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* docs: config guidance * feat: add comprehensive configuration guide and update README - Create docs/configuration-guide.md with detailed guidance on: - Embedding model selection (small/medium/large) - Index selection (HNSW vs DiskANN) - LLM engine and model comparison - Parameter tuning (build/search complexity, top-k) - Performance optimization tips - Deep dive into LEANN's recomputation feature - Update README.md to link to the configuration guide - Include latest 2025 model recommendations (Qwen3, DeepSeek-R1, O3-mini) * chore: move evaluation data .gitattributes to correct location * docs: Weaken DiskANN emphasis in README - Change backend description to emphasize HNSW as default - DiskANN positioned as optional for billion-scale datasets - Simplify evaluation commands to be more generic * docs: Adjust DiskANN positioning in features and roadmap - features.md: Put HNSW/FAISS first as default, DiskANN as optional - roadmap.md: Reorder to show HNSW integration before DiskANN - Consistent with positioning DiskANN as advanced option for large-scale use * docs: Improve configuration guide based on feedback - List specific files in default data/ directory (2 AI papers, literature, tech report) - Update examples to use English and better RAG-suitable queries - Change full dataset reference to use --max-items -1 - Adjust small model guidance about upgrading to larger models when time allows - Update top-k defaults to reflect actual default of 20 - Ensure consistent use of full model name Qwen/Qwen3-Embedding-0.6B - Reorder optimization steps, move MLX to third position - Remove incorrect chunk size tuning guidance - Change README from 'Having trouble' to 'Need best practices' * docs: Address all configuration guide feedback - Fix grammar: 'If time is not a constraint' instead of 'time expense is not large' - Highlight Qwen3-Embedding-0.6B performance (nearly OpenAI API level) - Add OpenAI quick start section with configuration example - Fold Cloud vs Local trade-offs into collapsible section - Update HNSW as 'default and recommended for extreme low storage' - Add DiskANN beta warning and explain PQ+rerank architecture - Expand Ollama models: add qwen3:0.6b, 4b, 7b variants - Note OpenAI as current default but recommend Ollama switch - Add 'need to install extra software' warning for Ollama - Remove incorrect latency numbers from search-complexity recommendations * docs: add a link
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1.5 KiB
Markdown
23 lines
1.5 KiB
Markdown
# ✨ Detailed Features
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## 🔥 Core Features
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- **🔄 Real-time Embeddings** - Eliminate heavy embedding storage with dynamic computation using optimized ZMQ servers and highly optimized search paradigm (overlapping and batching) with highly optimized embedding engine
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- **📈 Scalable Architecture** - Handles millions of documents on consumer hardware; the larger your dataset, the more LEANN can save
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- **🎯 Graph Pruning** - Advanced techniques to minimize the storage overhead of vector search to a limited footprint
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- **🏗️ Pluggable Backends** - HNSW/FAISS (default), with optional DiskANN for large-scale deployments
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## 🛠️ Technical Highlights
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- **🔄 Recompute Mode** - Highest accuracy scenarios while eliminating vector storage overhead
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- **⚡ Zero-copy Operations** - Minimize IPC overhead by transferring distances instead of embeddings
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- **🚀 High-throughput Embedding Pipeline** - Optimized batched processing for maximum efficiency
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- **🎯 Two-level Search** - Novel coarse-to-fine search overlap for accelerated query processing (optional)
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- **💾 Memory-mapped Indices** - Fast startup with raw text mapping to reduce memory overhead
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- **🚀 MLX Support** - Ultra-fast recompute/build with quantized embedding models, accelerating building and search ([minimal example](../examples/mlx_demo.py))
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## 🎨 Developer Experience
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- **Simple Python API** - Get started in minutes
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- **Extensible backend system** - Easy to add new algorithms
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- **Comprehensive examples** - From basic usage to production deployment
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