[FEATURE] Comprehensive Benchmarking Suite for SemanticaĀ #231
Description
Problem Statement
Semantica needs standardized benchmarks to measure performance (speed, throughput, latency, scalability), track improvements, and compare different configurations.
Why This Is Necessary for Semantica: Benchmarks are essential for validating Semantica's performance across different use cases, ensuring GraphRAG and Agentic systems meet speed and efficiency requirements, and tracking improvements over time.
Note: This is distinct from:
- Evaluation Framework ([FEATURE] Evaluation Framework for SemanticaĀ #228): Measures accuracy/quality - "how correct"
- Quality Assurance ([FEATURE] Knowledge Graph Quality Assurance ModuleĀ #229): Detects and fixes data quality issues - "how clean"
- Benchmarking (this issue): Measures performance - "how fast"
All three are complementary and necessary for a production-ready system.
Current Status: No benchmarking infrastructure exists. Greenfield opportunity for contributors. Contributions are welcome!
Benchmark Categories
All Semantica Modules Need Benchmarking: Vector Store, Graph Store, GraphRAG & Context, Knowledge Graph Building, Semantic Extraction, Deduplication, Embedding Generation, Pipeline Orchestration, Input Layer, Output & Export
Note: Specific benchmark metrics and datasets will be added incrementally based on use cases and requirements.
Performance Optimizations to Benchmark
Index type selection, batch size optimization, hybrid alpha tuning, blocking strategies, worker pool sizing, caching strategies, memory optimization
Features
Standardized datasets, benchmark methodology, comparison tools, continuous benchmarking (CI/CD), performance tracking, alert system, dataset generators, benchmark runners, result collectors, visualization tools
Files
Create benchmarks/ directory with:
Input Layer: test_ingestion.py, test_parsing.py, test_splitting.py, test_normalization.py
Core Processing: test_extraction.py, test_graph_building.py, test_ontology.py, test_reasoning.py
Storage: test_embeddings.py, test_vector_store.py, test_graph_store.py, test_triplet_store.py
Quality Assurance: test_deduplication.py, test_conflicts.py
Context & Memory: test_context.py, test_graphrag.py, test_agentic.py
Output & Orchestration: test_export.py, test_visualization.py, test_pipeline.py
Infrastructure: compare.py, benchmark_runner.py, data/, results/, utils/
Getting Started
Current State: No benchmarking infrastructure exists. Greenfield implementation opportunity!
Reference Patterns: tests/ directory for test patterns, cookbook/ for example datasets
Tools: Use pytest-benchmark for performance measurement and integrate with GitHub Actions for continuous benchmarking.
References
- pytest-benchmark: https://github.com/ionelmc/pytest-benchmark
- ANN Benchmarks: https://github.com/erikbern/ann-benchmarks
- FAISS Benchmarks: https://github.com/facebookresearch/faiss/wiki/Benchmarks
- Python Performance: https://docs.python.org/3/howto/optimization.html
- GitHub Actions: https://docs.github.com/en/actions
Labels: feature, benchmarking, performance, graphrag