Intelligent Document Retrieval with Multi-Layer Caching
A production-grade RAG architecture that combines Azure AI Search with intelligent caching strategies to deliver accurate document-based answers at scale. Designed for enterprise environments requiring high throughput, low latency, and cost-efficient LLM operations.
Key problems this architecture solves
Enterprise knowledge scattered across thousands of documents (PDF, DOCX, internal wikis) making information discovery slow and inefficient.
Traditional keyword search misses semantic context and relationships between concepts, leading to incomplete or irrelevant results.
Direct LLM queries without retrieval lead to hallucinations and incorrect information, undermining trust in AI-powered systems.
High costs and slow response times when processing large document sets repeatedly, making enterprise deployments economically unfeasible.
End-to-end data flow and components
Technical building blocks and Azure services
Centralized repository for enterprise documents (PDF, DOCX, TXT). Supports versioning and metadata tagging for efficient organization.
Serverless parsing and chunking pipeline. Extracts text, splits into 1000-token segments with 200-token overlap, preserves document structure and metadata.
Converts text chunks into high-dimensional vectors capturing semantic meaning. Enables similarity-based retrieval beyond keyword matching.
Combines vector similarity search with BM25 keyword ranking. Provides both semantic understanding and exact term matching for optimal retrieval accuracy.
Multi-tier caching strategy. Stores exact query matches and semantically similar queries to minimize redundant LLM calls and reduce response latency.
Generates contextual answers using retrieved document chunks. Engineered prompts ensure grounded responses with source citations and minimal hallucination.
Step-by-step journey through the system