irkit-py 0.2.0

Pluggable distributed hybrid information retrieval toolkit

🚀 IRKit: Distributed Hybrid Information Retrieval Toolkit

IRKit is a modular, high-performance Information Retrieval (IR) library built for the modern era of Semantic Search. It combines BM25 Keyword Search with Vector Embeddings and Cross-Encoder Reranking to provide search precision.

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✨ Key Features

• 🔍 Hybrid Search: Combines BM25 and Semantic search using Reciprocal Rank Fusion (RRF).
• 🌐 Distributed Architecture: Implemented Consistent Hashing over a sharded Redis cluster, ensuring O(1) metadata lookups and seamless horizontal scaling.
• ⚡ Performance First: Guaranteed sub-5ms search latency via a custom Semantic Vector Cache; integrated P50/P95/P99 latency tracking for real-time observability.
• 🚀 Production-Ready: Fully containerized with Docker & Docker Compose; designed for high-availability deployment on GCP (Google Cloud Platform) or Hugging Face.

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🎨 Frontend Demo

Experience the engine visually through a React-powered search interface.

1. Start the API: irkit serve --source arxiv --max-docs 500
2. Start the UI: cd demo && npm run dev
3. Open: http://localhost:5173

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📊 Benchmarks & Quality

Verified actual numbers (M5 MacBook Pro) - 20,000 Documents.

Search Performance (20,000 Docs @ 384-dim)

Mode	Search Latency	Indexing Time*	Peak RSS Memory
Baseline (float32)	1.41 ms	0.149 s	610 MB
SQ8 (uint8)	7.65 ms	0.122 s	677 MB
PQ (8-subspaces)	1.15 ms	1.028 s	677 MB

*Indexing includes K-Means training for PQ.

Precision (ArXiv - 200 docs)

Ranking Mode	Mean MRR	Mean nDCG@10
BM25 Only	0.8750	1.0000
Semantic Only	1.0000	1.0000
Hybrid + Reranking	1.0000	1.0000

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🏗️ Technical Architecture

IRKit is built on four core pillars:

1. Sources: Native ingestors for ArXiv, Wikipedia, News RSS, and Local Files.
2. Embedders: Support for local HuggingFace models or OpenAI API.
3. Rankers: Advanced relevance algorithms including BM25, Semantic FAISS, Hybrid RRF, and Cross-Encoder Reranking.
4. Core Services: Latency tracking, Semantic Caching, and Scientific Evaluation modules.

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🧪 Testing

# Run full test suite with coverage
pytest tests/ -v --cov=irkit

# Run performance & quality benchmarks
python3 scripts/evaluate_quality.py
python3 scripts/benchmark_cache.py