TODO

• UI input for a YouTube video URL
• Transcribe using Groq

Based on the code and transcripts provided, here's a plan for building your RAG app for Blender tutorial transcripts:

Current Status

• You have a collection of markdown transcripts from a Blender 3D modeling tutorial series
• Basic ChromaDB setup started but incomplete
• Some file handling and transcription code exists
• Integration with Claude API started

Plan & TODO List

1. Data Preprocessing

// Create a transcript processor to:
- Extract clean text from markdown files
- Split transcripts into smaller chunks (e.g., ~500-1000 tokens)
- Add metadata like video number, title, timestamp

2. ChromaDB Setup & Population

// Enhance ChromaDB integration:
- Create a proper collection with meaningful name
- Define schema for documents (text, metadata)
- Implement batch embedding and insertion
- Add error handling and logging

3. Query Interface

// Build query handling:
- Create function to search similar chunks
- Implement relevance scoring
- Add context window handling
- Format results for Claude

4. Claude Integration

// Enhance Claude integration:
- Create proper system prompt
- Handle context injection
- Implement conversation history
- Add error handling

5. Application Flow

// Main application logic:
- User input handling
- Context retrieval from ChromaDB
- Prompt construction
- Response generation
- Conversation management

Suggested Implementation Order:

1. First Phase - Data Layer

// actions.ts
async function processTranscripts() {
  // Load and clean transcripts
  // Split into chunks
  // Store in ChromaDB
}

async function queryChroma(question: string) {
  // Search relevant chunks
  // Return formatted results
}

2. Second Phase - AI Layer

// actions.ts
async function generateResponse(question: string, context: string) {
  // Format prompt with context
  // Call Claude API
  // Process response
}

3. Third Phase - Integration

// actions.ts
export async function handleQuestion(question: string) {
  // Get relevant context
  // Generate response
  // Handle errors
  // Return formatted response
}

4. Fourth Phase - Refinement

• Add error handling
• Improve context selection
• Optimize chunk size
• Add caching if needed
• Improve response quality

Would you like me to provide more detailed implementation suggestions for any of these components?