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OPTIMIZATIONS.md

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RAG System Optimizations Guide

This document details the optimizations implemented in the RAG system components.

1. Document Processor Optimizations

Async Processing

async def process_documents():
await self.init_collection()
self.init_vector_store()

The document processor now uses asynchronous processing to improve performance. This is implemented using Python's asyncio library.

Chunking Optimization

The chunking process has been optimized to reduce the number of tokens per chunk, which in turn reduces the computational load during indexing and querying.

Embedding Optimization

The embedding model is now loaded asynchronously, which reduces the startup time and improves the responsiveness of the system.

Batch Processing of Embeddings

  • Batch processing of embeddings
  • Reduces memory usage
  • Improves processing speed

Error Handling and Logging

try:
    documents = reader.load_data()
    logger.info(f"Loaded {len(documents)} documents")
except Exception as e:
    logger.error(f"Error loading documents: {str(e)}")
    raise
  • Comprehensive error tracking
  • Detailed logging for debugging
  • Proper resource cleanup

2. Document Processing Optimizations

Chunking Strategy

parser = SentenceSplitter(
    chunk_size=1024,
    chunk_overlap=200,
    paragraph_separator="\n\n"
)
  • Optimized chunk size for balance between context and performance
  • Overlap prevents loss of context at chunk boundaries
  • Paragraph-aware splitting for better semantic units

Vector Store Setup

vector_store = QdrantVectorStore(
    client=client,
    collection_name=collection_name,
    embedding_function=embeddings
)
  • Efficient vector storage configuration
  • Optimized for similarity search
  • Proper connection management

3. Query System Optimizations

Reranking Implementation

self.reranker = SentenceTransformerRerank(
    model=rerank_model,
    top_n=3
)
  • Improves result relevance
  • Two-stage retrieval process
  • Configurable top-k results

Query Engine Configuration

query_engine = self.index.as_query_engine(
    similarity_top_k=similarity_top_k,
    node_postprocessors=[self.reranker],
    response_mode="no_text"
)
  • Optimized similarity search
  • Post-processing for better results
  • Memory-efficient response handling

4. System-Wide Optimizations

Hardware Acceleration

if torch.backends.mps.is_available():
    device = torch.device("mps")
  • Metal Performance Shaders support for Apple Silicon
  • Automatic hardware detection
  • Fallback handling

Resource Management

async def close(self):
    await self.qdrant_client.close()
  • Proper cleanup of resources
  • Memory leak prevention
  • Connection pool management

Service Health Checks

async def wait_for_services(timeout: int = 30):
    # Service health checks
  • Ensures services are ready
  • Configurable timeout
  • Graceful error handling

Best Practices Implemented

  1. Type Hints

    • Improved code readability
    • Better IDE support
    • Runtime type checking capability

  2. Modular Design

    • Separate concerns
    • Easy to maintain
    • Reusable components

  3. Configuration Management

    • Externalized configuration
    • Environment-aware settings
    • Easy to modify parameters

  4. Performance Monitoring

    • Detailed logging
    • Performance metrics
    • Debug information

Usage Recommendations

  1. Memory Management

    • Monitor RAM usage
    • Adjust batch sizes if needed
    • Clean up resources properly

  2. Scaling Considerations

    • Adjust chunk sizes based on content
    • Monitor vector store performance
    • Configure timeouts appropriately

  3. Error Handling

    • Implement proper error recovery
    • Log errors comprehensively
    • Maintain system stability

Future Optimization Opportunities

  1. Caching Layer

    • Implement result caching
    • Cache frequently accessed embeddings
    • Cache vector store queries

  2. Parallel Processing

    • Parallel document processing
    • Batch embedding generation
    • Concurrent query handling

  3. Model Optimization

    • Model quantization
    • Optimized model loading
    • Model performance tuning

Maintenance and Monitoring

  1. Regular Health Checks

    • Monitor service availability
    • Check resource usage
    • Verify system performance

  2. Performance Metrics

    • Track query latency
    • Monitor embedding generation time
    • Measure result quality

  3. System Updates

    • Regular dependency updates
    • Security patches
    • Performance improvements