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Merge branch 'main' of github.com:crewAIInc/crewAI into add/agent-specific-knowledge
This commit is contained in:
@@ -13,10 +13,12 @@ from crewai.llm import LLM
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from crewai.knowledge.knowledge import Knowledge
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from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource
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from crewai.memory.contextual.contextual_memory import ContextualMemory
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from crewai.task import Task
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from crewai.tools import BaseTool
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from crewai.tools.agent_tools.agent_tools import AgentTools
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from crewai.utilities import Converter, Prompts
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from crewai.utilities.constants import TRAINED_AGENTS_DATA_FILE, TRAINING_DATA_FILE
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from crewai.utilities.converter import generate_model_description
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from crewai.utilities.token_counter_callback import TokenCalcHandler
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from crewai.utilities.training_handler import CrewTrainingHandler
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@@ -135,7 +137,7 @@ class Agent(BaseAgent):
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def post_init_setup(self):
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self._set_knowledge()
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self.agent_ops_agent_name = self.role
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unnacepted_attributes = [
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unaccepted_attributes = [
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"AWS_ACCESS_KEY_ID",
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"AWS_SECRET_ACCESS_KEY",
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"AWS_REGION_NAME",
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@@ -169,28 +171,23 @@ class Agent(BaseAgent):
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for provider, env_vars in ENV_VARS.items():
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if provider == set_provider:
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for env_var in env_vars:
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if env_var["key_name"] in unnacepted_attributes:
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continue
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# Check if the environment variable is set
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if "key_name" in env_var:
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env_value = os.environ.get(env_var["key_name"])
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key_name = env_var.get("key_name")
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if key_name and key_name not in unaccepted_attributes:
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env_value = os.environ.get(key_name)
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if env_value:
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# Map key names containing "API_KEY" to "api_key"
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key_name = (
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"api_key"
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if "API_KEY" in env_var["key_name"]
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else env_var["key_name"]
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"api_key" if "API_KEY" in key_name else key_name
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)
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# Map key names containing "API_BASE" to "api_base"
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key_name = (
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"api_base"
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if "API_BASE" in env_var["key_name"]
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else key_name
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"api_base" if "API_BASE" in key_name else key_name
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)
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# Map key names containing "API_VERSION" to "api_version"
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key_name = (
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"api_version"
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if "API_VERSION" in env_var["key_name"]
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if "API_VERSION" in key_name
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else key_name
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)
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llm_params[key_name] = env_value
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@@ -264,7 +261,7 @@ class Agent(BaseAgent):
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def execute_task(
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self,
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task: Any,
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task: Task,
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context: Optional[str] = None,
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tools: Optional[List[BaseTool]] = None,
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) -> str:
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@@ -283,6 +280,22 @@ class Agent(BaseAgent):
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task_prompt = task.prompt()
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# If the task requires output in JSON or Pydantic format,
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# append specific instructions to the task prompt to ensure
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# that the final answer does not include any code block markers
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if task.output_json or task.output_pydantic:
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# Generate the schema based on the output format
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if task.output_json:
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# schema = json.dumps(task.output_json, indent=2)
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schema = generate_model_description(task.output_json)
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elif task.output_pydantic:
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schema = generate_model_description(task.output_pydantic)
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task_prompt += "\n" + self.i18n.slice("formatted_task_instructions").format(
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output_format=schema
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)
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if context:
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task_prompt = self.i18n.slice("task_with_context").format(
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task=task_prompt, context=context
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