Creating a RAG System: Initialize with configurable semantic/keyword weights
Batch Document Addition: Efficient bulk document indexing
Document Retrieval: Search using hybrid semantic + keyword approach
Batch Document Removal: Efficient bulk document deletion
Performance Comparison: Shows speedup of batch vs sequential operations

What is the AI Backend?

The AI backend is not a rendering backend (like CLI, TUI, Web). It's a library of AI/ML capabilities:

RAG System: Combines semantic search (embeddings) + keyword search (full-text)
Knowledge Graph: Entity and relationship modeling using petgraph
Embeddings: Text-to-vector conversion for semantic similarity
Vector Store: HNSW-optimized approximate nearest neighbor search
Full-Text Indexer: Efficient keyword-based document search
Persistence: Save/load AI state to disk with version compatibility

Running the Example

# Build the example
just build::ai

# Run the example
cargo run --example main --features ai_backend

# Or directly
cargo run --example main --features ai_backend --release

Output Highlights

The example demonstrates:

Batch Add Performance: Adding 5 documents efficiently
Retrieval Quality: Combining semantic + keyword scores
Batch Remove: Efficiently removing multiple documents
Performance Benchmark: 20-document test showing ~2x speedup with batch ops

API Overview

// Create RAG system
let mut rag = RagSystem::new(RagConfig::default())?;

// Add documents (batch - efficient for large sets)
let docs = vec![
    ("id1".into(), "content1".into()),
    ("id2".into(), "content2".into()),
];
rag.add_documents_batch(docs)?;

// Retrieve relevant documents
let results = rag.retrieve("query text")?;
for result in results {
    println!("{}: {}", result.doc_id, result.content);
}

// Remove documents (batch - efficient)
let removed = rag.remove_documents_batch(&["id1", "id2"]);

// Save/Load
rag.save_to_file("rag.bin")?;
let loaded = RagSystem::load_from_file("rag.bin")?;

Configuration

RagConfig controls retrieval behavior:

RagConfig {
    semantic_weight: 0.6,     // Weight for vector similarity
    keyword_weight: 0.4,      // Weight for keyword matching
    max_results: 5,           // Maximum results to return
    min_score: 0.0,           // Minimum combined score threshold
}

Integration Points

The AI backend can be integrated with:

CLI Backend: Add AI-powered search to CLI prompts
TUI Backend: Add semantic search UI
Web Backend: Add AI features to HTTP forms
Custom Applications: Use as a library in any Rust project

Performance Notes

Vector Store: Uses HNSW for O(log N) approximate nearest neighbor search
Batch Operations: Avoid repeated index rebuilds (2x speedup typical)
Embeddings: Deterministic hash-based (production: integrate real ML models)
Full-Text Index: Simple substring matching (production: consider tantivy)

Next Steps

Integrate Knowledge Graph for relationship modeling
Use real embedding models (OpenAI, local transformers)
Add custom similarity metrics
Implement caching strategies
Build domain-specific RAG pipelines