docs: highlight diskann readiness and add performance comparison

docs/configuration-guide.md

@@ -86,16 +86,29 @@ For immediate testing without local model downloads:
 ```
 
 ### DiskANN
-**Best for**: Large datasets (> 10M vectors, 10GB+ index size) - **⚠️ Beta version, still in active development**
-- Uses Product Quantization (PQ) for coarse filtering during graph traversal
-- Novel approach: stores only PQ codes, performs rerank with exact computation in final step
-- Implements a corner case of double-queue: prunes all neighbors and recomputes at the end
+**Best for**: Performance-critical applications and large datasets - **Production-ready with automatic graph partitioning**
+
+**How it works:**
+- **Product Quantization (PQ) + Real-time Reranking**: Uses compressed PQ codes for fast graph traversal, then recomputes exact embeddings for final candidates
+- **Automatic Graph Partitioning**: When `is_recompute=True`, automatically partitions large indices and safely removes redundant files to save storage
+- **Superior Speed-Accuracy Trade-off**: Faster search than HNSW while maintaining high accuracy
+
+**Trade-offs compared to HNSW:**
+- ✅ **Faster search latency** (typically 2-8x speedup)
+- ✅ **Better scaling** for large datasets
+- ✅ **Smart storage management** with automatic partitioning
+- ⚠️ **Slightly larger index size** due to PQ tables and graph metadata
 
 ```bash
-# For billion-scale deployments
+# Recommended for most use cases
+--backend-name diskann --graph-degree 32 --build-complexity 64
+
+# For large-scale deployments
 --backend-name diskann --graph-degree 64 --build-complexity 128
 ```
 
+**Performance Benchmark**: Run `python benchmarks/diskann_vs_hnsw_speed_comparison.py` to compare DiskANN and HNSW on your system.
+
 ## LLM Selection: Engine and Model Comparison
 
 ### LLM Engines