Implement RAG (Retrieval-Augmented Generation) to Reduce Model Perplexity and Improve Performance

[Scarmonit]

Problem Statement

The BrowserOS agent currently relies solely on pre-trained LLM knowledge, which can lead to:

• Higher perplexity (uncertainty) in responses
• Outdated information
• Factual errors and hallucinations
• Limited domain-specific accuracy

Proposed Solution

Implement Retrieval-Augmented Generation (RAG) to enhance the agent's performance by combining generative AI with external knowledge retrieval.

Implementation Plan

Phase 1: Basic RAG Integration

1. Add Vector Database Integration

   • Integrate a vector store (Pinecone, Weaviate, or ChromaDB)
   • Add embeddings generation using OpenAI or Anthropic embeddings
   • Store relevant web content, documentation, and domain knowledge

2. Create RAG Service

   • Create src/lib/services/RAGService.ts
   • Implement document retrieval based on user queries
   • Add relevance scoring and ranking

3. Modify BrowserAgent.ts

   • Integrate RAG service before LLM invocation
   • Pass retrieved context to the LLM prompt
   • Implement context windowing to stay within token limits

Phase 2: Advanced Features

1. Implement Hybrid Search

   • Combine semantic search with keyword search
   • Add query expansion and rewriting

2. Add Caching Layer

   • Cache frequently accessed documents
   • Implement cache invalidation strategy

3. Implement RAFT (RAG + Fine-tuning)

   • Fine-tune model on domain-specific data
   • Combine with RAG for optimal performance

Phase 3: Evaluation & Monitoring

1. Add Metrics

   • Track retrieval precision and recall
   • Measure answer quality and factual accuracy
   • Monitor hallucination rates

2. Implement Feedback Loop

   • Collect user feedback on responses
   • Continuously improve retrieval quality

Expected Benefits

• Reduced model perplexity (uncertainty)
• More accurate and up-to-date information
• Fewer hallucinations
• Better domain-specific performance
• Verifiable sources for answers

Dependencies

Required packages (some already in package.json):

• @langchain/community ✅ (already installed)
• @langchain/core ✅ (already installed)
• Vector database client (Pinecone, ChromaDB, or Weaviate)
• Embeddings provider

References

• LangChain RAG Documentation
• RAG Best Practices
• RAFT: Adapting Language Model to Domain Specific RAG