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main.py
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main.py
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import json
import random
from abc import ABC, abstractmethod
from typing import List, Dict, Any
import requests
import logging
import numpy as np
import torch
from transformers import AutoTokenizer, AutoModel
from sentence_transformers import SentenceTransformer
from fastapi import FastAPI
from pydantic import BaseModel
from pymongo import MongoClient
import psycopg2
import redis
import faiss
import networkx as nx
from cryptography.fernet import Fernet
# Load configuration
with open('config.json', 'r') as config_file:
CONFIG = json.load(config_file)
# Set up logging
logging.basicConfig(filename='app.log', level=logging.INFO)
logger = logging.getLogger(__name__)
# Initialize FastAPI app
app = FastAPI()
# Define interfaces for databases and APIs
class Database(ABC):
@abstractmethod
def connect(self):
pass
@abstractmethod
def disconnect(self):
pass
def retry_operation(self, operation, max_retries=3):
for attempt in range(max_retries):
try:
return operation()
except Exception as e:
if attempt == max_retries - 1:
raise e
time.sleep(2 ** attempt) # Exponential backoff
class VectorDB(Database):
def __init__(self, config):
self.config = config
self.index = None
def connect(self):
logger.info(f"Connecting to Vector DB: {self.config['host']}")
dimension = 768 # Adjust based on your embedding size
self.index = faiss.IndexFlatL2(dimension)
def disconnect(self):
logger.info("Disconnecting from Vector DB")
self.index = None
def retrieve(self, query_vector):
def _retrieve():
distances, indices = self.index.search(query_vector, k=5)
return [(int(i), float(d)) for i, d in zip(indices[0], distances[0])]
return self.retry_operation(_retrieve)
class RDBMS(Database):
def __init__(self, config):
self.config = config
self.conn = None
def connect(self):
logger.info(f"Connecting to RDBMS: {self.config['host']}")
self.conn = psycopg2.connect(**self.config)
def disconnect(self):
logger.info("Disconnecting from RDBMS")
if self.conn:
self.conn.close()
def execute_query(self, query):
def _execute():
with self.conn.cursor() as cur:
cur.execute(query)
return cur.fetchall()
return self.retry_operation(_execute)
class NoSQLDB(Database):
def __init__(self, config):
self.config = config
self.client = None
self.db = None
def connect(self):
logger.info(f"Connecting to NoSQL DB: {self.config['host']}")
self.client = MongoClient(self.config['host'], self.config['port'])
self.db = self.client[self.config['database']]
def disconnect(self):
logger.info("Disconnecting from NoSQL DB")
if self.client:
self.client.close()
def find(self, collection, query):
def _find():
return list(self.db[collection].find(query))
return self.retry_operation(_find)
def fetch_from_api(url):
response = requests.get(url)
response.raise_for_status()
return response.json()
# Define specialized agents
class Agent(ABC):
@abstractmethod
def process(self, input_data):
pass
class CodeAgent(Agent):
def __init__(self):
self.model = AutoModel.from_pretrained("microsoft/codebert-base")
self.tokenizer = AutoTokenizer.from_pretrained("microsoft/codebert-base")
def process(self, task):
logger.info(f"Generating code for: {task}")
inputs = self.tokenizer(task, return_tensors="pt")
outputs = self.model(**inputs)
# This is a simplified example. In a real scenario, you'd use the model's output
# to generate actual code.
return f"def solution():\n # TODO: Implement {task}"
class CyberSecurityAgent(Agent):
def __init__(self):
self.model = SentenceTransformer('all-MiniLM-L6-v2')
def process(self, code):
logger.info("Evaluating security of the code")
embedding = self.model.encode(code)
# This is a simplified example. In a real scenario, you'd use the embedding
# to compare against known vulnerabilities or patterns.
return {"vulnerabilities": ["SQL Injection risk", "Unsanitized input"]}
class ProjectManagerAgent(Agent):
def __init__(self):
self.graph = nx.DiGraph()
def process(self, task):
logger.info(f"Managing task: {task}")
self.graph.add_node(task)
return {"status": "assigned", "deadline": "2024-07-01"}
class CyberResearchAgent(Agent):
def __init__(self):
self.cipher_suite = Fernet(Fernet.generate_key())
def process(self, topic):
logger.info(f"Researching cyber threats related to: {topic}")
encrypted_result = self.cipher_suite.encrypt(f"Threats related to {topic}".encode())
return {"threats": ["Zero-day exploit", "Ransomware attack"], "encrypted_data": encrypted_result}
# Enhanced Consensus Mechanism
class EnhancedConsensusMechanism:
def __init__(self):
self.agent_expertise = {
"CodeAgent": 0.8,
"CyberSecurityAgent": 0.9,
"ProjectManagerAgent": 0.7,
"CyberResearchAgent": 0.85
}
self.agent_performance_history = {
"CodeAgent": [],
"CyberSecurityAgent": [],
"ProjectManagerAgent": [],
"CyberResearchAgent": []
}
def calculate_proposal_score(self, proposal: Dict[str, Any], agent_type: str) -> float:
base_score = random.uniform(0.5, 1.0) # Simulating proposal quality
expertise_score = self.agent_expertise.get(agent_type, 0.5)
performance_history = self.agent_performance_history.get(agent_type, [])
performance_score = np.mean(performance_history) if performance_history else 0.5
final_score = (base_score * 0.4) + (expertise_score * 0.4) + (performance_score * 0.2)
return final_score
def update_performance_history(self, agent_type: str, score: float):
history = self.agent_performance_history.get(agent_type, [])
history.append(score)
if len(history) > 10: # Keep only the last 10 performances
history = history[-10:]
self.agent_performance_history[agent_type] = history
def vote(self, proposals: List[Dict[str, Any]]) -> Dict[str, Any]:
if not proposals:
raise ValueError("No proposals to vote on")
scored_proposals = []
for proposal in proposals:
agent_type = proposal.get("agent_type", "Unknown")
score = self.calculate_proposal_score(proposal, agent_type)
scored_proposals.append((score, proposal))
scored_proposals.sort(reverse=True, key=lambda x: x[0])
winning_score, winning_proposal = scored_proposals[0]
self.update_performance_history(winning_proposal.get("agent_type", "Unknown"), winning_score)
return winning_proposal
# Integrate LLMs for reasoning
class LLMReasoning:
def __init__(self):
self.models = {
"security": AutoModel.from_pretrained("distilbert-base-uncased"),
"code": AutoModel.from_pretrained("microsoft/codebert-base")
}
self.tokenizers = {
"security": AutoTokenizer.from_pretrained("distilbert-base-uncased"),
"code": AutoTokenizer.from_pretrained("microsoft/codebert-base")
}
def query(self, agent_type: str, question: str) -> str:
logger.info(f"Querying LLM for {agent_type}: {question}")
model = self.models.get(agent_type)
tokenizer = self.tokenizers.get(agent_type)
if not model or not tokenizer:
raise ValueError(f"No model or tokenizer found for agent type: {agent_type}")
inputs = tokenizer(question, return_tensors="pt")
outputs = model(**inputs)
# This is a simplified example. In a real scenario, you'd use the model's output
# to generate a more meaningful response.
return f"LLM response for {agent_type}: {question}"
# FastAPI routes
class TaskInput(BaseModel):
task: str
@app.post("/process_task")
async def process_task(task_input: TaskInput):
task = task_input.task
# Initialize components
vector_db = VectorDB(CONFIG['databases']['vector_db'])
rdbms = RDBMS(CONFIG['databases']['rdbms'])
nosql_db = NoSQLDB(CONFIG['databases']['nosql_db'])
code_agent = CodeAgent()
security_agent = CyberSecurityAgent()
pm_agent = ProjectManagerAgent()
research_agent = CyberResearchAgent()
consensus = EnhancedConsensusMechanism()
llm = LLMReasoning()
# Connect to databases
vector_db.connect()
rdbms.connect()
nosql_db.connect()
try:
# Step 1: Retrieve data from databases
query_vector = np.random.rand(1, 768).astype('float32') # Example query vector
vector_results = vector_db.retrieve(query_vector)
sql_query = "SELECT * FROM example_table LIMIT 5;"
rdbms_results = rdbms.execute_query(sql_query)
nosql_query = {"field": "value"}
nosql_results = nosql_db.find("example_collection", nosql_query)
# Step 2: Process task with specialized agents
code_result = await code_agent.process(task)
security_result = await security_agent.process(code_result)
pm_result = await pm_agent.process(task)
research_result = await research_agent.process(task)
# Step 3: Achieve consensus
proposals = [
{"agent_type": "CodeAgent", "result": code_result},
{"agent_type": "CyberSecurityAgent", "result": security_result},
{"agent_type": "ProjectManagerAgent", "result": pm_result},
{"agent_type": "CyberResearchAgent", "result": research_result}
]
final_decision = consensus.vote(proposals)
# Step 4: Use LLM for reasoning (if needed)
llm_response = await llm.query("code", f"What is the best way to implement {task}?")
return {
"vector_results": vector_results,
"rdbms_results": rdbms_results,
"nosql_results": nosql_results,
"final_decision": final_decision,
"llm_response": llm_response
}
finally:
# Disconnect from databases
vector_db.disconnect()
rdbms.disconnect()
nosql_db.disconnect()