# KardoAI Development Roadmap

**Version**: 0.1.0 (Planning Phase)  
**Last Updated**: October 24, 2025  
**Status**: Pre-Development

---

## 🎯 Vision

KardoAI is the native AI integration layer for KardoCore, designed to provide seamless AI capabilities across all framework modes (Core, Admin, Full CMS, Theme-only). The goal is to make AI features accessible, secure, and production-ready without requiring deep AI expertise from developers.

---

## 🚀 Top 3 Critical Development Steps

### Step 1: AI Provider Abstraction Layer (Foundation) 🔴 **HIGHEST PRIORITY**

**Objective**: Create a unified interface for multiple AI providers that allows developers to switch between providers without changing application code.

#### Why This is Critical
- **Provider Independence**: Avoid vendor lock-in
- **Cost Optimization**: Switch providers based on pricing
- **Reliability**: Fallback to alternative providers if one fails
- **Future-Proof**: Easy to add new providers as they emerge

#### Technical Implementation

**1.1 Create Base Provider Interface**

```python
# kardocore/ai/providers/base.py

from abc import ABC, abstractmethod
from typing import Optional, List, Dict, Any, AsyncIterator
from dataclasses import dataclass

@dataclass
class AIMessage:
    """Standard message format across all providers"""
    role: str  # system, user, assistant
    content: str
    metadata: Optional[Dict[str, Any]] = None

@dataclass
class AIResponse:
    """Standard response format"""
    content: str
    model: str
    provider: str
    usage: Dict[str, int]  # tokens, cost, etc.
    metadata: Dict[str, Any]

class BaseAIProvider(ABC):
    """Abstract base class for all AI providers"""
    
    def __init__(self, api_key: str, **config):
        self.api_key = api_key
        self.config = config
    
    @abstractmethod
    async def generate(
        self,
        prompt: str,
        *,
        system_prompt: Optional[str] = None,
        max_tokens: int = 1000,
        temperature: float = 0.7,
        **kwargs
    ) -> AIResponse:
        """Generate text completion"""
        pass
    
    @abstractmethod
    async def chat(
        self,
        messages: List[AIMessage],
        *,
        max_tokens: int = 1000,
        temperature: float = 0.7,
        **kwargs
    ) -> AIResponse:
        """Chat completion with conversation history"""
        pass
    
    @abstractmethod
    async def stream(
        self,
        prompt: str,
        *,
        system_prompt: Optional[str] = None,
        **kwargs
    ) -> AsyncIterator[str]:
        """Stream text generation"""
        pass
    
    @abstractmethod
    async def embed(
        self,
        text: str,
        *,
        model: Optional[str] = None
    ) -> List[float]:
        """Generate text embeddings"""
        pass
    
    @abstractmethod
    async def moderate(
        self,
        text: str
    ) -> Dict[str, Any]:
        """Content moderation"""
        pass
```

**1.2 Implement OpenAI Provider**

```python
# kardocore/ai/providers/openai.py

import openai
from .base import BaseAIProvider, AIMessage, AIResponse

class OpenAIProvider(BaseAIProvider):
    """OpenAI implementation"""
    
    def __init__(self, api_key: str, **config):
        super().__init__(api_key, **config)
        self.client = openai.AsyncOpenAI(api_key=api_key)
        self.default_model = config.get('model', 'gpt-4')
    
    async def generate(
        self,
        prompt: str,
        *,
        system_prompt: Optional[str] = None,
        max_tokens: int = 1000,
        temperature: float = 0.7,
        **kwargs
    ) -> AIResponse:
        messages = []
        if system_prompt:
            messages.append({"role": "system", "content": system_prompt})
        messages.append({"role": "user", "content": prompt})
        
        response = await self.client.chat.completions.create(
            model=kwargs.get('model', self.default_model),
            messages=messages,
            max_tokens=max_tokens,
            temperature=temperature
        )
        
        return AIResponse(
            content=response.choices[0].message.content,
            model=response.model,
            provider="openai",
            usage={
                "prompt_tokens": response.usage.prompt_tokens,
                "completion_tokens": response.usage.completion_tokens,
                "total_tokens": response.usage.total_tokens
            },
            metadata={"finish_reason": response.choices[0].finish_reason}
        )
    
    async def chat(
        self,
        messages: List[AIMessage],
        *,
        max_tokens: int = 1000,
        temperature: float = 0.7,
        **kwargs
    ) -> AIResponse:
        openai_messages = [
            {"role": msg.role, "content": msg.content}
            for msg in messages
        ]
        
        response = await self.client.chat.completions.create(
            model=kwargs.get('model', self.default_model),
            messages=openai_messages,
            max_tokens=max_tokens,
            temperature=temperature
        )
        
        return AIResponse(
            content=response.choices[0].message.content,
            model=response.model,
            provider="openai",
            usage={
                "prompt_tokens": response.usage.prompt_tokens,
                "completion_tokens": response.usage.completion_tokens,
                "total_tokens": response.usage.total_tokens
            },
            metadata={"finish_reason": response.choices[0].finish_reason}
        )
    
    async def stream(
        self,
        prompt: str,
        *,
        system_prompt: Optional[str] = None,
        **kwargs
    ) -> AsyncIterator[str]:
        messages = []
        if system_prompt:
            messages.append({"role": "system", "content": system_prompt})
        messages.append({"role": "user", "content": prompt})
        
        stream = await self.client.chat.completions.create(
            model=kwargs.get('model', self.default_model),
            messages=messages,
            stream=True
        )
        
        async for chunk in stream:
            if chunk.choices[0].delta.content:
                yield chunk.choices[0].delta.content
    
    async def embed(
        self,
        text: str,
        *,
        model: Optional[str] = None
    ) -> List[float]:
        response = await self.client.embeddings.create(
            model=model or "text-embedding-3-small",
            input=text
        )
        return response.data[0].embedding
    
    async def moderate(
        self,
        text: str
    ) -> Dict[str, Any]:
        response = await self.client.moderations.create(input=text)
        return {
            "flagged": response.results[0].flagged,
            "categories": response.results[0].categories.model_dump(),
            "category_scores": response.results[0].category_scores.model_dump()
        }
```

**1.3 Implement Anthropic Provider**

```python
# kardocore/ai/providers/anthropic.py

import anthropic
from .base import BaseAIProvider, AIMessage, AIResponse

class AnthropicProvider(BaseAIProvider):
    """Anthropic Claude implementation"""
    
    def __init__(self, api_key: str, **config):
        super().__init__(api_key, **config)
        self.client = anthropic.AsyncAnthropic(api_key=api_key)
        self.default_model = config.get('model', 'claude-3-5-sonnet-20241022')
    
    async def generate(
        self,
        prompt: str,
        *,
        system_prompt: Optional[str] = None,
        max_tokens: int = 1000,
        temperature: float = 0.7,
        **kwargs
    ) -> AIResponse:
        response = await self.client.messages.create(
            model=kwargs.get('model', self.default_model),
            max_tokens=max_tokens,
            temperature=temperature,
            system=system_prompt or "",
            messages=[{"role": "user", "content": prompt}]
        )
        
        return AIResponse(
            content=response.content[0].text,
            model=response.model,
            provider="anthropic",
            usage={
                "input_tokens": response.usage.input_tokens,
                "output_tokens": response.usage.output_tokens,
                "total_tokens": response.usage.input_tokens + response.usage.output_tokens
            },
            metadata={"stop_reason": response.stop_reason}
        )
    
    # ... implement other methods
```

**1.4 Create Provider Factory**

```python
# kardocore/ai/factory.py

from typing import Dict, Type
from .providers.base import BaseAIProvider
from .providers.openai import OpenAIProvider
from .providers.anthropic import AnthropicProvider
from .providers.google import GoogleAIProvider

class AIProviderFactory:
    """Factory for creating AI provider instances"""
    
    _providers: Dict[str, Type[BaseAIProvider]] = {
        "openai": OpenAIProvider,
        "anthropic": AnthropicProvider,
        "google": GoogleAIProvider,
    }
    
    @classmethod
    def create(cls, provider: str, api_key: str, **config) -> BaseAIProvider:
        """Create provider instance"""
        if provider not in cls._providers:
            raise ValueError(f"Unknown provider: {provider}")
        
        return cls._providers[provider](api_key, **config)
    
    @classmethod
    def register(cls, name: str, provider_class: Type[BaseAIProvider]):
        """Register custom provider"""
        cls._providers[name] = provider_class
```

**1.5 Create Main KardoAI Interface**

```python
# kardocore/ai/kardoai.py

from typing import Optional, List, Dict, Any
from .factory import AIProviderFactory
from .providers.base import BaseAIProvider, AIMessage, AIResponse

class KardoAI:
    """Main KardoAI interface"""
    
    def __init__(
        self,
        provider: str = "openai",
        api_key: Optional[str] = None,
        **config
    ):
        self.provider_name = provider
        self.provider: BaseAIProvider = AIProviderFactory.create(
            provider,
            api_key or config.get('api_key'),
            **config
        )
    
    async def generate(self, prompt: str, **kwargs) -> AIResponse:
        """Generate text completion"""
        return await self.provider.generate(prompt, **kwargs)
    
    async def chat(self, messages: List[AIMessage], **kwargs) -> AIResponse:
        """Chat completion"""
        return await self.provider.chat(messages, **kwargs)
    
    async def stream(self, prompt: str, **kwargs):
        """Stream generation"""
        async for chunk in self.provider.stream(prompt, **kwargs):
            yield chunk
    
    async def embed(self, text: str, **kwargs) -> List[float]:
        """Generate embeddings"""
        return await self.provider.embed(text, **kwargs)
    
    async def moderate(self, text: str) -> Dict[str, Any]:
        """Content moderation"""
        return await self.provider.moderate(text)
```

#### Deliverables
- ✅ Base provider interface
- ✅ OpenAI provider implementation
- ✅ Anthropic provider implementation
- ✅ Google AI provider implementation
- ✅ Provider factory
- ✅ Main KardoAI class
- ✅ Unit tests for all providers
- ✅ Documentation

#### Timeline
**2-3 weeks**

---

### Step 2: Semantic Search & Vector Database Integration 🟡 **HIGH PRIORITY**

**Objective**: Implement semantic search capabilities using embeddings and vector databases for intelligent content discovery.

#### Why This is Critical
- **Core AI Feature**: Semantic search is fundamental for modern CMS
- **User Value**: Better search results than keyword matching
- **Foundation**: Required for RAG (Retrieval Augmented Generation)
- **Competitive**: Essential feature for modern applications

#### Technical Implementation

**2.1 Vector Database Abstraction**

```python
# kardocore/ai/vector/base.py

from abc import ABC, abstractmethod
from typing import List, Dict, Any, Optional
from dataclasses import dataclass

@dataclass
class VectorDocument:
    """Document with vector embedding"""
    id: str
    content: str
    embedding: List[float]
    metadata: Dict[str, Any]

@dataclass
class SearchResult:
    """Search result with similarity score"""
    document: VectorDocument
    score: float
    distance: float

class BaseVectorDB(ABC):
    """Abstract base for vector databases"""
    
    @abstractmethod
    async def create_collection(
        self,
        name: str,
        dimension: int,
        **kwargs
    ):
        """Create a collection/index"""
        pass
    
    @abstractmethod
    async def add(
        self,
        collection: str,
        documents: List[VectorDocument]
    ):
        """Add documents to collection"""
        pass
    
    @abstractmethod
    async def search(
        self,
        collection: str,
        query_embedding: List[float],
        limit: int = 10,
        filters: Optional[Dict[str, Any]] = None
    ) -> List[SearchResult]:
        """Search for similar documents"""
        pass
    
    @abstractmethod
    async def delete(
        self,
        collection: str,
        document_ids: List[str]
    ):
        """Delete documents"""
        pass
```

**2.2 ChromaDB Implementation**

```python
# kardocore/ai/vector/chromadb.py

import chromadb
from chromadb.config import Settings
from .base import BaseVectorDB, VectorDocument, SearchResult

class ChromaDBVector(BaseVectorDB):
    """ChromaDB implementation"""
    
    def __init__(self, persist_directory: str = "./chroma_db"):
        self.client = chromadb.Client(Settings(
            persist_directory=persist_directory,
            anonymized_telemetry=False
        ))
    
    async def create_collection(
        self,
        name: str,
        dimension: int,
        **kwargs
    ):
        return self.client.create_collection(
            name=name,
            metadata={"dimension": dimension}
        )
    
    async def add(
        self,
        collection: str,
        documents: List[VectorDocument]
    ):
        coll = self.client.get_collection(collection)
        coll.add(
            ids=[doc.id for doc in documents],
            embeddings=[doc.embedding for doc in documents],
            documents=[doc.content for doc in documents],
            metadatas=[doc.metadata for doc in documents]
        )
    
    async def search(
        self,
        collection: str,
        query_embedding: List[float],
        limit: int = 10,
        filters: Optional[Dict[str, Any]] = None
    ) -> List[SearchResult]:
        coll = self.client.get_collection(collection)
        results = coll.query(
            query_embeddings=[query_embedding],
            n_results=limit,
            where=filters
        )
        
        search_results = []
        for i in range(len(results['ids'][0])):
            doc = VectorDocument(
                id=results['ids'][0][i],
                content=results['documents'][0][i],
                embedding=results['embeddings'][0][i] if results['embeddings'] else [],
                metadata=results['metadatas'][0][i]
            )
            search_results.append(SearchResult(
                document=doc,
                score=1 - results['distances'][0][i],  # Convert distance to similarity
                distance=results['distances'][0][i]
            ))
        
        return search_results
```

**2.3 Semantic Search Service**

```python
# kardocore/ai/search.py

from typing import List, Optional, Dict, Any
from .kardoai import KardoAI
from .vector.base import BaseVectorDB, VectorDocument, SearchResult

class SemanticSearch:
    """Semantic search service"""
    
    def __init__(
        self,
        ai: KardoAI,
        vector_db: BaseVectorDB,
        collection: str = "default"
    ):
        self.ai = ai
        self.vector_db = vector_db
        self.collection = collection
    
    async def index_document(
        self,
        doc_id: str,
        content: str,
        metadata: Optional[Dict[str, Any]] = None
    ):
        """Index a document for search"""
        # Generate embedding
        embedding = await self.ai.embed(content)
        
        # Create document
        doc = VectorDocument(
            id=doc_id,
            content=content,
            embedding=embedding,
            metadata=metadata or {}
        )
        
        # Add to vector DB
        await self.vector_db.add(self.collection, [doc])
    
    async def index_batch(
        self,
        documents: List[Dict[str, Any]]
    ):
        """Index multiple documents"""
        vector_docs = []
        
        for doc in documents:
            embedding = await self.ai.embed(doc['content'])
            vector_docs.append(VectorDocument(
                id=doc['id'],
                content=doc['content'],
                embedding=embedding,
                metadata=doc.get('metadata', {})
            ))
        
        await self.vector_db.add(self.collection, vector_docs)
    
    async def search(
        self,
        query: str,
        limit: int = 10,
        filters: Optional[Dict[str, Any]] = None
    ) -> List[SearchResult]:
        """Semantic search"""
        # Generate query embedding
        query_embedding = await self.ai.embed(query)
        
        # Search in vector DB
        results = await self.vector_db.search(
            self.collection,
            query_embedding,
            limit=limit,
            filters=filters
        )
        
        return results
    
    async def delete_document(self, doc_id: str):
        """Delete document from index"""
        await self.vector_db.delete(self.collection, [doc_id])
```

**2.4 Integration with KardoCore Models**

```python
# kardocore/ai/mixins.py

from typing import Optional
from .search import SemanticSearch

class SearchableMixin:
    """Mixin for models that support semantic search"""
    
    _search_service: Optional[SemanticSearch] = None
    
    @classmethod
    def setup_search(cls, search_service: SemanticSearch):
        """Setup semantic search for this model"""
        cls._search_service = search_service
    
    async def index_for_search(self):
        """Index this instance for search"""
        if not self._search_service:
            return
        
        content = self.get_search_content()
        metadata = self.get_search_metadata()
        
        await self._search_service.index_document(
            doc_id=str(self.id),
            content=content,
            metadata=metadata
        )
    
    def get_search_content(self) -> str:
        """Override to define searchable content"""
        raise NotImplementedError
    
    def get_search_metadata(self) -> dict:
        """Override to define metadata"""
        return {}
    
    @classmethod
    async def semantic_search(
        cls,
        query: str,
        limit: int = 10,
        **filters
    ):
        """Perform semantic search"""
        if not cls._search_service:
            raise RuntimeError("Search not configured for this model")
        
        results = await cls._search_service.search(
            query,
            limit=limit,
            filters=filters
        )
        
        # Convert results to model instances
        instances = []
        for result in results:
            instance = await cls.objects.get(id=result.document.id)
            instance._search_score = result.score
            instances.append(instance)
        
        return instances
```

#### Deliverables
- ✅ Vector database abstraction
- ✅ ChromaDB implementation
- ✅ Pinecone implementation (optional)
- ✅ Semantic search service
- ✅ Model mixin for searchable models
- ✅ CLI commands for indexing
- ✅ Admin UI for search
- ✅ Documentation and examples

#### Timeline
**2-3 weeks**

---

### Step 3: Content Generation & RAG System 🟢 **MEDIUM-HIGH PRIORITY**

**Objective**: Implement AI-powered content generation with Retrieval Augmented Generation (RAG) for accurate, context-aware content creation.

#### Why This is Critical
- **Killer Feature**: Content generation is highly valuable for CMS users
- **Differentiation**: RAG ensures accurate, contextual content
- **Productivity**: Dramatically speeds up content creation
- **Revenue**: Premium feature for monetization

#### Technical Implementation

**3.1 Content Generator Base**

```python
# kardocore/ai/content/generator.py

from typing import Optional, List, Dict, Any
from ..kardoai import KardoAI
from ..search import SemanticSearch

class ContentGenerator:
    """AI content generation service"""
    
    def __init__(
        self,
        ai: KardoAI,
        search: Optional[SemanticSearch] = None
    ):
        self.ai = ai
        self.search = search
    
    async def generate_article(
        self,
        topic: str,
        *,
        style: str = "professional",
        length: str = "medium",
        tone: str = "neutral",
        keywords: Optional[List[str]] = None,
        use_rag: bool = True
    ) -> Dict[str, Any]:
        """Generate a complete article"""
        
        # Build context from existing content (RAG)
        context = ""
        if use_rag and self.search:
            search_results = await self.search.search(topic, limit=5)
            context = self._build_context_from_results(search_results)
        
        # Build prompt
        prompt = self._build_article_prompt(
            topic=topic,
            style=style,
            length=length,
            tone=tone,
            keywords=keywords,
            context=context
        )
        
        # Generate content
        response = await self.ai.generate(
            prompt,
            system_prompt=self._get_system_prompt("article_writer"),
            max_tokens=self._get_length_tokens(length),
            temperature=0.7
        )
        
        # Parse response into structured format
        article = self._parse_article_response(response.content)
        
        return {
            "title": article['title'],
            "content": article['content'],
            "summary": article['summary'],
            "keywords": article['keywords'],
            "metadata": {
                "model": response.model,
                "provider": response.provider,
                "tokens_used": response.usage['total_tokens']
            }
        }
    
    async def generate_summary(
        self,
        content: str,
        max_length: int = 200
    ) -> str:
        """Generate summary of content"""
        prompt = f"Summarize the following content in {max_length} words or less:\n\n{content}"
        
        response = await self.ai.generate(
            prompt,
            system_prompt="You are a professional content summarizer.",
            max_tokens=max_length * 2
        )
        
        return response.content
    
    async def expand_content(
        self,
        outline: str,
        section: str
    ) -> str:
        """Expand an outline section into full content"""
        prompt = f"""
        Expand the following section from this outline:
        
        Outline:
        {outline}
        
        Section to expand:
        {section}
        
        Write detailed, engaging content for this section.
        """
        
        response = await self.ai.generate(
            prompt,
            system_prompt="You are a professional content writer.",
            max_tokens=1000
        )
        
        return response.content
    
    async def rewrite_content(
        self,
        content: str,
        style: str = "professional"
    ) -> str:
        """Rewrite content in different style"""
        prompt = f"Rewrite the following content in a {style} style:\n\n{content}"
        
        response = await self.ai.generate(
            prompt,
            max_tokens=len(content.split()) * 2
        )
        
        return response.content
    
    async def generate_seo_metadata(
        self,
        content: str
    ) -> Dict[str, Any]:
        """Generate SEO metadata"""
        prompt = f"""
        Generate SEO metadata for the following content:
        
        {content}
        
        Provide:
        1. Meta title (60 chars max)
        2. Meta description (160 chars max)
        3. Keywords (5-10)
        4. Suggested URL slug
        """
        
        response = await self.ai.generate(
            prompt,
            system_prompt="You are an SEO expert.",
            max_tokens=300
        )
        
        return self._parse_seo_metadata(response.content)
    
    def _build_context_from_results(
        self,
        results: List
    ) -> str:
        """Build context from search results"""
        context_parts = []
        for result in results:
            context_parts.append(f"--- Reference Document ---\n{result.document.content}\n")
        return "\n".join(context_parts)
    
    def _build_article_prompt(
        self,
        topic: str,
        style: str,
        length: str,
        tone: str,
        keywords: Optional[List[str]],
        context: str
    ) -> str:
        """Build article generation prompt"""
        prompt = f"""
        Write a {length} {style} article about: {topic}
        
        Tone: {tone}
        """
        
        if keywords:
            prompt += f"\nKeywords to include: {', '.join(keywords)}"
        
        if context:
            prompt += f"\n\nUse the following reference content for context:\n{context}"
        
        prompt += """
        
        Format the response as:
        
        TITLE: [Article title]
        
        SUMMARY: [Brief summary]
        
        CONTENT:
        [Full article content with proper paragraphs and structure]
        
        KEYWORDS: [Comma-separated keywords]
        """
        
        return prompt
    
    def _get_system_prompt(self, role: str) -> str:
        """Get system prompt for role"""
        prompts = {
            "article_writer": "You are a professional content writer who creates engaging, well-structured articles.",
            "seo_expert": "You are an SEO expert who optimizes content for search engines.",
            "editor": "You are a professional editor who improves content quality."
        }
        return prompts.get(role, "You are a helpful AI assistant.")
    
    def _get_length_tokens(self, length: str) -> int:
        """Get token count for length"""
        lengths = {
            "short": 500,
            "medium": 1000,
            "long": 2000,
            "very_long": 4000
        }
        return lengths.get(length, 1000)
    
    def _parse_article_response(self, content: str) -> Dict[str, str]:
        """Parse article response into structured format"""
        # Simple parsing logic
        lines = content.split('\n')
        article = {
            "title": "",
            "summary": "",
            "content": "",
            "keywords": []
        }
        
        current_section = None
        content_lines = []
        
        for line in lines:
            if line.startswith("TITLE:"):
                article["title"] = line.replace("TITLE:", "").strip()
            elif line.startswith("SUMMARY:"):
                article["summary"] = line.replace("SUMMARY:", "").strip()
            elif line.startswith("CONTENT:"):
                current_section = "content"
            elif line.startswith("KEYWORDS:"):
                keywords_str = line.replace("KEYWORDS:", "").strip()
                article["keywords"] = [k.strip() for k in keywords_str.split(",")]
            elif current_section == "content" and line.strip():
                content_lines.append(line)
        
        article["content"] = "\n".join(content_lines)
        return article
    
    def _parse_seo_metadata(self, content: str) -> Dict[str, Any]:
        """Parse SEO metadata from response"""
        # Implementation for parsing SEO metadata
        return {
            "meta_title": "",
            "meta_description": "",
            "keywords": [],
            "slug": ""
        }
```

**3.2 Admin Integration**

```python
# kardocore/admin/ai_widgets.py

class AIContentWidget:
    """Widget for AI content generation in admin"""
    
    def __init__(self, field_name: str):
        self.field_name = field_name
    
    def render(self, context):
        """Render AI widget in admin form"""
        return f"""
        <div class="ai-content-widget">
            <textarea name="{self.field_name}" id="{self.field_name}"></textarea>
            <div class="ai-controls">
                <button type="button" onclick="generateContent()">
                    ✨ Generate with AI
                </button>
                <button type="button" onclick="improveContent()">
                    🔧 Improve
                </button>
                <button type="button" onclick="summarize()">
                    📝 Summarize
                </button>
            </div>
        </div>
        """
```

#### Deliverables
- ✅ Content generator service
- ✅ RAG implementation
- ✅ Article generation
- ✅ Summary generation
- ✅ SEO metadata generation
- ✅ Admin UI widgets
- ✅ API endpoints
- ✅ Documentation and examples

#### Timeline
**3-4 weeks**

---

## 📅 Overall Timeline

| Phase | Duration | Status |
|-------|----------|--------|
| **Step 1**: Provider Abstraction | 2-3 weeks | ⏳ Not Started |
| **Step 2**: Semantic Search | 2-3 weeks | ⏳ Not Started |
| **Step 3**: Content Generation | 3-4 weeks | ⏳ Not Started |
| **Testing & Documentation** | 1-2 weeks | ⏳ Not Started |
| **Total** | **8-12 weeks** | - |

---

## 🎯 Success Metrics

### Technical Metrics
- ✅ 3+ AI providers supported
- ✅ <100ms provider switching overhead
- ✅ 95%+ uptime for AI services
- ✅ <2s average response time
- ✅ 80%+ test coverage

### User Metrics
- ✅ 90%+ user satisfaction with AI features
- ✅ 50%+ reduction in content creation time
- ✅ 80%+ accuracy in semantic search
- ✅ 70%+ of users actively using AI features

### Business Metrics
- ✅ 30%+ increase in user engagement
- ✅ 20%+ conversion to premium (AI features)
- ✅ 40%+ reduction in support tickets (AI help)

---

## 🔒 Security & Privacy Considerations

1. **API Key Management**
   - Secure storage of API keys
   - Environment variable configuration
   - Key rotation support

2. **Content Filtering**
   - Automatic moderation of generated content
   - User-configurable content policies
   - Audit logging

3. **Data Privacy**
   - No user data sent to AI providers without consent
   - Option to use local models
   - GDPR compliance

4. **Cost Control**
   - Token usage tracking
   - Rate limiting
   - Budget alerts
   - Cost attribution per user/project

---

## 📚 Documentation Requirements

1. **Developer Documentation**
   - API reference
   - Integration guide
   - Provider comparison
   - Best practices

2. **User Documentation**
   - Feature guides
   - Video tutorials
   - Use case examples
   - FAQ

3. **Admin Documentation**
   - Configuration guide
   - Cost management
   - Security setup
   - Monitoring

---

## 🚀 Next Actions

1. **Immediate (This Week)**
   - ✅ Review and approve this roadmap
   - ⏳ Set up development environment
   - ⏳ Create feature branch `feature/kardoai`
   - ⏳ Install required dependencies

2. **Week 1-2**
   - ⏳ Implement base provider interface
   - ⏳ Implement OpenAI provider
   - ⏳ Write unit tests
   - ⏳ Create basic documentation

3. **Week 3-4**
   - ⏳ Implement Anthropic provider
   - ⏳ Implement Google AI provider
   - ⏳ Create provider factory
   - ⏳ Integration testing

---

**This roadmap is a living document and will be updated as development progresses.**

