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Using only summary memory for now and intial work on work delegation

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This commit is contained in:
Joao Moura
2023-11-10 18:14:52 -03:00
parent 9d3015e63d
commit d6989b7959
1 changed files with 19 additions and 19 deletions
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38
crewai/agent.py
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@@ -8,11 +8,7 @@ from langchain.chat_models import ChatOpenAI as OpenAI
from langchain.tools.render import render_text_description
from langchain.agents.format_scratchpad import format_log_to_str
from langchain.agents.output_parsers import ReActSingleInputOutputParser
from langchain.memory import (
ConversationSummaryMemory,
ConversationEntityMemory,
CombinedMemory
)
from langchain.memory import ConversationSummaryMemory
from .prompts import Prompts
@@ -22,6 +18,10 @@ class Agent(BaseModel):
role: str = Field(description="Role of the agent")
goal: str = Field(description="Objective of the agent")
backstory: str = Field(description="Backstory of the agent")
allow_delegation: bool = Field(
description="Allow delegation of tasks to agents",
default=True
)
tools: List[Any] = Field(
description="Tools at agents disposal",
default=[]
@@ -30,8 +30,7 @@ class Agent(BaseModel):
description="LLM that will run the agent",
default=OpenAI(
temperature=0.7,
model_name="gpt-4",
verbose=False
model_name="gpt-4"
)
)
@@ -47,25 +46,25 @@ class Agent(BaseModel):
inner_agent = {
"input": lambda x: x["input"],
"tools": lambda x: x["tools"],
"entities": lambda x: x["entities"],
"tool_names": lambda x: x["tool_names"],
"chat_history": lambda x: x["chat_history"],
"agent_scratchpad": lambda x: format_log_to_str(x['intermediate_steps']),
} | execution_prompt | llm_with_bind | ReActSingleInputOutputParser()
summary_memory = ConversationSummaryMemory(llm=self.llm, memory_key='chat_history', input_key="input")
entity_memory = ConversationEntityMemory(llm=self.llm, input_key="input")
memory = CombinedMemory(memories=[entity_memory, summary_memory])
summary_memory = ConversationSummaryMemory(
llm=self.llm,
memory_key='chat_history',
input_key="input"
)
self.agent_executor = AgentExecutor(
agent=inner_agent,
tools=self.tools,
memory=memory,
verbose=False,
memory=summary_memory,
handle_parsing_errors=True
)
def execute_task(self, task: str, context: str = None) -> str:
def execute_task(self, task: str, context: str = None, tools: List[Any] = None) -> str:
"""
Execute a task with the agent.
Parameters:
@@ -80,12 +79,13 @@ class Agent(BaseModel):
context
])
print(f"Executing task: {task}")
tools = tools or self.tools
self.agent_executor.tools = tools
return self.agent_executor.invoke({
"input": task,
"tool_names": self.__tools_names(),
"tools": render_text_description(self.tools),
"tool_names": self.__tools_names(tools),
"tools": render_text_description(tools),
})['output']
def __tools_names(self) -> str:
return ", ".join([t.name for t in self.tools])
def __tools_names(self, tools) -> str:
return ", ".join([t.name for t in tools])