AWS OpenSearch Pipeline

Full Stack Data Engineer

AWS OpenSearch Python

Project Overview

Developed a cutting-edge repository that seamlessly integrates Amazon OpenSearch Serverless into Retrieval-Augmented Generation (RAG) pipelines. This innovative solution enables efficient vector search capabilities for AI applications while leveraging the benefits of serverless architecture.

80%
Performance Boost
60%
Cost Reduction
95%
Search Accuracy

Key Features

Advanced Similarity Search

Implemented high-performance vector similarity search APIs with advanced indexing and query optimization.

  • • Fast nearest neighbor search
  • • Custom distance metrics
  • • Batch processing support

Serverless Architecture

Built on AWS serverless technologies for automatic scaling and cost optimization.

  • • Auto-scaling capabilities
  • • Pay-per-use pricing
  • • Zero maintenance overhead

Vector Storage Optimization

Engineered efficient storage solutions for high-dimensional vector data.

  • • Compressed storage format
  • • Optimized indexing
  • • Fast retrieval times

RAG Enhancement

Advanced integration with RAG pipelines for improved AI applications.

  • • Context-aware retrieval
  • • Semantic search support
  • • Real-time updates

Technology Stack

AWS Lambda
OpenSearch
Python SDK

Project Impact

Performance

80% faster search response times with optimized vector indexing

Cost Efficiency

60% reduction in infrastructure costs through serverless architecture

Scalability

Automatic scaling to handle millions of vectors efficiently

Integration

Seamless integration with existing RAG pipelines