Machine Learning•2026
Contextual Semantic Search Engine
Document retrieval pipeline pairing dense vector embeddings with lexical search.
My RoleLead Developer
Timeline2026
Stack
Python,PyTorch,FastAPI,Vector Search,TypeScript,Next.js,
Links
[01] The Problem
Technical Constraints & Motivation
Keyword search fails on nuanced queries where vocabulary differs from source terminology, while pure vector search often misses exact technical identifiers.
[02] What I Built
Architecture & Implementation
An end-to-end document search pipeline that processes raw text, generates normalized embeddings, and retrieves ranked context chunks for downstream tasks.
- —Hybrid scoring combining lexical token matching with dense vector representations
- —Quantized vector index structure to reduce query memory footprint
- —Query inspection client built in Next.js with real-time score inspection
[03] Outcome & Results
Delivered System
Built a hybrid indexing pipeline that balances dense semantic similarity with keyword matching and exposes a clean query API.
System Interface & Wireframes1 Image
Contextual Semantic Search Engine query inspector and score breakdown[Placeholder Preview — Real Screenshot to be Added]