mirror of
https://github.com/crewAIInc/crewAI.git
synced 2026-01-10 08:38:30 +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:
committed by
GitHub
parent
88bffaa0d0
commit
da7d8256eb
@@ -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
|
||||
|
||||
Reference in New Issue
Block a user