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from abc import ABC, abstractmethod
from typing import Dict, Type, Any, Optional
import os
import json
import ollama
from src.utils.singleton import singleton
from src.utils.streamlit_interface import output_assistant_response
from google import genai
from google.genai import types
from google.genai.types import *
import os
from dotenv import load_dotenv
class Agent(ABC):
def __init__(self, agent_name: str, base_model: str, system_prompt: str, creation_cost: str, invoke_cost: str):
self.agent_name = agent_name
self.base_model = base_model
self.system_prompt = system_prompt
self.creation_cost = creation_cost
self.invoke_cost = invoke_cost
self.create_model()
@abstractmethod
def create_model(self) -> None:
"""Create and Initialize agent"""
pass
@abstractmethod
def ask_agent(self, prompt: str) -> str:
"""ask agent a question"""
pass
@abstractmethod
def delete_agent(self) ->None:
"""delete agent"""
pass
def get_costs(self):
return {
"create_cost": self.creation_cost,
"invoke_cost": self.invoke_cost
}
class OllamaAgent(Agent):
def create_model(self):
ollama_response = ollama.create(
model = self.agent_name,
from_ = self.base_model,
system = self.system_prompt,
stream = False
)
def ask_agent(self, prompt):
output_assistant_response(f"Asked Agent {self.agent_name} a question")
agent_response = ollama.chat(
model=self.agent_name,
messages=[{"role": "user", "content": prompt}],
)
output_assistant_response(f"Agent {self.agent_name} answered with {agent_response.message.content}")
return agent_response.message.content
def delete_agent(self):
ollama.delete(self.agent_name)
class GeminiAgent(Agent):
def __init__(self, agent_name: str, base_model: str, system_prompt: str, creation_cost: str, invoke_cost: str):
load_dotenv()
self.api_key = os.getenv("GEMINI_KEY")
if not self.api_key:
raise ValueError("Google API key is required for Gemini models. Set GOOGLE_API_KEY environment variable or pass api_key parameter.")
# Initialize the Gemini API
self.client = genai.Client(api_key=self.api_key)
# Call parent constructor after API setup
super().__init__(agent_name, base_model, system_prompt, creation_cost, invoke_cost)
def create_model(self):
self.messages = []
def ask_agent(self, prompt):
response = self.client.models.generate_content(
model=self.base_model,
contents=prompt,
config=types.GenerateContentConfig(
system_instruction=self.system_prompt,
)
)
return response.text
def delete_agent(self):
self.messages = []
@singleton
class AgentManager():
def __init__(self):
self._agents = {}
self._agent_types ={
"ollama": OllamaAgent,
"gemini": GeminiAgent
}
self._load_agents()
def create_agent(self, agent_name: str, base_model: str, system_prompt: str, description: str = "", create_cost: float = 0, invoke_cost: float = 0,
**additional_params) -> Agent:
if agent_name in self._agents:
raise ValueError(f"Agent {agent_name} already exists")
agent_type = self._get_agent_type(base_model)
agent_class = self._agent_types.get(agent_type)
if not agent_class:
raise ValueError(f"Unsupported base model {base_model}")
# create agent
self._agents[agent_name] = agent_class(agent_name, base_model, system_prompt, create_cost,invoke_cost )
#save agent to file
self._save_agent(
agent_name,
base_model,
system_prompt,
description=description,
create_cost=create_cost,
invoke_cost=invoke_cost,
**additional_params # For any future parameters we might want to add
)
return self._agents[agent_name]
def get_agent(self, agent_name: str) -> Agent:
"""Get existing agent by name"""
if agent_name not in self._agents:
raise ValueError(f"Agent {agent_name} does not exists")
return self._agents[agent_name]
def list_agents(self) -> dict:
"""Return agent information (name, description, costs)"""
try:
if os.path.exists("./models/models.json"):
with open("./models/models.json", "r", encoding="utf8") as f:
full_models = json.loads(f.read())
# Create a simplified version with only the description and costs
simplified_agents = {}
for name, data in full_models.items():
simplified_agents[name] = {
"description": data.get("description", ""),
"create_cost": data.get("create_cost", 0),
"invoke_cost": data.get("invoke_cost", 0)
}
return simplified_agents
else:
return {}
except Exception as e:
output_assistant_response(f"Error listing agents: {e}")
return {}
def delete_agent(self, agent_name: str) -> None:
if agent_name not in self._agents:
raise ValueError(f"Agent {agent_name} does not exist")
self._agents[agent_name].delete_agent()
del self._agents[agent_name]
try:
if os.path.exists("./models/models.json"):
with open("./models/models.json", "r", encoding="utf8") as f:
models = json.loads(f.read())
del models[agent_name]
with open("./models/models.json", "w", encoding="utf8") as f:
f.write(json.dumps(models, indent=4))
except Exception as e:
output_assistant_response(f"Error deleting agent: {e}")
def _save_agent(self, agent_name: str, base_model: str, system_prompt: str,
description: str = "", create_cost: float = 0, invoke_cost: float = 0,
**additional_params) -> None:
"""Save a single agent to the models.json file"""
try:
# Ensure the directory exists
os.makedirs("./models", exist_ok=True)
# Read existing models file or create empty dict if it doesn't exist
try:
with open("./models/models.json", "r", encoding="utf8") as f:
models = json.loads(f.read())
except (FileNotFoundError, json.JSONDecodeError):
models = {}
# Update the models dict with the new agent
models[agent_name] = {
"base_model": base_model,
"description": description,
"system_prompt": system_prompt,
"create_cost": create_cost,
"invoke_cost": invoke_cost,
}
# Add any additional parameters that were passed
for key, value in additional_params.items():
models[agent_name][key] = value
# Write the updated models back to the file
with open("./models/models.json", "w", encoding="utf8") as f:
f.write(json.dumps(models, indent=4))
except Exception as e:
output_assistant_response(f"Error saving agent {agent_name}: {e}")
def _get_agent_type(self, base_model)->str:
if base_model == "llama3.2":
return "ollama"
elif base_model == "mistral":
return "ollama"
elif "gemini" in base_model:
return "gemini"
else:
return "unknown"
def _load_agents(self) -> None:
"""Load agent configurations from disk"""
try:
if not os.path.exists("./models/models.json"):
return
with open("./models/models.json", "r", encoding="utf8") as f:
models = json.loads(f.read())
for name, data in models.items():
if name in self._agents:
continue
base_model = data["base_model"]
system_prompt = data["system_prompt"]
creation_cost = data["create_cost"]
invoke_cost = data["invoke_cost"]
model_type = self._get_agent_type(base_model)
manager_class = self._agent_types.get(model_type)
if manager_class:
# Create the agent with the appropriate manager class
self._agents[name] = manager_class(
name,
base_model,
system_prompt,
creation_cost,
invoke_cost
)
except Exception as e:
output_assistant_response(f"Error loading agents: {e}") |