🔍 Semantic Search Benchmark

⚡ High-Performance Semantic Search Comparison Framework

Compare different embedding models side-by-side with real benchmarks and metrics

Quick Start • Results • Models • Customization

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📊 What This Project Does

This is a complete benchmarking framework for comparing different semantic search embedding models. Instead of guessing which model works best for semantic search, get real data:

• ⚡ Speed Metrics: Document indexing time and query latency
• 🎯 Relevance Scores: Semantic distance measurements
• 📈 Comparative Analysis: Side-by-side performance tables
• 🧪 Real-World Testing: 20 diverse documents across 4 categories
• 🔄 Reproducible Results: Same test suite every run

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🎯 Models Compared

Model	Size	Speed	Accuracy	Best For
🔵 Default	Lightweight	⚡⚡⚡	⭐⭐	Quick prototypes
🟢 MiniLM-L6-v2	38M	⚡⚡⚡⚡	⭐⭐⭐⭐	Production (Balanced)
🟣 MPNet-base-v2	109M	⚡⚡	⭐⭐⭐⭐⭐	High-accuracy search

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📈 Benchmark Results

Performance Comparison

Testing: default
Documents added in: 3.573s
Avg query time: 0.4861s
Avg relevance distance: 0.8008 (lower is better)

Query                          Time (ms)    Distance
Sport-related query            640.50       0.9197
Tech-related query             455.72       0.8826
Finance-related query          434.48       0.6427
Health-related query           478.39       0.6865
Programming query              421.42       0.8725

─────────────────────────────────────────────────────────
Testing: all-MiniLM-L6-v2
Documents added in: 0.649s
Avg query time: 0.0313s
Avg relevance distance: 0.4004 (lower is better)

Query                          Time (ms)    Distance
Sport-related query            61.40        0.4599
Tech-related query             23.72        0.4413
Finance-related query          26.98        0.3214
Health-related query           26.36        0.3433
Programming query              18.19        0.4363

─────────────────────────────────────────────────────────
Testing: all-mpnet-base-v2
Documents added in: 1.396s
Avg query time: 0.1359s
Avg relevance distance: 0.4164 (lower is better)

Query                          Time (ms)    Distance
Sport-related query            156.71       0.5530
Tech-related query             140.52       0.4529
Finance-related query          160.53       0.3032
Health-related query           104.48       0.3088
Programming query              117.06       0.4640

🏆 Summary Comparison

Model	Add Time	Query Time	Relevance	Speed Rank	Accuracy Rank
Default	3.57s	486ms	0.8008	🥉	🥉
MiniLM-L6-v2	0.65s	31ms	0.4004	🥇	🥇
MPNet-base-v2	1.40s	136ms	0.4164	🥈	🥈

🎖️ Winners

Category	Winner	Metric
⚡ Fastest Document Indexing	all-MiniLM-L6-v2	0.649s
🚀 Fastest Query Time	all-MiniLM-L6-v2	31ms avg
🎯 Best Relevance Score	all-MiniLM-L6-v2	0.4004 distance

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🚀 Quick Start

📦 Installation

pip install -r requirements.txt

▶️ Run Benchmark

python benchmark.py

Expected output: ~2-3 minutes (first run downloads embedding models)

📂 Project Structure

semantic-search-benchmark/
├── benchmark.py              # Main benchmark runner
├── benchmark_data.py         # Dataset & test queries
├── test.py                   # Basic in-memory example
├── test_v2.py               # Persistent storage example
├── test_v3.py               # Semantic search demo
├── requirements.txt         # Dependencies
├── .gitignore              # Git ignore rules
└── README.md               # This file

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🔍 Test Dataset

📚 20 Documents Across 4 Categories

⚽ Sports (5 docs)

• Ronaldo scored an incredible goal last night
• Messi won the World Cup with Argentina
• Real Madrid won the Champions League final
• Liverpool defeated Manchester City
• Nadal won his 14th tennis grand slam

💻 Technology (5 docs)

• Python is a great programming language for beginners
• Machine learning is used in stock price prediction
• TensorFlow and PyTorch are popular deep learning frameworks
• Artificial intelligence is revolutionizing software development
• Cloud computing provides scalable infrastructure

💰 Finance (5 docs)

• The stock market crashed badly this week
• Interest rates are rising due to inflation
• Bitcoin reached a new all-time high
• The Federal Reserve raised interest rates
• Real estate prices continue to rise in major cities

🏥 Health (5 docs)

• Regular exercise improves cardiovascular health
• COVID-19 vaccines have saved millions of lives
• Mental health awareness is becoming increasingly important
• Healthy diet and sleep patterns prevent chronic diseases
• Meditation reduces stress and anxiety

🎯 Test Queries (5 Total)

1. ⚽ "football player scored a goal"
2. 🧠 "machine learning and artificial intelligence"
3. 📊 "money and market crash"
4. 💪 "exercise and health"
5. 🖥️ "programming languages and software"

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📊 Key Insights

💡 Learning Points

Model	Speed	Accuracy	Use Case
Default	Very Fast	Low	Quick tests, low-stakes
MiniLM	⚡ Fastest	⭐⭐⭐⭐ High	RECOMMENDED - Most scenarios
MPNet	Slower	🎯 Highest	Accuracy critical tasks

🎓 Recommendations

• 🏃 Need speed? → Use MiniLM (balanced winner)
• 🎯 Need accuracy? → Use MPNet (but slower)
• 🚀 Production ready? → Use MiniLM (best all-around)
• 🧪 Just testing? → Use Default (fastest to setup)

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🔧 Customization

➕ Add More Embedding Models

Edit benchmark.py:

models_to_test = [
    ("default", "./benchmark_db_default"),
    ("all-MiniLM-L6-v2", "./benchmark_db_minilm"),
    ("all-mpnet-base-v2", "./benchmark_db_mpnet"),
    ("your-model-name", "./benchmark_db_custom"),  # Add here
]

➕ Add More Test Queries

Edit benchmark_data.py:

QUERY_TESTS = [
    {
        "query": "your query here",
        "expected_category": "category",
        "description": "Your description"
    },
]

➕ Modify Test Documents

Edit benchmark_data.py DOCUMENTS list to test different domains.

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📚 Resources & References

Resource	Link	Description
📖 ChromaDB Docs	trychroma.com	Official documentation
🤗 Sentence Transformers	sbert.net	Embedding models & guide
🏆 Model Leaderboard	huggingface.co/spaces/mteb	Compare all models
🔬 MTEB Benchmark	github.com/embeddings-benchmark	Industry benchmarks

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🛠️ Requirements

• Python 3.8+
• chromadb >= 0.4.0
• sentence-transformers >= 2.2.0
• numpy >= 1.21.0

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📝 Files Overview

File	Purpose
benchmark.py	🎯 Core benchmarking logic & runner
benchmark_data.py	📚 Test dataset & queries
test.py	📖 Basic embedding example
test_v2.py	💾 Persistent storage demonstration
test_v3.py	🔍 Semantic search demo
requirements.txt	📦 Python dependencies
.gitignore	🚫 Git exclusion rules

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🚦 Getting Started

Step 1: Install Dependencies

pip install -r requirements.txt

Step 2: Run Benchmark

python benchmark.py

Step 3: Review Results

• Check terminal output for detailed metrics
• Compare models in the summary table
• Identify winner for your use case

Step 4: Customize (Optional)

• Add your own documents to benchmark_data.py
• Test additional embedding models
• Modify query tests for your domain

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📊 Performance Characteristics

⚡ Speed Profile

• Default: Instant setup, moderate query speed
• MiniLM: Best performance (fastest overall)
• MPNet: Higher latency, best accuracy

🎯 Accuracy Profile

• Default: Basic semantic understanding
• MiniLM: Good understanding of document relationships
• MPNet: Excellent semantic comprehension

💾 Memory Usage

• Default: Minimal footprint
• MiniLM: ~384MB loaded (38M params)
• MPNet: ~1.2GB loaded (109M params)

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🤝 Contributing

Have improvements? Found a bug? Want to add:

• More embedding models for comparison?
• Different benchmark datasets?
• Additional metrics?

Feel free to fork and submit pull requests!

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📄 License

MIT License - Feel free to use this in your projects!

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⭐ If This Helped You, Consider Giving It a Star! ⭐

Built with ❤️ for semantic search enthusiasts

Made to compare, benchmark, and optimize ChromaDB embedding models

Questions? Open an issue or check the docs 📚