VectorSearch.txt

Summary: How Search Works (Vector & Index Approach)

Document Representation (Vectorization)

Convert each document into a vector based on a vocabulary of words.

Example: "I love beach" → d1 = (1,0,1,0,0,0)

Inverted Index (Preprocessing)

Store a mapping of word → list of documents containing it.

Example:

love → [d1, d3]
beach → [d1, d2]
music → [d3]

Makes searching fast (O(1) lookup per word) instead of scanning all documents.

Query Handling

User types query → convert query to vector.

Use inverted index to fetch only documents containing query words.

Ranking (Similarity Calculation)

SimSca (dot product): counts word overlaps; favors long documents.

SimCos (cosine similarity): normalizes vector length; focuses on real similarity.

Rank documents by similarity to the query.

Result

Return top-ranked documents to the user.

Efficient, scalable, and avoids scanning all documents every search.

💡 Bonus Note:

This approach is very similar to how Google Search or chat-matching apps (like Omegle) store and retrieve data efficiently:

Key-value pairs for instant lookups (word → documents or user → partner)

Normalization / ranking for relevance