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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
class MathExpert:
def __init__(self, temperature, max_tokens):
system_prompt_path = os.path.join(os.getcwd(), "system_prompts", "06_math_expert.txt")
self.system_prompt = ""
with open(system_prompt_path, "r") as file:
self.system_prompt = file.read().strip()
llm = LLMFactory.create(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)
def get_system_prompt(self):
return self.system_prompt
async def query(self, question: str) -> str:
response = await self.agent.run(question, ctx=self.ctx)
response = str(response)
return response
class Researcher:
def __init__(self, temperature, max_tokens):
system_prompt_path = os.path.join(os.getcwd(), "system_prompts", "04_researcher.txt")
self.system_prompt = ""
with open(system_prompt_path, "r") as file:
self.system_prompt = file.read().strip()
llm = LLMFactory.create(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):
return self.system_prompt
async def query(self, question: str) -> str:
response = await self.agent.run(question, ctx=self.ctx)
response = str(response)
return response
class EncryptionExpert:
def __init__(self, temperature, max_tokens):
system_prompt_path = os.path.join(os.getcwd(), "system_prompts", "05_encryption_expert.txt")
self.system_prompt = ""
with open(system_prompt_path, "r") as file:
self.system_prompt = file.read().strip()
llm = LLMFactory.create(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
],
llm=llm
)
self.ctx = Context(self.agent)
def get_system_prompt(self):
return self.system_prompt
async def query(self, question: str) -> str:
response = await self.agent.run(question, ctx=self.ctx)
response = str(response)
return response
class ImageHandler:
pass
class VideoHandler:
pass
class RecursiveSolverAgent:
pass
class Solver:
def __init__(self, temperature, max_tokens):
print("Agent initialized.")
system_prompt_path = os.path.join(os.getcwd(), "system_prompts", "01_assistant.txt")
self.system_prompt = ""
with open(system_prompt_path, "r") as file:
self.system_prompt = file.read().strip()
llm = LLMFactory.create(self.system_prompt, temperature, max_tokens)
self.agent = AgentWorkflow.from_tools_or_functions(
[
FunctionTool.from_defaults(self.delegate_to_math_expert),
FunctionTool.from_defaults(self.set_final_answer)
],
llm=llm
)
self.ctx = Context(self.agent)
self.final_answer = ""
async def __call__(self, question: str) -> str:
print(f"Agent received question (first 50 chars): {question[:50]}...")
self.final_answer = ""
response = await self.query(question)
print(f"Agent processed the response: {response}")
if self.final_answer == "":
response = await self.query("I noticed the final_answer is an empty string. Have you forgot to set the final_answer ?")
return self.final_answer
def get_system_prompt(self):
return self.system_prompt
async def query(self, question: str) -> str:
response = await self.agent.run(question, ctx=self.ctx)
response = str(response)
final_answer = response
self.set_final_answer(final_answer)
return response
def set_final_answer(self, final_answer: str) -> str:
"""
Sets the final answer for the current querry.
Args:
final_answer (str): The final answer to be set for the agent.
Returns:
str: The final answer that was set.
"""
print("-> set_final_answer !")
self.final_answer = final_answer
def delegate_to_math_expert(self, question: str) -> str:
print("-> delegated to math agent !")
math_agent = MathExpert(temperature=0.7, max_tokens=100)
return math_agent.query(question)
if __name__ == "__main__":
encryption_agent = EncryptionExpert(temperature=0.7, max_tokens=2000)
# encryption_query = "Descifer this: 'Bmfy bfx ymj wjxzqy gjybjjs z-hqzo fsi zsnajwxnyfyjf-hwfntaf ns fuwnq 2025 ?'"
encryption_query = ".rewsna eht sa ""tfel"" drow eht fo etisoppo eht etirw ,ecnetnes siht dnatsrednu uoy fI"
# print(encryption_agent.get_system_prompt())
# encoding = encryption_agent.caesar_cipher_encode(encryption_query, 5)
# print(encoding)
# print(encryption_agent.caesar_cipher_decode(encoding, 5))
print(asyncio.run(encryption_agent.query(encryption_query)))
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