NotebookLM Clone - Document-Grounded AI Assistant

# RAG Pipeline with Citation Metadata
class RAGGenerator:
    def generate_response(self, query: str, conversation_history: List[Dict]) -> Dict:
        # Embed query for semantic search
        query_embedding = self.embedding_generator.embed_query(query)
        
        # Retrieve relevant chunks with metadata
        results = self.vector_db.search(
            query_embedding, 
            top_k=5,
            include_metadata=True  # Page numbers, timestamps, sources
        )
        
        # Get conversation context from memory
        memory_context = self.memory.get_context(session_id)
        
        # Generate cited response using retrieved chunks
        response = self.llm.generate(
            query=query,
            context=results,
            memory=memory_context,
            citations=True  # Enforce citation format
        )
        
        # Store conversation in memory layer
        self.memory.add_message(query, response)
        
        return response