# Conversations Module Implementation Plan

## 📋 **SCOPE & OBJECTIVES**

### **Primary Goals**
- Implement context-aware conversation management
- Support multi-turn conversations with memory
- Provide session management and persistence
- Enable conversation analytics and insights
- Support team collaboration features

### **Deliverables**
1. Conversation session management
2. Context-aware multi-turn interactions
3. Conversation history and persistence
4. User behavior analytics
5. Team collaboration insights
6. Conversation export and sharing
7. Memory optimization and cleanup

## 🛠️ **TECHNOLOGY STACK**

### **Core Dependencies**
- **datetime**: Session timestamp management
- **uuid**: Session ID generation
- **typing**: Type annotations for conversation data

### **Cognify API Endpoints Covered**
Based on OpenAPI analysis:
- `POST /api/v1/conversations/start` - Start conversation session
- `POST /api/v1/conversations/ask` - Context-aware queries
- `GET /api/v1/conversations/history` - Conversation history
- `GET /api/v1/conversations/context/{session_id}` - Session context
- `POST /api/v1/conversations/end/{session_id}` - End session
- `GET /api/v1/conversations/stats` - Conversation statistics
- `GET /api/v1/conversations/analytics/profile` - User behavior profile
- `GET /api/v1/conversations/analytics/team` - Team analytics

## 🏗️ **IMPLEMENTATION STRATEGY**

### **Step 1: Conversation Models**
```python
# conversations/models.py
from pydantic import BaseModel
from typing import Optional, List, Dict, Any
from datetime import datetime
from enum import Enum

class ConversationStatus(str, Enum):
    ACTIVE = "active"
    PAUSED = "paused"
    ENDED = "ended"

class ConversationTurn(BaseModel):
    id: str
    session_id: str
    query: str
    response: str
    timestamp: datetime
    processing_time_ms: float
    sources: List[Dict[str, Any]] = []
    metadata: Dict[str, Any] = {}

class ConversationSession(BaseModel):
    id: str
    user_id: str
    title: Optional[str] = None
    status: ConversationStatus
    collection_id: Optional[str] = None
    workspace_id: Optional[str] = None
    started_at: datetime
    last_activity: datetime
    ended_at: Optional[datetime] = None
    turn_count: int = 0
    total_tokens: int = 0
    metadata: Dict[str, Any] = {}

class ConversationContext(BaseModel):
    session_id: str
    recent_turns: List[ConversationTurn]
    user_preferences: Dict[str, Any] = {}
    conversation_summary: str = ""
    key_topics: List[str] = []
    relevant_documents: List[str] = []
```

### **Step 2: Conversations Module**
```python
# conversations/conversations_module.py
class ConversationsModule:
    def __init__(self, client: 'CognifyClient'):
        self.client = client
    
    # Session Management
    async def start_session(
        self,
        title: Optional[str] = None,
        collection_id: Optional[str] = None,
        workspace_id: Optional[str] = None,
        context: Optional[Dict[str, Any]] = None
    ) -> ConversationSession:
        """Start a new conversation session"""
    
    async def end_session(self, session_id: str) -> bool:
        """End a conversation session"""
    
    async def get_session_context(
        self,
        session_id: str,
        max_turns: int = 10
    ) -> ConversationContext:
        """Get detailed context for a conversation session"""
    
    # Context-Aware Queries
    async def ask(
        self,
        query: str,
        session_id: str,
        include_context: bool = True,
        max_context_turns: int = 5
    ) -> ConversationTurn:
        """Ask a question with conversation context"""
    
    async def continue_conversation(
        self,
        query: str,
        session_id: Optional[str] = None,
        auto_create_session: bool = True
    ) -> ConversationTurn:
        """Continue an existing conversation or start new one"""
    
    # History Management
    async def get_history(
        self,
        limit: int = 10,
        include_context: bool = False
    ) -> List[ConversationSession]:
        """Get user's conversation history"""
    
    async def get_session_turns(
        self,
        session_id: str,
        limit: int = 50,
        offset: int = 0
    ) -> List[ConversationTurn]:
        """Get all turns for a specific session"""
```

### **Step 3: Analytics & Insights**
```python
# conversations/analytics.py
class ConversationAnalytics:
    def __init__(self, conversations_module: ConversationsModule):
        self.conversations = conversations_module
    
    async def get_user_profile(
        self,
        days_back: int = 30
    ) -> Dict[str, Any]:
        """Get comprehensive user behavior profile"""
    
    async def get_team_analytics(
        self,
        days_back: int = 30
    ) -> Dict[str, Any]:
        """Get team analytics and collaboration insights"""
    
    async def get_conversation_stats(self) -> Dict[str, Any]:
        """Get conversation system statistics"""
    
    async def export_conversations(
        self,
        session_ids: Optional[List[str]] = None,
        format: str = "json",
        include_context: bool = True
    ) -> Union[str, bytes]:
        """Export conversations in specified format"""
```

### **Step 4: Context Management**
```python
# conversations/context_manager.py
class ConversationContextManager:
    def __init__(self):
        self.active_sessions: Dict[str, ConversationContext] = {}
        self.max_context_length = 4000
        self.max_turns_in_context = 10
    
    def add_turn(
        self,
        session_id: str,
        turn: ConversationTurn
    ) -> None:
        """Add a new turn to session context"""
    
    def get_context_for_query(
        self,
        session_id: str,
        max_turns: int = 5
    ) -> str:
        """Get formatted context for a new query"""
    
    def summarize_conversation(
        self,
        session_id: str
    ) -> str:
        """Generate a summary of the conversation"""
    
    def extract_key_topics(
        self,
        session_id: str
    ) -> List[str]:
        """Extract key topics from conversation"""
    
    def cleanup_old_sessions(
        self,
        max_age_hours: int = 24
    ) -> None:
        """Clean up old inactive sessions"""
```

## 🔗 **DEPENDENCIES**

### **Prerequisites**
- Core client architecture (Plan 02) completed
- Authentication module (Plan 03) implemented
- RAG module (Plan 06) for context-aware responses

### **External Dependencies**
- No additional external dependencies required

## ✅ **SUCCESS CRITERIA**

### **Functional Requirements**
- [ ] Conversation sessions working with persistence
- [ ] Context-aware multi-turn conversations
- [ ] Conversation history and retrieval
- [ ] User behavior analytics functional
- [ ] Team collaboration insights available
- [ ] Export and sharing capabilities

### **Performance Requirements**
- **Context Processing**: <200ms for context retrieval
- **Memory Usage**: Efficient context storage
- **Session Management**: Support for concurrent sessions
- **Analytics**: Real-time insights generation

### **API Design**
```python
# Start a conversation
session = await client.conversations.start_session(
    title="Authentication Implementation Discussion",
    collection_id="my_codebase"
)

# Context-aware conversation
turn1 = await client.conversations.ask(
    "How is user authentication implemented?",
    session_id=session.id
)

turn2 = await client.conversations.ask(
    "What about password hashing?",  # Context-aware
    session_id=session.id
)

turn3 = await client.conversations.ask(
    "Show me the login endpoint code",  # Builds on previous context
    session_id=session.id
)

# Get conversation history
history = await client.conversations.get_history(limit=20)

# Analytics
profile = await client.conversations.get_user_profile(days_back=30)
team_stats = await client.conversations.get_team_analytics()

# End session
await client.conversations.end_session(session.id)
```

## ⏱️ **TIMELINE**

### **Day 1: Core Session Management**
- Conversation models and types
- Session creation and management
- Basic context handling

### **Day 2: Context-Aware Queries**
- Multi-turn conversation logic
- Context building and formatting
- Memory management

### **Day 3: History & Analytics**
- Conversation history retrieval
- User behavior analytics
- Team collaboration insights

### **Day 4: Advanced Features**
- Export and sharing functionality
- Performance optimization
- Comprehensive testing

## 🧪 **TESTING STRATEGY**

### **Unit Tests**
```python
async def test_session_creation():
    session = await client.conversations.start_session(
        title="Test Session"
    )
    assert session.id
    assert session.status == ConversationStatus.ACTIVE

async def test_context_aware_conversation():
    session = await client.conversations.start_session()
    
    # First turn
    turn1 = await client.conversations.ask(
        "What is authentication?",
        session_id=session.id
    )
    
    # Second turn with context
    turn2 = await client.conversations.ask(
        "How is it implemented here?",  # Should understand "it" refers to auth
        session_id=session.id
    )
    
    assert turn1.response
    assert turn2.response
    assert "authentication" in turn2.response.lower()

async def test_conversation_history():
    # Create multiple sessions
    sessions = []
    for i in range(3):
        session = await client.conversations.start_session(
            title=f"Test Session {i}"
        )
        sessions.append(session)
    
    # Get history
    history = await client.conversations.get_history()
    assert len(history) >= 3
```

### **Integration Tests**
```python
async def test_full_conversation_workflow():
    # Start session
    session = await client.conversations.start_session(
        collection_id="test_collection"
    )
    
    # Multi-turn conversation
    queries = [
        "How does the authentication system work?",
        "What about the database schema?",
        "Show me the user model implementation",
        "How are passwords hashed?"
    ]
    
    turns = []
    for query in queries:
        turn = await client.conversations.ask(query, session.id)
        turns.append(turn)
    
    # Verify context awareness
    assert len(turns) == 4
    assert all(turn.response for turn in turns)
    
    # Get session context
    context = await client.conversations.get_session_context(session.id)
    assert len(context.recent_turns) == 4
    assert len(context.key_topics) > 0
    
    # End session
    await client.conversations.end_session(session.id)
```

### **Performance Tests**
- Context retrieval speed
- Memory usage with long conversations
- Concurrent session handling
- Analytics generation performance

## 📚 **DOCUMENTATION**

### **Conversation Guide**
```python
# Start a focused conversation
from cognify_sdk import CognifyClient

client = CognifyClient(api_key="your_key")

# Begin conversation about specific topic
session = await client.conversations.start_session(
    title="Database Design Review",
    collection_id="backend_code"
)

# Context-aware multi-turn conversation
await client.conversations.ask(
    "How is the user table structured?",
    session_id=session.id
)

await client.conversations.ask(
    "What about the relationships?",  # Understands context
    session_id=session.id
)

await client.conversations.ask(
    "Are there any performance issues?",  # Builds on previous context
    session_id=session.id
)

# Review conversation
turns = await client.conversations.get_session_turns(session.id)
for turn in turns:
    print(f"Q: {turn.query}")
    print(f"A: {turn.response}\n")
```

### **Analytics Usage**
```python
# User behavior insights
profile = await client.conversations.get_user_profile(days_back=30)
print(f"Total conversations: {profile['total_conversations']}")
print(f"Favorite topics: {profile['top_topics']}")

# Team collaboration
team_stats = await client.conversations.get_team_analytics()
print(f"Team conversations: {team_stats['total_team_conversations']}")
print(f"Most discussed topics: {team_stats['trending_topics']}")

# Export conversations
exported = await client.conversations.export_conversations(
    format="markdown",
    include_context=True
)
```

## 🔄 **RISK MITIGATION**

### **Technical Risks**
- **Context Length**: Intelligent summarization and pruning
- **Memory Usage**: Efficient session cleanup
- **Context Quality**: Advanced context building algorithms

### **Performance Risks**
- **Long Conversations**: Context summarization
- **Concurrent Sessions**: Efficient session management
- **Analytics Load**: Caching and optimization

---

**Plan Status**: 🟡 Ready for Implementation
**Dependencies**: Core client (02), Authentication (03), RAG (06)
**Estimated Effort**: 4 days
**Priority**: Medium (Advanced conversation features)
