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Feature/kickoff for each sync (#680)
* Sync with deep copy working now * async working!! * Clean up code for review * Fix naming --------- Co-authored-by: João Moura <joaomdmoura@gmail.com>
This commit is contained in:
Brandon Hancock (bhancock_ai)
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GitHub
GitHub
parent
2a0e21ca76
commit
946c56494e
@@ -1,3 +1,4 @@
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from copy import deepcopy
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import os
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import re
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import threading
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@@ -163,13 +164,16 @@ class Task(BaseModel):
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)
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if self.context:
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context = [] # type: ignore # Incompatible types in assignment (expression has type "list[Never]", variable has type "str | None")
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# type: ignore # Incompatible types in assignment (expression has type "list[Never]", variable has type "str | None")
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context = []
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for task in self.context:
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if task.async_execution:
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task.thread.join() # type: ignore # Item "None" of "Thread | None" has no attribute "join"
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if task and task.output:
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context.append(task.output.raw_output) # type: ignore # Item "str" of "str | None" has no attribute "append"
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context = "\n".join(context) # type: ignore # Argument 1 to "join" of "str" has incompatible type "str | None"; expected "Iterable[str]"
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# type: ignore # Item "str" of "str | None" has no attribute "append"
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context.append(task.output.raw_output)
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# type: ignore # Argument 1 to "join" of "str" has incompatible type "str | None"; expected "Iterable[str]"
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context = "\n".join(context)
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self.prompt_context = context
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tools = tools or self.tools
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@@ -232,7 +236,8 @@ class Task(BaseModel):
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if inputs:
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self.description = self._original_description.format(**inputs)
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self.expected_output = self._original_expected_output.format(**inputs)
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self.expected_output = self._original_expected_output.format(
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**inputs)
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def increment_tools_errors(self) -> None:
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"""Increment the tools errors counter."""
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@@ -242,6 +247,25 @@ class Task(BaseModel):
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"""Increment the delegations counter."""
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self.delegations += 1
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def copy(self):
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"""Create a deep copy of the Task."""
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exclude = {
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"id",
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"agent",
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"context",
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"tools",
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}
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copied_data = self.model_dump(exclude=exclude)
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copied_data = {k: v for k, v in copied_data.items() if v is not None}
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cloned_context = [task.copy() for task in self.context] if self.context else None
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cloned_agent = self.agent.copy() if self.agent else None
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cloned_tools = deepcopy(self.tools) if self.tools else None
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copied_task = Task(**copied_data, context=cloned_context, agent=cloned_agent, tools=cloned_tools)
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return copied_task
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def _export_output(self, result: str) -> Any:
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exported_result = result
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instructions = "I'm gonna convert this raw text into valid JSON."
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@@ -251,27 +275,35 @@ class Task(BaseModel):
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# try to convert task_output directly to pydantic/json
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try:
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exported_result = model.model_validate_json(result) # type: ignore # Item "None" of "type[BaseModel] | None" has no attribute "model_validate_json"
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# type: ignore # Item "None" of "type[BaseModel] | None" has no attribute "model_validate_json"
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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() # type: ignore # "str" has no attribute "model_dump"
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# type: ignore # "str" has no attribute "model_dump"
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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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# sometimes the response contains valid JSON in the middle of text
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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)) # type: ignore # Item "None" of "type[BaseModel] | None" has no attribute "model_validate_json"
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# type: ignore # Item "None" of "type[BaseModel] | None" has no attribute "model_validate_json"
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exported_result = model.model_validate_json(
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match.group(0))
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if self.output_json:
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return exported_result.model_dump() # type: ignore # "str" has no attribute "model_dump"
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# type: ignore # "str" has no attribute "model_dump"
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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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llm = self.agent.function_calling_llm or self.agent.llm # type: ignore # Item "None" of "Agent | None" has no attribute "function_calling_llm"
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# type: ignore # Item "None" of "Agent | None" has no attribute "function_calling_llm"
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llm = self.agent.function_calling_llm or self.agent.llm
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if not self._is_gpt(llm):
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model_schema = PydanticSchemaParser(model=model).get_schema() # type: ignore # Argument "model" to "PydanticSchemaParser" has incompatible type "type[BaseModel] | None"; expected "type[BaseModel]"
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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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# type: ignore # Argument "model" to "PydanticSchemaParser" has incompatible type "type[BaseModel] | None"; expected "type[BaseModel]"
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model_schema = PydanticSchemaParser(model=model).get_schema()
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instructions = f"{
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instructions}\n\nThe json should have the following structure, with the following keys:\n{model_schema}"
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converter = Converter(
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llm=llm, text=result, model=model, instructions=instructions
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@@ -284,14 +316,16 @@ class Task(BaseModel):
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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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content=f"{
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exported_result.message} Using raw output instead.",
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color="red",
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)
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exported_result = result
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if self.output_file:
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content = (
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exported_result if not self.output_pydantic else exported_result.json() # type: ignore # "str" has no attribute "json"
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# type: ignore # "str" has no attribute "json"
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exported_result if not self.output_pydantic else exported_result.json()
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)
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self._save_file(content)
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@@ -301,12 +335,14 @@ class Task(BaseModel):
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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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# type: ignore # Value of type variable "AnyOrLiteralStr" of "dirname" cannot be "str | None"
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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: # type: ignore # Argument 1 to "open" has incompatible type "str | None"; expected "int | str | bytes | PathLike[str] | PathLike[bytes]"
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# type: ignore # Argument 1 to "open" has incompatible type "str | None"; expected "int | str | bytes | PathLike[str] | PathLike[bytes]"
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with open(self.output_file, "w", encoding='utf-8') as file:
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file.write(result)
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return None
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