Semantic Search Engine - Compare Search Methods

Semantic Search (Embeddings) Keyword Search (BM25) Hybrid Search

Results per method: 10

Hybrid weight (semantic ← → keyword): 0.50

Document Corpus:

Semantic Search

Uses embeddings and cosine similarity to find semantically related documents

Time: - Avg Score: -

Enter a query to see results

Keyword Search (BM25)

Uses term frequency and document frequency for lexical matching

Time: - Avg Score: -

Enter a query to see results

Hybrid Search

Combines semantic and keyword scores for best of both worlds

Time: - Avg Score: -

Enter a query to see results

Relevance Score Comparison

Search Analysis

Overlap Analysis

Results overlap will appear here after searching

Performance Comparison

About Search Methods

Semantic Search

How it works: Converts both query and documents into high-dimensional embedding vectors. Computes cosine similarity to find semantically related content.

Strengths:

Finds conceptually similar documents
Handles synonyms and paraphrasing
Language-aware understanding

Weaknesses:

Slower (requires embedding generation)
May miss exact keyword matches
Computationally expensive

Keyword Search (BM25)

How it works: Uses BM25 (Best Matching 25) algorithm based on term frequency-inverse document frequency (TF-IDF) with saturation.

Strengths:

Very fast
Excellent for exact matches
Well-understood algorithm

Weaknesses:

Purely lexical matching
Misses semantic relationships
Sensitive to exact wording

Hybrid Search

How it works: Combines normalized scores from both semantic and keyword search methods using a weighted average.

Formula:

score = α × semantic_score + (1-α) × keyword_score

Benefits:

Best of both approaches
Configurable trade-offs
Robust to query types

Try These Queries

Corpus Statistics

Total Documents: 0

Avg Length: 0

Vocabulary Size: 0

Embedding Model: -