I'll help you create a complete RAG chatbot system using Llama models with the AI SDK. This will be a cloud-based solution using free providers like DeepInfra for Llama models.

Step-by-Step Setup Guide

1. Environment Setup

Add these environment variables to your project:

2. Get DeepInfra API Key

1. Go to DeepInfra
2. Sign up for a free account
3. Navigate to API Keys section
4. Create a new API key
5. Add it to your environment variables

3. How It Works

Document Processing:

• Upload text files (.txt, .md, .csv)
• Documents are chunked into smaller pieces
• Each chunk gets embedded using sentence transformers
• Embeddings are stored in an in-memory vector database

RAG Pipeline:

• User asks a question
• Question is embedded and compared to document embeddings
• Most similar document chunks are retrieved
• Retrieved context + user question sent to Llama 3.1
• AI generates response based on your documents

4. Key Features

✅ Free Llama 3.1 Integration via DeepInfra1
✅ Document Upload & Processing
✅ Vector Similarity Search
✅ Real-time Chat Interface
✅ Context-Aware Responses
✅ Document Management (upload/clear)

5. Usage Instructions

1. Upload Documents: Use the upload area to add your text files
2. Start Chatting: Ask questions about your uploaded content
3. Get Contextual Answers: The AI will reference your documents
4. Manage Knowledge Base: Clear and re-upload documents as needed

6. Customization Options

• Change Models: Switch between Llama 2/3 variants in the API routes
• Adjust Chunk Size: Modify chunkSize in document processor
• Tune Retrieval: Change topK parameter for more/fewer context chunks
• Model Parameters: Adjust temperature, maxTokens in the chat API

The system uses the AI SDK's unified interface2, making it easy to switch between different Llama models or providers. The RAG implementation follows best practices for document retrieval and generation3.

To configure the generation, complete these steps:

I can see the issue! The error is likely occurring because of empty text chunks or issues with the embedding model. Let me fix the code with better error handling and validation.

Key Changes Made:

1. Fixed Embedding Model

• Changed from sentence-transformers/all-MiniLM-L6-v2 to BAAI/bge-base-en-v1.5 which is more reliable on DeepInfra
• Added proper error handling for embedding operations

2. Improved Text Validation

• Filter out empty or very short text chunks (minimum 10 characters)
• Added file size validation (max 10MB)
• Better text preprocessing to remove problematic characters

3. Enhanced Error Handling

• Comprehensive try-catch blocks throughout the pipeline
• Detailed logging for debugging
• Graceful handling of individual file failures
• Better user feedback with specific error messages

4. Better Chunking Strategy

• Improved chunk size (400 characters with 50 overlap)
• Smart boundary detection (sentence/paragraph breaks)
• Minimum chunk length validation

5. Robust File Processing

• Individual file error handling (one bad file won't break the whole upload)
• File validation before processing
• Progress tracking and detailed results

The error should now be resolved. The system will:

• ✅ Validate all text content before embedding
• ✅ Handle empty or invalid files gracefully
• ✅ Provide detailed error messages for debugging
• ✅ Use a more reliable embedding model
• ✅ Process files individually to prevent cascade failures

Try uploading your documents again - you should now see detailed feedback about what's happening during the process!