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2025-07-15 22:28:59 -07:00
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@@ -70,10 +70,11 @@ uv sync
from leann.api import LeannBuilder, LeannSearcher
# 1. Build index (no embeddings stored!)
builder = LeannBuilder(backend_name="diskann")
builder = LeannBuilder(backend_name="hnsw")
builder.add_text("Python is a powerful programming language")
builder.add_text("Machine learning transforms industries")
builder.add_text("Neural networks process complex data")
builder.add_text("Leann is a great storage saving engine for RAG on your macbook")
builder.build_index("knowledge.leann")
# 2. Search with real-time embeddings
@@ -320,24 +321,18 @@ python examples/compare_faiss_vs_leann.py
| Metric | Faiss HNSW | LEANN HNSW | **Improvement** |
|--------|------------|-------------|-----------------|
| **Peak Memory** | 887.0 MB | 618.2 MB | **1.4x less** (268.8 MB saved) |
| **Storage Size** | 5.5 MB | 0.5 MB | **11.4x smaller** (5.0 MB saved) |
#### 📈 Key Takeaways
- **🧠 Memory Efficiency**: LEANN uses **30% less memory** during index building and querying
- **💾 Storage Optimization**: LEANN requires **91% less storage** for the same dataset
- **🔄 On-demand Computing**: Storage savings come from computing embeddings at query time instead of pre-storing them
- **⚖️ Fair Comparison**: Both systems tested on identical hardware with the same 2,573 document dataset
- **⚖️ Fair Comparison**: Both systems tested on identical hardware with the same 2,573 document dataset and the same embedding model and chunk method
> **Note**: Results may vary based on dataset size, hardware configuration, and query patterns. The comparison excludes text storage to focus purely on index structures.
### Run the comparison
```bash
python examples/compare_faiss_vs_leann.py
```
*Benchmark results obtained on Apple Silicon with consistent environmental conditions*
@@ -376,13 +371,13 @@ The script will print the recall and search time for each query, followed by the
### Storage Usage Comparison
| System | DPR(2.1M docs) | RPJ-wiki(60M docs) | Chat history() |
| System | DPR(2.1M docs) | RPJ-wiki(60M docs) | Chat history(5K messages) |
| --------------------- | ---------------- | ---------------- | ---------------- |
| Traditional Vector DB | 3.8 GB | 201 GB | TBD |
| **LEANN** | **324 MB** | **6 GB** | **TBD** |
| **Reduction** | **91% smaller** | **97% smaller** | **TBD** |
| Traditional Vector DB | 3.8 GB | 201 GB | **22.8 MB** |
| **LEANN** | **324 MB** | **6 GB** | **0.78 MB** |
| **Reduction** | **91% smaller** | **97% smaller** | **97% smaller** |
### Memory Usage Comparison
<!-- ### Memory Usage Comparison
| System j | DPR(2M docs) | RPJ-wiki(60M docs) | Chat history() |
| --------------------- | ---------------- | ---------------- | ---------------- |
@@ -395,7 +390,7 @@ The script will print the recall and search time for each query, followed by the
| Backend | Index Size | Query Time | Recall@3 |
| ------------------- | ---------- | ---------- | --------- |
| DiskANN | 1M docs | xms | 0.95 |
| HNSW | 1M docs | xms | 0.95 |
| HNSW | 1M docs | xms | 0.95 | -->
*Benchmarks run on Apple M3 Pro 36 GB*
@@ -406,14 +401,6 @@ The script will print the recall and search time for each query, followed by the
<img src="asset/arch.png" alt="LEANN Architecture" width="800">
</p>
### Key Components
1. **🧠 Embedding Engine**: Real-time transformer inference with caching
2. **📊 Graph Index**: Memory-efficient navigation structures
3. **🔄 Search Coordinator**: Orchestrates embedding + graph search
4. **⚡ Backend Adapters**: Pluggable algorithm implementations
## 🔬 Paper
If you find Leann useful, please cite: