Onboarding System (Cache-based RAG)

Intelligent onboarding system that prevents overload through efficient caching, embedding transformation, and RAG-based retrieval.

Architecture

Onboarding Documents
     ↓
[Text Processing & Chunking]
     ↓
[Embedding Generation] → [Cache Layer (Redis)] → [Vector DB (LanceDB/Qdrant)]
     ↓                                           ↓
[Query] → [Cache Check] → [Cache Hit?] → [Yes: Return Cached]
                 ↓           ↓
                [No]   [Vector Search]
                 ↓           ↓
          [Generate Answer] → [Store in Cache]

Features

1. Cache Layer

• Redis-based fast cache for common queries
• TTL-based automatic cache invalidation
• LRU eviction policy for memory efficiency
• Cache hit ratio monitoring

2. Embedding Transformation

• Multiple embedding model support (OpenAI, Cohere, Local)
• Batch embedding for efficiency
• Embedding caching to prevent recomputation
• Dimensionality reduction options (PCA, UMAP)

3. RAG Retrieval

• Hybrid search (semantic + keyword)
• Re-ranking for relevance optimization
• Context window management
• Source attribution

4. Onboarding Management

• Document versioning
• Incremental updates (only process changed content)
• Progress tracking
• Analytics dashboard

Setup

Prerequisites

# Install dependencies
pip install lancedb redis qdrant-client openai numpy scikit-learn

# Start Redis (optional, for cache layer)
brew install redis
brew services start redis

Configuration

Create onboarding-config.json:

{
  "embedding": {
    "model": "text-embedding-3-small",
    "dimensions": 1536,
    "batch_size": 100
  },
  "cache": {
    "enabled": true,
    "host": "localhost",
    "port": 6379,
    "ttl_seconds": 3600,
    "max_size_mb": 500
  },
  "vector_db": {
    "type": "lancedb",
    "path": "./data/onboarding-lancedb"
  },
  "rag": {
    "top_k": 5,
    "chunk_size": 500,
    "chunk_overlap": 50,
    "rerank_threshold": 0.7
  }
}

Usage

Initialize Onboarding System

from onboarding_system import OnboardingSystem

onboarding = OnboardingSystem(config="onboarding-config.json")
onboarding.initialize()

Add Onboarding Documents

# Single document
onboarding.add_document(
    path="docs/getting-started.md",
    category="setup",
    priority="high"
)

# Batch import
onboarding.import_directory(
    path="onboarding/",
    recursive=True
)

Query with Cache

# Automatic cache usage
result = onboarding.query(
    "How do I set up my first project?",
    use_cache=True
)

print(f"Answer: {result.answer}")
print(f"Sources: {result.sources}")
print(f"Cache hit: {result.from_cache}")

Update Documents

# Incremental update (only changed chunks)
onboarding.update_document(
    path="docs/getting-started.md"
)

Cache Management

# View cache stats
stats = onboarding.cache_stats()
print(f"Hit ratio: {stats['hit_ratio']}")

# Clear cache
onboarding.clear_cache()

# Warm up cache for common queries
onboarding.warmup_cache([
    "How do I get started?",
    "What are the basic features?",
    "How to configure settings?"
])

Performance Optimization

1. Caching Strategy

• Hot queries: Cache most frequent questions
• Warm queries: Pre-cache expected questions
• Cold queries: Vector search with cache storage

2. Embedding Efficiency

• Batch process documents
• Cache embeddings of unchanged chunks
• Reuse embeddings across queries

3. Memory Management

• Document chunking for context windows
• Lazy loading of vector databases
• Periodic cache cleanup

4. Parallel Processing

• Concurrent embedding generation
• Async vector search
• Background cache warming

Monitoring

Cache Performance

# Real-time monitoring
metrics = onboarding.get_metrics()
print(f"Cache hit ratio: {metrics['cache_hit_ratio']}")
print(f"Avg query time: {metrics['avg_query_time']}ms")
print(f"Cache size: {metrics['cache_size_mb']}MB")

Onboarding Progress

# Track onboarding completion
progress = onboarding.get_progress("user_id")
print(f"Completed sections: {progress['completed']}")
print(f"Next section: {progress['next']}")

Best Practices

1. Document Structure

onboarding/
├── 01-introduction/
│   ├── overview.md
│   └── quick-start.md
├── 02-setup/
│   ├── installation.md
│   └── configuration.md
├── 03-features/
│   └── feature-guide.md
└── 04-advanced/
    └── customization.md

2. Query Patterns

• Start broad, then refine
• Use natural language
• Include context when relevant

3. Cache Tuning

• Monitor hit ratios
• Adjust TTL based on content update frequency
• Set appropriate cache size limits

Integration with AFO Kingdom

Philosophy Alignment

• 眞 (Truth): Accurate retrieval from verified sources
• 善 (Goodness): Efficient system that respects resources
• 美 (Beauty): Clean API and smooth user experience
• 孝 (Serenity): Reliable, consistent performance

Integration Points

• Triple Memory: LanceDB integration
• Ultimate RAG: Retrieval pipeline
• Health Monitor: Performance metrics

Troubleshooting

Low Cache Hit Ratio

• Increase cache TTL
• Warm up cache with common queries
• Check query consistency

Slow Query Performance

• Increase batch size for embedding
• Enable parallel processing
• Check vector DB indexing

High Memory Usage

• Reduce cache size limit
• Implement LRU eviction
• Clear stale cache entries