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from llama_index.core.agent.workflow import AgentWorkflow
from llama_index.core.tools import FunctionTool
from llama_index.core.workflow import Context
import asyncio
import os
from llm_factory import LLMFactory
from toolbox import Toolbox
from args import Args
class Summarizer:
def __init__(self, temperature, max_tokens):
# Load the system prompt from a file
system_prompt_path = os.path.join(os.getcwd(), "system_prompts", "04_summarizer.txt")
self.system_prompt = ""
with open(system_prompt_path, "r") as file:
self.system_prompt = file.read().strip()
# Define the LLM and agent
llm = LLMFactory.create(Args.primary_llm_interface, self.system_prompt, temperature, max_tokens)
self.agent = AgentWorkflow.setup_agent(llm=llm)
self.ctx = Context(self.agent)
def get_system_prompt(self) -> str:
"""
Retrieves the system prompt.
Returns:
str: The system prompt string.
"""
return self.system_prompt
async def query(self, question: str) -> str:
"""
Asynchronously queries the agent with a given question and returns the response.
Args:
question (str): The question to be sent to the agent.
Returns:
str: The response from the agent as a string.
"""
response = await self.agent.run(question, ctx=self.ctx)
response = str(response)
return response
def clear_context(self):
"""
Clears the current context of the agent, resetting any conversation history.
This is useful when starting a new conversation or when the context needs to be refreshed.
"""
self.ctx = Context(self.agent)
class Researcher:
def __init__(self, temperature, max_tokens):
# Load the system prompt from a file
system_prompt_path = os.path.join(os.getcwd(), "system_prompts", "05_researcher.txt")
self.system_prompt = ""
with open(system_prompt_path, "r") as file:
self.system_prompt = file.read().strip()
# Define the LLM and agent
llm = LLMFactory.create(Args.primary_llm_interface, self.system_prompt, temperature, max_tokens)
self.agent = AgentWorkflow.from_tools_or_functions(
Toolbox.web_search.duck_duck_go_tools,
llm=llm
)
self.ctx = Context(self.agent)
def get_system_prompt(self) -> str:
"""
Retrieves the system prompt.
Returns:
str: The system prompt string.
"""
return self.system_prompt
async def query(self, question: str) -> str:
"""
Asynchronously queries the agent with a given question and returns the response.
Args:
question (str): The question to be sent to the agent.
Returns:
str: The response from the agent as a string.
"""
response = await self.agent.run(question, ctx=self.ctx)
response = str(response)
return response
def clear_context(self):
"""
Clears the current context of the agent, resetting any conversation history.
This is useful when starting a new conversation or when the context needs to be refreshed.
"""
self.ctx = Context(self.agent)
class EncryptionExpert:
def __init__(self, temperature, max_tokens):
# Load the system prompt from a file
system_prompt_path = os.path.join(os.getcwd(), "system_prompts", "06_encryption_expert.txt")
self.system_prompt = ""
with open(system_prompt_path, "r") as file:
self.system_prompt = file.read().strip()
# Define the LLM and agent
llm = LLMFactory.create(Args.primary_llm_interface, self.system_prompt, temperature, max_tokens)
self.agent = AgentWorkflow.from_tools_or_functions(
[
Toolbox.encryption.base64_encode,
Toolbox.encryption.base64_decode,
Toolbox.encryption.caesar_cipher_encode,
Toolbox.encryption.caesar_cipher_decode,
Toolbox.encryption.reverse_string
# TODO: Add more encryption tools
],
llm=llm
)
self.ctx = Context(self.agent)
# Initialize the tool agents
self.math_expert = MathExpert(temperature, max_tokens)
self.reasoner = Reasoner(temperature, max_tokens)
def get_system_prompt(self) -> str:
"""
Retrieves the system prompt.
Returns:
str: The system prompt string.
"""
return self.system_prompt
async def query(self, question: str) -> str:
"""
Asynchronously queries the agent with a given question and returns the response.
Args:
question (str): The question to be sent to the agent.
Returns:
str: The response from the agent as a string.
"""
response = await self.agent.run(question, ctx=self.ctx)
response = str(response)
return response
def clear_context(self):
"""
Clears the current context of the agent, resetting any conversation history.
This is useful when starting a new conversation or when the context needs to be refreshed.
Also clears the context of any tool agents.
"""
self.ctx = Context(self.agent)
# Clear context for tool agents
self.math_expert.clear_context()
self.reasoner.clear_context()
class MathExpert:
def __init__(self, temperature, max_tokens):
# Load the system prompt from a file
system_prompt_path = os.path.join(os.getcwd(), "system_prompts", "07_math_expert.txt")
self.system_prompt = ""
with open(system_prompt_path, "r") as file:
self.system_prompt = file.read().strip()
# Define the LLM and agent
llm = LLMFactory.create(Args.primary_llm_interface, self.system_prompt, temperature, max_tokens)
self.agent = AgentWorkflow.from_tools_or_functions(
[
Toolbox.math.symbolic_calc,
Toolbox.math.unit_converter,
],
llm=llm
)
self.ctx = Context(self.agent)
# Initialize the tool agents
self.reasoner = Reasoner(temperature, max_tokens)
def get_system_prompt(self) -> str:
"""
Retrieves the system prompt.
Returns:
str: The system prompt string.
"""
return self.system_prompt
async def query(self, question: str) -> str:
"""
Asynchronously queries the agent with a given question and returns the response.
Args:
question (str): The question to be sent to the agent.
Returns:
str: The response from the agent as a string.
"""
response = await self.agent.run(question, ctx=self.ctx)
response = str(response)
return response
def clear_context(self):
"""
Clears the current context of the agent, resetting any conversation history.
This is useful when starting a new conversation or when the context needs to be refreshed.
Also clears the context of any tool agents.
"""
self.ctx = Context(self.agent)
self.reasoner.clear_context()
class Reasoner:
def __init__(self, temperature, max_tokens):
# Load the system prompt from a file
system_prompt_path = os.path.join(os.getcwd(), "system_prompts", "08_reasoner.txt")
self.system_prompt = ""
with open(system_prompt_path, "r") as file:
self.system_prompt = file.read().strip()
# Define the LLM and agent
llm = LLMFactory.create(Args.primary_llm_interface, self.system_prompt, temperature, max_tokens)
self.agent = AgentWorkflow.setup_agent(llm=llm)
self.ctx = Context(self.agent)
async def query(self, question: str) -> str:
"""
Asynchronously queries the agent with a given question and returns the response.
Args:
question (str): The question to be sent to the agent.
Returns:
str: The response from the agent as a string.
"""
response = await self.agent.run(question, ctx=self.ctx)
response = str(response)
return response
def get_system_prompt(self) -> str:
"""
Retrieves the system prompt.
Returns:
str: The system prompt string.
"""
return self.system_prompt
def clear_context(self):
"""
Clears the current context of the agent, resetting any conversation history.
This is useful when starting a new conversation or when the context needs to be refreshed.
"""
self.ctx = Context(self.agent)
class ImageHandler:
def __init__(self, temperature, max_tokens):
# Load the system prompt from a file
system_prompt_path = os.path.join(os.getcwd(), "system_prompts", "09_image_handler.txt")
self.system_prompt = ""
with open(system_prompt_path, "r") as file:
self.system_prompt = file.read().strip()
pass
def get_system_prompt(self) -> str:
"""
Retrieves the system prompt.
Returns:
str: The system prompt string.
"""
return self.system_prompt
def clear_context(self):
"""
Clears the current context of the agent, resetting any conversation history.
This is useful when starting a new conversation or when the context needs to be refreshed.
"""
if hasattr(self, 'ctx') and hasattr(self, 'agent'):
self.ctx = Context(self.agent)
class VideoHandler:
def __init__(self, temperature, max_tokens):
# Load the system prompt from a file
system_prompt_path = os.path.join(os.getcwd(), "system_prompts", "10_video_handler.txt")
self.system_prompt = ""
with open(system_prompt_path, "r") as file:
self.system_prompt = file.read().strip()
# No implementation yet
pass
def get_system_prompt(self) -> str:
"""
Retrieves the system prompt.
Returns:
str: The system prompt string.
"""
return self.system_prompt
def clear_context(self):
"""
Clears the current context of the agent, resetting any conversation history.
This is useful when starting a new conversation or when the context needs to be refreshed.
"""
if hasattr(self, 'ctx') and hasattr(self, 'agent'):
self.ctx = Context(self.agent)
class Solver:
def __init__(self, temperature, max_tokens):
# Load the system prompt from a file
system_prompt_path = os.path.join(os.getcwd(), "system_prompts", "03_solver.txt")
self.system_prompt = ""
with open(system_prompt_path, "r") as file:
self.system_prompt = file.read().strip()
# Define the LLM and agent
llm = LLMFactory.create(Args.primary_llm_interface, self.system_prompt, temperature, max_tokens)
self.agent = AgentWorkflow.from_tools_or_functions(
[
self.call_summarizer,
self.call_researcher,
self.call_encryption_expert,
self.call_math_expert,
self.call_reasoner,
self.call_image_handler,
self.call_video_handler
],
llm=llm
)
self.ctx = Context(self.agent)
# Initialize the tool agents
self.summarizer = Summarizer(temperature, max_tokens)
self.researcher = Researcher(temperature, max_tokens)
self.encryption_expert = EncryptionExpert(temperature, max_tokens)
self.math_expert = MathExpert(temperature, max_tokens)
self.reasoner = Reasoner(temperature, max_tokens)
self.image_handler = ImageHandler(temperature, max_tokens)
self.video_handler = VideoHandler(temperature, max_tokens)
def get_system_prompt(self) -> str:
"""
Retrieves the system prompt.
Returns:
str: The system prompt string.
"""
return self.system_prompt
async def query(self, question: str) -> str:
"""
Asynchronously queries the agent with a given question and returns the response.
Args:
question (str): The question to be sent to the agent.
Returns:
str: The response from the agent as a string.
"""
response = await self.agent.run(question, ctx=self.ctx)
response = str(response)
return response
def clear_context(self):
"""
Clears the current context of the agent, resetting any conversation history.
This is useful when starting a new conversation or when the context needs to be refreshed.
Also clears the context of all tool agents.
"""
self.ctx = Context(self.agent)
# Clear context for all tool agents
self.summarizer.clear_context()
self.researcher.clear_context()
self.encryption_expert.clear_context()
self.math_expert.clear_context()
self.reasoner.clear_context()
self.image_handler.clear_context()
self.video_handler.clear_context()
async def call_summarizer(self, question: str) -> str:
return await self.summarizer.query(question)
async def call_researcher(self, question: str) -> str:
return await self.researcher.query(question)
async def call_encryption_expert(self, question: str) -> str:
return await self.encryption_expert.query(question)
async def call_math_expert(self, question: str) -> str:
return await self.math_expert.query(question)
async def call_reasoner(self, question: str) -> str:
return await self.reasoner.query(question)
async def call_image_handler(self, question: str) -> str:
# ImageHandler may not have a query method yet, but following the pattern
if hasattr(self.image_handler, 'query'):
return await self.image_handler.query(question)
return "Image handling is not implemented yet."
# TODO
async def call_video_handler(self, question: str) -> str:
# VideoHandler may not have a query method yet, but following the pattern
if hasattr(self.video_handler, 'query'):
return await self.video_handler.query(question)
return "Video handling is not implemented yet."
# TODO
# if __name__ == "__main__":
# pass
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