# Documentation Improvements

## 1. API Documentation

### Current Issues

- **No OpenAPI/Swagger Documentation**: No comprehensive API documentation
- **Missing Endpoint Documentation**: Many endpoints not documented
- **No Request/Response Examples**: No examples for API usage
- **No Error Documentation**: Error responses not documented
- **No Authentication Documentation**: Authentication methods not clearly documented

### Proposed Solutions

#### OpenAPI/Swagger Implementation

```python
# arklex/api/openapi.py
from fastapi import FastAPI
from fastapi.openapi.utils import get_openapi
from fastapi.middleware.cors import CORSMiddleware
from arklex.config.settings import settings

def create_app() -> FastAPI:
    app = FastAPI(
        title="Arklex AI API",
        description="Agent-First Framework API for building intelligent applications",
        version="1.0.0",
        docs_url="/docs",
        redoc_url="/redoc",
        openapi_url="/openapi.json"
    )
    
    # Add CORS middleware
    app.add_middleware(
        CORSMiddleware,
        allow_origins=settings.allowed_origins,
        allow_credentials=True,
        allow_methods=["*"],
        allow_headers=["*"],
    )
    
    return app

def custom_openapi():
    if app.openapi_schema:
        return app.openapi_schema
    
    openapi_schema = get_openapi(
        title="Arklex AI API",
        version="1.0.0",
        description="""
        ## Overview
        
        The Arklex AI API provides a comprehensive framework for building intelligent applications
        using agent-first architecture. This API enables you to:
        
        - Create and manage AI agents
        - Orchestrate complex workflows
        - Integrate with external services (Shopify, HubSpot, Google)
        - Process natural language queries
        - Manage document retrieval and RAG systems
        
        ## Authentication
        
        The API uses JWT token-based authentication. Include your token in the Authorization header:
        
        ```
        Authorization: Bearer <your-jwt-token>
        ```
        
        ## Rate Limiting
        
        API requests are rate-limited to 100 requests per minute per API key.
        
        ## Error Handling
        
        The API returns standard HTTP status codes:
        
        - 200: Success
        - 400: Bad Request
        - 401: Unauthorized
        - 403: Forbidden
        - 404: Not Found
        - 429: Too Many Requests
        - 500: Internal Server Error
        """,
        routes=app.routes,
    )
    
    # Add security schemes
    openapi_schema["components"]["securitySchemes"] = {
        "BearerAuth": {
            "type": "http",
            "scheme": "bearer",
            "bearerFormat": "JWT",
        }
    }
    
    # Add global security requirement
    openapi_schema["security"] = [{"BearerAuth": []}]
    
    app.openapi_schema = openapi_schema
    return app.openapi_schema

app = create_app()
app.openapi = custom_openapi
```

#### API Route Documentation

```python
# arklex/api/routes/agents.py
from fastapi import APIRouter, Depends, HTTPException, status
from typing import List, Optional
from pydantic import BaseModel
from arklex.core.auth import get_current_user
from arklex.core.agents import AgentManager

router = APIRouter(prefix="/agents", tags=["Agents"])

class AgentCreate(BaseModel):
    """Request model for creating a new agent."""
    name: str
    description: Optional[str] = None
    agent_type: str
    configuration: dict

class AgentResponse(BaseModel):
    """Response model for agent operations."""
    id: str
    name: str
    description: Optional[str]
    agent_type: str
    status: str
    created_at: str
    updated_at: str

@router.post("/", response_model=AgentResponse, status_code=status.HTTP_201_CREATED)
async def create_agent(
    agent_data: AgentCreate,
    current_user = Depends(get_current_user)
):
    """
    Create a new AI agent.
    
    This endpoint allows you to create a new AI agent with the specified configuration.
    The agent will be initialized and ready to process requests.
    
    **Parameters:**
    - name: The name of the agent
    - description: Optional description of the agent's purpose
    - agent_type: Type of agent (e.g., "openai", "anthropic", "custom")
    - configuration: Agent-specific configuration parameters
    
    **Returns:**
    - AgentResponse: The created agent with its ID and status
    
    **Example:**
    ```json
    {
        "name": "Customer Service Agent",
        "description": "Handles customer inquiries and support requests",
        "agent_type": "openai",
        "configuration": {
            "model": "gpt-4",
            "temperature": 0.7,
            "max_tokens": 1000
        }
    }
    ```
    """
    try:
        agent_manager = AgentManager()
        agent = await agent_manager.create_agent(agent_data.dict())
        return AgentResponse(**agent.dict())
    except Exception as e:
        raise HTTPException(
            status_code=status.HTTP_400_BAD_REQUEST,
            detail=f"Failed to create agent: {str(e)}"
        )

@router.get("/", response_model=List[AgentResponse])
async def list_agents(
    skip: int = 0,
    limit: int = 100,
    current_user = Depends(get_current_user)
):
    """
    List all available agents.
    
    Returns a paginated list of all agents accessible to the current user.
    
    **Parameters:**
    - skip: Number of agents to skip (for pagination)
    - limit: Maximum number of agents to return
    
    **Returns:**
    - List[AgentResponse]: List of agents with their details
    """
    agent_manager = AgentManager()
    agents = await agent_manager.list_agents(skip=skip, limit=limit)
    return [AgentResponse(**agent.dict()) for agent in agents]

@router.get("/{agent_id}", response_model=AgentResponse)
async def get_agent(
    agent_id: str,
    current_user = Depends(get_current_user)
):
    """
    Get a specific agent by ID.
    
    **Parameters:**
    - agent_id: The unique identifier of the agent
    
    **Returns:**
    - AgentResponse: The agent details
    
    **Raises:**
    - 404: Agent not found
    """
    agent_manager = AgentManager()
    agent = await agent_manager.get_agent(agent_id)
    if not agent:
        raise HTTPException(
            status_code=status.HTTP_404_NOT_FOUND,
            detail="Agent not found"
        )
    return AgentResponse(**agent.dict())
```

## 2. Architecture Documentation

### Current Issues

- **Outdated Architecture Diagrams**: Current diagrams don't reflect actual implementation
- **Missing Component Documentation**: Many components not documented
- **No Data Flow Diagrams**: No clear data flow documentation
- **No Integration Documentation**: External integrations not well documented
- **No Deployment Architecture**: No deployment architecture documentation

### Proposed Solutions

#### Updated Architecture Diagrams

```mermaid
# docs/architecture/system-overview.md
graph TB
    subgraph "Client Layer"
        Web[Web Client]
        Mobile[Mobile App]
        API[API Client]
    end
    
    subgraph "API Gateway"
        Gateway[API Gateway]
        Auth[Authentication]
        RateLimit[Rate Limiting]
    end
    
    subgraph "Application Layer"
        Orchestrator[Orchestrator]
        AgentManager[Agent Manager]
        WorkerManager[Worker Manager]
    end
    
    subgraph "Core Services"
        RAG[RAG Service]
        Memory[Memory Service]
        Evaluation[Evaluation Service]
    end
    
    subgraph "External Integrations"
        Shopify[Shopify API]
        HubSpot[HubSpot API]
        Google[Google APIs]
        OpenAI[OpenAI API]
    end
    
    subgraph "Data Layer"
        Database[(Database)]
        VectorDB[(Vector Database)]
        Cache[(Redis Cache)]
    end
    
    Web --> Gateway
    Mobile --> Gateway
    API --> Gateway
    
    Gateway --> Auth
    Gateway --> RateLimit
    Gateway --> Orchestrator
    
    Orchestrator --> AgentManager
    Orchestrator --> WorkerManager
    
    AgentManager --> RAG
    AgentManager --> Memory
    WorkerManager --> Evaluation
    
    RAG --> VectorDB
    Memory --> Database
    Evaluation --> Database
    
    Orchestrator --> Shopify
    Orchestrator --> HubSpot
    Orchestrator --> Google
    Orchestrator --> OpenAI
    
    RAG --> Cache
    Memory --> Cache
```

#### Component Documentation

```markdown
# docs/architecture/components.md

# Arklex AI Components

## Core Components

### 1. Orchestrator

The Orchestrator is the central component that coordinates all operations in the Arklex AI system.

**Responsibilities:**
- Route requests to appropriate agents
- Manage workflow execution
- Coordinate between different services
- Handle error recovery and retries

**Key Interfaces:**
- `execute_workflow(workflow_id: str, input_data: dict) -> dict`
- `create_agent(agent_config: dict) -> Agent`
- `get_agent_status(agent_id: str) -> AgentStatus`

**Dependencies:**
- Agent Manager
- Worker Manager
- Memory Service
- External APIs

### 2. Agent Manager

Manages the lifecycle of AI agents and their configurations.

**Responsibilities:**
- Create and configure agents
- Manage agent state
- Handle agent communication
- Monitor agent performance

**Key Interfaces:**
- `create_agent(config: AgentConfig) -> Agent`
- `update_agent(agent_id: str, config: AgentConfig) -> Agent`
- `delete_agent(agent_id: str) -> bool`
- `list_agents() -> List[Agent]`

### 3. Worker Manager

Manages background workers for processing tasks.

**Responsibilities:**
- Schedule and execute background tasks
- Manage worker pools
- Handle task queuing and prioritization
- Monitor worker health

**Key Interfaces:**
- `submit_task(task: Task) -> TaskId`
- `get_task_status(task_id: str) -> TaskStatus`
- `cancel_task(task_id: str) -> bool`

### 4. RAG Service

Handles document retrieval and question answering.

**Responsibilities:**
- Index and search documents
- Process natural language queries
- Retrieve relevant context
- Generate answers based on retrieved content

**Key Interfaces:**
- `index_document(document: Document) -> str`
- `search_documents(query: str, limit: int = 10) -> List[Document]`
- `answer_question(question: str, context: List[Document]) -> str`

### 5. Memory Service

Manages conversation history and context.

**Responsibilities:**
- Store conversation history
- Manage user context
- Provide conversation summaries
- Handle context window management

**Key Interfaces:**
- `store_message(user_id: str, message: Message) -> str`
- `get_conversation_history(user_id: str, limit: int = 50) -> List[Message]`
- `summarize_conversation(user_id: str) -> str`

## External Integrations

### 1. Shopify Integration

Handles all Shopify-related operations.

**Capabilities:**
- Product management
- Order processing
- Customer data synchronization
- Inventory management

**Key Endpoints:**
- `/shopify/products` - Product operations
- `/shopify/orders` - Order operations
- `/shopify/customers` - Customer operations

### 2. HubSpot Integration

Manages HubSpot CRM operations.

**Capabilities:**
- Contact management
- Deal tracking
- Email marketing
- Analytics integration

**Key Endpoints:**
- `/hubspot/contacts` - Contact operations
- `/hubspot/deals` - Deal operations
- `/hubspot/emails` - Email operations

### 3. Google Integration

Provides access to Google services.

**Capabilities:**
- Calendar management
- Email processing
- Document analysis
- Drive integration

**Key Endpoints:**
- `/google/calendar` - Calendar operations
- `/google/gmail` - Email operations
- `/google/drive` - Document operations
```

## 3. Developer Guides

### Current Issues

- **No Getting Started Guide**: No clear onboarding for new developers
- **Missing Tutorials**: No step-by-step tutorials for common tasks
- **No Best Practices**: No coding standards or best practices documentation
- **No Troubleshooting Guide**: No common issues and solutions
- **No Contribution Guidelines**: No clear contribution process

### Proposed Solutions

#### Getting Started Guide

```markdown
# docs/getting-started.md

# Getting Started with Arklex AI

## Prerequisites

Before you begin, ensure you have the following installed:

- Python 3.10 or higher
- Git
- Docker (optional, for containerized development)
- PostgreSQL (for local development)

## Installation

### 1. Clone the Repository

```bash
git clone https://github.com/your-org/arklex-ai.git
cd arklex-ai
```

### 2. Set Up Virtual Environment

```bash
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
```

### 3. Install Dependencies

```bash
pip install -r requirements.txt
pip install -r requirements-dev.txt
```

### 4. Configure Environment

Create a `.env` file in the root directory:

```env
# Database
DATABASE_URL=postgresql://user:password@localhost:5432/arklex

# API Keys
OPENAI_API_KEY=your-openai-api-key
ANTHROPIC_API_KEY=your-anthropic-api-key

# Security
SECRET_KEY=your-secret-key

# Environment
ENVIRONMENT=development
DEBUG=true
```

### 5. Set Up Database

```bash
# Create database
createdb arklex

# Run migrations
alembic upgrade head
```

### 6. Start the Application

```bash
# Development server
uvicorn arklex.main:app --reload --host 0.0.0.0 --port 8000

# Or with Docker
docker-compose up
```

## Your First Agent

### 1. Create a Simple Agent

```python
from arklex.orchestrator import AgentOrg
from arklex.env.agents import OpenAIAgent

# Initialize the orchestrator
orchestrator = AgentOrg()

# Create a simple agent
agent = OpenAIAgent(
    name="greeting_agent",
    description="A simple greeting agent",
    model="gpt-3.5-turbo",
    temperature=0.7
)

# Register the agent
orchestrator.register_agent(agent)
```

### 2. Test Your Agent

```python
# Test the agent
response = await orchestrator.run_agent(
    agent_id="greeting_agent",
    message="Hello, how are you?"
)

print(response)
```

### 3. Add Tools to Your Agent

```python
from arklex.env.tools import CalculatorTool

# Create a tool
calculator = CalculatorTool()

# Add tool to agent
agent.add_tool(calculator)

# Test with tool usage
response = await orchestrator.run_agent(
    agent_id="greeting_agent",
    message="What is 15 * 23?"
)
```

## Next Steps

- [Read the API Documentation](api-reference.md)
- [Explore the Architecture](architecture.md)
- [Check out the Examples](examples.md)
- [Join the Community](community.md)

```

#### Tutorial Series

```markdown
# docs/tutorials/index.md

# Tutorial Series

## Beginner Tutorials

### 1. Building Your First Agent
Learn how to create a simple AI agent that can respond to user queries.

**Duration:** 30 minutes
**Prerequisites:** Basic Python knowledge

[Start Tutorial →](tutorials/first-agent.md)

### 2. Adding Tools to Your Agent
Learn how to extend your agent's capabilities with custom tools.

**Duration:** 45 minutes
**Prerequisites:** Completed "Building Your First Agent"

[Start Tutorial →](tutorials/adding-tools.md)

### 3. Creating Multi-Agent Workflows
Learn how to orchestrate multiple agents to solve complex problems.

**Duration:** 60 minutes
**Prerequisites:** Completed "Adding Tools to Your Agent"

[Start Tutorial →](tutorials/multi-agent-workflows.md)

## Intermediate Tutorials

### 4. Building RAG Systems
Learn how to build document retrieval and question-answering systems.

**Duration:** 90 minutes
**Prerequisites:** Basic understanding of vector databases

[Start Tutorial →](tutorials/rag-systems.md)

### 5. Integrating External APIs
Learn how to integrate with Shopify, HubSpot, and other services.

**Duration:** 75 minutes
**Prerequisites:** API integration experience

[Start Tutorial →](tutorials/external-integrations.md)

### 6. Custom Tool Development
Learn how to create custom tools for your agents.

**Duration:** 60 minutes
**Prerequisites:** Python development experience

[Start Tutorial →](tutorials/custom-tools.md)

## Advanced Tutorials

### 7. Performance Optimization
Learn how to optimize your agents for production use.

**Duration:** 120 minutes
**Prerequisites:** Understanding of performance concepts

[Start Tutorial →](tutorials/performance-optimization.md)

### 8. Security Best Practices
Learn how to secure your agents and integrations.

**Duration:** 90 minutes
**Prerequisites:** Security awareness

[Start Tutorial →](tutorials/security-best-practices.md)

### 9. Deployment and Scaling
Learn how to deploy your agents to production.

**Duration:** 150 minutes
**Prerequisites:** DevOps experience

[Start Tutorial →](tutorials/deployment-scaling.md)
```

## 4. Code Documentation

### Current Issues

- **Missing Docstrings**: Many functions lack proper documentation
- **No Type Hints**: Missing type annotations in many modules
- **No Examples**: No usage examples in documentation
- **No API Reference**: No comprehensive API reference
- **No Changelog**: No version history documentation

### Proposed Solutions

#### Docstring Standards

```python
# arklex/core/agents/base.py
from abc import ABC, abstractmethod
from typing import Dict, Any, Optional, List
from arklex.utils.graph_state import MessageState

class BaseAgent(ABC):
    """
    Abstract base class for all AI agents in the Arklex system.
    
    This class defines the interface that all agents must implement.
    Agents are responsible for processing user messages and generating
    appropriate responses using various AI models and tools.
    
    Attributes:
        name (str): The unique name of the agent
        description (str): Human-readable description of the agent's purpose
        model (str): The AI model used by this agent
        temperature (float): Controls randomness in responses (0.0 to 1.0)
        tools (List[BaseTool]): List of tools available to this agent
        
    Example:
        >>> from arklex.env.agents import OpenAIAgent
        >>> agent = OpenAIAgent(
        ...     name="customer_service",
        ...     description="Handles customer inquiries",
        ...     model="gpt-4",
        ...     temperature=0.7
        ... )
        >>> response = await agent.execute("How can I help you?")
    """
    
    def __init__(
        self,
        name: str,
        description: str,
        model: str,
        temperature: float = 0.7,
        tools: Optional[List['BaseTool']] = None
    ):
        """
        Initialize a new agent.
        
        Args:
            name: Unique identifier for the agent
            description: Human-readable description of the agent's purpose
            model: AI model to use (e.g., "gpt-4", "claude-3")
            temperature: Controls response randomness (0.0 = deterministic, 1.0 = very random)
            tools: Optional list of tools the agent can use
            
        Raises:
            ValueError: If temperature is not between 0.0 and 1.0
            ValueError: If name is empty or contains invalid characters
        """
        if not name or not name.strip():
            raise ValueError("Agent name cannot be empty")
        
        if not 0.0 <= temperature <= 1.0:
            raise ValueError("Temperature must be between 0.0 and 1.0")
        
        self.name = name.strip()
        self.description = description
        self.model = model
        self.temperature = temperature
        self.tools = tools or []
        self._conversation_history: List[MessageState] = []
    
    @abstractmethod
    async def execute(self, message: str, context: Optional[Dict[str, Any]] = None) -> str:
        """
        Execute the agent with a given message.
        
        This is the main method that processes user input and generates
        a response. The implementation should handle the specific AI model
        interaction and tool usage.
        
        Args:
            message: The user's input message
            context: Optional context information (e.g., user session data)
            
        Returns:
            The agent's response message
            
        Raises:
            AgentExecutionError: If the agent fails to execute
            ModelConnectionError: If the AI model is unavailable
            ToolExecutionError: If a tool fails to execute
        """
        pass
    
    def add_tool(self, tool: 'BaseTool') -> None:
        """
        Add a tool to the agent's available tools.
        
        Tools extend the agent's capabilities by allowing it to perform
        specific actions like calculations, API calls, or data retrieval.
        
        Args:
            tool: The tool to add to the agent
            
        Example:
            >>> from arklex.env.tools import CalculatorTool
            >>> calculator = CalculatorTool()
            >>> agent.add_tool(calculator)
        """
        if tool not in self.tools:
            self.tools.append(tool)
    
    def remove_tool(self, tool_name: str) -> bool:
        """
        Remove a tool from the agent's available tools.
        
        Args:
            tool_name: Name of the tool to remove
            
        Returns:
            True if the tool was found and removed, False otherwise
        """
        for i, tool in enumerate(self.tools):
            if tool.name == tool_name:
                del self.tools[i]
                return True
        return False
    
    def get_conversation_history(self, limit: int = 50) -> List[MessageState]:
        """
        Get the agent's conversation history.
        
        Args:
            limit: Maximum number of messages to return
            
        Returns:
            List of recent message states
        """
        return self._conversation_history[-limit:]
    
    def clear_history(self) -> None:
        """Clear the agent's conversation history."""
        self._conversation_history.clear()
```

## 5. Troubleshooting Guide

### Current Issues

- **No Common Issues**: No documentation of common problems
- **No Debugging Guide**: No debugging procedures
- **No Error Reference**: No comprehensive error code documentation
- **No Performance Issues**: No performance troubleshooting
- **No Security Issues**: No security incident procedures

### Proposed Solutions

#### Troubleshooting Guide

```markdown
# docs/troubleshooting.md

# Troubleshooting Guide

## Common Issues

### 1. Agent Not Responding

**Symptoms:**
- Agent returns empty responses
- Agent times out
- Agent returns error messages

**Possible Causes:**
- Invalid API key
- Network connectivity issues
- Model service unavailable
- Rate limiting

**Solutions:**

1. **Check API Keys**
   ```bash
   # Verify your API keys are set correctly
   echo $OPENAI_API_KEY
   echo $ANTHROPIC_API_KEY
   ```

2. **Test API Connectivity**

   ```python
   import openai
   openai.api_key = "your-api-key"
   try:
       response = openai.ChatCompletion.create(
           model="gpt-3.5-turbo",
           messages=[{"role": "user", "content": "Hello"}]
       )
       print("API connection successful")
   except Exception as e:
       print(f"API connection failed: {e}")
   ```

3. **Check Rate Limits**

   ```python
   # Monitor your API usage
   from arklex.utils.monitoring import get_api_usage
   usage = get_api_usage()
   print(f"Current usage: {usage}")
   ```

### 2. Database Connection Issues

**Symptoms:**

- Database connection errors
- Slow query performance
- Connection pool exhaustion

**Solutions:**

1. **Check Database Connection**

   ```bash
   # Test database connectivity
   psql $DATABASE_URL -c "SELECT 1;"
   ```

2. **Verify Connection Pool Settings**

   ```python
   # Check connection pool configuration
   from arklex.core.database import get_pool_status
   status = get_pool_status()
   print(f"Pool status: {status}")
   ```

3. **Monitor Database Performance**

   ```sql
   -- Check for slow queries
   SELECT query, mean_time, calls 
   FROM pg_stat_statements 
   ORDER BY mean_time DESC 
   LIMIT 10;
   ```

### 3. Memory Issues

**Symptoms:**

- High memory usage
- Out of memory errors
- Slow response times

**Solutions:**

1. **Monitor Memory Usage**

   ```python
   import psutil
   process = psutil.Process()
   memory_info = process.memory_info()
   print(f"Memory usage: {memory_info.rss / 1024 / 1024} MB")
   ```

2. **Optimize Vector Search**

   ```python
   # Reduce vector search results
   from arklex.env.tools.RAG import RAGTool
   rag = RAGTool(max_results=5)  # Reduce from default 10
   ```

3. **Clear Conversation History**

   ```python
   # Clear agent history to free memory
   agent.clear_history()
   ```

### 4. Performance Issues

**Symptoms:**

- Slow response times
- High CPU usage
- Timeout errors

**Solutions:**

1. **Enable Caching**

   ```python
   # Enable Redis caching
   from arklex.core.cache import CacheManager
   cache = CacheManager(redis_url="redis://localhost:6379")
   ```

2. **Optimize Database Queries**

   ```sql
   -- Add indexes for frequently queried columns
   CREATE INDEX idx_messages_user_id ON messages(user_id);
   CREATE INDEX idx_messages_created_at ON messages(created_at);
   ```

3. **Use Connection Pooling**

   ```python
   # Configure connection pooling
   from arklex.core.database import DatabaseManager
   db = DatabaseManager(
       database_url="postgresql://...",
       pool_size=20,
       max_overflow=30
   )
   ```

## Error Reference

### Common Error Codes

| Error Code | Description | Solution |
|------------|-------------|----------|
| `AUTH_001` | Invalid API key | Check API key configuration |
| `AUTH_002` | Expired token | Refresh authentication token |
| `DB_001` | Database connection failed | Check database URL and connectivity |
| `DB_002` | Connection pool exhausted | Increase pool size or add connection limits |
| `MODEL_001` | Model service unavailable | Check model API status |
| `MODEL_002` | Rate limit exceeded | Implement rate limiting or upgrade plan |
| `TOOL_001` | Tool execution failed | Check tool configuration and dependencies |
| `TOOL_002` | External API error | Verify external service status |

### Debugging Procedures

1. **Enable Debug Logging**

   ```python
   import logging
   logging.basicConfig(level=logging.DEBUG)
   ```

2. **Use Debug Mode**

   ```bash
   # Start with debug mode
   DEBUG=true uvicorn arklex.main:app --reload
   ```

3. **Check Logs**

   ```bash
   # View application logs
   tail -f logs/arklex.log
   ```

## Performance Optimization

### 1. Database Optimization

```sql
-- Add indexes for better performance
CREATE INDEX CONCURRENTLY idx_messages_user_id_created_at 
ON messages(user_id, created_at);

-- Analyze table statistics
ANALYZE messages;
```

### 2. Caching Strategy

```python
# Implement Redis caching
from arklex.core.cache import CacheManager

cache = CacheManager(redis_url="redis://localhost:6379")

# Cache expensive operations
@cache.cache(ttl=3600)
async def expensive_operation(data):
    # ... expensive computation
    return result
```

### 3. Connection Pooling

```python
# Optimize database connections
from sqlalchemy import create_engine
from sqlalchemy.pool import QueuePool

engine = create_engine(
    DATABASE_URL,
    poolclass=QueuePool,
    pool_size=20,
    max_overflow=30,
    pool_pre_ping=True
)
```

## Security Issues

### 1. API Key Exposure

**Symptoms:**

- Unauthorized API usage
- Unexpected charges
- Security alerts

**Solutions:**

- Rotate API keys immediately
- Check for key exposure in logs
- Implement key rotation schedule
- Use environment variables for secrets

### 2. Rate Limiting

**Symptoms:**

- 429 errors
- Service degradation
- Cost spikes

**Solutions:**

- Implement client-side rate limiting
- Use exponential backoff
- Monitor API usage patterns
- Upgrade API plan if needed

### 3. Data Exposure

**Symptoms:**

- Sensitive data in logs
- Unauthorized data access
- Privacy violations

**Solutions:**

- Implement data masking
- Review logging practices
- Add access controls
- Encrypt sensitive data

```

## 6. Contribution Guidelines

### Current Issues

- **No Contribution Process**: No clear process for contributions
- **No Code Standards**: No coding standards documentation
- **No Review Process**: No code review guidelines
- **No Testing Requirements**: No testing requirements for contributions
- **No Release Process**: No release process documentation

### Proposed Solutions

#### Contribution Guidelines

```markdown
# docs/contributing.md

# Contributing to Arklex AI

Thank you for your interest in contributing to Arklex AI! This document provides guidelines for contributing to the project.

## Getting Started

### 1. Fork the Repository

1. Go to [https://github.com/your-org/arklex-ai](https://github.com/your-org/arklex-ai)
2. Click the "Fork" button
3. Clone your forked repository:
   ```bash
   git clone https://github.com/your-username/arklex-ai.git
   cd arklex-ai
   ```

### 2. Set Up Development Environment

```bash
# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt
pip install -r requirements-dev.txt

# Set up pre-commit hooks
pre-commit install
```

### 3. Create a Feature Branch

```bash
git checkout -b feature/your-feature-name
```

## Development Guidelines

### Code Style

We follow PEP 8 with some additional guidelines:

- Use type hints for all function parameters and return values
- Write docstrings for all public functions and classes
- Keep functions under 50 lines when possible
- Use meaningful variable and function names

### Testing Requirements

All contributions must include tests:

1. **Unit Tests**: Test individual functions and classes
2. **Integration Tests**: Test component interactions
3. **End-to-End Tests**: Test complete workflows

Example test structure:

```python
# tests/test_your_feature.py
import pytest
from arklex.your_module import YourClass

class TestYourClass:
    def test_initialization(self):
        """Test class initialization."""
        obj = YourClass()
        assert obj is not None
    
    def test_method_behavior(self):
        """Test method behavior."""
        obj = YourClass()
        result = obj.method("input")
        assert result == "expected_output"
    
    @pytest.mark.asyncio
    async def test_async_method(self):
        """Test async method."""
        obj = YourClass()
        result = await obj.async_method("input")
        assert result == "expected_output"
```

### Documentation Requirements

All new features must include:

1. **API Documentation**: Update OpenAPI/Swagger docs
2. **Code Documentation**: Add docstrings and type hints
3. **User Documentation**: Update user guides if needed
4. **Architecture Documentation**: Update diagrams if needed

### Commit Message Format

Use conventional commit format:

```
type(scope): description

[optional body]

[optional footer]
```

Examples:

- `feat(agents): add support for Claude 3`
- `fix(auth): resolve JWT token expiration issue`
- `docs(api): update authentication documentation`
- `test(rag): add integration tests for vector search`

## Pull Request Process

### 1. Create Pull Request

1. Push your changes to your fork
2. Create a pull request against the main branch
3. Fill out the pull request template

### 2. Pull Request Template

```markdown
## Description

Brief description of the changes.

## Type of Change

- [ ] Bug fix
- [ ] New feature
- [ ] Breaking change
- [ ] Documentation update

## Testing

- [ ] Unit tests pass
- [ ] Integration tests pass
- [ ] End-to-end tests pass
- [ ] Manual testing completed

## Checklist

- [ ] Code follows style guidelines
- [ ] Self-review completed
- [ ] Documentation updated
- [ ] Tests added/updated
- [ ] No breaking changes (or documented)

## Related Issues

Closes #123
```

### 3. Code Review Process

1. **Automated Checks**: All automated checks must pass
2. **Code Review**: At least one maintainer must approve
3. **Testing**: All tests must pass
4. **Documentation**: Documentation must be updated

## Release Process

### 1. Version Management

We use semantic versioning (MAJOR.MINOR.PATCH):

- **MAJOR**: Breaking changes
- **MINOR**: New features (backward compatible)
- **PATCH**: Bug fixes (backward compatible)

### 2. Release Checklist

- [ ] All tests pass
- [ ] Documentation updated
- [ ] Changelog updated
- [ ] Version bumped
- [ ] Release notes prepared
- [ ] Security review completed

### 3. Release Steps

```bash
# Update version
bump2version patch  # or minor/major

# Create release
git tag v1.2.3
git push origin v1.2.3

# Deploy to PyPI
python setup.py sdist bdist_wheel
twine upload dist/*
```

## Getting Help

- **Issues**: Use GitHub issues for bug reports and feature requests
- **Discussions**: Use GitHub discussions for questions and ideas
- **Discord**: Join our Discord server for real-time help
- **Documentation**: Check the docs folder for detailed guides

## Code of Conduct

Please read our [Code of Conduct](CODE_OF_CONDUCT.md) before contributing.

```

## Success Metrics

### Documentation Metrics

- [ ] 100% API endpoint documentation
- [ ] All components documented with examples
- [ ] Complete architecture diagrams
- [ ] Step-by-step tutorials for all major features
- [ ] Comprehensive troubleshooting guide
- [ ] Clear contribution guidelines

### Developer Experience Metrics

- [ ] <5 minutes setup time for new developers
- [ ] All examples run successfully
- [ ] Documentation search functionality
- [ ] Interactive API documentation
- [ ] Video tutorials for complex features
- [ ] Community-contributed examples

### Quality Metrics

- [ ] Zero broken links in documentation
- [ ] All code examples tested
- [ ] Documentation reviewed monthly
- [ ] User feedback incorporated
- [ ] Documentation versioned with code
- [ ] Automated documentation testing

## Implementation Timeline

### Phase 1: Foundation (Week 1-2)
- [ ] Set up OpenAPI/Swagger documentation
- [ ] Create getting started guide
- [ ] Update architecture diagrams
- [ ] Add basic API documentation

### Phase 2: Comprehensive Coverage (Week 3-4)
- [ ] Complete API endpoint documentation
- [ ] Create tutorial series
- [ ] Add troubleshooting guide
- [ ] Implement contribution guidelines

### Phase 3: Advanced Features (Week 5-6)
- [ ] Add interactive documentation
- [ ] Create video tutorials
- [ ] Implement documentation search
- [ ] Add community examples

### Phase 4: Maintenance (Week 7-8)
- [ ] Set up documentation automation
- [ ] Implement feedback system
- [ ] Create documentation metrics
- [ ] Establish review process
