🚀 Adaptive Tech Intelligence Assistant

# agents/base_agent.py from autogen import ConversableAgent from typing import Dict, List, Any import openai from transformers import AutoTokenizer, AutoModelForSequenceClassification class CognitiveAgent(ConversableAgent): def __init__(self, name: str, llm_config: Dict, local_model_path: str = None): super().__init__(name=name, llm_config=llm_config) self.memory = {} self.feedback_history = [] self.local_model = None if local_model_path: self.load_local_model(local_model_path) def load_local_model(self, model_path: str): """Load fine-tuned local model for specific tasks""" self.tokenizer = AutoTokenizer.from_pretrained(model_path) self.local_model = AutoModelForSequenceClassification.from_pretrained(model_path) def cognitive_loop(self, task: Dict, max_iterations: int = 3) -> Dict: """Implement cognitive loop with plan-execute-verify-critique""" result = {"iterations": [], "final_output": None} for i in range(max_iterations): # Plan plan = self.plan_task(task) # Execute execution_result = self.execute_plan(plan) # Verify verification = self.verify_result(execution_result, task) # Critique critique = self.critique_result(execution_result, verification) result["iterations"].append({ "iteration": i + 1, "plan": plan, "execution": execution_result, "verification": verification, "critique": critique }) if verification["approved"] or i == max_iterations - 1: result["final_output"] = execution_result break # Improve based on critique task = self.incorporate_feedback(task, critique) return result

# agents/scout_agent.py class ScoutAgent(CognitiveAgent): def __init__(self, sources_config: Dict): super().__init__( name="scout_agent", llm_config={"model": "gpt-4", "temperature": 0.1} ) self.sources = sources_config self.content_cache = {} async def monitor_sources(self) -> List[Dict]: """Continuously monitor configured sources""" new_content = [] for source_type, configs in self.sources.items(): if source_type == "arxiv": content = await self.fetch_arxiv_papers(configs) elif source_type == "github": content = await self.fetch_github_trends(configs) elif source_type == "news": content = await self.fetch_news_articles(configs) new_content.extend(content) return await self.deduplicate_content(new_content) async def fetch_arxiv_papers(self, config: Dict) -> List[Dict]: """Fetch latest papers from arXiv in specified categories""" # Implementation for arXiv API integration pass

# agents/classifier_agent.py class ClassifierAgent(CognitiveAgent): def __init__(self): super().__init__( name="classifier_agent", llm_config={"model": "claude-3-sonnet", "temperature": 0.2}, local_model_path="./models/tech_classifier" ) def classify_content(self, content: Dict) -> Dict: """Classify and tag content using hybrid approach""" task = { "type": "classification", "content": content, "categories": ["AI", "Quantum", "Robotics", "Smart_Devices"], "device_types": ["Humanoid", "Wearable", "Autonomous", "IoT"], "impact_level": ["Breakthrough", "Incremental", "Application"] } # Use cognitive loop for complex classification result = self.cognitive_loop(task) # Combine remote LLM with local fine-tuned model local_prediction = self.local_classify(content) remote_classification = result["final_output"] return self.merge_classifications(local_prediction, remote_classification) def local_classify(self, content: Dict) -> Dict: """Use local fine-tuned model for fast, specialized classification""" if self.local_model: inputs = self.tokenizer(content["text"], return_tensors="pt", truncation=True) outputs = self.local_model(**inputs) # Process outputs and return classification pass return {}

# cognitive_engine.py class CognitiveEngine: def __init__(self): self.agents = self.initialize_agents() self.memory_manager = MemoryManager() self.feedback_processor = FeedbackProcessor() def initialize_agents(self) -> Dict: """Initialize all AutoGen agents""" return { "planner": PlannerAgent(), "scout": ScoutAgent(sources_config=self.load_sources_config()), "classifier": ClassifierAgent(), "summarizer": SummarizerAgent(), "trend_detector": TrendDetectorAgent(), "verifier": VerifierAgent(), "critic": CriticAgent() } async def run_intelligence_cycle(self) -> Dict: """Execute complete intelligence gathering and analysis cycle""" # Phase 1: Data Collection raw_content = await self.agents["scout"].monitor_sources() # Phase 2: Classification & Processing classified_content = [] for item in raw_content: classification = self.agents["classifier"].classify_content(item) classified_content.append({**item, **classification}) # Phase 3: Summarization with Cognitive Loop summaries = await self.cognitive_summarization(classified_content) # Phase 4: Trend Analysis trends = self.agents["trend_detector"].analyze_trends(classified_content) # Phase 5: Quality Assurance verified_output = self.agents["verifier"].verify_complete_output({ "summaries": summaries, "trends": trends, "raw_count": len(raw_content) }) return verified_output async def cognitive_summarization(self, content: List[Dict]) -> Dict: """Implement cognitive loop for summarization""" summarization_task = { "content": content, "user_preferences": self.memory_manager.get_user_preferences(), "output_formats": ["executive", "technical", "trend_digest"] } # Planner breaks down the task plan = self.agents["planner"].create_summarization_plan(summarization_task) # Execute with cognitive loop results = {} for subtask in plan["subtasks"]: result = self.agents["summarizer"].cognitive_loop(subtask, max_iterations=3) results[subtask["id"]] = result["final_output"] return results