mirror of
https://github.com/crewAIInc/crewAI.git
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fix: ensure lite agents course-correct on validation errors
* fix: ensure lite agents course-correct on validation errors * chore: update cassettes and test expectations * fix: ensure multiple guardrails propogate
This commit is contained in:
@@ -1,6 +1,7 @@
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import asyncio
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import asyncio
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from collections.abc import Callable
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from collections.abc import Callable
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import inspect
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import inspect
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import json
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from typing import (
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from typing import (
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Any,
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Any,
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Literal,
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Literal,
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@@ -58,7 +59,11 @@ from crewai.utilities.agent_utils import (
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process_llm_response,
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process_llm_response,
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render_text_description_and_args,
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render_text_description_and_args,
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)
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)
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from crewai.utilities.converter import generate_model_description
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from crewai.utilities.converter import (
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Converter,
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ConverterError,
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generate_model_description,
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)
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from crewai.utilities.guardrail import process_guardrail
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from crewai.utilities.guardrail import process_guardrail
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from crewai.utilities.guardrail_types import GuardrailCallable, GuardrailType
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from crewai.utilities.guardrail_types import GuardrailCallable, GuardrailType
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from crewai.utilities.i18n import I18N, get_i18n
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from crewai.utilities.i18n import I18N, get_i18n
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@@ -241,7 +246,11 @@ class LiteAgent(FlowTrackable, BaseModel):
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"""Return the original role for compatibility with tool interfaces."""
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"""Return the original role for compatibility with tool interfaces."""
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return self.role
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return self.role
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def kickoff(self, messages: str | list[LLMMessage]) -> LiteAgentOutput:
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def kickoff(
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self,
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messages: str | list[LLMMessage],
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response_format: type[BaseModel] | None = None,
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) -> LiteAgentOutput:
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"""
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"""
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Execute the agent with the given messages.
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Execute the agent with the given messages.
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@@ -249,6 +258,8 @@ class LiteAgent(FlowTrackable, BaseModel):
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messages: Either a string query or a list of message dictionaries.
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messages: Either a string query or a list of message dictionaries.
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If a string is provided, it will be converted to a user message.
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If a string is provided, it will be converted to a user message.
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If a list is provided, each dict should have 'role' and 'content' keys.
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If a list is provided, each dict should have 'role' and 'content' keys.
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response_format: Optional Pydantic model for structured output. If provided,
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overrides self.response_format for this execution.
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Returns:
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Returns:
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LiteAgentOutput: The result of the agent execution.
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LiteAgentOutput: The result of the agent execution.
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@@ -269,9 +280,13 @@ class LiteAgent(FlowTrackable, BaseModel):
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self.tools_results = []
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self.tools_results = []
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# Format messages for the LLM
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# Format messages for the LLM
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self._messages = self._format_messages(messages)
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self._messages = self._format_messages(
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messages, response_format=response_format
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)
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return self._execute_core(agent_info=agent_info)
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return self._execute_core(
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agent_info=agent_info, response_format=response_format
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)
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except Exception as e:
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except Exception as e:
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self._printer.print(
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self._printer.print(
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@@ -289,7 +304,9 @@ class LiteAgent(FlowTrackable, BaseModel):
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)
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)
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raise e
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raise e
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def _execute_core(self, agent_info: dict[str, Any]) -> LiteAgentOutput:
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def _execute_core(
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self, agent_info: dict[str, Any], response_format: type[BaseModel] | None = None
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) -> LiteAgentOutput:
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# Emit event for agent execution start
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# Emit event for agent execution start
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crewai_event_bus.emit(
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crewai_event_bus.emit(
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self,
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self,
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@@ -303,15 +320,29 @@ class LiteAgent(FlowTrackable, BaseModel):
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# Execute the agent using invoke loop
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# Execute the agent using invoke loop
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agent_finish = self._invoke_loop()
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agent_finish = self._invoke_loop()
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formatted_result: BaseModel | None = None
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formatted_result: BaseModel | None = None
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if self.response_format:
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active_response_format = response_format or self.response_format
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if active_response_format:
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try:
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try:
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# Cast to BaseModel to ensure type safety
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model_schema = generate_model_description(active_response_format)
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result = self.response_format.model_validate_json(agent_finish.output)
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schema = json.dumps(model_schema, indent=2)
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instructions = self.i18n.slice("formatted_task_instructions").format(
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output_format=schema
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)
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converter = Converter(
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llm=self.llm,
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text=agent_finish.output,
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model=active_response_format,
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instructions=instructions,
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)
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result = converter.to_pydantic()
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if isinstance(result, BaseModel):
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if isinstance(result, BaseModel):
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formatted_result = result
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formatted_result = result
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except Exception as e:
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except ConverterError as e:
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self._printer.print(
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self._printer.print(
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content=f"Failed to parse output into response format: {e!s}",
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content=f"Failed to parse output into response format after retries: {e.message}",
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color="yellow",
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color="yellow",
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)
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)
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@@ -400,8 +431,14 @@ class LiteAgent(FlowTrackable, BaseModel):
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"""
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"""
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return await asyncio.to_thread(self.kickoff, messages)
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return await asyncio.to_thread(self.kickoff, messages)
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def _get_default_system_prompt(self) -> str:
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def _get_default_system_prompt(
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"""Get the default system prompt for the agent."""
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self, response_format: type[BaseModel] | None = None
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) -> str:
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"""Get the default system prompt for the agent.
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Args:
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response_format: Optional response format to use instead of self.response_format
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"""
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base_prompt = ""
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base_prompt = ""
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if self._parsed_tools:
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if self._parsed_tools:
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# Use the prompt template for agents with tools
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# Use the prompt template for agents with tools
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@@ -422,21 +459,31 @@ class LiteAgent(FlowTrackable, BaseModel):
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goal=self.goal,
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goal=self.goal,
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)
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)
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# Add response format instructions if specified
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active_response_format = response_format or self.response_format
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if self.response_format:
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if active_response_format:
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schema = generate_model_description(self.response_format)
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model_description = generate_model_description(active_response_format)
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schema_json = json.dumps(model_description, indent=2)
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base_prompt += self.i18n.slice("lite_agent_response_format").format(
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base_prompt += self.i18n.slice("lite_agent_response_format").format(
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response_format=schema
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response_format=schema_json
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)
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)
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return base_prompt
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return base_prompt
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def _format_messages(self, messages: str | list[LLMMessage]) -> list[LLMMessage]:
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def _format_messages(
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"""Format messages for the LLM."""
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self,
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messages: str | list[LLMMessage],
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response_format: type[BaseModel] | None = None,
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) -> list[LLMMessage]:
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"""Format messages for the LLM.
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Args:
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messages: Input messages to format
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response_format: Optional response format to use instead of self.response_format
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"""
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if isinstance(messages, str):
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if isinstance(messages, str):
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messages = [{"role": "user", "content": messages}]
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messages = [{"role": "user", "content": messages}]
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system_prompt = self._get_default_system_prompt()
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system_prompt = self._get_default_system_prompt(response_format=response_format)
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# Add system message at the beginning
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# Add system message at the beginning
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formatted_messages: list[LLMMessage] = [
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formatted_messages: list[LLMMessage] = [
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@@ -506,6 +553,10 @@ class LiteAgent(FlowTrackable, BaseModel):
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self._append_message(formatted_answer.text, role="assistant")
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self._append_message(formatted_answer.text, role="assistant")
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except OutputParserError as e: # noqa: PERF203
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except OutputParserError as e: # noqa: PERF203
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self._printer.print(
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content="Failed to parse LLM output. Retrying...",
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color="yellow",
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)
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formatted_answer = handle_output_parser_exception(
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formatted_answer = handle_output_parser_exception(
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e=e,
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e=e,
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messages=self._messages,
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messages=self._messages,
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@@ -525,7 +525,11 @@ class Task(BaseModel):
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tools=tools,
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tools=tools,
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)
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)
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pydantic_output, json_output = self._export_output(result)
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if not self._guardrails and not self._guardrail:
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pydantic_output, json_output = self._export_output(result)
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else:
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pydantic_output, json_output = None, None
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task_output = TaskOutput(
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task_output = TaskOutput(
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name=self.name or self.description,
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name=self.name or self.description,
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description=self.description,
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description=self.description,
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@@ -22,12 +22,12 @@
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"summarize_instruction": "Summarize the following text, make sure to include all the important information: {group}",
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"summarize_instruction": "Summarize the following text, make sure to include all the important information: {group}",
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"summary": "This is a summary of our conversation so far:\n{merged_summary}",
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"summary": "This is a summary of our conversation so far:\n{merged_summary}",
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"manager_request": "Your best answer to your coworker asking you this, accounting for the context shared.",
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"manager_request": "Your best answer to your coworker asking you this, accounting for the context shared.",
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"formatted_task_instructions": "Ensure your final answer contains only the content in the following format: {output_format}\n\nEnsure the final output does not include any code block markers like ```json or ```python.",
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"formatted_task_instructions": "Ensure your final answer strictly adheres to the following OpenAPI schema: {output_format}\n\nDo not include the OpenAPI schema in the final output. Ensure the final output does not include any code block markers like ```json or ```python.",
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"conversation_history_instruction": "You are a member of a crew collaborating to achieve a common goal. Your task is a specific action that contributes to this larger objective. For additional context, please review the conversation history between you and the user that led to the initiation of this crew. Use any relevant information or feedback from the conversation to inform your task execution and ensure your response aligns with both the immediate task and the crew's overall goals.",
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"conversation_history_instruction": "You are a member of a crew collaborating to achieve a common goal. Your task is a specific action that contributes to this larger objective. For additional context, please review the conversation history between you and the user that led to the initiation of this crew. Use any relevant information or feedback from the conversation to inform your task execution and ensure your response aligns with both the immediate task and the crew's overall goals.",
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"feedback_instructions": "User feedback: {feedback}\nInstructions: Use this feedback to enhance the next output iteration.\nNote: Do not respond or add commentary.",
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"feedback_instructions": "User feedback: {feedback}\nInstructions: Use this feedback to enhance the next output iteration.\nNote: Do not respond or add commentary.",
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"lite_agent_system_prompt_with_tools": "You are {role}. {backstory}\nYour personal goal is: {goal}\n\nYou ONLY have access to the following tools, and should NEVER make up tools that are not listed here:\n\n{tools}\n\nIMPORTANT: Use the following format in your response:\n\n```\nThought: you should always think about what to do\nAction: the action to take, only one name of [{tool_names}], just the name, exactly as it's written.\nAction Input: the input to the action, just a simple JSON object, enclosed in curly braces, using \" to wrap keys and values.\nObservation: the result of the action\n```\n\nOnce all necessary information is gathered, return the following format:\n\n```\nThought: I now know the final answer\nFinal Answer: the final answer to the original input question\n```",
|
"lite_agent_system_prompt_with_tools": "You are {role}. {backstory}\nYour personal goal is: {goal}\n\nYou ONLY have access to the following tools, and should NEVER make up tools that are not listed here:\n\n{tools}\n\nIMPORTANT: Use the following format in your response:\n\n```\nThought: you should always think about what to do\nAction: the action to take, only one name of [{tool_names}], just the name, exactly as it's written.\nAction Input: the input to the action, just a simple JSON object, enclosed in curly braces, using \" to wrap keys and values.\nObservation: the result of the action\n```\n\nOnce all necessary information is gathered, return the following format:\n\n```\nThought: I now know the final answer\nFinal Answer: the final answer to the original input question\n```",
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"lite_agent_system_prompt_without_tools": "You are {role}. {backstory}\nYour personal goal is: {goal}\n\nTo give my best complete final answer to the task respond using the exact following format:\n\nThought: I now can give a great answer\nFinal Answer: Your final answer must be the great and the most complete as possible, it must be outcome described.\n\nI MUST use these formats, my job depends on it!",
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"lite_agent_system_prompt_without_tools": "You are {role}. {backstory}\nYour personal goal is: {goal}\n\nTo give my best complete final answer to the task respond using the exact following format:\n\nThought: I now can give a great answer\nFinal Answer: Your final answer must be the great and the most complete as possible, it must be outcome described.\n\nI MUST use these formats, my job depends on it!",
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"lite_agent_response_format": "\nIMPORTANT: Your final answer MUST contain all the information requested in the following format: {response_format}\n\nIMPORTANT: Ensure the final output does not include any code block markers like ```json or ```python.",
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"lite_agent_response_format": "Ensure your final answer strictly adheres to the following OpenAPI schema: {response_format}\n\nDo not include the OpenAPI schema in the final output. Ensure the final output does not include any code block markers like ```json or ```python.",
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"knowledge_search_query": "The original query is: {task_prompt}.",
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"knowledge_search_query": "The original query is: {task_prompt}.",
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"knowledge_search_query_system_prompt": "Your goal is to rewrite the user query so that it is optimized for retrieval from a vector database. Consider how the query will be used to find relevant documents, and aim to make it more specific and context-aware. \n\n Do not include any other text than the rewritten query, especially any preamble or postamble and only add expected output format if its relevant to the rewritten query. \n\n Focus on the key words of the intended task and to retrieve the most relevant information. \n\n There will be some extra context provided that might need to be removed such as expected_output formats structured_outputs and other instructions."
|
"knowledge_search_query_system_prompt": "Your goal is to rewrite the user query so that it is optimized for retrieval from a vector database. Consider how the query will be used to find relevant documents, and aim to make it more specific and context-aware. \n\n Do not include any other text than the rewritten query, especially any preamble or postamble and only add expected output format if its relevant to the rewritten query. \n\n Focus on the key words of the intended task and to retrieve the most relevant information. \n\n There will be some extra context provided that might need to be removed such as expected_output formats structured_outputs and other instructions."
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},
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},
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@@ -10,9 +10,9 @@ from pydantic import BaseModel, ValidationError
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from typing_extensions import Unpack
|
from typing_extensions import Unpack
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|
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from crewai.agents.agent_builder.utilities.base_output_converter import OutputConverter
|
from crewai.agents.agent_builder.utilities.base_output_converter import OutputConverter
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|
from crewai.utilities.i18n import get_i18n
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from crewai.utilities.internal_instructor import InternalInstructor
|
from crewai.utilities.internal_instructor import InternalInstructor
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from crewai.utilities.printer import Printer
|
from crewai.utilities.printer import Printer
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from crewai.utilities.pydantic_schema_parser import PydanticSchemaParser
|
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|
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|
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if TYPE_CHECKING:
|
if TYPE_CHECKING:
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@@ -22,6 +22,7 @@ if TYPE_CHECKING:
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from crewai.llms.base_llm import BaseLLM
|
from crewai.llms.base_llm import BaseLLM
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|
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_JSON_PATTERN: Final[re.Pattern[str]] = re.compile(r"({.*})", re.DOTALL)
|
_JSON_PATTERN: Final[re.Pattern[str]] = re.compile(r"({.*})", re.DOTALL)
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|
_I18N = get_i18n()
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|
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|
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class ConverterError(Exception):
|
class ConverterError(Exception):
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@@ -87,8 +88,7 @@ class Converter(OutputConverter):
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result = self.model.model_validate(result)
|
result = self.model.model_validate(result)
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elif isinstance(result, str):
|
elif isinstance(result, str):
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try:
|
try:
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parsed = json.loads(result)
|
result = self.model.model_validate_json(result)
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result = self.model.model_validate(parsed)
|
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except Exception as parse_err:
|
except Exception as parse_err:
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raise ConverterError(
|
raise ConverterError(
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f"Failed to convert partial JSON result into Pydantic: {parse_err}"
|
f"Failed to convert partial JSON result into Pydantic: {parse_err}"
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@@ -172,6 +172,16 @@ def convert_to_model(
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model = output_pydantic or output_json
|
model = output_pydantic or output_json
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if model is None:
|
if model is None:
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return result
|
return result
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|
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|
if converter_cls:
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|
return convert_with_instructions(
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|
result=result,
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|
model=model,
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|
is_json_output=bool(output_json),
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|
agent=agent,
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|
converter_cls=converter_cls,
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|
)
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|
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try:
|
try:
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escaped_result = json.dumps(json.loads(result, strict=False))
|
escaped_result = json.dumps(json.loads(result, strict=False))
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return validate_model(
|
return validate_model(
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@@ -251,7 +261,7 @@ def handle_partial_json(
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except json.JSONDecodeError:
|
except json.JSONDecodeError:
|
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pass
|
pass
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except ValidationError:
|
except ValidationError:
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pass
|
raise
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except Exception as e:
|
except Exception as e:
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Printer().print(
|
Printer().print(
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content=f"Unexpected error during partial JSON handling: {type(e).__name__}: {e}. Attempting alternative conversion method.",
|
content=f"Unexpected error during partial JSON handling: {type(e).__name__}: {e}. Attempting alternative conversion method.",
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@@ -335,25 +345,26 @@ def get_conversion_instructions(
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Returns:
|
Returns:
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|
|
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"""
|
"""
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instructions = "Please convert the following text into valid JSON."
|
instructions = ""
|
||||||
if (
|
if (
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llm
|
llm
|
||||||
and not isinstance(llm, str)
|
and not isinstance(llm, str)
|
||||||
and hasattr(llm, "supports_function_calling")
|
and hasattr(llm, "supports_function_calling")
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||||||
and llm.supports_function_calling()
|
and llm.supports_function_calling()
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):
|
):
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model_schema = PydanticSchemaParser(model=model).get_schema()
|
schema_dict = generate_model_description(model)
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instructions += (
|
schema = json.dumps(schema_dict, indent=2)
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f"\n\nOutput ONLY the valid JSON and nothing else.\n\n"
|
formatted_task_instructions = _I18N.slice("formatted_task_instructions").format(
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||||||
f"Use this format exactly:\n```json\n{model_schema}\n```"
|
output_format=schema
|
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)
|
)
|
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@@ -340,7 +340,7 @@ def test_output_pydantic_hierarchical():
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)
|
)
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result = crew.kickoff()
|
result = crew.kickoff()
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assert isinstance(result.pydantic, ScoreOutput)
|
assert isinstance(result.pydantic, ScoreOutput)
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assert result.to_dict() == {"score": 0}
|
assert result.to_dict() == {"score": 4}
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@pytest.mark.vcr(filter_headers=["authorization"])
|
@pytest.mark.vcr(filter_headers=["authorization"])
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@@ -599,7 +599,7 @@ def test_output_pydantic_to_another_task():
|
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assert isinstance(pydantic_result, ScoreOutput), (
|
assert isinstance(pydantic_result, ScoreOutput), (
|
||||||
"Expected pydantic result to be of type ScoreOutput"
|
"Expected pydantic result to be of type ScoreOutput"
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)
|
)
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assert pydantic_result.score == 4
|
assert pydantic_result.score == 5
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@pytest.mark.vcr(filter_headers=["authorization"])
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@pytest.mark.vcr(filter_headers=["authorization"])
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@@ -630,7 +630,7 @@ def test_output_json_to_another_task():
|
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|
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crew = Crew(agents=[scorer], tasks=[task1, task2])
|
crew = Crew(agents=[scorer], tasks=[task1, task2])
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result = crew.kickoff()
|
result = crew.kickoff()
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assert '{"score": 4}' == result.json
|
assert '{"score": 3}' == result.json
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@pytest.mark.vcr(filter_headers=["authorization"])
|
@pytest.mark.vcr(filter_headers=["authorization"])
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@@ -181,7 +181,7 @@ def test_task_guardrail_process_output(task_output):
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result = guardrail(task_output)
|
result = guardrail(task_output)
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assert result[0] is False
|
assert result[0] is False
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|
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assert "exceeding the guardrail limit of fewer than" in result[1].lower()
|
assert result[1] == "The task result contains more than 10 words, violating the guardrail. The text provided contains about 21 words."
|
||||||
|
|
||||||
guardrail = LLMGuardrail(
|
guardrail = LLMGuardrail(
|
||||||
description="Ensure the result has less than 500 words", llm=LLM(model="gpt-4o")
|
description="Ensure the result has less than 500 words", llm=LLM(model="gpt-4o")
|
||||||
@@ -252,10 +252,7 @@ def test_guardrail_emits_events(sample_agent):
|
|||||||
{
|
{
|
||||||
"success": False,
|
"success": False,
|
||||||
"result": None,
|
"result": None,
|
||||||
"error": "The task result does not comply with the guardrail because none of "
|
"error": "The output indicates that none of the authors mentioned are from Italy, while the guardrail requires authors to be from Italy. Therefore, the output does not comply with the guardrail.",
|
||||||
"the listed authors are from Italy. All authors mentioned are from "
|
|
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"different countries, including Germany, the UK, the USA, and others, "
|
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"which violates the requirement that authors must be Italian.",
|
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"retry_count": 0,
|
"retry_count": 0,
|
||||||
},
|
},
|
||||||
{"success": True, "result": result.raw, "error": None, "retry_count": 1},
|
{"success": True, "result": result.raw, "error": None, "retry_count": 1},
|
||||||
|
|||||||
@@ -227,28 +227,22 @@ def test_get_conversion_instructions_gpt() -> None:
|
|||||||
with patch.object(LLM, "supports_function_calling") as supports_function_calling:
|
with patch.object(LLM, "supports_function_calling") as supports_function_calling:
|
||||||
supports_function_calling.return_value = True
|
supports_function_calling.return_value = True
|
||||||
instructions = get_conversion_instructions(SimpleModel, llm)
|
instructions = get_conversion_instructions(SimpleModel, llm)
|
||||||
model_schema = PydanticSchemaParser(model=SimpleModel).get_schema()
|
# Now using OpenAPI schema format for all models
|
||||||
expected_instructions = (
|
assert "Ensure your final answer strictly adheres to the following OpenAPI schema:" in instructions
|
||||||
"Please convert the following text into valid JSON.\n\n"
|
assert '"type": "json_schema"' in instructions
|
||||||
"Output ONLY the valid JSON and nothing else.\n\n"
|
assert '"name": "SimpleModel"' in instructions
|
||||||
"Use this format exactly:\n```json\n"
|
assert "Do not include the OpenAPI schema in the final output" in instructions
|
||||||
f"{model_schema}\n```"
|
|
||||||
)
|
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||||||
assert instructions == expected_instructions
|
|
||||||
|
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||||||
|
|
||||||
def test_get_conversion_instructions_non_gpt() -> None:
|
def test_get_conversion_instructions_non_gpt() -> None:
|
||||||
llm = LLM(model="ollama/llama3.1", base_url="http://localhost:11434")
|
llm = LLM(model="ollama/llama3.1", base_url="http://localhost:11434")
|
||||||
with patch.object(LLM, "supports_function_calling", return_value=False):
|
with patch.object(LLM, "supports_function_calling", return_value=False):
|
||||||
instructions = get_conversion_instructions(SimpleModel, llm)
|
instructions = get_conversion_instructions(SimpleModel, llm)
|
||||||
# Check that the JSON schema is properly formatted
|
# Now using OpenAPI schema format for all models
|
||||||
assert "Please convert the following text into valid JSON" in instructions
|
assert "Ensure your final answer strictly adheres to the following OpenAPI schema:" in instructions
|
||||||
assert "Output ONLY the valid JSON and nothing else" in instructions
|
assert '"type": "json_schema"' in instructions
|
||||||
assert "Use this format exactly" in instructions
|
assert '"name": "SimpleModel"' in instructions
|
||||||
assert "```json" in instructions
|
assert "Do not include the OpenAPI schema in the final output" in instructions
|
||||||
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|
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|
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||||||
assert "'type': 'json_schema'" not in instructions
|
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||||||
|
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||||||
|
|
||||||
# Tests for is_gpt
|
# Tests for is_gpt
|
||||||
|
|||||||
Reference in New Issue
Block a user