mirror of
https://github.com/crewAIInc/crewAI.git
synced 2026-01-10 00:28:31 +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
88bffaa0d0
commit
da7d8256eb
@@ -1,6 +1,5 @@
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import json
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import os
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import re
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import threading
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import uuid
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from concurrent.futures import Future
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@@ -8,7 +7,6 @@ from copy import copy
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from hashlib import md5
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from typing import Any, Dict, List, Optional, Tuple, Type, Union
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from langchain_openai import ChatOpenAI
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from opentelemetry.trace import Span
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from pydantic import UUID4, BaseModel, Field, field_validator, model_validator
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from pydantic_core import PydanticCustomError
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@@ -17,10 +15,8 @@ from crewai.agents.agent_builder.base_agent import BaseAgent
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from crewai.tasks.output_format import OutputFormat
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from crewai.tasks.task_output import TaskOutput
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from crewai.telemetry.telemetry import Telemetry
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from crewai.utilities.converter import Converter, ConverterError
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from crewai.utilities.converter import Converter, convert_to_model
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from crewai.utilities.i18n import I18N
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from crewai.utilities.printer import Printer
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from crewai.utilities.pydantic_schema_parser import PydanticSchemaParser
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class Task(BaseModel):
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@@ -254,9 +250,7 @@ class Task(BaseModel):
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content = (
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json_output
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if json_output
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else pydantic_output.model_dump_json()
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if pydantic_output
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else result
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else pydantic_output.model_dump_json() if pydantic_output else result
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)
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self._save_file(content)
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@@ -326,18 +320,6 @@ class Task(BaseModel):
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return copied_task
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def _create_converter(self, *args, **kwargs) -> Converter:
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"""Create a converter instance."""
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if self.agent and not self.converter_cls:
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converter = self.agent.get_output_converter(*args, **kwargs)
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elif self.converter_cls:
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converter = self.converter_cls(*args, **kwargs)
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if not converter:
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raise Exception("No output converter found or set.")
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return converter
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def _export_output(
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self, result: str
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) -> Tuple[Optional[BaseModel], Optional[Dict[str, Any]]]:
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@@ -345,75 +327,26 @@ class Task(BaseModel):
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json_output: Optional[Dict[str, Any]] = None
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if self.output_pydantic or self.output_json:
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model_output = self._convert_to_model(result)
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pydantic_output = (
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model_output if isinstance(model_output, BaseModel) else None
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model_output = convert_to_model(
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result,
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self.output_pydantic,
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self.output_json,
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self.agent,
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self.converter_cls,
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)
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if isinstance(model_output, str):
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if isinstance(model_output, BaseModel):
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pydantic_output = model_output
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elif isinstance(model_output, dict):
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json_output = model_output
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elif isinstance(model_output, str):
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try:
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json_output = json.loads(model_output)
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except json.JSONDecodeError:
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json_output = None
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else:
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json_output = model_output if isinstance(model_output, dict) else None
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return pydantic_output, json_output
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def _convert_to_model(self, result: str) -> Union[dict, BaseModel, str]:
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model = self.output_pydantic or self.output_json
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if model is None:
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return result
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try:
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return self._validate_model(result, model)
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except Exception:
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return self._handle_partial_json(result, model)
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def _validate_model(
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self, result: str, model: Type[BaseModel]
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) -> Union[dict, BaseModel]:
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exported_result = model.model_validate_json(result)
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if self.output_json:
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return exported_result.model_dump()
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return exported_result
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def _handle_partial_json(
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self, result: str, model: Type[BaseModel]
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) -> Union[dict, BaseModel, str]:
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match = re.search(r"({.*})", result, re.DOTALL)
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if match:
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try:
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exported_result = model.model_validate_json(match.group(0))
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if self.output_json:
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return exported_result.model_dump()
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return exported_result
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except Exception:
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pass
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return self._convert_with_instructions(result, model)
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def _convert_with_instructions(
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self, result: str, model: Type[BaseModel]
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) -> Union[dict, BaseModel, str]:
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llm = self.agent.function_calling_llm or self.agent.llm # type: ignore # Item "None" of "BaseAgent | None" has no attribute "function_calling_llm"
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instructions = self._get_conversion_instructions(model, llm)
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converter = self._create_converter(
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llm=llm, text=result, model=model, instructions=instructions
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)
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exported_result = (
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converter.to_pydantic() if self.output_pydantic else converter.to_json()
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)
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if isinstance(exported_result, ConverterError):
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Printer().print(
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content=f"{exported_result.message} Using raw output instead.",
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color="red",
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)
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return result
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return exported_result
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def _get_output_format(self) -> OutputFormat:
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if self.output_json:
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return OutputFormat.JSON
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@@ -421,26 +354,6 @@ class Task(BaseModel):
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return OutputFormat.PYDANTIC
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return OutputFormat.RAW
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def _get_conversion_instructions(self, model: Type[BaseModel], llm: Any) -> str:
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instructions = "I'm gonna convert this raw text into valid JSON."
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if not self._is_gpt(llm):
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model_schema = PydanticSchemaParser(model=model).get_schema()
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instructions = f"{instructions}\n\nThe json should have the following structure, with the following keys:\n{model_schema}"
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return instructions
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def _save_output(self, content: str) -> None:
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if not self.output_file:
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raise Exception("Output file path is not set.")
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directory = os.path.dirname(self.output_file)
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if directory and not os.path.exists(directory):
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os.makedirs(directory)
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with open(self.output_file, "w", encoding="utf-8") as file:
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file.write(content)
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def _is_gpt(self, llm) -> bool:
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return isinstance(llm, ChatOpenAI) and llm.openai_api_base is None
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def _save_file(self, result: Any) -> None:
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directory = os.path.dirname(self.output_file) # type: ignore # Value of type variable "AnyOrLiteralStr" of "dirname" cannot be "str | None"
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@@ -1,9 +1,14 @@
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import json
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import re
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from typing import Any, Optional, Type, Union
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from langchain.schema import HumanMessage, SystemMessage
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from langchain_openai import ChatOpenAI
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from pydantic import BaseModel, ValidationError
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from crewai.agents.agent_builder.utilities.base_output_converter import OutputConverter
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from crewai.utilities.printer import Printer
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from crewai.utilities.pydantic_schema_parser import PydanticSchemaParser
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class ConverterError(Exception):
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@@ -72,3 +77,153 @@ class Converter(OutputConverter):
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def is_gpt(self) -> bool:
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"""Return if llm provided is of gpt from openai."""
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return isinstance(self.llm, ChatOpenAI) and self.llm.openai_api_base is None
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def convert_to_model(
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result: str,
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output_pydantic: Optional[Type[BaseModel]],
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output_json: Optional[Type[BaseModel]],
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agent: Any,
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converter_cls: Optional[Type[Converter]] = None,
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) -> Union[dict, BaseModel, str]:
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model = output_pydantic or output_json
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if model is None:
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return result
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try:
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escaped_result = json.dumps(json.loads(result, strict=False))
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return validate_model(escaped_result, model, bool(output_json))
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except json.JSONDecodeError as e:
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Printer().print(
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content=f"Error parsing JSON: {e}. Attempting to handle partial JSON.",
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color="yellow",
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)
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return handle_partial_json(
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result, model, bool(output_json), agent, converter_cls
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)
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except ValidationError as e:
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Printer().print(
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content=f"Pydantic validation error: {e}. Attempting to handle partial JSON.",
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color="yellow",
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)
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return handle_partial_json(
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result, model, bool(output_json), agent, converter_cls
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)
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except Exception as e:
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Printer().print(
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content=f"Unexpected error during model conversion: {type(e).__name__}: {e}. Returning original result.",
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color="red",
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)
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return result
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def validate_model(
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result: str, model: Type[BaseModel], is_json_output: bool
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) -> Union[dict, BaseModel]:
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exported_result = model.model_validate_json(result)
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if is_json_output:
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return exported_result.model_dump()
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return exported_result
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def handle_partial_json(
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result: str,
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model: Type[BaseModel],
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is_json_output: bool,
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agent: Any,
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converter_cls: Optional[Type[Converter]] = None,
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) -> Union[dict, BaseModel, str]:
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match = re.search(r"({.*})", result, re.DOTALL)
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if match:
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try:
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exported_result = model.model_validate_json(match.group(0))
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if is_json_output:
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return exported_result.model_dump()
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return exported_result
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except json.JSONDecodeError as e:
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Printer().print(
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content=f"Error parsing JSON: {e}. The extracted JSON-like string is not valid JSON. Attempting alternative conversion method.",
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color="yellow",
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)
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except ValidationError as e:
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Printer().print(
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content=f"Pydantic validation error: {e}. The JSON structure doesn't match the expected model. Attempting alternative conversion method.",
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color="yellow",
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)
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except Exception as e:
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Printer().print(
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content=f"Unexpected error during partial JSON handling: {type(e).__name__}: {e}. Attempting alternative conversion method.",
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color="red",
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)
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return convert_with_instructions(
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result, model, is_json_output, agent, converter_cls
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)
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def convert_with_instructions(
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result: str,
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model: Type[BaseModel],
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is_json_output: bool,
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agent: Any,
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converter_cls: Optional[Type[Converter]] = None,
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) -> Union[dict, BaseModel, str]:
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llm = agent.function_calling_llm or agent.llm
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instructions = get_conversion_instructions(model, llm)
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converter = create_converter(
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agent=agent,
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converter_cls=converter_cls,
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llm=llm,
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text=result,
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model=model,
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instructions=instructions,
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)
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exported_result = (
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converter.to_pydantic() if not is_json_output else converter.to_json()
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)
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if isinstance(exported_result, ConverterError):
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Printer().print(
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content=f"{exported_result.message} Using raw output instead.",
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color="red",
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)
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return result
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return exported_result
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def get_conversion_instructions(model: Type[BaseModel], llm: Any) -> str:
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instructions = "I'm gonna convert this raw text into valid JSON."
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if not is_gpt(llm):
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model_schema = PydanticSchemaParser(model=model).get_schema()
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instructions = f"{instructions}\n\nThe json should have the following structure, with the following keys:\n{model_schema}"
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return instructions
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def is_gpt(llm: Any) -> bool:
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from langchain_openai import ChatOpenAI
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return isinstance(llm, ChatOpenAI) and llm.openai_api_base is None
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def create_converter(
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agent: Optional[Any] = None,
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converter_cls: Optional[Type[Converter]] = None,
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*args,
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**kwargs,
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) -> Converter:
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if agent and not converter_cls:
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if hasattr(agent, "get_output_converter"):
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converter = agent.get_output_converter(*args, **kwargs)
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else:
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raise AttributeError("Agent does not have a 'get_output_converter' method")
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elif converter_cls:
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converter = converter_cls(*args, **kwargs)
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else:
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raise ValueError("Either agent or converter_cls must be provided")
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if not converter:
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raise Exception("No output converter found or set.")
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return converter
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