Lost and Found Website Idea

For a general-purpose lost & found system handling millions of items, people, pets, documents, etc., you need search algorithms that balance scalability, accuracy, and flexibility across categories.

Here’s a structured breakdown:

1. Core Search Approaches

• Full-Text Search (Keyword Matching)
• Use Inverted Index (like in Lucene, ElasticSearch, Solr).
• Fast lookup for item descriptions, names, locations, dates.
• Example: Searching “red wallet Mumbai” directly returns indexed documents.
• Vector Similarity Search (Semantic Search)
• Convert descriptions, images, even metadata into embeddings (e.g., OpenAI, Sentence-BERT, CLIP).
• Use ANN (Approximate Nearest Neighbor) algorithms:
• HNSW (Hierarchical Navigable Small World)
• IVF + PQ (Inverted File Index with Product Quantization)
• FAISS, Milvus, Weaviate, Pinecone
• Handles fuzzy matching like “lost spectacles” ≈ “missing eyeglasses”.

2. Hybrid Search (Best for Lost & Found)

Combine keyword + semantic + metadata filtering:

• Keyword → quick, exact matches (e.g., item tags, serial numbers).
• Vector → semantic, fuzzy matches (similar descriptions, image similarity).