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Enterprise RAG System

Production-ready RAG implementation with vector search optimization, serving 10K+ daily queries for enterprise knowledge management.

Multi-modal Embeddings

Advanced embedding models for text, images, and documents with GPU-accelerated processing

Vector Search at Scale

Sub-millisecond vector similarity search across millions of embeddings using optimized CUDA kernels

Real-time Knowledge Synthesis

Dynamic knowledge graph construction and real-time context assembly for precise AI responses

Technical Implementation

Built a scalable RAG system using modern vector databases and efficient retrieval algorithms to support enterprise knowledge management with reliable performance and accuracy.

Key Features

  • 92% accuracy on internal knowledge retrieval tasks
  • Sub-200ms query response times for typical use cases
  • Support for 100K+ document corpus with regular updates
  • Multi-format document processing (PDF, Word, text)

Architecture Components

  • FastAPI backend with async request handling
  • Redis caching layer for frequently accessed embeddings
  • Automated document processing pipeline
  • Vector database optimization for similarity search
  • Configurable chunking strategies for different content types

Production Metrics

  • Daily queries: 10K+ with 99.5% uptime
  • Document processing: 5,000 documents/hour
  • Response time: P95 under 200ms
  • Cost efficiency: 30% reduction vs previous system

Technologies

PythonFastAPIVector DBRedisLangChainTransformersPostgreSQLDocker