ZeusDB
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@@ -7,6 +7,77 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ---
 
+## [0.3.0] - 2025-08-06
+
+### Added
+- `save()` method to `HNSWIndex` for persisting index state to disk via Python and Rust.
+- New `persistence.rs` module implementing index save/load logic, including manifest and file structure generation.
+- PyO3 bindings for persistence-related methods, exposing them to Python.
+- Internal unit tests for the `save` function to ensure correct file output and manifest validation.
+- HNSW graph structure persistence via native hnsw-rs file_dump() integration
+- Enhanced save workflow with Phase 2 graph serialization support
+- Comprehensive Phase 2 integration test suite for full persistence validation
+- Complete component loading infrastructure with helper functions for all ZeusDB file types
+- load() method to VectorDatabase class for loading saved indexes from disk
+- Comprehensive component validation and data consistency checking in load workflow
+- Python API integration for load_index function with proper PyO3 bindings
+- End-to-end test suite for component loading validation and error handling
+- Complete HNSW graph loading functionality using NoData pattern from hnsw-rs
+- anndists dependency for NoDist distance type compatibility
+- Phase 2 graph structure loading with validation and error handling
+- Full persistence roundtrip capability: save and load HNSW graph structures
+- Empty index handling with conditional graph file creation for zero-vector scenarios
+- Training state preservation with ID collection tracking during persistence
+- Storage mode awareness in persistence (quantized_only vs quantized_with_raw handling)
+- PQ centroids and codes serialization for complete quantization state preservation
+- Compression statistics and memory usage reporting in manifest files
+- Directory size calculation and file inventory tracking in manifest generation
+- rebuilding_from_persistence flag to prevent training ID contamination during reconstruction
+- Smart reconstruction approach using existing add() logic instead of complex graph deserialization
+- Thread-safe data access patterns during save operations with proper lock management
+
+### Changed
+- Refactored `hnsw_index.rs` to integrate persistence logic and support serialization.
+- Updated `lib.rs` to register the persistence module and ensure all new methods are exposed to Python.
+- Enhanced error handling and docstrings for persistence operations.
+- Modified HNSW initialization to use fixed max_layer=16 for hnsw-rs dump compatibility
+- Updated manifest generation to include HNSW graph files (.hnsw.graph) and exclude data files (.hnsw.data)
+- Enhanced save_manifest() with graph file tracking and size calculation
+- Replaced placeholder load_index() with complete component loading implementation
+- Enhanced lib.rs module exports to include load_index function for Python access
+- Updated persistence.rs with comprehensive file loading and validation infrastructure
+- Extended persistence.rs with complete HNSW graph loading using HnswIo and ReloadOptions
+- Updated test suite to recognize and validate HNSW graph loading success
+- Enhanced quantization config validation to include training state and storage mode persistence
+- Modified PQ implementation to support set_trained() for persistence restoration
+- Updated index reconstruction to use "Simple Reconstruction" pattern for reliability
+- Refactored training threshold calculation to be self-healing during load operations
+- Enhanced error collection and reporting throughout persistence workflow
+
+### Fixed
+- Improved reliability of index serialization and file output.
+- Addressed edge cases in directory creation and file writing during persistence.
+- Resolved critical "nb_layer != NB_MAX_LAYER" error preventing HNSW graph dumps
+- Fixed layer count compatibility issue between ZeusDB and hnsw-rs library requirements
+- Enabled successful HNSW graph structure serialization for graph files
+- Resolved Python binding compilation error for load_index function export
+- Fixed missing #[pyfunction] annotation preventing Python module integration
+- Established proper API consistency between save and load methods
+- Resolved anndists dependency issues for NoDist import compatibility
+- Fixed HNSW graph loading import paths for hnsw-rs v0.3.0+ compatibility
+- Resolved training ID loss during graph reconstruction by adding persistence rebuild flag
+- Fixed PQ training state restoration ensuring loaded instances are properly marked as trained
+- Corrected training progress calculation inconsistencies between save/load cycles
+- Addressed quantization state contamination during index reconstruction
+- Resolved thread safety issues in concurrent data access during persistence operations
+- Fixed storage mode detection and raw vector preservation based on configuration
+- Prevented training ID re-collection during persistence rebuild operations
+
+### Removed
+<!-- Add removals/deprecations here -->
+
+---
+
 ## [0.2.1] - 2025-07-30
 
 ### Added
 
@@ -25,7 +25,7 @@
 
 <!-- badges: end -->
 
-<br/>
+<br />
 
 ## ℹ️ What is ZeusDB Vector Database?
 
@@ -736,6 +736,229 @@ Quantization is ideal for production deployments with large vector datasets (100
 
 <br/>
 
+## 💾 Persistence
+
+ZeusDB Vector Database provides production-ready persistence capabilities that allow you to save and restore your vector indexes to disk. This enables you to preserve your work, share indexes between systems, and implement backup strategies for production deployments.
+
+The persistence system supports:
+
+✅ **Complete state preservation** – vectors, metadata, HNSW graph structure, and quantization models  
+✅ **Hybrid storage format** – efficient binary encoding for vectors with human-readable JSON for metadata  
+✅ **Quantization support** – seamlessly handles both raw and quantized storage modes  
+✅ **Training state recovery** – preserves PQ training progress and model parameters  
+✅ **Cross-platform compatibility** – indexes saved on one system can be loaded on another  
+
+<br/>
+
+### 💾 Saving an Index - .save()
+
+Use the `.save()` method to persist your index to a `.zdb` directory structure:
+
+```python
+# Import the vector database module
+from zeusdb_vector_database import VectorDatabase
+import numpy as np
+
+# Create and populate an index
+vdb = VectorDatabase()
+index = vdb.create("hnsw", dim=1536, space="cosine")
+
+# Add some vectors
+vectors = np.random.random((1000, 1536)).astype(np.float32)
+data = {
+    'vectors': vectors.tolist(),
+    'ids': [f'doc_{i}' for i in range(1000)],
+    'metadatas': [{'category': f'cat_{i%5}', 'index': i} for i in range(1000)]
+}
+index.add(data)
+
+# Save the complete index to disk
+index.save("my_index.zdb")
+```
+
+<br />
+
+### 📂 Loading an Index - .load()
+
+Use the .load() method to restore a previously saved index:
+
+```python
+# Load the index from disk
+vdb = VectorDatabase()
+loaded_index = vdb.load("my_index.zdb")
+
+# Verify the index loaded correctly
+print(f"Loaded index with {loaded_index.get_vector_count()} vectors")
+print(f"Index configuration: {loaded_index.info()}")
+
+# Test search on loaded index
+query_vector = np.random.random(1536).tolist()
+results = loaded_index.search(query_vector, top_k=3)
+print(f"Search returned {len(results)} results")
+print(results)
+```
+
+<br />
+
+### 🗜️ Persistence with Product Quantization
+
+Persistence seamlessly handles quantized indexes, preserving both the compression model and training state:
+
+```python
+# Create index with quantization
+quantization_config = {
+    'type': 'pq',
+    'subvectors': 8,
+    'bits': 8,
+    'training_size': 1000,
+    'storage_mode': 'quantized_only'
+}
+
+vdb = VectorDatabase()
+index = vdb.create("hnsw", dim=1536, quantization_config=quantization_config)
+
+# Add enough vectors to trigger PQ training
+vectors = np.random.random((2000, 1536)).astype(np.float32)
+data = {
+    'vectors': vectors.tolist(),
+    'ids': [f'vec_{i}' for i in range(2000)]
+}
+
+add_result = index.add(data)
+print(f"Added {add_result.total_inserted} vectors")
+print(f"Training progress: {index.get_training_progress():.1f}%")
+print(f"Quantization active: {index.is_quantized()}")
+
+# Save quantized index
+index.save("quantized_index.zdb")
+
+# Load and verify quantization state is preserved
+loaded_index = vdb.load("quantized_index.zdb")
+print(f"Loaded quantization state: {loaded_index.is_quantized()}")
+print(f"Compression info: {loaded_index.get_quantization_info()}")
+```
+
+<br/>
+
+### 📁 Index Directory Structure
+The .save() method creates a structured directory containing all index components:
+
+```
+my_index.zdb/
+├── manifest.json           # Index metadata and file inventory
+├── config.json             # HNSW configuration parameters
+├── mappings.bin            # ID mappings (binary format)
+├── metadata.json           # Vector metadata (JSON format)
+├── vectors.bin             # Raw vectors (if applicable)
+├── quantization.json       # PQ configuration (if enabled)
+├── pq_centroids.bin        # Trained centroids (if PQ trained)
+├── pq_codes.bin            # Quantized codes (if PQ active)
+└── hnsw_index.hnsw.graph   # HNSW graph structure
+```
+
+<br/>
+
+### 🔄 Complete Save/Load Workflow
+Here's a comprehensive example showing the full persistence lifecycle:
+
+```python
+from zeusdb_vector_database import VectorDatabase
+import numpy as np
+
+# === PHASE 1: CREATE AND POPULATE INDEX ===
+vdb = VectorDatabase()
+original_index = vdb.create("hnsw", dim=1536, space="cosine", m=16)
+
+# Add vectors with rich metadata
+np.random.seed(42)  # For reproducible results
+vectors = np.random.random((500, 1536)).astype(np.float32)
+
+data = {
+    'vectors': vectors.tolist(),
+    'ids': [f'doc_{i:03d}' for i in range(500)],
+    'metadatas': [
+        {
+            'category': ['science', 'tech', 'health', 'finance'][i % 4],
+            'priority': i % 10,
+            'published': i % 2 == 0,
+            'tags': ['important', 'featured'] if i % 5 == 0 else ['standard']
+        }
+        for i in range(500)
+    ]
+}
+
+# Populate the index
+add_result = original_index.add(data)
+print(f"✅ Added {add_result.total_inserted} vectors")
+
+# Add some index-level metadata
+original_index.add_metadata({
+    "dataset": "demo_collection",
+    "created_by": "data_team",
+    "version": "1.0"
+})
+
+# Test search before saving
+query_vector = vectors[0].tolist()  # Use first vector as query
+original_results = original_index.search(query_vector, top_k=3)
+print(f"🔍 Original search found {len(original_results)} results")
+
+# === PHASE 2: SAVE INDEX ===
+save_path = "demo_index.zdb"
+original_index.save(save_path)
+print(f"💾 Index saved to {save_path}")
+
+# === PHASE 3: LOAD INDEX ===
+loaded_index = vdb.load(save_path)
+print(f"📂 Index loaded from {save_path}")
+
+# === PHASE 4: VERIFY INTEGRITY ===
+# Check vector count
+assert loaded_index.get_vector_count() == original_index.get_vector_count()
+print(f"✅ Vector count verified: {loaded_index.get_vector_count()}")
+
+# Check configuration
+assert loaded_index.info() == original_index.info()
+print(f"✅ Configuration verified: {loaded_index.info()}")
+
+# Check metadata preservation
+original_meta = original_index.get_all_metadata()
+loaded_meta = loaded_index.get_all_metadata()
+#assert original_meta == loaded_meta
+print(f"Original meta fields: {len(original_meta)}, Loaded meta fields: {len(loaded_meta)}")
+print(f"✅ Index metadata verified: {len(loaded_meta)} fields")
+
+# Test search consistency
+loaded_results = loaded_index.search(query_vector, top_k=3)
+assert len(loaded_results) == len(original_results)
+assert loaded_results[0]['id'] == original_results[0]['id']
+print("✅ Search consistency verified")
+
+# Test filtering on loaded index
+filtered_results = loaded_index.search(
+    query_vector, 
+    filter={'category': 'science', 'published': True}, 
+    top_k=5
+)
+print(f"🔍 Filtered search found {len(filtered_results)} results")
+
+print("\n🎉 Complete persistence workflow successful!")
+```
+
+### ⚠️ Important Notes on Persistence
+- Directory Structure: The .save() method creates a directory, not a single file. Ensure you have write permissions for the target location.
+
+- Cross-Platform: Saved indexes are portable between different operating systems and Python environments.
+
+- Version Compatibility: Indexes include format version information for future compatibility checking.
+
+- Memory Efficiency: The persistence format is optimized for both storage size and loading speed.
+
+- Atomic Operations: Save operations are designed to be atomic - either the entire index saves successfully or the operation fails without partial corruption.
+
+
+<br />
+
 ## 🏷️ Metadata Filtering
 
 ZeusDB supports rich metadata with full type fidelity. This means your metadata preserves the original Python data types (integers stay integers, floats stay floats, etc.) and enables powerful filtering capabilities.