Restore working code

Cargo.toml

@@ -1,8 +1,8 @@
 [package]
 name = "herodb"
 version = "0.0.1"
-authors = ["Pin Fang <fpfangpin@hotmail.com>"]
-edition = "2021"
+authors = ["ThreeFold Tech NV"]
+edition = "2024"
 
 [dependencies]
 anyhow = "1.0.59"
@@ -24,18 +24,7 @@ age = "0.10"
 secrecy = "0.8"
 ed25519-dalek = "2"
 base64 = "0.22"
-# Lance vector database dependencies
-lance = "0.33"
-lance-index = "0.33"
-lance-linalg = "0.33"
-# Use Arrow version compatible with Lance 0.33
-arrow = "55.2"
-arrow-array = "55.2"
-arrow-schema = "55.2"
-parquet = "55.2"
-uuid = { version = "1.10", features = ["v4"] }
-reqwest = { version = "0.11", features = ["json"] }
-image = "0.25"
+jsonrpsee = { version = "0.26.0", features = ["http-client", "ws-client", "server", "macros"] }
 
 [dev-dependencies]
 redis = { version = "0.24", features = ["aio", "tokio-comp"] }

README.md

@@ -47,13 +47,13 @@ You can start HeroDB with different backends and encryption options:
 #### `redb` with Encryption
 
 ```bash
-./target/release/herodb --dir /tmp/herodb_encrypted --port 6379 --encrypt --key mysecretkey
+./target/release/herodb --dir /tmp/herodb_encrypted --port 6379 --encrypt --encryption_key mysecretkey
 ```
 
 #### `sled` with Encryption
 
 ```bash
-./target/release/herodb --dir /tmp/herodb_sled_encrypted --port 6379 --sled --encrypt --key mysecretkey
+./target/release/herodb --dir /tmp/herodb_sled_encrypted --port 6379 --sled --encrypt --encryption_key mysecretkey
 ```
 
 ## Usage with Redis Clients

docs/lance_vector_db.md

@@ -1,454 +0,0 @@
-# Lance Vector Database Operations
-
-HeroDB includes a powerful vector database integration using Lance, enabling high-performance vector storage, search, and multimodal data management. By default, it uses Ollama for local text embeddings, with support for custom external embedding services.
-
-## Overview
-
-The Lance vector database integration provides:
-
-- **High-performance vector storage** using Lance's columnar format
-- **Local Ollama integration** for text embeddings (default, no external dependencies)
-- **Custom embedding service support** for advanced use cases
-- **Text embedding support** (images via custom services)
-- **Vector similarity search** with configurable parameters
-- **Scalable indexing** with IVF_PQ (Inverted File with Product Quantization)
-- **Redis-compatible command interface**
-
-## Architecture
-
-```
-┌─────────────────┐    ┌──────────────────┐    ┌─────────────────┐
-│   HeroDB        │    │  External        │    │   Lance         │
-│   Redis Server  │◄──►│  Embedding       │    │   Vector Store  │
-│                 │    │  Service         │    │                 │
-└─────────────────┘    └──────────────────┘    └─────────────────┘
-         │                       │                       │
-         │                       │                       │
-    Redis Protocol          HTTP API              Arrow/Parquet
-    Commands                JSON Requests         Columnar Storage
-```
-
-### Key Components
-
-1. **Lance Store**: High-performance columnar vector storage
-2. **Ollama Integration**: Local embedding service (default)
-3. **Custom Embedding Service**: Optional HTTP API for advanced use cases
-4. **Redis Command Interface**: Familiar Redis-style commands
-5. **Arrow Schema**: Flexible schema definition for metadata
-
-## Configuration
-
-### Default Setup (Ollama)
-
-HeroDB uses Ollama by default for text embeddings. No configuration is required if Ollama is running locally:
-
-```bash
-# Install Ollama (if not already installed)
-# Visit: https://ollama.ai
-
-# Pull the embedding model
-ollama pull nomic-embed-text
-
-# Ollama automatically runs on localhost:11434
-# HeroDB will use this by default
-```
-
-**Default Configuration:**
-- **URL**: `http://localhost:11434`
-- **Model**: `nomic-embed-text`
-- **Dimensions**: 768 (for nomic-embed-text)
-
-### Custom Embedding Service (Optional)
-
-To use a custom embedding service instead of Ollama:
-
-```bash
-# Set custom embedding service URL
-redis-cli HSET config:core:aiembed url "http://your-embedding-service:8080/embed"
-
-# Optional: Set authentication if required
-redis-cli HSET config:core:aiembed token "your-api-token"
-```
-
-### Embedding Service API Contracts
-
-#### Ollama API (Default)
-HeroDB calls Ollama using this format:
-
-```bash
-POST http://localhost:11434/api/embeddings
-Content-Type: application/json
-
-{
-  "model": "nomic-embed-text",
-  "prompt": "Your text to embed"
-}
-```
-
-Response:
-```json
-{
-  "embedding": [0.1, 0.2, 0.3, ...]
-}
-```
-
-#### Custom Service API
-Your custom embedding service should accept POST requests with this JSON format:
-
-```json
-{
-  "texts": ["text1", "text2"],           // Optional: array of texts
-  "images": ["base64_image1", "base64_image2"], // Optional: base64 encoded images
-  "model": "your-model-name"             // Optional: model specification
-}
-```
-
-And return responses in this format:
-
-```json
-{
-  "embeddings": [[0.1, 0.2, ...], [0.3, 0.4, ...]],  // Array of embedding vectors
-  "model": "model-name",                               // Model used
-  "usage": {                                          // Optional usage stats
-    "tokens": 100,
-    "requests": 2
-  }
-}
-```
-
-## Commands Reference
-
-### Dataset Management
-
-#### LANCE CREATE
-Create a new vector dataset with specified dimensions and optional schema.
-
-```bash
-LANCE CREATE <dataset> DIM <dimension> [SCHEMA field:type ...]
-```
-
-**Parameters:**
-- `dataset`: Name of the dataset
-- `dimension`: Vector dimension (e.g., 384, 768, 1536)
-- `field:type`: Optional metadata fields (string, int, float, bool)
-
-**Examples:**
-```bash
-# Create a simple dataset for 384-dimensional vectors
-LANCE CREATE documents DIM 384
-
-# Create dataset with metadata schema
-LANCE CREATE products DIM 768 SCHEMA category:string price:float available:bool
-```
-
-#### LANCE LIST
-List all available datasets.
-
-```bash
-LANCE LIST
-```
-
-**Returns:** Array of dataset names
-
-#### LANCE INFO
-Get information about a specific dataset.
-
-```bash
-LANCE INFO <dataset>
-```
-
-**Returns:** Dataset metadata including name, version, row count, and schema
-
-#### LANCE DROP
-Delete a dataset and all its data.
-
-```bash
-LANCE DROP <dataset>
-```
-
-### Data Operations
-
-#### LANCE STORE
-Store multimodal data (text/images) with automatic embedding generation.
-
-```bash
-LANCE STORE <dataset> [TEXT <text>] [IMAGE <base64>] [key value ...]
-```
-
-**Parameters:**
-- `dataset`: Target dataset name
-- `TEXT`: Text content to embed
-- `IMAGE`: Base64-encoded image to embed
-- `key value`: Metadata key-value pairs
-
-**Examples:**
-```bash
-# Store text with metadata
-LANCE STORE documents TEXT "Machine learning is transforming industries" category "AI" author "John Doe"
-
-# Store image with metadata
-LANCE STORE images IMAGE "iVBORw0KGgoAAAANSUhEUgAA..." category "nature" tags "landscape,mountains"
-
-# Store both text and image
-LANCE STORE multimodal TEXT "Beautiful sunset" IMAGE "base64data..." location "California"
-```
-
-**Returns:** Unique ID of the stored item
-
-### Search Operations
-
-#### LANCE SEARCH
-Search using a raw vector.
-
-```bash
-LANCE SEARCH <dataset> VECTOR <vector> K <k> [NPROBES <n>] [REFINE <r>]
-```
-
-**Parameters:**
-- `dataset`: Dataset to search
-- `vector`: Comma-separated vector values (e.g., "0.1,0.2,0.3")
-- `k`: Number of results to return
-- `NPROBES`: Number of partitions to search (optional)
-- `REFINE`: Refine factor for better accuracy (optional)
-
-**Example:**
-```bash
-LANCE SEARCH documents VECTOR "0.1,0.2,0.3,0.4" K 5 NPROBES 10
-```
-
-#### LANCE SEARCH.TEXT
-Search using text query (automatically embedded).
-
-```bash
-LANCE SEARCH.TEXT <dataset> <query_text> K <k> [NPROBES <n>] [REFINE <r>]
-```
-
-**Parameters:**
-- `dataset`: Dataset to search
-- `query_text`: Text query to search for
-- `k`: Number of results to return
-- `NPROBES`: Number of partitions to search (optional)
-- `REFINE`: Refine factor for better accuracy (optional)
-
-**Example:**
-```bash
-LANCE SEARCH.TEXT documents "artificial intelligence applications" K 10 NPROBES 20
-```
-
-**Returns:** Array of results with distance scores and metadata
-
-### Embedding Operations
-
-#### LANCE EMBED.TEXT
-Generate embeddings for text without storing.
-
-```bash
-LANCE EMBED.TEXT <text1> [text2] [text3] ...
-```
-
-**Example:**
-```bash
-LANCE EMBED.TEXT "Hello world" "Machine learning" "Vector database"
-```
-
-**Returns:** Array of embedding vectors
-
-### Index Management
-
-#### LANCE CREATE.INDEX
-Create a vector index for faster search performance.
-
-```bash
-LANCE CREATE.INDEX <dataset> <index_type> [PARTITIONS <n>] [SUBVECTORS <n>]
-```
-
-**Parameters:**
-- `dataset`: Dataset to index
-- `index_type`: Index type (currently supports "IVF_PQ")
-- `PARTITIONS`: Number of partitions (default: 256)
-- `SUBVECTORS`: Number of sub-vectors for PQ (default: 16)
-
-**Example:**
-```bash
-LANCE CREATE.INDEX documents IVF_PQ PARTITIONS 512 SUBVECTORS 32
-```
-
-## Usage Patterns
-
-### 1. Document Search System
-
-```bash
-# Setup
-LANCE CREATE documents DIM 384 SCHEMA title:string content:string category:string
-
-# Store documents
-LANCE STORE documents TEXT "Introduction to machine learning algorithms" title "ML Basics" category "education"
-LANCE STORE documents TEXT "Deep learning neural networks explained" title "Deep Learning" category "education"
-LANCE STORE documents TEXT "Building scalable web applications" title "Web Dev" category "programming"
-
-# Create index for better performance
-LANCE CREATE.INDEX documents IVF_PQ PARTITIONS 256
-
-# Search
-LANCE SEARCH.TEXT documents "neural networks" K 5
-```
-
-### 2. Image Similarity Search
-
-```bash
-# Setup
-LANCE CREATE images DIM 512 SCHEMA filename:string tags:string
-
-# Store images (base64 encoded)
-LANCE STORE images IMAGE "iVBORw0KGgoAAAANSUhEUgAA..." filename "sunset.jpg" tags "nature,landscape"
-LANCE STORE images IMAGE "iVBORw0KGgoAAAANSUhEUgBB..." filename "city.jpg" tags "urban,architecture"
-
-# Search by image
-LANCE STORE temp_search IMAGE "query_image_base64..."
-# Then use the returned ID to get embedding and search
-```
-
-### 3. Multimodal Content Management
-
-```bash
-# Setup
-LANCE CREATE content DIM 768 SCHEMA type:string source:string
-
-# Store mixed content
-LANCE STORE content TEXT "Product description for smartphone" type "product" source "catalog"
-LANCE STORE content IMAGE "product_image_base64..." type "product_image" source "catalog"
-
-# Search across all content types
-LANCE SEARCH.TEXT content "smartphone features" K 10
-```
-
-## Performance Considerations
-
-### Vector Dimensions
-- **384**: Good for general text (e.g., sentence-transformers)
-- **768**: Standard for BERT-like models
-- **1536**: OpenAI text-embedding-ada-002
-- **Higher dimensions**: Better accuracy but slower search
-
-### Index Configuration
-- **More partitions**: Better for larger datasets (>100K vectors)
-- **More sub-vectors**: Better compression but slower search
-- **NPROBES**: Higher values = better accuracy, slower search
-
-### Best Practices
-
-1. **Create indexes** for datasets with >1000 vectors
-2. **Use appropriate dimensions** based on your embedding model
-3. **Configure NPROBES** based on accuracy vs speed requirements
-4. **Batch operations** when possible for better performance
-5. **Monitor embedding service** response times and rate limits
-
-## Error Handling
-
-Common error scenarios and solutions:
-
-### Embedding Service Errors
-```bash
-# Error: Embedding service not configured
-ERR Embedding service URL not configured. Set it with: HSET config:core:aiembed url <YOUR_EMBEDDING_SERVICE_URL>
-
-# Error: Service unavailable
-ERR Embedding service returned error 404 Not Found
-```
-
-**Solution:** Ensure embedding service is running and URL is correct.
-
-### Dataset Errors
-```bash
-# Error: Dataset doesn't exist
-ERR Dataset 'mydata' does not exist
-
-# Error: Dimension mismatch
-ERR Vector dimension mismatch: expected 384, got 768
-```
-
-**Solution:** Create dataset first or check vector dimensions.
-
-### Search Errors
-```bash
-# Error: Invalid vector format
-ERR Invalid vector format
-
-# Error: No index available
-ERR No index available for fast search
-```
-
-**Solution:** Check vector format or create an index.
-
-## Integration Examples
-
-### With Python
-```python
-import redis
-import json
-
-r = redis.Redis(host='localhost', port=6379)
-
-# Create dataset
-r.execute_command('LANCE', 'CREATE', 'docs', 'DIM', '384')
-
-# Store document
-result = r.execute_command('LANCE', 'STORE', 'docs', 
-                          'TEXT', 'Machine learning tutorial',
-                          'category', 'education')
-print(f"Stored with ID: {result}")
-
-# Search
-results = r.execute_command('LANCE', 'SEARCH.TEXT', 'docs', 
-                           'machine learning', 'K', '5')
-print(f"Search results: {results}")
-```
-
-### With Node.js
-```javascript
-const redis = require('redis');
-const client = redis.createClient();
-
-// Create dataset
-await client.sendCommand(['LANCE', 'CREATE', 'docs', 'DIM', '384']);
-
-// Store document
-const id = await client.sendCommand(['LANCE', 'STORE', 'docs', 
-                                   'TEXT', 'Deep learning guide',
-                                   'category', 'AI']);
-
-// Search
-const results = await client.sendCommand(['LANCE', 'SEARCH.TEXT', 'docs',
-                                        'deep learning', 'K', '10']);
-```
-
-## Monitoring and Maintenance
-
-### Health Checks
-```bash
-# Check if Lance store is available
-LANCE LIST
-
-# Check dataset health
-LANCE INFO mydataset
-
-# Test embedding service
-LANCE EMBED.TEXT "test"
-```
-
-### Maintenance Operations
-```bash
-# Backup: Use standard Redis backup procedures
-# The Lance data is stored separately in the data directory
-
-# Cleanup: Remove unused datasets
-LANCE DROP old_dataset
-
-# Reindex: Drop and recreate indexes if needed
-LANCE DROP dataset_name
-LANCE CREATE dataset_name DIM 384
-# Re-import data
-LANCE CREATE.INDEX dataset_name IVF_PQ
-```
-
-This integration provides a powerful foundation for building AI-powered applications with vector search capabilities while maintaining the familiar Redis interface.

examples/README.md

@@ -1,191 +1,6 @@
-# HeroDB Examples
+# HeroDB Tantivy Search Examples
 
-This directory contains examples demonstrating HeroDB's capabilities including full-text search powered by Tantivy and vector database operations using Lance.
-
-## Available Examples
-
-1. **[Tantivy Search Demo](#tantivy-search-demo-bash-script)** - Full-text search capabilities
-2. **[Lance Vector Database Demo](#lance-vector-database-demo-bash-script)** - Vector database and AI operations
-3. **[AGE Encryption Demo](age_bash_demo.sh)** - Cryptographic operations
-4. **[Simple Demo](simple_demo.sh)** - Basic Redis operations
-
----
-
-## Lance Vector Database Demo (Bash Script)
-
-### Overview
-The `lance_vector_demo.sh` script provides a comprehensive demonstration of HeroDB's vector database capabilities using Lance. It showcases vector storage, similarity search, multimodal data handling, and AI-powered operations with external embedding services.
-
-### Prerequisites
-1. **HeroDB Server**: The server must be running (default port 6379)
-2. **Redis CLI**: The `redis-cli` tool must be installed and available in your PATH
-3. **Embedding Service** (optional): For full functionality, set up an external embedding service
-
-### Running the Demo
-
-#### Step 1: Start HeroDB Server
-```bash
-# From the project root directory
-cargo run -- --dir ./test_data --port 6379
-```
-
-#### Step 2: Run the Demo (in a new terminal)
-```bash
-# From the project root directory
-./examples/lance_vector_demo.sh
-```
-
-### What the Demo Covers
-
-The script demonstrates comprehensive vector database operations:
-
-1. **Dataset Management**
-   - Creating vector datasets with custom dimensions
-   - Defining schemas with metadata fields
-   - Listing and inspecting datasets
-   - Dataset information and statistics
-
-2. **Embedding Operations**
-   - Text embedding generation via external services
-   - Multimodal embedding support (text + images)
-   - Batch embedding operations
-
-3. **Data Storage**
-   - Storing text documents with automatic embedding
-   - Storing images with metadata
-   - Multimodal content storage
-   - Rich metadata support
-
-4. **Vector Search**
-   - Similarity search with raw vectors
-   - Text-based semantic search
-   - Configurable search parameters (K, NPROBES, REFINE)
-   - Cross-modal search capabilities
-
-5. **Index Management**
-   - Creating IVF_PQ indexes for performance
-   - Custom index parameters
-   - Performance optimization
-
-6. **Advanced Features**
-   - Error handling and recovery
-   - Performance testing concepts
-   - Monitoring and maintenance
-   - Cleanup operations
-
-### Key Lance Commands Demonstrated
-
-#### Dataset Management
-```bash
-# Create vector dataset
-LANCE CREATE documents DIM 384
-
-# Create dataset with schema
-LANCE CREATE products DIM 768 SCHEMA category:string price:float available:bool
-
-# List datasets
-LANCE LIST
-
-# Get dataset information
-LANCE INFO documents
-```
-
-#### Data Operations
-```bash
-# Store text with metadata
-LANCE STORE documents TEXT "Machine learning tutorial" category "education" author "John Doe"
-
-# Store image with metadata
-LANCE STORE images IMAGE "base64_encoded_image..." filename "photo.jpg" tags "nature,landscape"
-
-# Store multimodal content
-LANCE STORE content TEXT "Product description" IMAGE "base64_image..." type "product"
-```
-
-#### Search Operations
-```bash
-# Search with raw vector
-LANCE SEARCH documents VECTOR "0.1,0.2,0.3,0.4" K 5
-
-# Semantic text search
-LANCE SEARCH.TEXT documents "artificial intelligence" K 10 NPROBES 20
-
-# Generate embeddings
-LANCE EMBED.TEXT "Hello world" "Machine learning"
-```
-
-#### Index Management
-```bash
-# Create performance index
-LANCE CREATE.INDEX documents IVF_PQ PARTITIONS 256 SUBVECTORS 16
-
-# Drop dataset
-LANCE DROP old_dataset
-```
-
-### Configuration
-
-#### Setting Up Embedding Service
-```bash
-# Configure embedding service URL
-redis-cli HSET config:core:aiembed url "http://your-embedding-service:8080/embed"
-
-# Optional: Set authentication token
-redis-cli HSET config:core:aiembed token "your-api-token"
-```
-
-#### Embedding Service API
-Your embedding service should accept POST requests:
-```json
-{
-  "texts": ["text1", "text2"],
-  "images": ["base64_image1", "base64_image2"],
-  "model": "your-model-name"
-}
-```
-
-And return responses:
-```json
-{
-  "embeddings": [[0.1, 0.2, ...], [0.3, 0.4, ...]],
-  "model": "model-name",
-  "usage": {"tokens": 100, "requests": 2}
-}
-```
-
-### Interactive Features
-
-The demo script includes:
-- **Colored output** for better readability
-- **Step-by-step execution** with explanations
-- **Error handling** demonstrations
-- **Automatic cleanup** options
-- **Performance testing** concepts
-- **Real-world usage** examples
-
-### Use Cases Demonstrated
-
-1. **Document Search System**
-   - Semantic document retrieval
-   - Metadata filtering
-   - Relevance ranking
-
-2. **Image Similarity Search**
-   - Visual content matching
-   - Tag-based filtering
-   - Multimodal queries
-
-3. **Product Recommendations**
-   - Feature-based similarity
-   - Category filtering
-   - Price range queries
-
-4. **Content Management**
-   - Mixed media storage
-   - Cross-modal search
-   - Rich metadata support
-
----
+This directory contains examples demonstrating HeroDB's full-text search capabilities powered by Tantivy.
 
 ## Tantivy Search Demo (Bash Script)
 

examples/lance_vector_demo.sh

@@ -1,426 +0,0 @@
-#!/bin/bash
-
-# Lance Vector Database Demo Script
-# This script demonstrates all Lance vector database operations in HeroDB
-
-set -e  # Exit on any error
-
-# Configuration
-REDIS_HOST="localhost"
-REDIS_PORT="6379"
-REDIS_CLI="redis-cli -h $REDIS_HOST -p $REDIS_PORT"
-
-# Colors for output
-RED='\033[0;31m'
-GREEN='\033[0;32m'
-YELLOW='\033[1;33m'
-BLUE='\033[0;34m'
-NC='\033[0m' # No Color
-
-# Helper functions
-log_info() {
-    echo -e "${BLUE}[INFO]${NC} $1"
-}
-
-log_success() {
-    echo -e "${GREEN}[SUCCESS]${NC} $1"
-}
-
-log_warning() {
-    echo -e "${YELLOW}[WARNING]${NC} $1"
-}
-
-log_error() {
-    echo -e "${RED}[ERROR]${NC} $1"
-}
-
-execute_command() {
-    local cmd="$1"
-    local description="$2"
-    
-    echo
-    log_info "Executing: $description"
-    echo "Command: $cmd"
-    
-    if result=$($cmd 2>&1); then
-        log_success "Result: $result"
-    else
-        log_error "Failed: $result"
-        return 1
-    fi
-}
-
-# Check if HeroDB is running
-check_herodb() {
-    log_info "Checking if HeroDB is running..."
-    if ! $REDIS_CLI ping > /dev/null 2>&1; then
-        log_error "HeroDB is not running. Please start it first:"
-        echo "  cargo run -- --dir ./test_data --port $REDIS_PORT"
-        exit 1
-    fi
-    log_success "HeroDB is running"
-}
-
-# Setup embedding service configuration
-setup_embedding_service() {
-    log_info "Setting up embedding service configuration..."
-    
-    # Note: This is a mock URL for demonstration
-    # In production, replace with your actual embedding service
-    execute_command \
-        "$REDIS_CLI HSET config:core:aiembed url 'http://localhost:8080/embed'" \
-        "Configure embedding service URL"
-    
-    # Optional: Set authentication token
-    # execute_command \
-    #     "$REDIS_CLI HSET config:core:aiembed token 'your-api-token'" \
-    #     "Configure embedding service token"
-    
-    log_warning "Note: Embedding service at http://localhost:8080/embed is not running."
-    log_warning "Some operations will fail, but this demonstrates the command structure."
-}
-
-# Dataset Management Operations
-demo_dataset_management() {
-    echo
-    echo "=========================================="
-    echo "         DATASET MANAGEMENT DEMO"
-    echo "=========================================="
-    
-    # List datasets (should be empty initially)
-    execute_command \
-        "$REDIS_CLI LANCE LIST" \
-        "List all datasets (initially empty)"
-    
-    # Create a simple dataset
-    execute_command \
-        "$REDIS_CLI LANCE CREATE documents DIM 384" \
-        "Create a simple document dataset with 384 dimensions"
-    
-    # Create a dataset with schema
-    execute_command \
-        "$REDIS_CLI LANCE CREATE products DIM 768 SCHEMA category:string price:float available:bool description:string" \
-        "Create products dataset with custom schema"
-    
-    # Create an image dataset
-    execute_command \
-        "$REDIS_CLI LANCE CREATE images DIM 512 SCHEMA filename:string tags:string width:int height:int" \
-        "Create images dataset for multimodal content"
-    
-    # List datasets again
-    execute_command \
-        "$REDIS_CLI LANCE LIST" \
-        "List all datasets (should show 3 datasets)"
-    
-    # Get info about datasets
-    execute_command \
-        "$REDIS_CLI LANCE INFO documents" \
-        "Get information about documents dataset"
-    
-    execute_command \
-        "$REDIS_CLI LANCE INFO products" \
-        "Get information about products dataset"
-}
-
-# Embedding Operations
-demo_embedding_operations() {
-    echo
-    echo "=========================================="
-    echo "         EMBEDDING OPERATIONS DEMO"
-    echo "=========================================="
-    
-    log_warning "The following operations will fail because no embedding service is running."
-    log_warning "This demonstrates the command structure and error handling."
-    
-    # Try to embed text (will fail without embedding service)
-    execute_command \
-        "$REDIS_CLI LANCE EMBED.TEXT 'Hello world'" \
-        "Generate embedding for single text" || true
-    
-    # Try to embed multiple texts
-    execute_command \
-        "$REDIS_CLI LANCE EMBED.TEXT 'Machine learning' 'Artificial intelligence' 'Deep learning'" \
-        "Generate embeddings for multiple texts" || true
-}
-
-# Data Storage Operations
-demo_data_storage() {
-    echo
-    echo "=========================================="
-    echo "         DATA STORAGE DEMO"
-    echo "=========================================="
-    
-    log_warning "Storage operations will fail without embedding service, but show command structure."
-    
-    # Store text documents
-    execute_command \
-        "$REDIS_CLI LANCE STORE documents TEXT 'Introduction to machine learning algorithms and their applications in modern AI systems' category 'education' author 'John Doe' difficulty 'beginner'" \
-        "Store a document with text and metadata" || true
-    
-    execute_command \
-        "$REDIS_CLI LANCE STORE documents TEXT 'Deep learning neural networks for computer vision tasks' category 'research' author 'Jane Smith' difficulty 'advanced'" \
-        "Store another document" || true
-    
-    # Store product information
-    execute_command \
-        "$REDIS_CLI LANCE STORE products TEXT 'High-performance laptop with 16GB RAM and SSD storage' category 'electronics' price '1299.99' available 'true'" \
-        "Store product with text description" || true
-    
-    # Store image with metadata (using placeholder base64)
-    execute_command \
-        "$REDIS_CLI LANCE STORE images IMAGE 'iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAYAAAAfFcSJAAAADUlEQVR42mP8/5+hHgAHggJ/PchI7wAAAABJRU5ErkJggg==' filename 'sample.png' tags 'test,demo' width '1' height '1'" \
-        "Store image with metadata (1x1 pixel PNG)" || true
-    
-    # Store multimodal content
-    execute_command \
-        "$REDIS_CLI LANCE STORE images TEXT 'Beautiful sunset over mountains' IMAGE 'iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAYAAAAfFcSJAAAADUlEQVR42mP8/5+hHgAHggJ/PchI7wAAAABJRU5ErkJggg==' filename 'sunset.png' tags 'nature,landscape' location 'California'" \
-        "Store multimodal content (text + image)" || true
-}
-
-# Search Operations
-demo_search_operations() {
-    echo
-    echo "=========================================="
-    echo "         SEARCH OPERATIONS DEMO"
-    echo "=========================================="
-    
-    log_warning "Search operations will fail without data, but show command structure."
-    
-    # Search with raw vector
-    execute_command \
-        "$REDIS_CLI LANCE SEARCH documents VECTOR '0.1,0.2,0.3,0.4,0.5' K 5" \
-        "Search with raw vector (5 results)" || true
-    
-    # Search with vector and parameters
-    execute_command \
-        "$REDIS_CLI LANCE SEARCH documents VECTOR '0.1,0.2,0.3,0.4,0.5' K 10 NPROBES 20 REFINE 2" \
-        "Search with vector and advanced parameters" || true
-    
-    # Text-based search
-    execute_command \
-        "$REDIS_CLI LANCE SEARCH.TEXT documents 'machine learning algorithms' K 5" \
-        "Search using text query" || true
-    
-    # Text search with parameters
-    execute_command \
-        "$REDIS_CLI LANCE SEARCH.TEXT products 'laptop computer' K 3 NPROBES 10" \
-        "Search products using text with parameters" || true
-    
-    # Search in image dataset
-    execute_command \
-        "$REDIS_CLI LANCE SEARCH.TEXT images 'sunset landscape' K 5" \
-        "Search images using text description" || true
-}
-
-# Index Management Operations
-demo_index_management() {
-    echo
-    echo "=========================================="
-    echo "         INDEX MANAGEMENT DEMO"
-    echo "=========================================="
-    
-    # Create indexes for better search performance
-    execute_command \
-        "$REDIS_CLI LANCE CREATE.INDEX documents IVF_PQ" \
-        "Create default IVF_PQ index for documents"
-    
-    execute_command \
-        "$REDIS_CLI LANCE CREATE.INDEX products IVF_PQ PARTITIONS 512 SUBVECTORS 32" \
-        "Create IVF_PQ index with custom parameters for products"
-    
-    execute_command \
-        "$REDIS_CLI LANCE CREATE.INDEX images IVF_PQ PARTITIONS 256 SUBVECTORS 16" \
-        "Create IVF_PQ index for images dataset"
-    
-    log_success "Indexes created successfully"
-}
-
-# Advanced Usage Examples
-demo_advanced_usage() {
-    echo
-    echo "=========================================="
-    echo "         ADVANCED USAGE EXAMPLES"
-    echo "=========================================="
-    
-    # Create a specialized dataset for semantic search
-    execute_command \
-        "$REDIS_CLI LANCE CREATE semantic_search DIM 1536 SCHEMA title:string content:string url:string timestamp:string source:string" \
-        "Create dataset for semantic search with rich metadata"
-    
-    # Demonstrate batch operations concept
-    log_info "Batch operations example (would store multiple items):"
-    echo "  for doc in documents:"
-    echo "    LANCE STORE semantic_search TEXT \"\$doc_content\" title \"\$title\" url \"\$url\""
-    
-    # Show monitoring commands
-    log_info "Monitoring and maintenance commands:"
-    execute_command \
-        "$REDIS_CLI LANCE LIST" \
-        "List all datasets for monitoring"
-    
-    # Show dataset statistics
-    for dataset in documents products images semantic_search; do
-        execute_command \
-            "$REDIS_CLI LANCE INFO $dataset" \
-            "Get statistics for $dataset" || true
-    done
-}
-
-# Cleanup Operations
-demo_cleanup() {
-    echo
-    echo "=========================================="
-    echo "         CLEANUP OPERATIONS DEMO"
-    echo "=========================================="
-    
-    log_info "Demonstrating cleanup operations..."
-    
-    # Drop individual datasets
-    execute_command \
-        "$REDIS_CLI LANCE DROP semantic_search" \
-        "Drop semantic_search dataset"
-    
-    # List remaining datasets
-    execute_command \
-        "$REDIS_CLI LANCE LIST" \
-        "List remaining datasets"
-    
-    # Ask user if they want to clean up all test data
-    echo
-    read -p "Do you want to clean up all test datasets? (y/N): " -n 1 -r
-    echo
-    if [[ $REPLY =~ ^[Yy]$ ]]; then
-        execute_command \
-            "$REDIS_CLI LANCE DROP documents" \
-            "Drop documents dataset"
-        
-        execute_command \
-            "$REDIS_CLI LANCE DROP products" \
-            "Drop products dataset"
-        
-        execute_command \
-            "$REDIS_CLI LANCE DROP images" \
-            "Drop images dataset"
-        
-        execute_command \
-            "$REDIS_CLI LANCE LIST" \
-            "Verify all datasets are cleaned up"
-        
-        log_success "All test datasets cleaned up"
-    else
-        log_info "Keeping test datasets for further experimentation"
-    fi
-}
-
-# Error Handling Demo
-demo_error_handling() {
-    echo
-    echo "=========================================="
-    echo "         ERROR HANDLING DEMO"
-    echo "=========================================="
-    
-    log_info "Demonstrating various error conditions..."
-    
-    # Try to access non-existent dataset
-    execute_command \
-        "$REDIS_CLI LANCE INFO nonexistent_dataset" \
-        "Try to get info for non-existent dataset" || true
-    
-    # Try to search non-existent dataset
-    execute_command \
-        "$REDIS_CLI LANCE SEARCH nonexistent_dataset VECTOR '0.1,0.2' K 5" \
-        "Try to search non-existent dataset" || true
-    
-    # Try to drop non-existent dataset
-    execute_command \
-        "$REDIS_CLI LANCE DROP nonexistent_dataset" \
-        "Try to drop non-existent dataset" || true
-    
-    # Try invalid vector format
-    execute_command \
-        "$REDIS_CLI LANCE SEARCH documents VECTOR 'invalid,vector,format' K 5" \
-        "Try search with invalid vector format" || true
-    
-    log_info "Error handling demonstration complete"
-}
-
-# Performance Testing Demo
-demo_performance_testing() {
-    echo
-    echo "=========================================="
-    echo "         PERFORMANCE TESTING DEMO"
-    echo "=========================================="
-    
-    log_info "Creating performance test dataset..."
-    execute_command \
-        "$REDIS_CLI LANCE CREATE perf_test DIM 128 SCHEMA batch_id:string item_id:string" \
-        "Create performance test dataset"
-    
-    log_info "Performance testing would involve:"
-    echo "  1. Bulk loading thousands of vectors"
-    echo "  2. Creating indexes with different parameters"
-    echo "  3. Measuring search latency with various K values"
-    echo "  4. Testing different NPROBES settings"
-    echo "  5. Monitoring memory usage"
-    
-    log_info "Example performance test commands:"
-    echo "  # Test search speed with different parameters"
-    echo "  time redis-cli LANCE SEARCH.TEXT perf_test 'query' K 10"
-    echo "  time redis-cli LANCE SEARCH.TEXT perf_test 'query' K 10 NPROBES 50"
-    echo "  time redis-cli LANCE SEARCH.TEXT perf_test 'query' K 100 NPROBES 100"
-    
-    # Clean up performance test dataset
-    execute_command \
-        "$REDIS_CLI LANCE DROP perf_test" \
-        "Clean up performance test dataset"
-}
-
-# Main execution
-main() {
-    echo "=========================================="
-    echo "    LANCE VECTOR DATABASE DEMO SCRIPT"
-    echo "=========================================="
-    echo
-    echo "This script demonstrates all Lance vector database operations."
-    echo "Note: Some operations will fail without a running embedding service."
-    echo "This is expected and demonstrates error handling."
-    echo
-    
-    # Check prerequisites
-    check_herodb
-    
-    # Setup
-    setup_embedding_service
-    
-    # Run demos
-    demo_dataset_management
-    demo_embedding_operations
-    demo_data_storage
-    demo_search_operations
-    demo_index_management
-    demo_advanced_usage
-    demo_error_handling
-    demo_performance_testing
-    
-    # Cleanup
-    demo_cleanup
-    
-    echo
-    echo "=========================================="
-    echo "         DEMO COMPLETE"
-    echo "=========================================="
-    echo
-    log_success "Lance vector database demo completed successfully!"
-    echo
-    echo "Next steps:"
-    echo "1. Set up a real embedding service (OpenAI, Hugging Face, etc.)"
-    echo "2. Update the embedding service URL configuration"
-    echo "3. Try storing and searching real data"
-    echo "4. Experiment with different vector dimensions and index parameters"
-    echo "5. Build your AI-powered application!"
-    echo
-    echo "For more information, see docs/lance_vector_db.md"
-}
-
-# Run the demo
-main "$@"

src/cmd.rs

@@ -1,8 +1,6 @@
 use crate::{error::DBError, protocol::Protocol, server::Server};
 use tokio::time::{timeout, Duration};
 use futures::future::select_all;
-use std::sync::Arc;
-use base64::Engine;
 
 #[derive(Debug, Clone)]
 pub enum Cmd {
@@ -86,49 +84,6 @@ pub enum Cmd {
     AgeSignName(String, String),           // name, message
     AgeVerifyName(String, String, String), // name, message, signature_b64
     AgeList,
-
-    // Lance vector database commands
-    LanceCreate {
-        dataset: String,
-        dim: usize,
-        schema: Vec<(String, String)>, // field_name, field_type pairs
-    },
-    LanceStore {
-        dataset: String,
-        text: Option<String>,
-        image_base64: Option<String>,
-        metadata: std::collections::HashMap<String, String>,
-    },
-    LanceSearch {
-        dataset: String,
-        vector: Vec<f32>,
-        k: usize,
-        nprobes: Option<usize>,
-        refine_factor: Option<usize>,
-    },
-    LanceSearchText {
-        dataset: String,
-        query_text: String,
-        k: usize,
-        nprobes: Option<usize>,
-        refine_factor: Option<usize>,
-    },
-    LanceEmbedText {
-        texts: Vec<String>,
-    },
-    LanceCreateIndex {
-        dataset: String,
-        index_type: String,
-        num_partitions: Option<usize>,
-        num_sub_vectors: Option<usize>,
-    },
-    LanceList,
-    LanceDrop {
-        dataset: String,
-    },
-    LanceInfo {
-        dataset: String,
-    },
 }
 
 impl Cmd {
@@ -661,237 +616,6 @@ impl Cmd {
                                 _ => return Err(DBError(format!("unsupported AGE subcommand {:?}", cmd))),
                             }
                         }
-                        "lance" => {
-                            if cmd.len() < 2 {
-                                return Err(DBError("wrong number of arguments for LANCE".to_string()));
-                            }
-                            match cmd[1].to_lowercase().as_str() {
-                                "create" => {
-                                    if cmd.len() < 4 {
-                                        return Err(DBError("LANCE CREATE <dataset> DIM <dimension> [SCHEMA field:type ...]".to_string()));
-                                    }
-                                    let dataset = cmd[2].clone();
-                                    
-                                    // Parse DIM parameter
-                                    if cmd[3].to_lowercase() != "dim" {
-                                        return Err(DBError("Expected DIM after dataset name".to_string()));
-                                    }
-                                    if cmd.len() < 5 {
-                                        return Err(DBError("Missing dimension value".to_string()));
-                                    }
-                                    let dim = cmd[4].parse::<usize>().map_err(|_| DBError("Invalid dimension value".to_string()))?;
-                                    
-                                    // Parse optional SCHEMA
-                                    let mut schema = Vec::new();
-                                    let mut i = 5;
-                                    if i < cmd.len() && cmd[i].to_lowercase() == "schema" {
-                                        i += 1;
-                                        while i < cmd.len() {
-                                            let field_spec = &cmd[i];
-                                            let parts: Vec<&str> = field_spec.split(':').collect();
-                                            if parts.len() != 2 {
-                                                return Err(DBError("Schema fields must be in format field:type".to_string()));
-                                            }
-                                            schema.push((parts[0].to_string(), parts[1].to_string()));
-                                            i += 1;
-                                        }
-                                    }
-                                    
-                                    Cmd::LanceCreate { dataset, dim, schema }
-                                }
-                                "store" => {
-                                    if cmd.len() < 3 {
-                                        return Err(DBError("LANCE STORE <dataset> [TEXT <text>] [IMAGE <base64>] [metadata...]".to_string()));
-                                    }
-                                    let dataset = cmd[2].clone();
-                                    let mut text = None;
-                                    let mut image_base64 = None;
-                                    let mut metadata = std::collections::HashMap::new();
-                                    
-                                    let mut i = 3;
-                                    while i < cmd.len() {
-                                        match cmd[i].to_lowercase().as_str() {
-                                            "text" => {
-                                                if i + 1 >= cmd.len() {
-                                                    return Err(DBError("TEXT requires a value".to_string()));
-                                                }
-                                                text = Some(cmd[i + 1].clone());
-                                                i += 2;
-                                            }
-                                            "image" => {
-                                                if i + 1 >= cmd.len() {
-                                                    return Err(DBError("IMAGE requires a base64 value".to_string()));
-                                                }
-                                                image_base64 = Some(cmd[i + 1].clone());
-                                                i += 2;
-                                            }
-                                            _ => {
-                                                // Parse as metadata key:value
-                                                if i + 1 >= cmd.len() {
-                                                    return Err(DBError("Metadata requires key value pairs".to_string()));
-                                                }
-                                                metadata.insert(cmd[i].clone(), cmd[i + 1].clone());
-                                                i += 2;
-                                            }
-                                        }
-                                    }
-                                    
-                                    Cmd::LanceStore { dataset, text, image_base64, metadata }
-                                }
-                                "search" => {
-                                    if cmd.len() < 5 {
-                                        return Err(DBError("LANCE SEARCH <dataset> VECTOR <vector> K <k> [NPROBES <n>] [REFINE <r>]".to_string()));
-                                    }
-                                    let dataset = cmd[2].clone();
-                                    
-                                    if cmd[3].to_lowercase() != "vector" {
-                                        return Err(DBError("Expected VECTOR after dataset name".to_string()));
-                                    }
-                                    
-                                    // Parse vector - expect comma-separated floats in brackets or just comma-separated
-                                    let vector_str = &cmd[4];
-                                    let vector_str = vector_str.trim_start_matches('[').trim_end_matches(']');
-                                    let vector: Result<Vec<f32>, _> = vector_str
-                                        .split(',')
-                                        .map(|s| s.trim().parse::<f32>())
-                                        .collect();
-                                    let vector = vector.map_err(|_| DBError("Invalid vector format".to_string()))?;
-                                    
-                                    if cmd.len() < 7 || cmd[5].to_lowercase() != "k" {
-                                        return Err(DBError("Expected K after vector".to_string()));
-                                    }
-                                    let k = cmd[6].parse::<usize>().map_err(|_| DBError("Invalid K value".to_string()))?;
-                                    
-                                    let mut nprobes = None;
-                                    let mut refine_factor = None;
-                                    let mut i = 7;
-                                    while i < cmd.len() {
-                                        match cmd[i].to_lowercase().as_str() {
-                                            "nprobes" => {
-                                                if i + 1 >= cmd.len() {
-                                                    return Err(DBError("NPROBES requires a value".to_string()));
-                                                }
-                                                nprobes = Some(cmd[i + 1].parse::<usize>().map_err(|_| DBError("Invalid NPROBES value".to_string()))?);
-                                                i += 2;
-                                            }
-                                            "refine" => {
-                                                if i + 1 >= cmd.len() {
-                                                    return Err(DBError("REFINE requires a value".to_string()));
-                                                }
-                                                refine_factor = Some(cmd[i + 1].parse::<usize>().map_err(|_| DBError("Invalid REFINE value".to_string()))?);
-                                                i += 2;
-                                            }
-                                            _ => {
-                                                return Err(DBError(format!("Unknown parameter: {}", cmd[i])));
-                                            }
-                                        }
-                                    }
-                                    
-                                    Cmd::LanceSearch { dataset, vector, k, nprobes, refine_factor }
-                                }
-                                "search.text" => {
-                                    if cmd.len() < 6 {
-                                        return Err(DBError("LANCE SEARCH.TEXT <dataset> <query_text> K <k> [NPROBES <n>] [REFINE <r>]".to_string()));
-                                    }
-                                    let dataset = cmd[2].clone();
-                                    let query_text = cmd[3].clone();
-                                    
-                                    if cmd[4].to_lowercase() != "k" {
-                                        return Err(DBError("Expected K after query text".to_string()));
-                                    }
-                                    let k = cmd[5].parse::<usize>().map_err(|_| DBError("Invalid K value".to_string()))?;
-                                    
-                                    let mut nprobes = None;
-                                    let mut refine_factor = None;
-                                    let mut i = 6;
-                                    while i < cmd.len() {
-                                        match cmd[i].to_lowercase().as_str() {
-                                            "nprobes" => {
-                                                if i + 1 >= cmd.len() {
-                                                    return Err(DBError("NPROBES requires a value".to_string()));
-                                                }
-                                                nprobes = Some(cmd[i + 1].parse::<usize>().map_err(|_| DBError("Invalid NPROBES value".to_string()))?);
-                                                i += 2;
-                                            }
-                                            "refine" => {
-                                                if i + 1 >= cmd.len() {
-                                                    return Err(DBError("REFINE requires a value".to_string()));
-                                                }
-                                                refine_factor = Some(cmd[i + 1].parse::<usize>().map_err(|_| DBError("Invalid REFINE value".to_string()))?);
-                                                i += 2;
-                                            }
-                                            _ => {
-                                                return Err(DBError(format!("Unknown parameter: {}", cmd[i])));
-                                            }
-                                        }
-                                    }
-                                    
-                                    Cmd::LanceSearchText { dataset, query_text, k, nprobes, refine_factor }
-                                }
-                                "embed.text" => {
-                                    if cmd.len() < 3 {
-                                        return Err(DBError("LANCE EMBED.TEXT <text1> [text2] ...".to_string()));
-                                    }
-                                    let texts = cmd[2..].to_vec();
-                                    Cmd::LanceEmbedText { texts }
-                                }
-                                "create.index" => {
-                                    if cmd.len() < 5 {
-                                        return Err(DBError("LANCE CREATE.INDEX <dataset> <index_type> [PARTITIONS <n>] [SUBVECTORS <n>]".to_string()));
-                                    }
-                                    let dataset = cmd[2].clone();
-                                    let index_type = cmd[3].clone();
-                                    
-                                    let mut num_partitions = None;
-                                    let mut num_sub_vectors = None;
-                                    let mut i = 4;
-                                    while i < cmd.len() {
-                                        match cmd[i].to_lowercase().as_str() {
-                                            "partitions" => {
-                                                if i + 1 >= cmd.len() {
-                                                    return Err(DBError("PARTITIONS requires a value".to_string()));
-                                                }
-                                                num_partitions = Some(cmd[i + 1].parse::<usize>().map_err(|_| DBError("Invalid PARTITIONS value".to_string()))?);
-                                                i += 2;
-                                            }
-                                            "subvectors" => {
-                                                if i + 1 >= cmd.len() {
-                                                    return Err(DBError("SUBVECTORS requires a value".to_string()));
-                                                }
-                                                num_sub_vectors = Some(cmd[i + 1].parse::<usize>().map_err(|_| DBError("Invalid SUBVECTORS value".to_string()))?);
-                                                i += 2;
-                                            }
-                                            _ => {
-                                                return Err(DBError(format!("Unknown parameter: {}", cmd[i])));
-                                            }
-                                        }
-                                    }
-                                    
-                                    Cmd::LanceCreateIndex { dataset, index_type, num_partitions, num_sub_vectors }
-                                }
-                                "list" => {
-                                    if cmd.len() != 2 {
-                                        return Err(DBError("LANCE LIST takes no arguments".to_string()));
-                                    }
-                                    Cmd::LanceList
-                                }
-                                "drop" => {
-                                    if cmd.len() != 3 {
-                                        return Err(DBError("LANCE DROP <dataset>".to_string()));
-                                    }
-                                    let dataset = cmd[2].clone();
-                                    Cmd::LanceDrop { dataset }
-                                }
-                                "info" => {
-                                    if cmd.len() != 3 {
-                                        return Err(DBError("LANCE INFO <dataset>".to_string()));
-                                    }
-                                    let dataset = cmd[2].clone();
-                                    Cmd::LanceInfo { dataset }
-                                }
-                                _ => return Err(DBError(format!("unsupported LANCE subcommand {:?}", cmd))),
-                            }
-                        }
                         _ => Cmd::Unknow(cmd[0].clone()),
                     },
                     protocol,
@@ -1006,18 +730,6 @@ impl Cmd {
             Cmd::AgeSignName(name, message) => Ok(crate::age::cmd_age_sign_name(server, &name, &message).await),
             Cmd::AgeVerifyName(name, message, sig_b64) => Ok(crate::age::cmd_age_verify_name(server, &name, &message, &sig_b64).await),
             Cmd::AgeList => Ok(crate::age::cmd_age_list(server).await),
-            
-            // Lance vector database commands
-            Cmd::LanceCreate { dataset, dim, schema } => lance_create_cmd(server, &dataset, dim, &schema).await,
-            Cmd::LanceStore { dataset, text, image_base64, metadata } => lance_store_cmd(server, &dataset, text.as_deref(), image_base64.as_deref(), &metadata).await,
-            Cmd::LanceSearch { dataset, vector, k, nprobes, refine_factor } => lance_search_cmd(server, &dataset, &vector, k, nprobes, refine_factor).await,
-            Cmd::LanceSearchText { dataset, query_text, k, nprobes, refine_factor } => lance_search_text_cmd(server, &dataset, &query_text, k, nprobes, refine_factor).await,
-            Cmd::LanceEmbedText { texts } => lance_embed_text_cmd(server, &texts).await,
-            Cmd::LanceCreateIndex { dataset, index_type, num_partitions, num_sub_vectors } => lance_create_index_cmd(server, &dataset, &index_type, num_partitions, num_sub_vectors).await,
-            Cmd::LanceList => lance_list_cmd(server).await,
-            Cmd::LanceDrop { dataset } => lance_drop_cmd(server, &dataset).await,
-            Cmd::LanceInfo { dataset } => lance_info_cmd(server, &dataset).await,
-            
             Cmd::Unknow(s) => Ok(Protocol::err(&format!("ERR unknown command `{}`", s))),
         }
     }
@@ -1801,243 +1513,3 @@ fn command_cmd(args: &[String]) -> Result<Protocol, DBError> {
         _ => Ok(Protocol::Array(vec![])),
     }
 }
-
-// Helper function to create Arrow schema from field specifications
-fn create_schema_from_fields(dim: usize, fields: &[(String, String)]) -> arrow::datatypes::Schema {
-    let mut schema_fields = Vec::new();
-    
-    // Always add the vector field first
-    let vector_field = arrow::datatypes::Field::new(
-        "vector",
-        arrow::datatypes::DataType::FixedSizeList(
-            Arc::new(arrow::datatypes::Field::new("item", arrow::datatypes::DataType::Float32, true)),
-            dim as i32
-        ),
-        false
-    );
-    schema_fields.push(vector_field);
-    
-    // Add custom fields
-    for (name, field_type) in fields {
-        let data_type = match field_type.to_lowercase().as_str() {
-            "string" | "text" => arrow::datatypes::DataType::Utf8,
-            "int" | "integer" => arrow::datatypes::DataType::Int64,
-            "float" => arrow::datatypes::DataType::Float64,
-            "bool" | "boolean" => arrow::datatypes::DataType::Boolean,
-            _ => arrow::datatypes::DataType::Utf8, // Default to string
-        };
-        schema_fields.push(arrow::datatypes::Field::new(name, data_type, true));
-    }
-    
-    arrow::datatypes::Schema::new(schema_fields)
-}
-
-// Lance vector database command implementations
-async fn lance_create_cmd(
-    server: &Server,
-    dataset: &str,
-    dim: usize,
-    schema: &[(String, String)],
-) -> Result<Protocol, DBError> {
-    match server.lance_store() {
-        Ok(lance_store) => {
-            match lance_store.create_dataset(dataset, create_schema_from_fields(dim, schema)).await {
-                Ok(_) => Ok(Protocol::SimpleString("OK".to_string())),
-                Err(e) => Ok(Protocol::err(&sanitize_error_message(&format!("ERR {}", e)))),
-            }
-        }
-        Err(e) => Ok(Protocol::err(&sanitize_error_message(&format!("ERR Lance store not available: {}", e)))),
-    }
-}
-
-async fn lance_store_cmd(
-    server: &Server,
-    dataset: &str,
-    text: Option<&str>,
-    image_base64: Option<&str>,
-    metadata: &std::collections::HashMap<String, String>,
-) -> Result<Protocol, DBError> {
-    match server.lance_store() {
-        Ok(lance_store) => {
-            match lance_store.store_multimodal(server, dataset, text.map(|s| s.to_string()),
-                image_base64.and_then(|s| base64::engine::general_purpose::STANDARD.decode(s).ok()),
-                metadata.clone()).await {
-                Ok(id) => Ok(Protocol::BulkString(id)),
-                Err(e) => Ok(Protocol::err(&sanitize_error_message(&format!("ERR {}", e)))),
-            }
-        }
-        Err(e) => Ok(Protocol::err(&sanitize_error_message(&format!("ERR Lance store not available: {}", e)))),
-    }
-}
-
-async fn lance_search_cmd(
-    server: &Server,
-    dataset: &str,
-    vector: &[f32],
-    k: usize,
-    nprobes: Option<usize>,
-    refine_factor: Option<usize>,
-) -> Result<Protocol, DBError> {
-    match server.lance_store() {
-        Ok(lance_store) => {
-            match lance_store.search_vectors(dataset, vector.to_vec(), k, nprobes, refine_factor).await {
-                Ok(results) => {
-                    let mut response = Vec::new();
-                    for (distance, metadata) in results {
-                        let mut item = Vec::new();
-                        item.push(Protocol::BulkString("distance".to_string()));
-                        item.push(Protocol::BulkString(distance.to_string()));
-                        for (key, value) in metadata {
-                            item.push(Protocol::BulkString(key));
-                            item.push(Protocol::BulkString(value));
-                        }
-                        response.push(Protocol::Array(item));
-                    }
-                    Ok(Protocol::Array(response))
-                }
-                Err(e) => Ok(Protocol::err(&sanitize_error_message(&format!("ERR {}", e)))),
-            }
-        }
-        Err(e) => Ok(Protocol::err(&sanitize_error_message(&format!("ERR Lance store not available: {}", e)))),
-    }
-}
-
-async fn lance_search_text_cmd(
-    server: &Server,
-    dataset: &str,
-    query_text: &str,
-    k: usize,
-    nprobes: Option<usize>,
-    refine_factor: Option<usize>,
-) -> Result<Protocol, DBError> {
-    match server.lance_store() {
-        Ok(lance_store) => {
-            match lance_store.search_with_text(server, dataset, query_text.to_string(), k, nprobes, refine_factor).await {
-                Ok(results) => {
-                    let mut response = Vec::new();
-                    for (distance, metadata) in results {
-                        let mut item = Vec::new();
-                        item.push(Protocol::BulkString("distance".to_string()));
-                        item.push(Protocol::BulkString(distance.to_string()));
-                        for (key, value) in metadata {
-                            item.push(Protocol::BulkString(key));
-                            item.push(Protocol::BulkString(value));
-                        }
-                        response.push(Protocol::Array(item));
-                    }
-                    Ok(Protocol::Array(response))
-                }
-                Err(e) => Ok(Protocol::err(&sanitize_error_message(&format!("ERR {}", e)))),
-            }
-        }
-        Err(e) => Ok(Protocol::err(&sanitize_error_message(&format!("ERR Lance store not available: {}", e)))),
-    }
-}
-
-// Helper function to sanitize error messages for Redis protocol
-fn sanitize_error_message(msg: &str) -> String {
-    // Remove newlines, carriage returns, and limit length
-    let sanitized = msg
-        .replace('\n', " ")
-        .replace('\r', " ")
-        .replace('\t', " ");
-    
-    // Limit to 200 characters to avoid overly long error messages
-    if sanitized.len() > 200 {
-        format!("{}...", &sanitized[..197])
-    } else {
-        sanitized
-    }
-}
-
-async fn lance_embed_text_cmd(
-    server: &Server,
-    texts: &[String],
-) -> Result<Protocol, DBError> {
-    match server.lance_store() {
-        Ok(lance_store) => {
-            match lance_store.embed_text(server, texts.to_vec()).await {
-                Ok(embeddings) => {
-                    let mut response = Vec::new();
-                    for embedding in embeddings {
-                        let vector_strings: Vec<Protocol> = embedding
-                            .iter()
-                            .map(|f| Protocol::BulkString(f.to_string()))
-                            .collect();
-                        response.push(Protocol::Array(vector_strings));
-                    }
-                    Ok(Protocol::Array(response))
-                }
-                Err(e) => Ok(Protocol::err(&sanitize_error_message(&format!("ERR {}", e)))),
-            }
-        }
-        Err(e) => Ok(Protocol::err(&sanitize_error_message(&format!("ERR Lance store not available: {}", e)))),
-    }
-}
-
-async fn lance_create_index_cmd(
-    server: &Server,
-    dataset: &str,
-    index_type: &str,
-    num_partitions: Option<usize>,
-    num_sub_vectors: Option<usize>,
-) -> Result<Protocol, DBError> {
-    match server.lance_store() {
-        Ok(lance_store) => {
-            match lance_store.create_index(dataset, index_type, num_partitions, num_sub_vectors).await {
-                Ok(_) => Ok(Protocol::SimpleString("OK".to_string())),
-                Err(e) => Ok(Protocol::err(&sanitize_error_message(&format!("ERR {}", e)))),
-            }
-        }
-        Err(e) => Ok(Protocol::err(&sanitize_error_message(&format!("ERR Lance store not available: {}", e)))),
-    }
-}
-
-async fn lance_list_cmd(server: &Server) -> Result<Protocol, DBError> {
-    match server.lance_store() {
-        Ok(lance_store) => {
-            match lance_store.list_datasets().await {
-                Ok(datasets) => {
-                    let response: Vec<Protocol> = datasets
-                        .into_iter()
-                        .map(Protocol::BulkString)
-                        .collect();
-                    Ok(Protocol::Array(response))
-                }
-                Err(e) => Ok(Protocol::err(&sanitize_error_message(&format!("ERR {}", e)))),
-            }
-        }
-        Err(e) => Ok(Protocol::err(&sanitize_error_message(&format!("ERR Lance store not available: {}", e)))),
-    }
-}
-
-async fn lance_drop_cmd(server: &Server, dataset: &str) -> Result<Protocol, DBError> {
-    match server.lance_store() {
-        Ok(lance_store) => {
-            match lance_store.drop_dataset(dataset).await {
-                Ok(_) => Ok(Protocol::SimpleString("OK".to_string())),
-                Err(e) => Ok(Protocol::err(&sanitize_error_message(&format!("ERR {}", e)))),
-            }
-        }
-        Err(e) => Ok(Protocol::err(&sanitize_error_message(&format!("ERR Lance store not available: {}", e)))),
-    }
-}
-
-async fn lance_info_cmd(server: &Server, dataset: &str) -> Result<Protocol, DBError> {
-    match server.lance_store() {
-        Ok(lance_store) => {
-            match lance_store.get_dataset_info(dataset).await {
-                Ok(info) => {
-                    let mut response = Vec::new();
-                    for (key, value) in info {
-                        response.push(Protocol::BulkString(key));
-                        response.push(Protocol::BulkString(value));
-                    }
-                    Ok(Protocol::Array(response))
-                }
-                Err(e) => Ok(Protocol::err(&sanitize_error_message(&format!("ERR {}", e)))),
-            }
-        }
-        Err(e) => Ok(Protocol::err(&sanitize_error_message(&format!("ERR Lance store not available: {}", e)))),
-    }
-}

src/error.rs

@@ -9,12 +9,6 @@ use bincode;
 #[derive(Debug)]
 pub struct DBError(pub String);
 
-impl std::fmt::Display for DBError {
-    fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
-        write!(f, "{}", self.0)
-    }
-}
-
 impl From<std::io::Error> for DBError {
     fn from(item: std::io::Error) -> Self {
         DBError(item.to_string().clone())
@@ -98,40 +92,3 @@ impl From<chacha20poly1305::Error> for DBError {
         DBError(item.to_string())
     }
 }
-
-// Lance and related dependencies error handling
-impl From<lance::Error> for DBError {
-    fn from(item: lance::Error) -> Self {
-        DBError(item.to_string())
-    }
-}
-
-impl From<arrow::error::ArrowError> for DBError {
-    fn from(item: arrow::error::ArrowError) -> Self {
-        DBError(item.to_string())
-    }
-}
-
-impl From<reqwest::Error> for DBError {
-    fn from(item: reqwest::Error) -> Self {
-        DBError(item.to_string())
-    }
-}
-
-impl From<image::ImageError> for DBError {
-    fn from(item: image::ImageError) -> Self {
-        DBError(item.to_string())
-    }
-}
-
-impl From<uuid::Error> for DBError {
-    fn from(item: uuid::Error) -> Self {
-        DBError(item.to_string())
-    }
-}
-
-impl From<base64::DecodeError> for DBError {
-    fn from(item: base64::DecodeError) -> Self {
-        DBError(item.to_string())
-    }
-}

src/lance_store.rs

@@ -1,609 +0,0 @@
-use std::collections::HashMap;
-use std::path::PathBuf;
-use std::sync::Arc;
-use tokio::sync::RwLock;
-
-use arrow::array::{Float32Array, StringArray, ArrayRef, FixedSizeListArray, Array};
-use arrow::datatypes::{DataType, Field, Schema, SchemaRef};
-use arrow::record_batch::{RecordBatch, RecordBatchReader};
-use arrow::error::ArrowError;
-use lance::dataset::{Dataset, WriteParams, WriteMode};
-use lance::index::vector::VectorIndexParams;
-use lance_index::vector::pq::PQBuildParams;
-use lance_index::vector::ivf::IvfBuildParams;
-use lance_index::DatasetIndexExt;
-use lance_linalg::distance::MetricType;
-use futures::TryStreamExt;
-use base64::Engine;
-
-use serde::{Deserialize, Serialize};
-use crate::error::DBError;
-
-// Simple RecordBatchReader implementation for Vec<RecordBatch>
-struct VecRecordBatchReader {
-    batches: std::vec::IntoIter<Result<RecordBatch, ArrowError>>,
-}
-
-impl VecRecordBatchReader {
-    fn new(batches: Vec<RecordBatch>) -> Self {
-        let result_batches = batches.into_iter().map(Ok).collect::<Vec<_>>();
-        Self {
-            batches: result_batches.into_iter(),
-        }
-    }
-}
-
-impl Iterator for VecRecordBatchReader {
-    type Item = Result<RecordBatch, ArrowError>;
-
-    fn next(&mut self) -> Option<Self::Item> {
-        self.batches.next()
-    }
-}
-
-impl RecordBatchReader for VecRecordBatchReader {
-    fn schema(&self) -> SchemaRef {
-        // This is a simplified implementation - in practice you'd want to store the schema
-        Arc::new(Schema::empty())
-    }
-}
-
-#[derive(Debug, Serialize, Deserialize)]
-struct EmbeddingRequest {
-    texts: Option<Vec<String>>,
-    images: Option<Vec<String>>, // base64 encoded
-    model: Option<String>,
-}
-
-#[derive(Debug, Serialize, Deserialize)]
-struct EmbeddingResponse {
-    embeddings: Vec<Vec<f32>>,
-    model: String,
-    usage: Option<HashMap<String, u32>>,
-}
-
-// Ollama-specific request/response structures
-#[derive(Debug, Serialize, Deserialize)]
-struct OllamaEmbeddingRequest {
-    model: String,
-    prompt: String,
-}
-
-#[derive(Debug, Serialize, Deserialize)]
-struct OllamaEmbeddingResponse {
-    embedding: Vec<f32>,
-}
-
-pub struct LanceStore {
-    datasets: Arc<RwLock<HashMap<String, Arc<Dataset>>>>,
-    data_dir: PathBuf,
-    http_client: reqwest::Client,
-}
-
-impl LanceStore {
-    pub async fn new(data_dir: PathBuf) -> Result<Self, DBError> {
-        // Create data directory if it doesn't exist
-        std::fs::create_dir_all(&data_dir)
-            .map_err(|e| DBError(format!("Failed to create Lance data directory: {}", e)))?;
-        
-        let http_client = reqwest::Client::builder()
-            .timeout(std::time::Duration::from_secs(30))
-            .build()
-            .map_err(|e| DBError(format!("Failed to create HTTP client: {}", e)))?;
-        
-        Ok(Self {
-            datasets: Arc::new(RwLock::new(HashMap::new())),
-            data_dir,
-            http_client,
-        })
-    }
-    
-    /// Get embedding service URL from Redis config, default to local Ollama
-    async fn get_embedding_url(&self, server: &crate::server::Server) -> Result<String, DBError> {
-        // Get the embedding URL from Redis config directly from storage
-        let storage = server.current_storage()?;
-        match storage.hget("config:core:aiembed", "url")? {
-            Some(url) => Ok(url),
-            None => Ok("http://localhost:11434".to_string()), // Default to local Ollama
-        }
-    }
-    
-    /// Check if we're using Ollama (default) or custom embedding service
-    async fn is_ollama_service(&self, server: &crate::server::Server) -> Result<bool, DBError> {
-        let url = self.get_embedding_url(server).await?;
-        Ok(url.contains("localhost:11434") || url.contains("127.0.0.1:11434"))
-    }
-    
-    /// Call external embedding service (Ollama or custom)
-    async fn call_embedding_service(
-        &self,
-        server: &crate::server::Server,
-        texts: Option<Vec<String>>,
-        images: Option<Vec<String>>,
-    ) -> Result<Vec<Vec<f32>>, DBError> {
-        let base_url = self.get_embedding_url(server).await?;
-        let is_ollama = self.is_ollama_service(server).await?;
-        
-        if is_ollama {
-            // Use Ollama API format
-            if let Some(texts) = texts {
-                let mut embeddings = Vec::new();
-                for text in texts {
-                    let url = format!("{}/api/embeddings", base_url);
-                    let request = OllamaEmbeddingRequest {
-                        model: "nomic-embed-text".to_string(),
-                        prompt: text,
-                    };
-                    
-                    let response = self.http_client
-                        .post(&url)
-                        .json(&request)
-                        .send()
-                        .await
-                        .map_err(|e| DBError(format!("Failed to call Ollama embedding service: {}", e)))?;
-                    
-                    if !response.status().is_success() {
-                        let status = response.status();
-                        let error_text = response.text().await.unwrap_or_default();
-                        return Err(DBError(format!(
-                            "Ollama embedding service returned error {}: {}",
-                            status, error_text
-                        )));
-                    }
-                    
-                    let ollama_response: OllamaEmbeddingResponse = response
-                        .json()
-                        .await
-                        .map_err(|e| DBError(format!("Failed to parse Ollama embedding response: {}", e)))?;
-                    
-                    embeddings.push(ollama_response.embedding);
-                }
-                Ok(embeddings)
-            } else if let Some(_images) = images {
-                // Ollama doesn't support image embeddings with this API yet
-                Err(DBError("Image embeddings not supported with Ollama. Please configure a custom embedding service.".to_string()))
-            } else {
-                Err(DBError("No text or images provided for embedding".to_string()))
-            }
-        } else {
-            // Use custom embedding service API format
-            let request = EmbeddingRequest {
-                texts,
-                images,
-                model: None, // Let the service use its default
-            };
-            
-            let response = self.http_client
-                .post(&base_url)
-                .json(&request)
-                .send()
-                .await
-                .map_err(|e| DBError(format!("Failed to call embedding service: {}", e)))?;
-            
-            if !response.status().is_success() {
-                let status = response.status();
-                let error_text = response.text().await.unwrap_or_default();
-                return Err(DBError(format!(
-                    "Embedding service returned error {}: {}",
-                    status, error_text
-                )));
-            }
-            
-            let embedding_response: EmbeddingResponse = response
-                .json()
-                .await
-                .map_err(|e| DBError(format!("Failed to parse embedding response: {}", e)))?;
-            
-            Ok(embedding_response.embeddings)
-        }
-    }
-    
-    pub async fn embed_text(
-        &self, 
-        server: &crate::server::Server,
-        texts: Vec<String>
-    ) -> Result<Vec<Vec<f32>>, DBError> {
-        if texts.is_empty() {
-            return Ok(Vec::new());
-        }
-        
-        self.call_embedding_service(server, Some(texts), None).await
-    }
-    
-    pub async fn embed_image(
-        &self,
-        server: &crate::server::Server,
-        image_bytes: Vec<u8>
-    ) -> Result<Vec<f32>, DBError> {
-        // Convert image bytes to base64
-        let base64_image = base64::engine::general_purpose::STANDARD.encode(&image_bytes);
-        
-        let embeddings = self.call_embedding_service(
-            server, 
-            None, 
-            Some(vec![base64_image])
-        ).await?;
-        
-        embeddings.into_iter()
-            .next()
-            .ok_or_else(|| DBError("No embedding returned for image".to_string()))
-    }
-    
-    pub async fn create_dataset(
-        &self,
-        name: &str,
-        schema: Schema,
-    ) -> Result<(), DBError> {
-        let dataset_path = self.data_dir.join(format!("{}.lance", name));
-        
-        // Create empty dataset with schema
-        let write_params = WriteParams {
-            mode: WriteMode::Create,
-            ..Default::default()
-        };
-        
-        // Create an empty RecordBatch with the schema
-        let empty_batch = RecordBatch::new_empty(Arc::new(schema));
-        
-        // Use RecordBatchReader for Lance 0.33
-        let reader = VecRecordBatchReader::new(vec![empty_batch]);
-        let dataset = Dataset::write(
-            reader,
-            dataset_path.to_str().unwrap(),
-            Some(write_params)
-        ).await
-        .map_err(|e| DBError(format!("Failed to create dataset: {}", e)))?;
-        
-        let mut datasets = self.datasets.write().await;
-        datasets.insert(name.to_string(), Arc::new(dataset));
-        
-        Ok(())
-    }
-    
-    pub async fn write_vectors(
-        &self,
-        dataset_name: &str,
-        vectors: Vec<Vec<f32>>,
-        metadata: Option<HashMap<String, Vec<String>>>,
-    ) -> Result<usize, DBError> {
-        let dataset_path = self.data_dir.join(format!("{}.lance", dataset_name));
-        
-        // Open or get cached dataset
-        let _dataset = self.get_or_open_dataset(dataset_name).await?;
-        
-        // Build RecordBatch
-        let num_vectors = vectors.len();
-        if num_vectors == 0 {
-            return Ok(0);
-        }
-        
-        let dim = vectors.first()
-            .ok_or_else(|| DBError("Empty vectors".to_string()))?
-            .len();
-        
-        // Flatten vectors
-        let flat_vectors: Vec<f32> = vectors.into_iter().flatten().collect();
-        let values_array = Float32Array::from(flat_vectors);
-        let field = Arc::new(Field::new("item", DataType::Float32, true));
-        let vector_array = FixedSizeListArray::try_new(
-            field,
-            dim as i32,
-            Arc::new(values_array),
-            None
-        ).map_err(|e| DBError(format!("Failed to create vector array: {}", e)))?;
-        
-        let mut arrays: Vec<ArrayRef> = vec![Arc::new(vector_array)];
-        let mut fields = vec![Field::new(
-            "vector",
-            DataType::FixedSizeList(
-                Arc::new(Field::new("item", DataType::Float32, true)),
-                dim as i32
-            ),
-            false
-        )];
-        
-        // Add metadata columns if provided
-        if let Some(metadata) = metadata {
-            for (key, values) in metadata {
-                if values.len() != num_vectors {
-                    return Err(DBError(format!(
-                        "Metadata field '{}' has {} values but expected {}", 
-                        key, values.len(), num_vectors
-                    )));
-                }
-                let array = StringArray::from(values);
-                arrays.push(Arc::new(array));
-                fields.push(Field::new(&key, DataType::Utf8, true));
-            }
-        }
-        
-        let schema = Arc::new(Schema::new(fields));
-        let batch = RecordBatch::try_new(schema, arrays)
-            .map_err(|e| DBError(format!("Failed to create RecordBatch: {}", e)))?;
-        
-        // Append to dataset
-        let write_params = WriteParams {
-            mode: WriteMode::Append,
-            ..Default::default()
-        };
-        
-        let reader = VecRecordBatchReader::new(vec![batch]);
-        Dataset::write(
-            reader,
-            dataset_path.to_str().unwrap(),
-            Some(write_params)
-        ).await
-        .map_err(|e| DBError(format!("Failed to write to dataset: {}", e)))?;
-        
-        // Refresh cached dataset
-        let mut datasets = self.datasets.write().await;
-        datasets.remove(dataset_name);
-        
-        Ok(num_vectors)
-    }
-    
-    pub async fn search_vectors(
-        &self,
-        dataset_name: &str,
-        query_vector: Vec<f32>,
-        k: usize,
-        nprobes: Option<usize>,
-        _refine_factor: Option<usize>,
-    ) -> Result<Vec<(f32, HashMap<String, String>)>, DBError> {
-        let dataset = self.get_or_open_dataset(dataset_name).await?;
-        
-        // Build query
-        let query_array = Float32Array::from(query_vector.clone());
-        let mut query = dataset.scan();
-        query.nearest(
-            "vector",
-            &query_array,
-            k,
-        ).map_err(|e| DBError(format!("Failed to build search query: {}", e)))?;
-        
-        if let Some(nprobes) = nprobes {
-            query.nprobs(nprobes);
-        }
-        
-        // Note: refine_factor might not be available in this Lance version
-        // if let Some(refine) = refine_factor {
-        //     query.refine_factor(refine);
-        // }
-        
-        // Execute search
-        let results = query
-            .try_into_stream()
-            .await
-            .map_err(|e| DBError(format!("Failed to execute search: {}", e)))?
-            .try_collect::<Vec<_>>()
-            .await
-            .map_err(|e| DBError(format!("Failed to collect results: {}", e)))?;
-        
-        // Process results
-        let mut output = Vec::new();
-        for batch in results {
-            // Get distances
-            let distances = batch
-                .column_by_name("_distance")
-                .ok_or_else(|| DBError("No distance column".to_string()))?
-                .as_any()
-                .downcast_ref::<Float32Array>()
-                .ok_or_else(|| DBError("Invalid distance type".to_string()))?;
-            
-            // Get metadata
-            for i in 0..batch.num_rows() {
-                let distance = distances.value(i);
-                let mut metadata = HashMap::new();
-                
-                for field in batch.schema().fields() {
-                    if field.name() != "vector" && field.name() != "_distance" {
-                        if let Some(col) = batch.column_by_name(field.name()) {
-                            if let Some(str_array) = col.as_any().downcast_ref::<StringArray>() {
-                                if !str_array.is_null(i) {
-                                    metadata.insert(
-                                        field.name().to_string(),
-                                        str_array.value(i).to_string()
-                                    );
-                                }
-                            }
-                        }
-                    }
-                }
-                
-                output.push((distance, metadata));
-            }
-        }
-        
-        Ok(output)
-    }
-    
-    pub async fn store_multimodal(
-        &self,
-        server: &crate::server::Server,
-        dataset_name: &str,
-        text: Option<String>,
-        image_bytes: Option<Vec<u8>>,
-        metadata: HashMap<String, String>,
-    ) -> Result<String, DBError> {
-        // Generate ID
-        let id = uuid::Uuid::new_v4().to_string();
-        
-        // Generate embeddings using external service
-        let embedding = if let Some(text) = text.as_ref() {
-            self.embed_text(server, vec![text.clone()]).await?
-                .into_iter()
-                .next()
-                .ok_or_else(|| DBError("No embedding returned".to_string()))?
-        } else if let Some(img) = image_bytes.as_ref() {
-            self.embed_image(server, img.clone()).await?
-        } else {
-            return Err(DBError("No text or image provided".to_string()));
-        };
-        
-        // Prepare metadata
-        let mut full_metadata = metadata;
-        full_metadata.insert("id".to_string(), id.clone());
-        if let Some(text) = text {
-            full_metadata.insert("text".to_string(), text);
-        }
-        if let Some(img) = image_bytes {
-            full_metadata.insert("image_base64".to_string(), base64::engine::general_purpose::STANDARD.encode(img));
-        }
-        
-        // Convert metadata to column vectors
-        let mut metadata_cols = HashMap::new();
-        for (key, value) in full_metadata {
-            metadata_cols.insert(key, vec![value]);
-        }
-        
-        // Write to dataset
-        self.write_vectors(dataset_name, vec![embedding], Some(metadata_cols)).await?;
-        
-        Ok(id)
-    }
-    
-    pub async fn search_with_text(
-        &self,
-        server: &crate::server::Server,
-        dataset_name: &str,
-        query_text: String,
-        k: usize,
-        nprobes: Option<usize>,
-        refine_factor: Option<usize>,
-    ) -> Result<Vec<(f32, HashMap<String, String>)>, DBError> {
-        // Embed the query text using external service
-        let embeddings = self.embed_text(server, vec![query_text]).await?;
-        let query_vector = embeddings.into_iter()
-            .next()
-            .ok_or_else(|| DBError("No embedding returned for query".to_string()))?;
-        
-        // Search with the embedding
-        self.search_vectors(dataset_name, query_vector, k, nprobes, refine_factor).await
-    }
-    
-    pub async fn create_index(
-        &self,
-        dataset_name: &str,
-        index_type: &str,
-        num_partitions: Option<usize>,
-        num_sub_vectors: Option<usize>,
-    ) -> Result<(), DBError> {
-        let _dataset = self.get_or_open_dataset(dataset_name).await?;
-        
-        match index_type.to_uppercase().as_str() {
-            "IVF_PQ" => {
-                let ivf_params = IvfBuildParams {
-                    num_partitions: num_partitions.unwrap_or(256),
-                    ..Default::default()
-                };
-                let pq_params = PQBuildParams {
-                    num_sub_vectors: num_sub_vectors.unwrap_or(16),
-                    ..Default::default()
-                };
-                let params = VectorIndexParams::with_ivf_pq_params(
-                    MetricType::L2,
-                    ivf_params,
-                    pq_params,
-                );
-                
-                // Get a mutable reference to the dataset
-                let mut dataset_mut = Dataset::open(self.data_dir.join(format!("{}.lance", dataset_name)).to_str().unwrap())
-                    .await
-                    .map_err(|e| DBError(format!("Failed to open dataset for indexing: {}", e)))?;
-                
-                dataset_mut.create_index(
-                    &["vector"],
-                    lance_index::IndexType::Vector,
-                    None,
-                    &params,
-                    true
-                ).await
-                .map_err(|e| DBError(format!("Failed to create index: {}", e)))?;
-            }
-            _ => return Err(DBError(format!("Unsupported index type: {}", index_type))),
-        }
-        
-        Ok(())
-    }
-    
-    async fn get_or_open_dataset(&self, name: &str) -> Result<Arc<Dataset>, DBError> {
-        let mut datasets = self.datasets.write().await;
-        
-        if let Some(dataset) = datasets.get(name) {
-            return Ok(dataset.clone());
-        }
-        
-        let dataset_path = self.data_dir.join(format!("{}.lance", name));
-        if !dataset_path.exists() {
-            return Err(DBError(format!("Dataset '{}' does not exist", name)));
-        }
-        
-        let dataset = Dataset::open(dataset_path.to_str().unwrap())
-            .await
-            .map_err(|e| DBError(format!("Failed to open dataset: {}", e)))?;
-        
-        let dataset = Arc::new(dataset);
-        datasets.insert(name.to_string(), dataset.clone());
-        
-        Ok(dataset)
-    }
-    
-    pub async fn list_datasets(&self) -> Result<Vec<String>, DBError> {
-        let mut datasets = Vec::new();
-        
-        let entries = std::fs::read_dir(&self.data_dir)
-            .map_err(|e| DBError(format!("Failed to read data directory: {}", e)))?;
-        
-        for entry in entries {
-            let entry = entry.map_err(|e| DBError(format!("Failed to read entry: {}", e)))?;
-            let path = entry.path();
-            
-            if path.is_dir() {
-                if let Some(name) = path.file_name() {
-                    if let Some(name_str) = name.to_str() {
-                        if name_str.ends_with(".lance") {
-                            let dataset_name = name_str.trim_end_matches(".lance");
-                            datasets.push(dataset_name.to_string());
-                        }
-                    }
-                }
-            }
-        }
-        
-        Ok(datasets)
-    }
-    
-    pub async fn drop_dataset(&self, name: &str) -> Result<(), DBError> {
-        // Remove from cache
-        let mut datasets = self.datasets.write().await;
-        datasets.remove(name);
-        
-        // Delete from disk
-        let dataset_path = self.data_dir.join(format!("{}.lance", name));
-        if dataset_path.exists() {
-            std::fs::remove_dir_all(dataset_path)
-                .map_err(|e| DBError(format!("Failed to delete dataset: {}", e)))?;
-        }
-        
-        Ok(())
-    }
-    
-    pub async fn get_dataset_info(&self, name: &str) -> Result<HashMap<String, String>, DBError> {
-        let dataset = self.get_or_open_dataset(name).await?;
-        
-        let mut info = HashMap::new();
-        info.insert("name".to_string(), name.to_string());
-        info.insert("version".to_string(), dataset.version().version.to_string());
-        info.insert("num_rows".to_string(), dataset.count_rows(None).await?.to_string());
-        
-        // Get schema info
-        let schema = dataset.schema();
-        let fields: Vec<String> = schema.fields
-            .iter()
-            .map(|f| format!("{}:{}", f.name, f.data_type()))
-            .collect();
-        info.insert("schema".to_string(), fields.join(", "));
-        
-        Ok(info)
-    }
-}

src/lib.rs

@@ -2,9 +2,10 @@ pub mod age;   // NEW
 pub mod cmd;
 pub mod crypto;
 pub mod error;
-pub mod lance_store;    // Add Lance store module
 pub mod options;
 pub mod protocol;
+pub mod rpc;
+pub mod rpc_server;
 pub mod server;
 pub mod storage;
 pub mod storage_trait;  // Add this

src/main.rs

@@ -3,6 +3,7 @@
 use tokio::net::TcpListener;
 
 use herodb::server;
+use herodb::rpc_server;
 
 use clap::Parser;
 
@@ -31,6 +32,14 @@ struct Args {
     #[arg(long)]
     encrypt: bool,
 
+    /// Enable RPC management server
+    #[arg(long)]
+    enable_rpc: bool,
+
+    /// RPC server port (default: 8080)
+    #[arg(long, default_value = "8080")]
+    rpc_port: u16,
+
     /// Use the sled backend
     #[arg(long)]
     sled: bool,
@@ -50,7 +59,7 @@ async fn main() {
 
     // new DB option
     let option = herodb::options::DBOption {
-        dir: args.dir,
+        dir: args.dir.clone(),
         port,
         debug: args.debug,
         encryption_key: args.encryption_key,
@@ -62,12 +71,36 @@ async fn main() {
         },
     };
 
+    let backend = option.backend.clone();
+
     // new server
-    let server = server::Server::new(option).await;
+    let mut server = server::Server::new(option).await;
+
+    // Initialize the default database storage
+    let _ = server.current_storage();
 
     // Add a small delay to ensure the port is ready
     tokio::time::sleep(std::time::Duration::from_millis(100)).await;
 
+    // Start RPC server if enabled
+    let rpc_handle = if args.enable_rpc {
+        let rpc_addr = format!("127.0.0.1:{}", args.rpc_port).parse().unwrap();
+        let base_dir = args.dir.clone();
+
+        match rpc_server::start_rpc_server(rpc_addr, base_dir, backend).await {
+            Ok(handle) => {
+                println!("RPC management server started on port {}", args.rpc_port);
+                Some(handle)
+            }
+            Err(e) => {
+                eprintln!("Failed to start RPC server: {}", e);
+                None
+            }
+        }
+    } else {
+        None
+    };
+
     // accept new connections
     loop {
         let stream = listener.accept().await;

src/rpc.rs

@@ -0,0 +1,342 @@
+use std::collections::HashMap;
+use std::sync::Arc;
+use tokio::sync::RwLock;
+use jsonrpsee::{core::RpcResult, proc_macros::rpc};
+use serde::{Deserialize, Serialize};
+
+use crate::server::Server;
+use crate::options::DBOption;
+
+/// Database backend types
+#[derive(Debug, Clone, Serialize, Deserialize)]
+pub enum BackendType {
+    Redb,
+    Sled,
+    // Future: InMemory, Custom(String)
+}
+
+/// Database configuration
+#[derive(Debug, Clone, Serialize, Deserialize)]
+pub struct DatabaseConfig {
+    pub name: Option<String>,
+    pub storage_path: Option<String>,
+    pub max_size: Option<u64>,
+    pub redis_version: Option<String>,
+}
+
+/// Database information returned by metadata queries
+#[derive(Debug, Clone, Serialize, Deserialize)]
+pub struct DatabaseInfo {
+    pub id: u64,
+    pub name: Option<String>,
+    pub backend: BackendType,
+    pub encrypted: bool,
+    pub redis_version: Option<String>,
+    pub storage_path: Option<String>,
+    pub size_on_disk: Option<u64>,
+    pub key_count: Option<u64>,
+    pub created_at: u64,
+    pub last_access: Option<u64>,
+}
+
+/// RPC trait for HeroDB management
+#[rpc(server, client, namespace = "herodb")]
+pub trait Rpc {
+    /// Create a new database with specified configuration
+    #[method(name = "createDatabase")]
+    async fn create_database(
+        &self,
+        backend: BackendType,
+        config: DatabaseConfig,
+        encryption_key: Option<String>,
+    ) -> RpcResult<u64>;
+
+    /// Set encryption for an existing database (write-only key)
+    #[method(name = "setEncryption")]
+    async fn set_encryption(&self, db_id: u64, encryption_key: String) -> RpcResult<bool>;
+
+    /// List all managed databases
+    #[method(name = "listDatabases")]
+    async fn list_databases(&self) -> RpcResult<Vec<DatabaseInfo>>;
+
+    /// Get detailed information about a specific database
+    #[method(name = "getDatabaseInfo")]
+    async fn get_database_info(&self, db_id: u64) -> RpcResult<DatabaseInfo>;
+
+    /// Delete a database
+    #[method(name = "deleteDatabase")]
+    async fn delete_database(&self, db_id: u64) -> RpcResult<bool>;
+
+    /// Get server statistics
+    #[method(name = "getServerStats")]
+    async fn get_server_stats(&self) -> RpcResult<HashMap<String, serde_json::Value>>;
+}
+
+/// RPC Server implementation
+pub struct RpcServerImpl {
+    /// Base directory for database files
+    base_dir: String,
+    /// Managed database servers
+    servers: Arc<RwLock<HashMap<u64, Arc<Server>>>>,
+    /// Next unencrypted database ID to assign
+    next_unencrypted_id: Arc<RwLock<u64>>,
+    /// Next encrypted database ID to assign
+    next_encrypted_id: Arc<RwLock<u64>>,
+    /// Default backend type
+    backend: crate::options::BackendType,
+}
+
+impl RpcServerImpl {
+    /// Create a new RPC server instance
+    pub fn new(base_dir: String, backend: crate::options::BackendType) -> Self {
+        Self {
+            base_dir,
+            servers: Arc::new(RwLock::new(HashMap::new())),
+            next_unencrypted_id: Arc::new(RwLock::new(0)),
+            next_encrypted_id: Arc::new(RwLock::new(10)),
+            backend,
+        }
+    }
+
+    /// Get or create a server instance for the given database ID
+    async fn get_or_create_server(&self, db_id: u64) -> Result<Arc<Server>, jsonrpsee::types::ErrorObjectOwned> {
+        // Check if server already exists
+        {
+            let servers = self.servers.read().await;
+            if let Some(server) = servers.get(&db_id) {
+                return Ok(server.clone());
+            }
+        }
+
+        // Check if database file exists
+        let db_path = std::path::PathBuf::from(&self.base_dir).join(format!("{}.db", db_id));
+        if !db_path.exists() {
+            return Err(jsonrpsee::types::ErrorObjectOwned::owned(
+                -32000,
+                format!("Database {} not found", db_id),
+                None::<()>
+            ));
+        }
+
+        // Create server instance with default options
+        let db_option = DBOption {
+            dir: self.base_dir.clone(),
+            port: 0, // Not used for RPC-managed databases
+            debug: false,
+            encryption_key: None,
+            encrypt: false,
+            backend: self.backend.clone(),
+        };
+
+        let mut server = Server::new(db_option).await;
+
+        // Set the selected database to the db_id for proper file naming
+        server.selected_db = db_id;
+
+        // Store the server
+        let mut servers = self.servers.write().await;
+        servers.insert(db_id, Arc::new(server.clone()));
+
+        Ok(Arc::new(server))
+    }
+
+    /// Discover existing database files in the base directory
+    async fn discover_databases(&self) -> Vec<u64> {
+        let mut db_ids = Vec::new();
+
+        if let Ok(entries) = std::fs::read_dir(&self.base_dir) {
+            for entry in entries.flatten() {
+                if let Ok(file_name) = entry.file_name().into_string() {
+                    // Check if it's a database file (ends with .db)
+                    if file_name.ends_with(".db") {
+                        // Extract database ID from filename (e.g., "11.db" -> 11)
+                        if let Some(id_str) = file_name.strip_suffix(".db") {
+                            if let Ok(db_id) = id_str.parse::<u64>() {
+                                db_ids.push(db_id);
+                            }
+                        }
+                    }
+                }
+            }
+        }
+
+        db_ids
+    }
+
+    /// Get the next available database ID
+    async fn get_next_db_id(&self, is_encrypted: bool) -> u64 {
+        if is_encrypted {
+            let mut id = self.next_encrypted_id.write().await;
+            let current_id = *id;
+            *id += 1;
+            current_id
+        } else {
+            let mut id = self.next_unencrypted_id.write().await;
+            let current_id = *id;
+            *id += 1;
+            current_id
+        }
+    }
+}
+
+#[jsonrpsee::core::async_trait]
+impl RpcServer for RpcServerImpl {
+    async fn create_database(
+        &self,
+        backend: BackendType,
+        config: DatabaseConfig,
+        encryption_key: Option<String>,
+    ) -> RpcResult<u64> {
+        let db_id = self.get_next_db_id(encryption_key.is_some()).await;
+
+        // Handle both Redb and Sled backends
+        match backend {
+            BackendType::Redb | BackendType::Sled => {
+                // Create database directory
+                let db_dir = if let Some(path) = &config.storage_path {
+                    std::path::PathBuf::from(path)
+                } else {
+                    std::path::PathBuf::from(&self.base_dir).join(format!("rpc_db_{}", db_id))
+                };
+
+                // Ensure directory exists
+                std::fs::create_dir_all(&db_dir)
+                    .map_err(|e| jsonrpsee::types::ErrorObjectOwned::owned(
+                        -32000,
+                        format!("Failed to create directory: {}", e),
+                        None::<()>
+                    ))?;
+
+                // Create DB options
+                let encrypt = encryption_key.is_some();
+                let option = DBOption {
+                    dir: db_dir.to_string_lossy().to_string(),
+                    port: 0, // Not used for RPC-managed databases
+                    debug: false,
+                    encryption_key,
+                    encrypt,
+                    backend: match backend {
+                        BackendType::Redb => crate::options::BackendType::Redb,
+                        BackendType::Sled => crate::options::BackendType::Sled,
+                    },
+                };
+
+                // Create server instance
+                let mut server = Server::new(option).await;
+
+                // Set the selected database to the db_id for proper file naming
+                server.selected_db = db_id;
+
+                // Initialize the storage to create the database file
+                let _ = server.current_storage();
+
+                // Store the server
+                let mut servers = self.servers.write().await;
+                servers.insert(db_id, Arc::new(server));
+
+                Ok(db_id)
+            }
+        }
+    }
+
+    async fn set_encryption(&self, db_id: u64, _encryption_key: String) -> RpcResult<bool> {
+        // Note: In a real implementation, we'd need to modify the existing database
+        // For now, return false as encryption can only be set during creation
+        let _servers = self.servers.read().await;
+        // TODO: Implement encryption setting for existing databases
+        Ok(false)
+    }
+
+    async fn list_databases(&self) -> RpcResult<Vec<DatabaseInfo>> {
+        let db_ids = self.discover_databases().await;
+        let mut result = Vec::new();
+
+        for db_id in db_ids {
+            // Try to get or create server for this database
+            if let Ok(server) = self.get_or_create_server(db_id).await {
+                let backend = match server.option.backend {
+                    crate::options::BackendType::Redb => BackendType::Redb,
+                    crate::options::BackendType::Sled => BackendType::Sled,
+                };
+
+                let info = DatabaseInfo {
+                    id: db_id,
+                    name: None, // TODO: Store name in server metadata
+                    backend,
+                    encrypted: server.option.encrypt,
+                    redis_version: Some("7.0".to_string()), // Default Redis compatibility
+                    storage_path: Some(server.option.dir.clone()),
+                    size_on_disk: None, // TODO: Calculate actual size
+                    key_count: None, // TODO: Get key count from storage
+                    created_at: std::time::SystemTime::now()
+                        .duration_since(std::time::UNIX_EPOCH)
+                        .unwrap()
+                        .as_secs(),
+                    last_access: None,
+                };
+                result.push(info);
+            }
+        }
+
+        Ok(result)
+    }
+
+    async fn get_database_info(&self, db_id: u64) -> RpcResult<DatabaseInfo> {
+        let server = self.get_or_create_server(db_id).await?;
+
+        let backend = match server.option.backend {
+            crate::options::BackendType::Redb => BackendType::Redb,
+            crate::options::BackendType::Sled => BackendType::Sled,
+        };
+
+        Ok(DatabaseInfo {
+            id: db_id,
+            name: None,
+            backend,
+            encrypted: server.option.encrypt,
+            redis_version: Some("7.0".to_string()),
+            storage_path: Some(server.option.dir.clone()),
+            size_on_disk: None,
+            key_count: None,
+            created_at: std::time::SystemTime::now()
+                .duration_since(std::time::UNIX_EPOCH)
+                .unwrap()
+                .as_secs(),
+            last_access: None,
+        })
+    }
+
+    async fn delete_database(&self, db_id: u64) -> RpcResult<bool> {
+        let mut servers = self.servers.write().await;
+
+        if let Some(_server) = servers.remove(&db_id) {
+            // Clean up database files
+            let db_path = std::path::PathBuf::from(&self.base_dir).join(format!("{}.db", db_id));
+            if db_path.exists() {
+                if db_path.is_dir() {
+                    std::fs::remove_dir_all(&db_path).ok();
+                } else {
+                    std::fs::remove_file(&db_path).ok();
+                }
+            }
+            Ok(true)
+        } else {
+            Ok(false)
+        }
+    }
+
+    async fn get_server_stats(&self) -> RpcResult<HashMap<String, serde_json::Value>> {
+        let db_ids = self.discover_databases().await;
+        let mut stats = HashMap::new();
+
+        stats.insert("total_databases".to_string(), serde_json::json!(db_ids.len()));
+        stats.insert("uptime".to_string(), serde_json::json!(
+            std::time::SystemTime::now()
+                .duration_since(std::time::UNIX_EPOCH)
+                .unwrap()
+                .as_secs()
+        ));
+
+        Ok(stats)
+    }
+}

src/rpc_server.rs

@@ -0,0 +1,49 @@
+use std::net::SocketAddr;
+use jsonrpsee::server::{ServerBuilder, ServerHandle};
+use jsonrpsee::RpcModule;
+
+use crate::rpc::{RpcServer, RpcServerImpl};
+
+/// Start the RPC server on the specified address
+pub async fn start_rpc_server(addr: SocketAddr, base_dir: String, backend: crate::options::BackendType) -> Result<ServerHandle, Box<dyn std::error::Error + Send + Sync>> {
+    // Create the RPC server implementation
+    let rpc_impl = RpcServerImpl::new(base_dir, backend);
+
+    // Create the RPC module
+    let mut module = RpcModule::new(());
+    module.merge(RpcServer::into_rpc(rpc_impl))?;
+
+    // Build the server with both HTTP and WebSocket support
+    let server = ServerBuilder::default()
+        .build(addr)
+        .await?;
+
+    // Start the server
+    let handle = server.start(module);
+
+    println!("RPC server started on {}", addr);
+
+    Ok(handle)
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    use std::time::Duration;
+
+    #[tokio::test]
+    async fn test_rpc_server_startup() {
+        let addr = "127.0.0.1:0".parse().unwrap(); // Use port 0 for auto-assignment
+        let base_dir = "/tmp/test_rpc".to_string();
+        let backend = crate::options::BackendType::Redb; // Default for test
+
+        let handle = start_rpc_server(addr, base_dir, backend).await.unwrap();
+
+        // Give the server a moment to start
+        tokio::time::sleep(Duration::from_millis(100)).await;
+
+        // Stop the server
+        handle.stop().unwrap();
+        handle.stopped().await;
+    }
+}

src/server.rs

@@ -9,7 +9,6 @@ use std::sync::atomic::{AtomicU64, Ordering};
 
 use crate::cmd::Cmd;
 use crate::error::DBError;
-use crate::lance_store::LanceStore;
 use crate::options;
 use crate::protocol::Protocol;
 use crate::storage::Storage;
@@ -27,9 +26,6 @@ pub struct Server {
     // BLPOP waiter registry: per (db_index, key) FIFO of waiters
     pub list_waiters: Arc<Mutex<HashMap<u64, HashMap<String, Vec<Waiter>>>>>,
     pub waiter_seq: Arc<AtomicU64>,
-    
-    // Lance vector store
-    pub lance_store: Option<Arc<LanceStore>>,
 }
 
 pub struct Waiter {
@@ -46,16 +42,6 @@ pub enum PopSide {
 
 impl Server {
     pub async fn new(option: options::DBOption) -> Self {
-        // Initialize Lance store
-        let lance_data_dir = std::path::PathBuf::from(&option.dir).join("lance");
-        let lance_store = match LanceStore::new(lance_data_dir).await {
-            Ok(store) => Some(Arc::new(store)),
-            Err(e) => {
-                eprintln!("Warning: Failed to initialize Lance store: {}", e.0);
-                None
-            }
-        };
-
         Server {
             db_cache: Arc::new(std::sync::RwLock::new(HashMap::new())),
             option,
@@ -65,17 +51,9 @@ impl Server {
 
             list_waiters: Arc::new(Mutex::new(HashMap::new())),
             waiter_seq: Arc::new(AtomicU64::new(1)),
-            lance_store,
         }
     }
 
-    pub fn lance_store(&self) -> Result<Arc<LanceStore>, DBError> {
-        self.lance_store
-            .as_ref()
-            .cloned()
-            .ok_or_else(|| DBError("Lance store not initialized".to_string()))
-    }
-
     pub fn current_storage(&self) -> Result<Arc<dyn StorageBackend>, DBError> {
         let mut cache = self.db_cache.write().unwrap();
         

tests/rpc_tests.rs

@@ -0,0 +1,62 @@
+use std::net::SocketAddr;
+use jsonrpsee::http_client::HttpClientBuilder;
+use jsonrpsee::core::client::ClientT;
+use serde_json::json;
+
+use herodb::rpc::{RpcClient, BackendType, DatabaseConfig};
+
+#[tokio::test]
+async fn test_rpc_server_basic() {
+    // This test would require starting the RPC server in a separate thread
+    // For now, we'll just test that the types compile correctly
+
+    // Test serialization of types
+    let backend = BackendType::Redb;
+    let config = DatabaseConfig {
+        name: Some("test_db".to_string()),
+        storage_path: Some("/tmp/test".to_string()),
+        max_size: Some(1024 * 1024),
+        redis_version: Some("7.0".to_string()),
+    };
+
+    let backend_json = serde_json::to_string(&backend).unwrap();
+    let config_json = serde_json::to_string(&config).unwrap();
+
+    assert_eq!(backend_json, "\"Redb\"");
+    assert!(config_json.contains("test_db"));
+}
+
+#[tokio::test]
+async fn test_database_config_serialization() {
+    let config = DatabaseConfig {
+        name: Some("my_db".to_string()),
+        storage_path: None,
+        max_size: Some(1000000),
+        redis_version: Some("7.0".to_string()),
+    };
+
+    let json = serde_json::to_value(&config).unwrap();
+    assert_eq!(json["name"], "my_db");
+    assert_eq!(json["max_size"], 1000000);
+    assert_eq!(json["redis_version"], "7.0");
+}
+
+#[tokio::test]
+async fn test_backend_type_serialization() {
+    // Test that both Redb and Sled backends serialize correctly
+    let redb_backend = BackendType::Redb;
+    let sled_backend = BackendType::Sled;
+
+    let redb_json = serde_json::to_string(&redb_backend).unwrap();
+    let sled_json = serde_json::to_string(&sled_backend).unwrap();
+
+    assert_eq!(redb_json, "\"Redb\"");
+    assert_eq!(sled_json, "\"Sled\"");
+
+    // Test deserialization
+    let redb_deserialized: BackendType = serde_json::from_str(&redb_json).unwrap();
+    let sled_deserialized: BackendType = serde_json::from_str(&sled_json).unwrap();
+
+    assert!(matches!(redb_deserialized, BackendType::Redb));
+    assert!(matches!(sled_deserialized, BackendType::Sled));
+}

tests/usage_suite.rs

@@ -501,11 +501,11 @@ async fn test_07_age_stateless_suite() {
     let mut s = connect(port).await;
 
     // GENENC -> [recipient, identity]
-    let gen = send_cmd(&mut s, &["AGE", "GENENC"]).await;
+    let genenc = send_cmd(&mut s, &["AGE", "GENENC"]).await;
     assert!(
-        gen.starts_with("*2\r\n$"),
+        genenc.starts_with("*2\r\n$"),
         "AGE GENENC should return array [recipient, identity], got:\n{}",
-        gen
+        genenc
     );
 
     // Parse simple RESP array of two bulk strings to extract keys
@@ -520,7 +520,7 @@ async fn test_07_age_stateless_suite() {
         let ident = lines.next().unwrap_or("").to_string();
         (recip, ident)
     }
-    let (recipient, identity) = parse_two_bulk_array(&gen);
+    let (recipient, identity) = parse_two_bulk_array(&genenc);
     assert!(
         recipient.starts_with("age1") && identity.starts_with("AGE-SECRET-KEY-1"),
         "Unexpected AGE key formats.\nrecipient: {}\nidentity: {}",