videodb

VideoDB Documentation

Pages

Quick Start Guide

Semantic Search

Semantic search operates on the conceptual meaning of a query rather than simple string matching. This approach allows for more intelligent and context-aware results. Key Features of Semantic Search:

Concept-Based Querying

Users can pose questions or use natural language.

The system understands the intent behind the query.

Vector Embeddings

Queries and documents are transformed into high-dimensional vector spaces.

These spaces capture semantic relationships between words and concepts.

Similarity Algorithms

K-Nearest Neighbors (KNN) or other vector similarity algorithms are employed.

Cosine similarity (angle between vectors) is a common measure.

Query-Document Matching

The query's vector is compared to indexed document vectors.

Documents with the closest vector representations are returned.

Scoring Mechanism

Each returned document is assigned a relevance score.

Scores typically reflect the degree of semantic similarity to the query.

⁠

5. Semantic Search Parameters.png

⁠

⁠

Want to print your doc?
This is not the way.

Try clicking the ⋯ next to your doc name or using a keyboard shortcut (

CtrlP

) instead.