JSON structure doesn't match the expected model. Attempting alternative conversion method.",
|
||||
color="yellow",
|
||||
)
|
||||
except Exception as e:
|
||||
Printer().print(
|
||||
content=f"Unexpected error during partial JSON handling: {type(e).__name__}: {e}. Attempting alternative conversion method.",
|
||||
color="red",
|
||||
)
|
||||
|
||||
return convert_with_instructions(
|
||||
result, model, is_json_output, agent, converter_cls
|
||||
)
|
||||
|
||||
|
||||
def convert_with_instructions(
|
||||
result: str,
|
||||
model: Type[BaseModel],
|
||||
is_json_output: bool,
|
||||
agent: Any,
|
||||
converter_cls: Optional[Type[Converter]] = None,
|
||||
) -> Union[dict, BaseModel, str]:
|
||||
llm = agent.function_calling_llm or agent.llm
|
||||
instructions = get_conversion_instructions(model, llm)
|
||||
|
||||
converter = create_converter(
|
||||
agent=agent,
|
||||
converter_cls=converter_cls,
|
||||
llm=llm,
|
||||
text=result,
|
||||
model=model,
|
||||
instructions=instructions,
|
||||
)
|
||||
exported_result = (
|
||||
converter.to_pydantic() if not is_json_output else converter.to_json()
|
||||
)
|
||||
|
||||
if isinstance(exported_result, ConverterError):
|
||||
Printer().print(
|
||||
content=f"{exported_result.message} Using raw output instead.",
|
||||
color="red",
|
||||
)
|
||||
return result
|
||||
|
||||
return exported_result
|
||||
|
||||
|
||||
def get_conversion_instructions(model: Type[BaseModel], llm: Any) -> str:
|
||||
instructions = "I'm gonna convert this raw text into valid JSON."
|
||||
if not is_gpt(llm):
|
||||
model_schema = PydanticSchemaParser(model=model).get_schema()
|
||||
instructions = f"{instructions}\n\nThe json should have the following structure, with the following keys:\n{model_schema}"
|
||||
return instructions
|
||||
|
||||
|
||||
def is_gpt(llm: Any) -> bool:
|
||||
from langchain_openai import ChatOpenAI
|
||||
|
||||
return isinstance(llm, ChatOpenAI) and llm.openai_api_base is None
|
||||
|
||||
|
||||
def create_converter(
|
||||
agent: Optional[Any] = None,
|
||||
converter_cls: Optional[Type[Converter]] = None,
|
||||
*args,
|
||||
**kwargs,
|
||||
) -> Converter:
|
||||
if agent and not converter_cls:
|
||||
if hasattr(agent, "get_output_converter"):
|
||||
converter = agent.get_output_converter(*args, **kwargs)
|
||||
else:
|
||||
raise AttributeError("Agent does not have a 'get_output_converter' method")
|
||||
elif converter_cls:
|
||||
converter = converter_cls(*args, **kwargs)
|
||||
else:
|
||||
raise ValueError("Either agent or converter_cls must be provided")
|
||||
|
||||
if not converter:
|
||||
raise Exception("No output converter found or set.")
|
||||
|
||||
return converter
|
||||
|
||||
@@ -5,13 +5,12 @@ import json
|
||||
from unittest.mock import MagicMock, patch
|
||||
|
||||
import pytest
|
||||
from pydantic import BaseModel
|
||||
from pydantic_core import ValidationError
|
||||
|
||||
from crewai import Agent, Crew, Process, Task
|
||||
from crewai.tasks.conditional_task import ConditionalTask
|
||||
from crewai.tasks.task_output import TaskOutput
|
||||
from crewai.utilities.converter import Converter
|
||||
from pydantic import BaseModel
|
||||
from pydantic_core import ValidationError
|
||||
|
||||
|
||||
def test_task_tool_reflect_agent_tools():
|
||||
|
||||
266
tests/utilities/test_converter.py
Normal file
266
tests/utilities/test_converter.py
Normal file
@@ -0,0 +1,266 @@
|
||||
import json
|
||||
from unittest.mock import MagicMock, Mock, patch
|
||||
|
||||
import pytest
|
||||
from crewai.utilities.converter import (
|
||||
Converter,
|
||||
ConverterError,
|
||||
convert_to_model,
|
||||
convert_with_instructions,
|
||||
create_converter,
|
||||
get_conversion_instructions,
|
||||
handle_partial_json,
|
||||
is_gpt,
|
||||
validate_model,
|
||||
)
|
||||
from pydantic import BaseModel
|
||||
|
||||
|
||||
# Sample Pydantic models for testing
|
||||
class EmailResponse(BaseModel):
|
||||
previous_message_content: str
|
||||
|
||||
|
||||
class EmailResponses(BaseModel):
|
||||
responses: list[EmailResponse]
|
||||
|
||||
|
||||
class SimpleModel(BaseModel):
|
||||
name: str
|
||||
age: int
|
||||
|
||||
|
||||
class NestedModel(BaseModel):
|
||||
id: int
|
||||
data: SimpleModel
|
||||
|
||||
|
||||
# Fixtures
|
||||
@pytest.fixture
|
||||
def mock_agent():
|
||||
agent = Mock()
|
||||
agent.function_calling_llm = None
|
||||
agent.llm = Mock()
|
||||
return agent
|
||||
|
||||
|
||||
# Tests for convert_to_model
|
||||
def test_convert_to_model_with_valid_json():
|
||||
result = '{"name": "John", "age": 30}'
|
||||
output = convert_to_model(result, SimpleModel, None, None)
|
||||
assert isinstance(output, SimpleModel)
|
||||
assert output.name == "John"
|
||||
assert output.age == 30
|
||||
|
||||
|
||||
def test_convert_to_model_with_invalid_json():
|
||||
result = '{"name": "John", "age": "thirty"}'
|
||||
with patch("crewai.utilities.converter.handle_partial_json") as mock_handle:
|
||||
mock_handle.return_value = "Fallback result"
|
||||
output = convert_to_model(result, SimpleModel, None, None)
|
||||
assert output == "Fallback result"
|
||||
|
||||
|
||||
def test_convert_to_model_with_no_model():
|
||||
result = "Plain text"
|
||||
output = convert_to_model(result, None, None, None)
|
||||
assert output == "Plain text"
|
||||
|
||||
|
||||
def test_convert_to_model_with_special_characters():
|
||||
json_string_test = """
|
||||
{
|
||||
"responses": [
|
||||
{
|
||||
"previous_message_content": "Hi Tom,\r\n\r\nNiamh has chosen the Mika phonics on"
|
||||
}
|
||||
]
|
||||
}
|
||||
"""
|
||||
output = convert_to_model(json_string_test, EmailResponses, None, None)
|
||||
assert isinstance(output, EmailResponses)
|
||||
assert len(output.responses) == 1
|
||||
assert (
|
||||
output.responses[0].previous_message_content
|
||||
== "Hi Tom,\r\n\r\nNiamh has chosen the Mika phonics on"
|
||||
)
|
||||
|
||||
|
||||
def test_convert_to_model_with_escaped_special_characters():
|
||||
json_string_test = json.dumps(
|
||||
{
|
||||
"responses": [
|
||||
{
|
||||
"previous_message_content": "Hi Tom,\r\n\r\nNiamh has chosen the Mika phonics on"
|
||||
}
|
||||
]
|
||||
}
|
||||
)
|
||||
output = convert_to_model(json_string_test, EmailResponses, None, None)
|
||||
assert isinstance(output, EmailResponses)
|
||||
assert len(output.responses) == 1
|
||||
assert (
|
||||
output.responses[0].previous_message_content
|
||||
== "Hi Tom,\r\n\r\nNiamh has chosen the Mika phonics on"
|
||||
)
|
||||
|
||||
|
||||
def test_convert_to_model_with_multiple_special_characters():
|
||||
json_string_test = """
|
||||
{
|
||||
"responses": [
|
||||
{
|
||||
"previous_message_content": "Line 1\r\nLine 2\tTabbed\nLine 3\r\n\rEscaped newline"
|
||||
}
|
||||
]
|
||||
}
|
||||
"""
|
||||
output = convert_to_model(json_string_test, EmailResponses, None, None)
|
||||
assert isinstance(output, EmailResponses)
|
||||
assert len(output.responses) == 1
|
||||
assert (
|
||||
output.responses[0].previous_message_content
|
||||
== "Line 1\r\nLine 2\tTabbed\nLine 3\r\n\rEscaped newline"
|
||||
)
|
||||
|
||||
|
||||
# Tests for validate_model
|
||||
def test_validate_model_pydantic_output():
|
||||
result = '{"name": "Alice", "age": 25}'
|
||||
output = validate_model(result, SimpleModel, False)
|
||||
assert isinstance(output, SimpleModel)
|
||||
assert output.name == "Alice"
|
||||
assert output.age == 25
|
||||
|
||||
|
||||
def test_validate_model_json_output():
|
||||
result = '{"name": "Bob", "age": 40}'
|
||||
output = validate_model(result, SimpleModel, True)
|
||||
assert isinstance(output, dict)
|
||||
assert output == {"name": "Bob", "age": 40}
|
||||
|
||||
|
||||
# Tests for handle_partial_json
|
||||
def test_handle_partial_json_with_valid_partial():
|
||||
result = 'Some text {"name": "Charlie", "age": 35} more text'
|
||||
output = handle_partial_json(result, SimpleModel, False, None)
|
||||
assert isinstance(output, SimpleModel)
|
||||
assert output.name == "Charlie"
|
||||
assert output.age == 35
|
||||
|
||||
|
||||
def test_handle_partial_json_with_invalid_partial(mock_agent):
|
||||
result = "No valid JSON here"
|
||||
with patch("crewai.utilities.converter.convert_with_instructions") as mock_convert:
|
||||
mock_convert.return_value = "Converted result"
|
||||
output = handle_partial_json(result, SimpleModel, False, mock_agent)
|
||||
assert output == "Converted result"
|
||||
|
||||
|
||||
# Tests for convert_with_instructions
|
||||
@patch("crewai.utilities.converter.create_converter")
|
||||
@patch("crewai.utilities.converter.get_conversion_instructions")
|
||||
def test_convert_with_instructions_success(
|
||||
mock_get_instructions, mock_create_converter, mock_agent
|
||||
):
|
||||
mock_get_instructions.return_value = "Instructions"
|
||||
mock_converter = Mock()
|
||||
mock_converter.to_pydantic.return_value = SimpleModel(name="David", age=50)
|
||||
mock_create_converter.return_value = mock_converter
|
||||
|
||||
result = "Some text to convert"
|
||||
output = convert_with_instructions(result, SimpleModel, False, mock_agent)
|
||||
|
||||
assert isinstance(output, SimpleModel)
|
||||
assert output.name == "David"
|
||||
assert output.age == 50
|
||||
|
||||
|
||||
@patch("crewai.utilities.converter.create_converter")
|
||||
@patch("crewai.utilities.converter.get_conversion_instructions")
|
||||
def test_convert_with_instructions_failure(
|
||||
mock_get_instructions, mock_create_converter, mock_agent
|
||||
):
|
||||
mock_get_instructions.return_value = "Instructions"
|
||||
mock_converter = Mock()
|
||||
mock_converter.to_pydantic.return_value = ConverterError("Conversion failed")
|
||||
mock_create_converter.return_value = mock_converter
|
||||
|
||||
result = "Some text to convert"
|
||||
with patch("crewai.utilities.converter.Printer") as mock_printer:
|
||||
output = convert_with_instructions(result, SimpleModel, False, mock_agent)
|
||||
assert output == result
|
||||
mock_printer.return_value.print.assert_called_once()
|
||||
|
||||
|
||||
# Tests for get_conversion_instructions
|
||||
def test_get_conversion_instructions_gpt():
|
||||
mock_llm = Mock()
|
||||
mock_llm.openai_api_base = None
|
||||
with patch("crewai.utilities.converter.is_gpt", return_value=True):
|
||||
instructions = get_conversion_instructions(SimpleModel, mock_llm)
|
||||
assert instructions == "I'm gonna convert this raw text into valid JSON."
|
||||
|
||||
|
||||
def test_get_conversion_instructions_non_gpt():
|
||||
mock_llm = Mock()
|
||||
with patch("crewai.utilities.converter.is_gpt", return_value=False):
|
||||
with patch("crewai.utilities.converter.PydanticSchemaParser") as mock_parser:
|
||||
mock_parser.return_value.get_schema.return_value = "Sample schema"
|
||||
instructions = get_conversion_instructions(SimpleModel, mock_llm)
|
||||
assert "Sample schema" in instructions
|
||||
|
||||
|
||||
# Tests for is_gpt
|
||||
def test_is_gpt_true():
|
||||
from langchain_openai import ChatOpenAI
|
||||
|
||||
mock_llm = Mock(spec=ChatOpenAI)
|
||||
mock_llm.openai_api_base = None
|
||||
assert is_gpt(mock_llm) is True
|
||||
|
||||
|
||||
def test_is_gpt_false():
|
||||
mock_llm = Mock()
|
||||
assert is_gpt(mock_llm) is False
|
||||
|
||||
|
||||
class CustomConverter(Converter):
|
||||
pass
|
||||
|
||||
|
||||
def test_create_converter_with_mock_agent():
|
||||
mock_agent = MagicMock()
|
||||
mock_agent.get_output_converter.return_value = MagicMock(spec=Converter)
|
||||
|
||||
converter = create_converter(
|
||||
agent=mock_agent,
|
||||
llm=Mock(),
|
||||
text="Sample",
|
||||
model=SimpleModel,
|
||||
instructions="Convert",
|
||||
)
|
||||
|
||||
assert isinstance(converter, Converter)
|
||||
mock_agent.get_output_converter.assert_called_once()
|
||||
|
||||
|
||||
def test_create_converter_with_custom_converter():
|
||||
converter = create_converter(
|
||||
converter_cls=CustomConverter,
|
||||
llm=Mock(),
|
||||
text="Sample",
|
||||
model=SimpleModel,
|
||||
instructions="Convert",
|
||||
)
|
||||
|
||||
assert isinstance(converter, CustomConverter)
|
||||
|
||||
|
||||
def test_create_converter_fails_without_agent_or_converter_cls():
|
||||
with pytest.raises(
|
||||
ValueError, match="Either agent or converter_cls must be provided"
|
||||
):
|
||||
create_converter(
|
||||
llm=Mock(), text="Sample", model=SimpleModel, instructions="Convert"
|
||||
)
|
||||
Reference in New Issue
Block a user