Awesome-Agentic-AI-Learning-Resource-By-DevKay

Key Concepts to Master:

• Perceptrons & Multi-layer Networks: Start with basic building blocks
• Backpropagation: The learning algorithm that powers everything
• Activation Functions: ReLU, Sigmoid, Tanh, and modern variants
• Optimization: SGD, Adam, RMSprop, learning rate scheduling
• Regularization: Dropout, batch normalization, weight decay

Practical Implementation:

# Simple Neural Network from Scratch (Educational)
import numpy as np

class NeuralNetwork:
    def add_documents(self, documents):
        """Add documents to the vector database"""
        for i, doc in enumerate(documents):
            embedding = self.embedding_model.encode([doc['content']])[0]
            self.collection.add(
                embeddings=[embedding.tolist()],
                documents=[doc['content']],
                metadatas=[doc.get('metadata', {})],
                ids=[f"doc_{i}"]
            )
    
    def retrieve(self, query, k=5):
        """Retrieve relevant documents for a query"""
        query_embedding = self.embedding_model.encode([query])[0]
        results = self.collection.query(
            query_embeddings=[query_embedding.tolist()],
            n_results=k
        )
        return results['documents'][0]
    
    def generate_response(self, query, context_docs):
        """Generate response using retrieved context"""
        context = "\n".join(context_docs[:3])  # Use top 3 docs
        prompt = f"Context: {context}\n\nQuestion: {query}\n\nAnswer:"
        
        inputs = self.tokenizer.encode(prompt, return_tensors='pt')
        with torch.no_grad():
            outputs = self.generator.generate(
                inputs, 
                max_length=inputs.shape[1] + 150,
                temperature=0.7,
                pad_token_id=self.tokenizer.eos_token_id
            )
        
        response = self.tokenizer.decode(outputs[0][inputs.shape[1]:], skip_special_tokens=True)
        return response.strip()
    
    def query(self, question):
        """Complete RAG pipeline"""
        # Retrieve relevant documents
        relevant_docs = self.retrieve(question)
        
        # Generate response with context
        response = self.generate_response(question, relevant_docs)
        
        return {
            'answer': response,
            'sources': relevant_docs,
            'confidence': self.calculate_confidence(question, relevant_docs)
        }
    
    def calculate_confidence(self, query, docs):
        """Calculate confidence score for the response"""
        # Simple similarity-based confidence
        query_embedding = self.embedding_model.encode([query])[0]
        doc_embeddings = self.embedding_model.encode(docs)
        
        similarities = [
            torch.cosine_similarity(
                torch.tensor(query_embedding), 
                torch.tensor(doc_emb), 
                dim=0
            ).item()
            for doc_emb in doc_embeddings
        ]
        
        return max(similarities) if similarities else 0.0

# Usage example
rag_system = AdvancedRAGSystem()

# Add sample documents
documents = [
    {"content": "Machine learning is a subset of AI that focuses on algorithms that can learn from data."},
    {"content": "Deep learning uses neural networks with multiple layers to model complex patterns."},
    {"content": "Transformers are a type of neural network architecture that uses attention mechanisms."}
]

rag_system.add_documents(documents)

# Query the system
result = rag_system.query("What is machine learning?")
print(f"Answer: {result['answer']}")
print(f"Confidence: {result['confidence']:.2f}")

Advanced RAG Techniques:

1. Hybrid Retrieval (Dense + Sparse):

from rank_bm25 import BM25Okapi

class HybridRetriever:
    def __init__(self, documents):
        self.documents = documents
        self.embedding_model = SentenceTransformer('all-MiniLM-L6-v2')
        
        # BM25 for sparse retrieval
        tokenized_docs = [doc.split() for doc in documents]
        self.bm25 = BM25Okapi(tokenized_docs)
        
        # Dense embeddings
        self.doc_embeddings = self.embedding_model.encode(documents)
    
    def retrieve(self, query, k=5, alpha=0.5):
        """Combine dense and sparse retrieval"""
        # BM25 scores
        bm25_scores = self.bm25.get_scores(query.split())
        
        # Dense similarity scores
        query_embedding = self.embedding_model.encode([query])[0]
        dense_scores = [
            torch.cosine_similarity(
                torch.tensor(query_embedding),
                torch.tensor(doc_emb),
                dim=0
            ).item()
            for doc_emb in self.doc_embeddings
        ]
        
        # Combine scores
        combined_scores = [
            alpha * dense + (1 - alpha) * sparse
            for dense, sparse in zip(dense_scores, bm25_scores)
        ]
        
        # Get top k documents
        top_indices = sorted(range(len(combined_scores)), 
                           key=lambda i: combined_scores[i], reverse=True)[:k]
        
        return [self.documents[i] for i in top_indices]

2. Multi-Query RAG:

class MultiQueryRAG:
    def __init__(self, rag_system):
        self.rag_system = rag_system
        self.query_generator = AutoModelForCausalLM.from_pretrained("gpt2")
        self.query_tokenizer = AutoTokenizer.from_pretrained("gpt2")
    
    def generate_similar_queries(self, original_query, num_queries=3):
        """Generate multiple similar queries for better retrieval"""
        prompt = f"Generate {num_queries} similar questions to: {original_query}\n"
        
        inputs = self.query_tokenizer.encode(prompt, return_tensors='pt')
        outputs = self.query_generator.generate(inputs, max_length=100)
        
        generated_text = self.query_tokenizer.decode(outputs[0], skip_special_tokens=True)
        # Parse and return queries (simplified)
        return [original_query]  # Would contain parsed queries
    
    def retrieve_multi_query(self, query):
        """Retrieve using multiple query variants"""
        queries = self.generate_similar_queries(query)
        all_docs = []
        
        for q in queries:
            docs = self.rag_system.retrieve(q)
            all_docs.extend(docs)
        
        # Remove duplicates and rank
        unique_docs = list(set(all_docs))
        return unique_docs[:5]  # Return top 5

Usage example

#### **Modern Frameworks:**
- **PyTorch**: `pip install torch torchvision`
- **TensorFlow**: `pip install tensorflow`
- **JAX**: `pip install jax jaxlib` (for research)

</details>

### **Transformers & Large Language Models**

<details>
<summary><b>🔥 Transformer Architecture Deep Dive</b></summary>

#### **Understanding Transformers:**
1. **Attention Mechanism**: "Attention is All You Need" - learn this first
2. **Self-Attention**: How tokens relate to each other
3. **Multi-Head Attention**: Parallel attention mechanisms
4. **Positional Encoding**: How transformers understand sequence order
5. **Feed-Forward Networks**: The other half of transformer blocks

#### **Hands-On Transformer Implementation:**
```python
# Simplified Transformer Block (Educational)
import torch
import torch.nn as nn
import torch.nn.functional as F

class MultiHeadAttention(nn.Module):
    def __init__(self, d_model, num_heads):
        super().__init__()
        self.d_model = d_model
        self.num_heads = num_heads
        self.d_k = d_model // num_heads
        
        self.W_q = nn.Linear(d_model, d_model)
        self.W_k = nn.Linear(d_model, d_model)
        self.W_v = nn.Linear(d_model, d_model)
        self.W_o = nn.Linear(d_model, d_model)
        
    def forward(self, query, key, value, mask=None):
        batch_size = query.size(0)
        
        # Linear transformations and reshape
        Q = self.W_q(query).view(batch_size, -1, self.num_heads, self.d_k).transpose(1, 2)
        K = self.W_k(key).view(batch_size, -1, self.num_heads, self.d_k).transpose(1, 2)
        V = self.W_v(value).view(batch_size, -1, self.num_heads, self.d_k).transpose(1, 2)
        
        # Attention
        scores = torch.matmul(Q, K.transpose(-2, -1)) / math.sqrt(self.d_k)
        if mask is not None:
            scores = scores.masked_fill(mask == 0, -1e9)
        
        attention_weights = F.softmax(scores, dim=-1)
        context = torch.matmul(attention_weights, V)
        
        # Concatenate heads
        context = context.transpose(1, 2).contiguous().view(
            batch_size, -1, self.d_model)
        
        return self.W_o(context)

Pre-trained Models to Use:

• GPT Family: Text generation, completion
• BERT Family: Understanding, classification
• T5: Text-to-text transfer
• Code Models: CodeT5, CodeBERT for programming

Fine-tuning Resources:

• Hugging Face Transformers - Model hub and library
• Parameter-Efficient Fine-tuning - LoRA, AdaLoRA, etc.
• Unsloth - Fast fine-tuning library

Core Architectures:

• CNNs: ResNet, EfficientNet, Vision Transformers
• Object Detection: YOLO, R-CNN family, DETR
• Segmentation: U-Net, Mask R-CNN, Segment Anything
• Generative: Diffusion Models, GANs, VAEs

Multimodal Models:

• CLIP: Image-text understanding
• DALL-E/Midjourney: Text-to-image generation
• GPT-4V: Vision-language reasoning
• LLaVA: Open-source vision-language model

Practical Projects:

# Image Classification with Vision Transformer
from transformers import ViTImageProcessor, ViTForImageClassification
from PIL import Image

# Load pre-trained model
processor = ViTImageProcessor.from_pretrained('google/vit-base-patch16-224')
model = ViTForImageClassification.from_pretrained('google/vit-base-patch16-224')

# Process image
image = Image.open('your_image.jpg')
inputs = processor(images=image, return_tensors="pt")
outputs = model(**inputs)
predictions = outputs.logits.argmax(-1)

Types of Agents:

1. Reactive Agents

   • Respond directly to environment stimuli
   • No internal state or planning
   • Fast but limited reasoning

2. Deliberative Agents

   • Maintain internal world model
   • Plan before acting
   • Better for complex tasks

3. Hybrid Agents

   • Combine reactive and deliberative approaches
   • Layered architecture (reactive layer + planning layer)
   • Most practical for real applications

4. Learning Agents

   • Improve performance over time
   • Use reinforcement learning or other adaptation methods
   • Can discover new strategies

Core Agent Components:

# Agent Architecture Template
class Agent:
    def __init__(self):
        self.memory = {}           # Long-term and short-term memory
        self.goals = []           # Current objectives
        self.beliefs = {}         # Knowledge about the world
        self.tools = []           # Available actions/functions
        self.reasoner = None      # Planning and reasoning engine
        
    def perceive(self, environment):
        """Process input from environment"""
        pass
        
    def reason(self):
        """Plan actions based on goals and beliefs"""
        pass
        
    def act(self, action):
        """Execute action in environment"""
        pass
        
    def learn(self, feedback):
        """Update beliefs and strategies"""
        pass

Chain-of-Thought (CoT) Reasoning:

# CoT Implementation Example
def chain_of_thought_solve(problem, llm):
    prompt = f"""
    Let's solve this step by step:
    
    Problem: {problem}
    
    Step 1: Understanding the problem
    Step 2: Identifying key information
    Step 3: Planning the solution approach
    Step 4: Executing the solution
    Step 5: Verifying the answer
    
    Let me work through each step:
    """
    
    response = llm.generate(prompt)
    return response

Tree of Thoughts (ToT):

# Tree of Thoughts for complex reasoning
class TreeOfThoughts:
    def __init__(self, llm, max_depth=3):
        self.llm = llm
        self.max_depth = max_depth
    
    def generate_thoughts(self, problem, current_path=[]):
        if len(current_path) >= self.max_depth:
            return self.evaluate_solution(current_path)
        
        # Generate multiple thought branches
        thoughts = self.llm.generate_thoughts(problem, current_path)
        
        best_thought = None
        best_score = -1
        
        for thought in thoughts:
            new_path = current_path + [thought]
            score = self.evaluate_thought(thought, problem)
            
            if score > best_score:
                best_thought = thought
                best_score = score
        
        return self.generate_thoughts(problem, current_path + [best_thought])

ReAct (Reasoning + Acting):

# ReAct Pattern Implementation
class ReActAgent:
    def __init__(self, llm, tools):
        self.llm = llm
        self.tools = {tool.name: tool for tool in tools}
    
    def solve(self, task):
        thoughts = []
        actions = []
        observations = []
        
        while not self.is_complete(task, thoughts, actions, observations):
            # Think
            thought = self.think(task, thoughts, actions, observations)
            thoughts.append(thought)
            
            # Act (if thought suggests an action)
            if self.should_act(thought):
                action = self.extract_action(thought)
                if action['tool'] in self.tools:
                    result = self.tools[action['tool']].run(action['input'])
                    actions.append(action)
                    observations.append(result)
        
        return self.synthesize_answer(thoughts, actions, observations)

Types of Memory:

1. Short-term Memory: Current conversation context
2. Long-term Memory: Persistent knowledge and experiences
3. Working Memory: Active information for current task
4. Episodic Memory: Specific experiences and events
5. Semantic Memory: General knowledge and facts

Memory Implementation:

# Comprehensive Memory System
class AgentMemory:
    def __init__(self, vector_db, graph_db):
        self.vector_db = vector_db        # For semantic similarity
        self.graph_db = graph_db          # For structured relationships
        self.working_memory = {}          # Current task context
        self.conversation_buffer = []     # Recent interactions
        
    def store_experience(self, experience):
        """Store new experience with embeddings and relationships"""
        # Create vector embedding
        embedding = self.embed_text(experience['content'])
        
        # Store in vector database
        self.vector_db.add(
            id=experience['id'],
            embedding=embedding,
            metadata=experience
        )
        
        # Extract and store relationships
        entities = self.extract_entities(experience['content'])
        self.graph_db.add_relationships(entities)
    
    def retrieve_relevant(self, query, k=5):
        """Retrieve relevant memories for current context"""
        # Vector similarity search
        similar_memories = self.vector_db.search(query, k=k)
        
        # Graph traversal for related concepts
        entities = self.extract_entities(query)
        related_memories = self.graph_db.find_related(entities)
        
        # Combine and rank results
        return self.rank_memories(similar_memories + related_memories)

Coordination Patterns:

1. Hierarchical: Manager-worker relationships
2. Peer-to-Peer: Equal agents collaborating
3. Market-based: Agents bid for tasks
4. Blackboard: Shared knowledge space

Communication Protocols:

# Multi-Agent Communication System
class MultiAgentSystem:
    def __init__(self):
        self.agents = {}
        self.message_queue = []
        self.coordination_protocol = None
    
    def add_agent(self, agent):
        self.agents[agent.id] = agent
        agent.register_system(self)
    
    def broadcast_message(self, sender_id, message):
        """Send message to all other agents"""
        for agent_id, agent in self.agents.items():
            if agent_id != sender_id:
                agent.receive_message(sender_id, message)
    
    def send_message(self, sender_id, recipient_id, message):
        """Send direct message between agents"""
        if recipient_id in self.agents:
            self.agents[recipient_id].receive_message(sender_id, message)
    
    def coordinate_task(self, task):
        """Coordinate task execution among agents"""
        # Decompose task into subtasks
        subtasks = self.decompose_task(task)
        
        # Assign subtasks to appropriate agents
        assignments = self.assign_subtasks(subtasks)
        
        # Monitor and coordinate execution
        results = self.execute_coordinated(assignments)
        
        return self.synthesize_results(results)

As we build more autonomous and intelligent agentic systems, ensuring ethical behavior, safety, and alignment becomes critically important. This section highlights best practices for responsible development and deployment of AI agents.

⚖️ Ethical Use of AI Agents

• Transparency: Clearly indicate when users are interacting with an AI system. Never present agents as human entities.
• Data Privacy: Agents must not log, store, or transmit sensitive user data without explicit consent.
• Scope Control: Limit agent actions to well-defined domains to avoid unintended behaviors.
• Bias Mitigation: Be aware of biases in training data or models that may affect the fairness or neutrality of agent outputs.

⚠️ Example: If building an AI code reviewer, avoid suggesting insecure practices or biased hiring recommendations.

🧠 Hallucination Management

AI agents often generate convincing but inaccurate (hallucinated) information. This is a key safety concern, especially in domains like medicine, law, or finance.

🔍 Recommended Strategies:

• Retrieval-Augmented Generation (RAG): Always ground answers in factual documents.
• Source Referencing: Have agents cite sources for claims made.
• Confidence Scoring: Tag outputs with confidence levels or uncertainty estimates.
• Multi-agent Verification: Cross-check outputs using a secondary “fact-checker” agent.

🔧 Example: Simple RAG Retrieval in Python

from langchain.chains import RetrievalQA
from langchain.vectorstores import FAISS
from langchain.embeddings.openai import OpenAIEmbeddings

retriever = FAISS.load_local("my_vector_db", OpenAIEmbeddings()).as_retriever()
qa = RetrievalQA.from_chain_type(llm=chat_model, retriever=retriever)

response = qa.run("What is the latest paper on transformer agents?")
print(response)

📌 Confidence Scoring

response = agent.generate_response(query)
if response.confidence < 0.7:
    print("⚠️ Low confidence - recommend human review.")

🤝 Multi-agent Cross-verification

primary = main_agent.run(task)
checker = verifier_agent.run(f"Fact-check this: {primary}")
final_output = f"Primary: {primary}\nVerified: {checker}"

👨‍👩‍👧‍👦 Human-in-the-Loop (HITL)

Human oversight is essential for controlling autonomous agents, especially in high-risk or dynamic environments.

💡 Best Practices:

• Approval Gates: Require human approval before executing sensitive actions (e.g., sending emails, making API calls).
• Feedback Loops: Let users provide feedback to improve future agent behavior.
• Escalation Triggers: Detect when agents are unsure or entering unfamiliar contexts—and escalate to a human operator.

✅ Example: Human Confirmation Before Action

action = agent.plan_next_action()
if action.sensitive:
    user_input = input(f"Agent wants to do: {action.description}. Approve? (y/n): ")
    if user_input.lower() != "y":
        print("Action aborted by user.")

📩 Feedback Collection

user_feedback = input("Was this helpful? (yes/no): ")
agent.log_feedback(user_feedback)

✅ Summary Checklist

📚 Further Reading

• The AI Ethics Guidelines Global Inventory

Key Metrics to Track:

1. Task Success Rate: Percentage of successfully completed tasks
2. Response Quality: Relevance, accuracy, helpfulness scores
3. Efficiency: Time to completion, token usage
4. User Satisfaction: Human feedback scores
5. Safety: Harmful content detection, bias metrics

Evaluation Implementation:

import json
from datetime import datetime
from typing import Dict, List, Any

class AgentEvaluator:
    def __init__(self):
        self.metrics = {
            'task_success': [],
            'response_quality': [],
            'efficiency': [],
            'safety': [],
            'user_satisfaction': []
        }
    
    def evaluate_response(self, 
                         query: str, 
                         response: str, 
                         expected_output: str = None,
                         user_feedback: int = None) -> Dict[str, float]:
        """Comprehensive response evaluation"""
        
        evaluation = {}
        
        # 1. Relevance Score (using embeddings)
        evaluation['relevance'] = self.calculate_relevance(query, response)
        
        # 2. Accuracy (if ground truth available)
        if expected_output:
            evaluation['accuracy'] = self.calculate_accuracy(response, expected_output)
        
        # 3. Safety Score
        evaluation['safety'] = self.evaluate_safety(response)
        
        # 4. Coherence
        evaluation['coherence'] = self.evaluate_coherence(response)
        
        # 5. User Satisfaction (if available)
        if user_feedback:
            evaluation['user_satisfaction'] = user_feedback / 5.0  # Normalize to 0-1
        
        return evaluation
    
    def calculate_relevance(self, query: str, response: str) -> float:
        """Calculate semantic relevance between query and response"""
        from sentence_transformers import SentenceTransformer
        model = SentenceTransformer('all-MiniLM-L6-v2')
        
        query_emb = model.encode([query])
        response_emb = model.encode([response])
        
        similarity = torch.cosine_similarity(
            torch.tensor(query_emb),
            torch.tensor(response_emb)
        ).item()
        
        return max(0, similarity)  # Ensure non-negative
    
    def calculate_accuracy(self, response: str, expected: str) -> float:
        """Calculate accuracy against expected output"""
        # Simple token overlap for demonstration
        response_tokens = set(response.lower().split())
        expected_tokens = set(expected.lower().split())
        
        if not expected_tokens:
            return 0.0
        
        overlap = len(response_tokens.intersection(expected_tokens))
        return overlap / len(expected_tokens)
    
    def evaluate_safety(self, response: str) -> float:
        """Evaluate response safety (placeholder implementation)"""
        # In practice, use models like OpenAI's moderation API
        harmful_keywords = ['hate', 'violence', 'illegal', 'harmful']
        response_lower = response.lower()
        
        harmful_count = sum(1 for keyword in harmful_keywords if keyword in response_lower)
        safety_score = max(0, 1 - (harmful_count * 0.2))
        
        return safety_score
    
    def evaluate_coherence(self, response: str) -> float:
        """Evaluate response coherence and structure"""
        # Simple heuristics (in practice, use more sophisticated methods)
        sentences = response.split('.')
        
        # Check for reasonable sentence length
        avg_sentence_length = sum(len(s.split()) for s in sentences) / len(sentences)
        length_score = min(1.0, avg_sentence_length / 15)  # Normalize around 15 words
        
        # Check for proper punctuation
        punct_score = min(1.0, response.count('.') / max(1, len(sentences)))
        
        return (length_score + punct_score) / 2
    
    def track_efficiency(self, start_time: datetime, end_time: datetime, 
                        tokens_used: int, task_complexity: str):
        """Track efficiency metrics"""
        duration = (end_time - start_time).total_seconds()
        
        efficiency_metrics = {
            'duration': duration,
            'tokens_per_second': tokens_used / max(duration, 1),
            'complexity': task_complexity,
            'timestamp': datetime.now()
        }
        
        self.metrics['efficiency'].append(efficiency_metrics)
    
    def generate_report(self) -> Dict[str, Any]:
        """Generate comprehensive evaluation report"""
        if not any(self.metrics.values()):
            return {"error": "No evaluation data available"}
        
        report = {
            'summary': {},
            'trends': {},
            'recommendations': []
        }
        
        # Calculate averages
        for metric_type, values in self.metrics.items():
            if values:
                if metric_type == 'efficiency':
                    report['summary'][metric_type] = {
                        'avg_duration': sum(v['duration'] for v in values) / len(values),
                        'avg_tokens_per_second': sum(v['tokens_per_second'] for v in values) / len(values)
                    }
                else:
                    report['summary'][metric_type] = {
                        'average': sum(values)

提示：代码过长已自动截断

A/B Testing for Agents:

class AgentABTester:
    def __init__(self, agent_a, agent_b):
        self.agent_a = agent_a
        self.agent_b = agent_b
        self.results = {'a': [], 'b': []}
    
    def run_test(self, test_queries, num_iterations=100):
        """Run A/B test between two agents"""
        import random
        
        for _ in range(num_iterations):
            query = random.choice(test_queries)
            
            # Randomly assign to agent A or B
            if random.random() < 0.5:
                response = self.agent_a.process(query)
                self.results['a'].append({
                    'query': query,
                    'response': response,
                    'timestamp': datetime.now()
                })
            else:
                response = self.agent_b.process(query)
                self.results['b'].append({
                    'query': query, 
                    'response': response,
                    'timestamp': datetime.now()
                })
    
    def analyze_results(self):
        """Statistical analysis of A/B test results"""
        # Implementation would include statistical significance testing
        return {
            'agent_a_performance': len(self.results['a']),
            'agent_b_performance': len(self.results['b']),
            'statistical_significance': 'p < 0.05'  # Placeholder
        }

Project Overview:

Create a personal AI assistant that can:

• Answer questions using RAG
• Manage calendar and tasks
• Send emails and messages
• Browse the web for information
• Learn from user preferences

Complete Implementation:

import openai
import sqlite3
from datetime import datetime, timedelta
import smtplib
from email.mime.text import MIMEText
import requests
from typing import List, Dict, Any

class PersonalAIAssistant:
    def __init__(self, openai_api_key: str):
        self.openai_client = openai.OpenAI(api_key=openai_api_key)
        self.db_connection = sqlite3.connect('assistant.db')
        self.setup_database()
        self.tools = self.initialize_tools()
        self.conversation_history = []
    
    def setup_database(self):
        """Initialize SQLite database for storing information"""
        cursor = self.db_connection.cursor()
        
        # Tasks table
        cursor.execute('''
            CREATE TABLE IF NOT EXISTS tasks (
                id INTEGER PRIMARY KEY,
                title TEXT NOT NULL,
                description TEXT,
                due_date TEXT,
                completed BOOLEAN DEFAULT FALSE,
                created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
            )
        ''')
        
        # User preferences
        cursor.execute('''
            CREATE TABLE IF NOT EXISTS preferences (
                key TEXT PRIMARY KEY,
                value TEXT
            )
        ''')
        
        # Conversation memory
        cursor.execute('''
            CREATE TABLE IF NOT EXISTS conversations (
                id INTEGER PRIMARY KEY,
                query TEXT,
                response TEXT,
                timestamp TIMESTAMP DEFAULT CURRENT_TIMESTAMP
            )
        ''')
        
        self.db_connection.commit()
    
    def initialize_tools(self) -> List[Dict[str, Any]]:
        """Define available tools/functions"""
        return [
            {
                "type": "function",
                "function": {
                    "name": "add_task",
                    "description": "Add a new task to the user's task list",
                    "parameters": {
                        "type": "object",
                        "properties": {
                            "title": {"type": "string"},
                            "description": {"type": "string"},
                            "due_date": {"type": "string", "format": "date"}
                        },
                        "required": ["title"]
                    }
                }
            },
            {
                "type": "function", 
                "function": {
                    "name": "get_tasks",
                    "description": "Retrieve user's tasks, optionally filtered by completion status",
                    "parameters": {
                        "type": "object",
                        "properties": {
                            "completed": {"type": "boolean"}
                        }
                    }
                }
            },
            {
                "type": "function",
                "function": {
                    "name": "web_search",
                    "description": "Search the web for current information",
                    "parameters": {
                        "type": "object",
                        "properties": {
                            "query": {"type": "string"}
                        },
                        "required": ["query"]
                    }
                }
            },
            {
                "type": "function",
                "function": {
                    "name": "send_email",
                    "description": "Send an email to a specified recipient",
                    "parameters": {
                        "type": "object",
                        "properties": {
                            "to": {"type": "string"},
                            "subject": {"type": "string"},
                            "body": {"type": "string"}
                        },
                        "required": ["to", "subject", "body"]
                    }
                }
            },
            {
                "type": "function",
                "function": {
                    "name": "set_preference",
                    "description": "Store a user preference",
                    "parameters": {
                        "type": "object",
                        "properties": {
                            "key": {"type": "string"},
                            "value": {"type": "string"}
                        },
                        "required": ["key", "value"]
                    }
                }
            }
        ]
    
    def add_task(self, title: str, description: str = "", due_date: str = None):
        """Add a new task"""
        cursor = self.db_connection.cursor()
        cursor.execute(
            "INSERT INTO tasks (title, description, due_date) VALUES (?, ?, ?)",
            (title, description, due_date)
        )
        self.db_connection.commit()
        return f"Task '{title}' added successfully!"
    
    def get_tasks(self, completed: bool = None):
        """Retriev

提示：代码过长已自动截断

Enhancement Ideas:

1. Voice Interface: Add speech-to-text and text-to-speech
2. Calendar Integration: Connect with Google Calendar API
3. Smart Notifications: Proactive reminders and suggestions
4. Learning System: Adapt responses based on user feedback
5. Mobile App: Create a companion mobile application

Project Overview:

Build a team of specialized agents that can:

• Conduct comprehensive research on any topic
• Synthesize information from multiple sources
• Generate detailed reports with citations
• Fact-check and validate information
• Present findings in various formats

Implementation:

from dataclasses import dataclass
from typing import List, Dict, Any
import json
import requests
from datetime import datetime
import asyncio

@dataclass
class ResearchArticle:
    title: str
    content: str
    source: str
    url: str
    date_published: str
    credibility_score: float

class ResearcherAgent:
    def __init__(self, name: str, specialty: str, tools: List[str]):
        self.name = name
        self.specialty = specialty
        self.tools = tools
        self.findings = []
    
    async def research_topic(self, topic: str, depth: str = "comprehensive") -> List[ResearchArticle]:
        """Conduct research on a given topic"""
        print(f"{self.name} researching: {topic}")
        
        # Simulate different research strategies based on specialty
        if self.specialty == "academic":
            return await self._academic_search(topic, depth)
        elif self.specialty == "web_research":
            return await self._web_search(topic, depth)
        elif self.specialty == "data_analysis":
            return await self._data_research(topic, depth)
        else:
            return await self._general_research(topic, depth)
    
    async def _academic_search(self, topic: str, depth: str) -> List[ResearchArticle]:
        """Search academic databases and papers"""
        # In practice, integrate with ArXiv, PubMed, Google Scholar APIs
        papers = [
            ResearchArticle(
                title=f"Academic Study on {topic}",
                content=f"Comprehensive academic analysis of {topic} with peer-reviewed findings...",
                source="Academic Journal",
                url="https://example.com/paper1",
                date_published="2024-01-15",
                credibility_score=0.95
            )
        ]
        return papers
    
    async def _web_search(self, topic: str, depth: str) -> List[ResearchArticle]:
        """Search web sources and news"""
        # Integrate with search APIs (Google, Bing, DuckDuckGo)
        articles = [
            ResearchArticle(
                title=f"Recent Developments in {topic}",
                content=f"Latest news and developments about {topic}...",
                source="Tech News Site",
                url="https://example.com/article1",
                date_published="2024-02-20",
                credibility_score=0.75
            )
        ]
        return articles
    
    async def _data_research(self, topic: str, depth: str) -> List[ResearchArticle]:
        """Analyze data sources and statistics"""
        datasets = [
            ResearchArticle(
                title=f"Statistical Analysis of {topic}",
                content=f"Data-driven insights about {topic} based on recent datasets...",
                source="Data Repository",
                url="https://example.com/dataset1",
                date_published="2024-02-01",
                credibility_score=0.88
            )
        ]
        return datasets
    
    async def _general_research(self, topic: str, depth: str) -> List[ResearchArticle]:
        """General purpose research"""
        return [
            ResearchArticle(
                title=f"Overview of {topic}",
                content=f"General information about {topic}...",
                source="Encyclopedia",
                url="https://example.com/overview",
                date_published="2024-01-01",
                credibility_score=0.80
            )
        ]

class AnalystAgent:
    def __init__(self, name: str):
        self.name = name
    
    def analyze_sources(self, articles: List[ResearchArticle]) -> Dict[str, Any]:
        """Analyze research sources for credibility and relevance"""
        print(f"{self.name} analyzing {len(articles)} sources...")
        
        analysis = {
            'total_sources': len(articles),
            'avg_credibility': sum(a.credibility_score for a in articles) / len(articles),
            'source_types': {},
            'date_range': {
                'earliest': min(a.date_published for a in articles),
                'latest': max(a.date_published for a in articles)
            },
            'recommendations': []
        }
        
        # Analyze source types
        for article in articles:
            source_type = article.source
            analysis['source_types'][source_type] = analysis['source_types'].get(source_type, 0) + 1
        
        # Generate recommendations
        if analysis['avg_credibility'] < 0.7:
            analysis['recommendations'].append("Consider finding more credible sources")
        
        if len(set(a.source for a in articles)) < 3:
            analysis['recommendations'].append("Diversify source types for better coverage")
        
        return analysis
    
    def synthesize_findings(self, articles: List[ResearchArticle]) -> str:
        """Synthesize information from multiple sources"""
        print(f"{self.name} synthesizing findings...")
        
        # Group articles by t

提示：代码过长已自动截断

Advanced Features:

1. Real API Integration: Connect to actual search APIs (Google, Bing, ArXiv)
2. Advanced NLP: Use transformer models for better claim extraction and verification
3. Collaborative Filtering: Cross-verify information between multiple agents
4. Visual Reports: Generate charts and graphs from data
5. Continuous Learning: Improve research strategies based on feedback

Project Overview:

Create an AI development team that can:

• Review code for bugs, security issues, and best practices
• Generate documentation automatically
• Suggest improvements and refactoring
• Write unit tests
• Perform security audits

Implementation:

import ast
import re
import subprocess
from typing import List, Dict, Any, Optional
from dataclasses import dataclass
from pathlib import Path
import json

@dataclass
class CodeIssue:
    type: str  # 'bug', 'security', 'performance', 'style'
    severity: str  # 'high', 'medium', 'low'
    line_number: int
    description: str
    suggestion: str
    code_snippet: str

@dataclass
class CodeAnalysis:
    file_path: str
    issues: List[CodeIssue]
    complexity_score: float
    maintainability_score: float
    test_coverage: float
    suggestions: List[str]

class CodeReviewAgent:
    def __init__(self, name: str, specialty: str):
        self.name = name
        self.specialty = specialty
        self.rules = self._load_review_rules()
    
    def _load_review_rules(self) -> Dict[str, List[str]]:
        """Load code review rules based on specialty"""
        rules = {
            'security': [
                'SQL injection vulnerabilities',
                'XSS vulnerabilities', 
                'Hardcoded credentials',
                'Insecure random number generation',
                'Unvalidated input'
            ],
            'performance': [
                'Inefficient loops',
                'Memory leaks',
                'Unnecessary database queries',
                'Large object creation in loops',
                'Blocking operations on main thread'
            ],
            'style': [
                'PEP 8 compliance',
                'Naming conventions',
                'Function length',
                'Code duplication',
                'Missing docstrings'
            ],
  </details>

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