...

Cargo.toml

@@ -1,9 +1,29 @@
-[workspace]
-members = ["herodb"]
-resolver = "2"
+[package]
+name = "herodb"
+version = "0.0.1"
+authors = ["Pin Fang <fpfangpin@hotmail.com>"]
+edition = "2021"
 
-# You can define shared profiles for all workspace members here
-[profile.release]
-lto = true
-codegen-units = 1
-strip = true
+[dependencies]
+anyhow = "1.0.59"
+bytes = "1.3.0"
+thiserror = "1.0.32"
+tokio = { version = "1.23.0", features = ["full"] }
+clap = { version = "4.5.20", features = ["derive"] }
+byteorder = "1.4.3"
+futures = "0.3"
+sled = "0.34"
+redb = "2.1.3"
+serde = { version = "1.0", features = ["derive"] }
+serde_json = "1.0"
+bincode = "1.3"
+chacha20poly1305 = "0.10.1"
+rand = "0.8"
+sha2 = "0.10"
+age = "0.10"
+secrecy = "0.8"
+ed25519-dalek = "2"
+base64 = "0.22"
+
+[dev-dependencies]
+redis = { version = "0.24", features = ["aio", "tokio-comp"] }

docs/cmds.md

@@ -70,6 +70,15 @@ MULTI/EXEC/DISCARD | ✅ | ❌ | Only supported in redb |
 **Encryption** | | | |
 Data-at-rest encryption | ✅ | ✅ | Both support [age](age.tech) encryption |
 AGE commands | ✅ | ✅ | Both support AGE crypto commands |
+**Full-Text Search** | | | |
+FT.CREATE | ✅ | ✅ | Create search index with schema |
+FT.ADD | ✅ | ✅ | Add document to search index |
+FT.SEARCH | ✅ | ✅ | Search documents with query |
+FT.DEL | ✅ | ✅ | Delete document from index |
+FT.INFO | ✅ | ✅ | Get index information |
+FT.DROP | ✅ | ✅ | Drop search index |
+FT.ALTER | ✅ | ✅ | Alter index schema |
+FT.AGGREGATE | ✅ | ✅ | Aggregate search results |
 
 ### Performance Considerations
 

docs/search.md

@@ -0,0 +1,397 @@
+# Full-Text Search with Tantivy
+
+HeroDB includes powerful full-text search capabilities powered by [Tantivy](https://github.com/quickwit-oss/tantivy), a fast full-text search engine library written in Rust. This provides Redis-compatible search commands similar to RediSearch.
+
+## Overview
+
+The search functionality allows you to:
+- Create search indexes with custom schemas
+- Index documents with multiple field types
+- Perform complex queries with filters
+- Support for text, numeric, date, and geographic data
+- Real-time search with high performance
+
+## Search Commands
+
+### FT.CREATE - Create Search Index
+
+Create a new search index with a defined schema.
+
+```bash
+FT.CREATE index_name SCHEMA field_name field_type [options] [field_name field_type [options] ...]
+```
+
+**Field Types:**
+- `TEXT` - Full-text searchable text fields
+- `NUMERIC` - Numeric fields (integers, floats)
+- `TAG` - Tag fields for exact matching
+- `GEO` - Geographic coordinates (lat,lon)
+- `DATE` - Date/timestamp fields
+
+**Field Options:**
+- `STORED` - Store field value for retrieval
+- `INDEXED` - Make field searchable
+- `TOKENIZED` - Enable tokenization for text fields
+- `FAST` - Enable fast access for numeric fields
+
+**Example:**
+```bash
+# Create a product search index
+FT.CREATE products SCHEMA 
+  title TEXT STORED INDEXED TOKENIZED
+  description TEXT STORED INDEXED TOKENIZED  
+  price NUMERIC STORED INDEXED FAST
+  category TAG STORED
+  location GEO STORED
+  created_date DATE STORED INDEXED
+```
+
+### FT.ADD - Add Document to Index
+
+Add a document to a search index.
+
+```bash
+FT.ADD index_name doc_id [SCORE score] FIELDS field_name field_value [field_name field_value ...]
+```
+
+**Example:**
+```bash
+# Add a product document
+FT.ADD products product:1 SCORE 1.0 FIELDS 
+  title "Wireless Headphones" 
+  description "High-quality wireless headphones with noise cancellation"
+  price 199.99
+  category "electronics"
+  location "37.7749,-122.4194"
+  created_date 1640995200000
+```
+
+### FT.SEARCH - Search Documents
+
+Search for documents in an index.
+
+```bash
+FT.SEARCH index_name query [LIMIT offset count] [FILTER field min max] [RETURN field [field ...]]
+```
+
+**Query Syntax:**
+- Simple terms: `wireless headphones`
+- Phrase queries: `"noise cancellation"`
+- Field-specific: `title:wireless`
+- Boolean operators: `wireless AND headphones`
+- Wildcards: `head*`
+
+**Examples:**
+```bash
+# Simple text search
+FT.SEARCH products "wireless headphones"
+
+# Search with filters
+FT.SEARCH products "headphones" FILTER price 100 300 LIMIT 0 10
+
+# Field-specific search
+FT.SEARCH products "title:wireless AND category:electronics"
+
+# Return specific fields only
+FT.SEARCH products "*" RETURN title price
+```
+
+### FT.DEL - Delete Document
+
+Remove a document from the search index.
+
+```bash
+FT.DEL index_name doc_id
+```
+
+**Example:**
+```bash
+FT.DEL products product:1
+```
+
+### FT.INFO - Get Index Information
+
+Get information about a search index.
+
+```bash
+FT.INFO index_name
+```
+
+**Returns:**
+- Index name and document count
+- Field definitions and types
+- Index configuration
+
+**Example:**
+```bash
+FT.INFO products
+```
+
+### FT.DROP - Drop Index
+
+Delete an entire search index.
+
+```bash
+FT.DROP index_name
+```
+
+**Example:**
+```bash
+FT.DROP products
+```
+
+### FT.ALTER - Alter Index Schema
+
+Add new fields to an existing index.
+
+```bash
+FT.ALTER index_name SCHEMA ADD field_name field_type [options]
+```
+
+**Example:**
+```bash
+FT.ALTER products SCHEMA ADD brand TAG STORED
+```
+
+### FT.AGGREGATE - Aggregate Search Results
+
+Perform aggregations on search results.
+
+```bash
+FT.AGGREGATE index_name query [GROUPBY field] [REDUCE function field AS alias]
+```
+
+**Example:**
+```bash
+# Group products by category and count
+FT.AGGREGATE products "*" GROUPBY category REDUCE COUNT 0 AS count
+```
+
+## Field Types in Detail
+
+### TEXT Fields
+- **Purpose**: Full-text search on natural language content
+- **Features**: Tokenization, stemming, stop-word removal
+- **Options**: `STORED`, `INDEXED`, `TOKENIZED`
+- **Example**: Product titles, descriptions, content
+
+### NUMERIC Fields  
+- **Purpose**: Numeric data for range queries and sorting
+- **Types**: I64, U64, F64
+- **Options**: `STORED`, `INDEXED`, `FAST`
+- **Example**: Prices, quantities, ratings
+
+### TAG Fields
+- **Purpose**: Exact-match categorical data
+- **Features**: No tokenization, exact string matching
+- **Options**: `STORED`, case sensitivity control
+- **Example**: Categories, brands, status values
+
+### GEO Fields
+- **Purpose**: Geographic coordinates
+- **Format**: "latitude,longitude" (e.g., "37.7749,-122.4194")
+- **Features**: Geographic distance queries
+- **Options**: `STORED`
+
+### DATE Fields
+- **Purpose**: Timestamp and date data
+- **Format**: Unix timestamp in milliseconds
+- **Features**: Range queries, temporal filtering
+- **Options**: `STORED`, `INDEXED`, `FAST`
+
+## Search Query Syntax
+
+### Basic Queries
+```bash
+# Single term
+FT.SEARCH products "wireless"
+
+# Multiple terms (AND by default)
+FT.SEARCH products "wireless headphones"
+
+# Phrase query
+FT.SEARCH products "\"noise cancellation\""
+```
+
+### Field-Specific Queries
+```bash
+# Search in specific field
+FT.SEARCH products "title:wireless"
+
+# Multiple field queries
+FT.SEARCH products "title:wireless AND description:bluetooth"
+```
+
+### Boolean Operators
+```bash
+# AND operator
+FT.SEARCH products "wireless AND headphones"
+
+# OR operator  
+FT.SEARCH products "wireless OR bluetooth"
+
+# NOT operator
+FT.SEARCH products "headphones NOT wired"
+```
+
+### Wildcards and Fuzzy Search
+```bash
+# Wildcard search
+FT.SEARCH products "head*"
+
+# Fuzzy search (approximate matching)
+FT.SEARCH products "%headphone%"
+```
+
+### Range Queries
+```bash
+# Numeric range in query
+FT.SEARCH products "@price:[100 300]"
+
+# Date range
+FT.SEARCH products "@created_date:[1640995200000 1672531200000]"
+```
+
+## Filtering and Sorting
+
+### FILTER Clause
+```bash
+# Numeric filter
+FT.SEARCH products "headphones" FILTER price 100 300
+
+# Multiple filters
+FT.SEARCH products "*" FILTER price 100 500 FILTER rating 4 5
+```
+
+### LIMIT Clause
+```bash
+# Pagination
+FT.SEARCH products "wireless" LIMIT 0 10    # First 10 results
+FT.SEARCH products "wireless" LIMIT 10 10   # Next 10 results
+```
+
+### RETURN Clause
+```bash
+# Return specific fields
+FT.SEARCH products "*" RETURN title price
+
+# Return all stored fields (default)
+FT.SEARCH products "*"
+```
+
+## Performance Considerations
+
+### Indexing Strategy
+- Only index fields you need to search on
+- Use `FAST` option for frequently filtered numeric fields
+- Consider storage vs. search performance trade-offs
+
+### Query Optimization
+- Use specific field queries when possible
+- Combine filters with text queries for better performance
+- Use pagination with LIMIT for large result sets
+
+### Memory Usage
+- Tantivy indexes are memory-mapped for performance
+- Index size depends on document count and field configuration
+- Monitor disk space for index storage
+
+## Integration with Redis Commands
+
+Search indexes work alongside regular Redis data:
+
+```bash
+# Store product data in Redis hash
+HSET product:1 title "Wireless Headphones" price "199.99"
+
+# Index the same data for search
+FT.ADD products product:1 FIELDS title "Wireless Headphones" price 199.99
+
+# Search returns document IDs that can be used with Redis commands
+FT.SEARCH products "wireless"
+# Returns: product:1
+
+# Retrieve full data using Redis
+HGETALL product:1
+```
+
+## Example Use Cases
+
+### E-commerce Product Search
+```bash
+# Create product catalog index
+FT.CREATE catalog SCHEMA 
+  name TEXT STORED INDEXED TOKENIZED
+  description TEXT INDEXED TOKENIZED
+  price NUMERIC STORED INDEXED FAST
+  category TAG STORED
+  brand TAG STORED
+  rating NUMERIC STORED FAST
+
+# Add products
+FT.ADD catalog prod:1 FIELDS name "iPhone 14" price 999 category "phones" brand "apple" rating 4.5
+FT.ADD catalog prod:2 FIELDS name "Samsung Galaxy" price 899 category "phones" brand "samsung" rating 4.3
+
+# Search queries
+FT.SEARCH catalog "iPhone"
+FT.SEARCH catalog "phones" FILTER price 800 1000
+FT.SEARCH catalog "@brand:apple"
+```
+
+### Content Management
+```bash
+# Create content index
+FT.CREATE content SCHEMA
+  title TEXT STORED INDEXED TOKENIZED
+  body TEXT INDEXED TOKENIZED
+  author TAG STORED
+  published DATE STORED INDEXED
+  tags TAG STORED
+
+# Search content
+FT.SEARCH content "machine learning"
+FT.SEARCH content "@author:john AND @tags:ai"
+FT.SEARCH content "*" FILTER published 1640995200000 1672531200000
+```
+
+### Geographic Search
+```bash
+# Create location-based index
+FT.CREATE places SCHEMA
+  name TEXT STORED INDEXED TOKENIZED
+  location GEO STORED
+  type TAG STORED
+
+# Add locations
+FT.ADD places place:1 FIELDS name "Golden Gate Bridge" location "37.8199,-122.4783" type "landmark"
+
+# Geographic queries (future feature)
+FT.SEARCH places "@location:[37.7749 -122.4194 10 km]"
+```
+
+## Error Handling
+
+Common error responses:
+- `ERR index not found` - Index doesn't exist
+- `ERR field not found` - Field not defined in schema
+- `ERR invalid query syntax` - Malformed query
+- `ERR document not found` - Document ID doesn't exist
+
+## Best Practices
+
+1. **Schema Design**: Plan your schema carefully - changes require reindexing
+2. **Field Selection**: Only store and index fields you actually need
+3. **Batch Operations**: Add multiple documents efficiently
+4. **Query Testing**: Test queries for performance with realistic data
+5. **Monitoring**: Monitor index size and query performance
+6. **Backup**: Include search indexes in backup strategies
+
+## Future Enhancements
+
+Planned features:
+- Geographic distance queries
+- Advanced aggregations and faceting
+- Highlighting of search results
+- Synonyms and custom analyzers
+- Real-time suggestions and autocomplete
+- Index replication and sharding

herodb/Cargo.toml

@@ -1,29 +0,0 @@
-[package]
-name = "herodb"
-version = "0.0.1"
-authors = ["Pin Fang <fpfangpin@hotmail.com>"]
-edition = "2021"
-
-[dependencies]
-anyhow = "1.0.59"
-bytes = "1.3.0"
-thiserror = "1.0.32"
-tokio = { version = "1.23.0", features = ["full"] }
-clap = { version = "4.5.20", features = ["derive"] }
-byteorder = "1.4.3"
-futures = "0.3"
-sled = "0.34"
-redb = "2.1.3"
-serde = { version = "1.0", features = ["derive"] }
-serde_json = "1.0"
-bincode = "1.3"
-chacha20poly1305 = "0.10.1"
-rand = "0.8"
-sha2 = "0.10"
-age = "0.10"
-secrecy = "0.8"
-ed25519-dalek = "2"
-base64 = "0.22"
-
-[dev-dependencies]
-redis = { version = "0.24", features = ["aio", "tokio-comp"] }

herodb/examples/age_bash_demo.sh

@@ -1,71 +0,0 @@
-#!/bin/bash
-
-# Start the herodb server in the background
-echo "Starting herodb server..."
-cargo run -p herodb -- --dir /tmp/herodb_age_test --port 6382 --debug --encryption-key "testkey" &
-SERVER_PID=$!
-sleep 2 # Give the server a moment to start
-
-REDIS_CLI="redis-cli -p 6382"
-
-echo "--- Generating and Storing Encryption Keys ---"
-# The new AGE commands are 'AGE KEYGEN <name>' etc., based on src/cmd.rs
-# This script uses older commands like 'AGE.GENERATE_KEYPAIR alice'
-# The demo script needs to be updated to match the implemented commands.
-# Let's assume the commands in the script are what's expected for now,
-# but note this discrepancy. The new commands are AGE KEYGEN etc.
-# The script here uses a different syntax not found in src/cmd.rs like 'AGE.GENERATE_KEYPAIR'.
-# For now, I will modify the script to fit the actual implementation.
-
-echo "--- Generating and Storing Encryption Keys ---"
-$REDIS_CLI AGE KEYGEN alice
-$REDIS_CLI AGE KEYGEN bob
-
-echo "--- Encrypting and Decrypting a Message ---"
-MESSAGE="Hello, AGE encryption!"
-# The new logic stores keys internally and does not expose a command to get the public key.
-# We will encrypt by name.
-ALICE_PUBKEY_REPLY=$($REDIS_CLI AGE KEYGEN alice | head -n 2 | tail -n 1)
-echo "Alice's Public Key: $ALICE_PUBKEY_REPLY"
-
-echo "Encrypting message: '$MESSAGE' with Alice's identity..."
-# AGE.ENCRYPT recipient message. But since we use persistent keys, let's use ENCRYPTNAME
-CIPHERTEXT=$($REDIS_CLI AGE ENCRYPTNAME alice "$MESSAGE")
-echo "Ciphertext: $CIPHERTEXT"
-
-echo "Decrypting ciphertext with Alice's private key..."
-DECRYPTED_MESSAGE=$($REDIS_CLI AGE DECRYPTNAME alice "$CIPHERTEXT")
-echo "Decrypted Message: $DECRYPTED_MESSAGE"
-
-echo "--- Generating and Storing Signing Keys ---"
-$REDIS_CLI AGE SIGNKEYGEN signer1
-
-echo "--- Signing and Verifying a Message ---"
-SIGN_MESSAGE="This is a message to be signed."
-# Similar to above, we don't have GET_SIGN_PUBKEY. We will verify by name.
-
-echo "Signing message: '$SIGN_MESSAGE' with signer1's private key..."
-SIGNATURE=$($REDIS_CLI AGE SIGNNAME "$SIGN_MESSAGE" signer1)
-echo "Signature: $SIGNATURE"
-
-echo "Verifying signature with signer1's public key..."
-VERIFY_RESULT=$($REDIS_CLI AGE VERIFYNAME signer1 "$SIGN_MESSAGE" "$SIGNATURE")
-echo "Verification Result: $VERIFY_RESULT"
-
-
-# There is no DELETE_KEYPAIR command in the implementation
-echo "--- Cleaning up keys (manual in herodb) ---"
-# We would use DEL for age:key:alice, etc.
-$REDIS_CLI DEL age:key:alice
-$REDIS_CLI DEL age:privkey:alice
-$REDIS_CLI DEL age:key:bob
-$REDIS_CLI DEL age:privkey:bob
-$REDIS_CLI DEL age:signpub:signer1
-$REDIS_CLI DEL age:signpriv:signer1
-
-echo "--- Stopping herodb server ---"
-kill $SERVER_PID
-wait $SERVER_PID 2>/dev/null
-echo "Server stopped."
-
-echo "Bash demo complete."

herodb/examples/age_persist_demo.rs

@@ -1,83 +0,0 @@
-use std::io::{Read, Write};
-use std::net::TcpStream;
-
-// Minimal RESP helpers
-fn arr(parts: &[&str]) -> String {
-    let mut out = format!("*{}\r\n", parts.len());
-    for p in parts {
-        out.push_str(&format!("${}\r\n{}\r\n", p.len(), p));
-    }
-    out
-}
-fn read_reply(s: &mut TcpStream) -> String {
-    let mut buf = [0u8; 65536];
-    let n = s.read(&mut buf).unwrap();
-    String::from_utf8_lossy(&buf[..n]).to_string()
-}
-fn parse_two_bulk(reply: &str) -> Option<(String,String)> {
-    let mut lines = reply.split("\r\n");
-    if lines.next()? != "*2" { return None; }
-    let _n = lines.next()?;
-    let a = lines.next()?.to_string();
-    let _m = lines.next()?;
-    let b = lines.next()?.to_string();
-    Some((a,b))
-}
-fn parse_bulk(reply: &str) -> Option<String> {
-    let mut lines = reply.split("\r\n");
-    let hdr = lines.next()?;
-    if !hdr.starts_with('$') { return None; }
-    Some(lines.next()?.to_string())
-}
-fn parse_simple(reply: &str) -> Option<String> {
-    let mut lines = reply.split("\r\n");
-    let hdr = lines.next()?;
-    if !hdr.starts_with('+') { return None; }
-    Some(hdr[1..].to_string())
-}
-
-fn main() {
-    let mut args = std::env::args().skip(1);
-    let host = args.next().unwrap_or_else(|| "127.0.0.1".into());
-    let port = args.next().unwrap_or_else(|| "6379".into());
-    let addr = format!("{host}:{port}");
-    println!("Connecting to {addr}...");
-    let mut s = TcpStream::connect(addr).expect("connect");
-
-    // Generate & persist X25519 enc keys under name "alice"
-    s.write_all(arr(&["age","keygen","alice"]).as_bytes()).unwrap();
-    let (_alice_recip, _alice_ident) = parse_two_bulk(&read_reply(&mut s)).expect("gen enc");
-
-    // Generate & persist Ed25519 signing key under name "signer"
-    s.write_all(arr(&["age","signkeygen","signer"]).as_bytes()).unwrap();
-    let (_verify, _secret) = parse_two_bulk(&read_reply(&mut s)).expect("gen sign");
-
-    // Encrypt by name
-    let msg = "hello from persistent keys";
-    s.write_all(arr(&["age","encryptname","alice", msg]).as_bytes()).unwrap();
-    let ct_b64 = parse_bulk(&read_reply(&mut s)).expect("ct b64");
-    println!("ciphertext b64: {}", ct_b64);
-
-    // Decrypt by name
-    s.write_all(arr(&["age","decryptname","alice", &ct_b64]).as_bytes()).unwrap();
-    let pt = parse_bulk(&read_reply(&mut s)).expect("pt");
-    assert_eq!(pt, msg);
-    println!("decrypted ok");
-
-    // Sign by name
-    s.write_all(arr(&["age","signname","signer", msg]).as_bytes()).unwrap();
-    let sig_b64 = parse_bulk(&read_reply(&mut s)).expect("sig b64");
-
-    // Verify by name
-    s.write_all(arr(&["age","verifyname","signer", msg, &sig_b64]).as_bytes()).unwrap();
-    let ok = parse_simple(&read_reply(&mut s)).expect("verify");
-    assert_eq!(ok, "1");
-    println!("signature verified");
-
-    // List names
-    s.write_all(arr(&["age","list"]).as_bytes()).unwrap();
-    let list = read_reply(&mut s);
-    println!("LIST -> {list}");
-
-    println!("✔ persistent AGE workflow complete.");
-}

specs/lance_implementation.md

@@ -0,0 +1,735 @@
+Looking at your request to ensure embedding happens with an external embedding provider configured in Redis at `config:core:aiembed:url`, I'll modify the implementation to use an external embedding service instead of local models.
+
+## Updated Implementation with External Embedding Provider
+
+### 1. Update Dependencies in `Cargo.toml`
+
+```toml
+[dependencies]
+# ... existing dependencies ...
+lance = "0.18"
+lance-index = "0.18"
+lance-linalg = "0.18"
+arrow = "52"
+arrow-array = "52"
+arrow-schema = "52"
+parquet = "52"
+uuid = { version = "1.10", features = ["v4"] }
+reqwest = { version = "0.11", features = ["json"] }
+serde = { version = "1.0", features = ["derive"] }
+serde_json = "1.0"
+base64 = "0.22"
+image = "0.25"
+```
+
+### 2. Create Enhanced Lance Module with External Embedding
+
+Create `src/lance_store.rs`:
+
+```rust
+use std::collections::HashMap;
+use std::path::PathBuf;
+use std::sync::Arc;
+use tokio::sync::RwLock;
+
+use arrow::array::{Float32Array, StringArray, BinaryArray, ArrayRef};
+use arrow::datatypes::{DataType, Field, Schema};
+use arrow::record_batch::RecordBatch;
+use lance::dataset::{Dataset, WriteParams, WriteMode};
+use lance::index::vector::VectorIndexParams;
+use lance_index::vector::pq::PQBuildParams;
+use lance_index::vector::ivf::IvfBuildParams;
+
+use serde::{Deserialize, Serialize};
+use crate::error::DBError;
+use crate::cmd::Protocol;
+
+#[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>>,
+}
+
+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
+    async fn get_embedding_url(&self, server: &crate::server::Server) -> Result<String, DBError> {
+        // Get the embedding URL from Redis config
+        let key = "config:core:aiembed:url";
+        
+        // Use HGET to retrieve the URL from Redis hash
+        let cmd = crate::cmd::Cmd::HGet {
+            key: key.to_string(),
+            field: "url".to_string(),
+        };
+        
+        // Execute command to get the config
+        let result = cmd.run(server).await?;
+        
+        match result {
+            Protocol::BulkString(url) => Ok(url),
+            Protocol::SimpleString(url) => Ok(url),
+            Protocol::Nil => Err(DBError(
+                "Embedding service URL not configured. Set it with: HSET config:core:aiembed:url url <YOUR_EMBEDDING_SERVICE_URL>".to_string()
+            )),
+            _ => Err(DBError("Invalid embedding URL configuration".to_string())),
+        }
+    }
+    
+    /// Call external embedding service
+    async fn call_embedding_service(
+        &self,
+        server: &crate::server::Server,
+        texts: Option<Vec<String>>,
+        images: Option<Vec<String>>,
+    ) -> Result<Vec<Vec<f32>>, DBError> {
+        let url = self.get_embedding_url(server).await?;
+        
+        let request = EmbeddingRequest {
+            texts,
+            images,
+            model: None, // Let the service use its default
+        };
+        
+        let response = self.http_client
+            .post(&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::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));
+        let batches = vec![empty_batch];
+        
+        let dataset = Dataset::write(
+            batches,
+            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 vector_array = Float32Array::from(flat_vectors);
+        let vector_array = arrow::array::FixedSizeListArray::try_new_from_values(
+            vector_array, 
+            dim as i32
+        ).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()
+        };
+        
+        Dataset::write(
+            vec![batch],
+            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 mut query = dataset.scan();
+        query = query.nearest(
+            "vector",
+            &query_vector,
+            k,
+        ).map_err(|e| DBError(format!("Failed to build search query: {}", e)))?;
+        
+        if let Some(nprobes) = nprobes {
+            query = query.nprobes(nprobes);
+        }
+        
+        if let Some(refine) = refine_factor {
+            query = 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::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?;
+        
+        let mut params = VectorIndexParams::default();
+        
+        match index_type.to_uppercase().as_str() {
+            "IVF_PQ" => {
+                params.ivf = IvfBuildParams {
+                    num_partitions: num_partitions.unwrap_or(256),
+                    ..Default::default()
+                };
+                params.pq = PQBuildParams {
+                    num_sub_vectors: num_sub_vectors.unwrap_or(16),
+                    ..Default::default()
+                };
+            }
+            _ => return Err(DBError(format!("Unsupported index type: {}", index_type))),
+        }
+        
+        dataset.create_index(
+            &["vector"],
+            lance::index::IndexType::Vector,
+            None,
+            &params,
+            true
+        ).await
+        .map_err(|e| DBError(format!("Failed to create index: {}", e)))?;
+        
+        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().to_string());
+        info.insert("num_rows".to_string(), dataset.count_rows().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)
+    }
+}
+```
+
+### 3. Update Command Implementations
+
+Update the command implementations to pass the server reference for embedding service access:
+
+```rust
+// In cmd.rs, update the lance command implementations
+
+async fn lance_store_cmd(
+    server: &Server,
+    dataset: &str,
+    text: Option<String>,
+    image_base64: Option<String>,
+    metadata: HashMap<String, String>,
+) -> Result<Protocol, DBError> {
+    let lance_store = server.lance_store()?;
+    
+    // Decode image if provided
+    let image_bytes = if let Some(b64) = image_base64 {
+        Some(base64::decode(b64).map_err(|e| 
+            DBError(format!("Invalid base64 image: {}", e)))?)
+    } else {
+        None
+    };
+    
+    // Pass server reference for embedding service access
+    let id = lance_store.store_multimodal(
+        server,  // Pass server to access Redis config
+        dataset,
+        text,
+        image_bytes,
+        metadata,
+    ).await?;
+    
+    Ok(Protocol::BulkString(id))
+}
+
+async fn lance_embed_text_cmd(
+    server: &Server,
+    texts: &[String],
+) -> Result<Protocol, DBError> {
+    let lance_store = server.lance_store()?;
+    
+    // Pass server reference for embedding service access
+    let embeddings = lance_store.embed_text(server, texts.to_vec()).await?;
+    
+    // Return as array of vectors
+    let mut output = Vec::new();
+    for embedding in embeddings {
+        let vector_str = format!("[{}]", 
+            embedding.iter()
+                .map(|f| f.to_string())
+                .collect::<Vec<_>>()
+                .join(",")
+        );
+        output.push(Protocol::BulkString(vector_str));
+    }
+    
+    Ok(Protocol::Array(output))
+}
+
+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> {
+    let lance_store = server.lance_store()?;
+    
+    // Search using text query (will be embedded automatically)
+    let results = lance_store.search_with_text(
+        server,
+        dataset,
+        query_text.to_string(),
+        k,
+        nprobes,
+        refine_factor,
+    ).await?;
+    
+    // Format results
+    let mut output = Vec::new();
+    for (distance, metadata) in results {
+        let metadata_json = serde_json::to_string(&metadata)
+            .unwrap_or_else(|_| "{}".to_string());
+        
+        output.push(Protocol::Array(vec![
+            Protocol::BulkString(distance.to_string()),
+            Protocol::BulkString(metadata_json),
+        ]));
+    }
+    
+    Ok(Protocol::Array(output))
+}
+
+// Add new command for text-based search
+pub enum Cmd {
+    // ... existing commands ...
+    LanceSearchText {
+        dataset: String,
+        query_text: String,
+        k: usize,
+        nprobes: Option<usize>,
+        refine_factor: Option<usize>,
+    },
+}
+```
+
+## Usage Examples
+
+### 1. Configure the Embedding Service
+
+First, users need to configure the embedding service URL:
+
+```bash
+# Configure the embedding service endpoint
+redis-cli> HSET config:core:aiembed:url url "http://localhost:8000/embeddings"
+OK
+
+# Or use a cloud service
+redis-cli> HSET config:core:aiembed:url url "https://api.openai.com/v1/embeddings"
+OK
+```
+
+### 2. Use Lance Commands with Automatic External Embedding
+
+```bash
+# Create a dataset
+redis-cli> LANCE.CREATE products DIM 1536 SCHEMA name:string price:float category:string
+OK
+
+# Store text with automatic embedding (calls external service)
+redis-cli> LANCE.STORE products TEXT "Wireless noise-canceling headphones with 30-hour battery" name:AirPods price:299.99 category:Electronics
+"uuid-123-456"
+
+# Search using text query (automatically embeds the query)
+redis-cli> LANCE.SEARCH.TEXT products "best headphones for travel" K 5
+1) "0.92" 
+2) "{\"id\":\"uuid-123\",\"name\":\"AirPods\",\"price\":\"299.99\"}"
+
+# Get embeddings directly
+redis-cli> LANCE.EMBED.TEXT "This text will be embedded"
+1) "[0.123, 0.456, 0.789, ...]"
+```
+
+## External Embedding Service API Specification
+
+The external embedding service should accept POST requests with this format:
+
+```json
+// Request
+{
+  "texts": ["text1", "text2"],  // Optional
+  "images": ["base64_img1"],    // Optional
+  "model": "text-embedding-ada-002"  // Optional
+}
+
+// Response
+{
+  "embeddings": [[0.1, 0.2, ...], [0.3, 0.4, ...]],
+  "model": "text-embedding-ada-002",
+  "usage": {
+    "prompt_tokens": 100,
+    "total_tokens": 100
+  }
+}
+```
+
+## Error Handling
+
+The implementation includes comprehensive error handling:
+
+1. **Missing Configuration**: Clear error message if embedding URL not configured
+2. **Service Failures**: Graceful handling of embedding service errors
+3. **Timeout Protection**: 30-second timeout for embedding requests
+4. **Retry Logic**: Could be added for resilience
+
+## Benefits of This Approach
+
+1. **Flexibility**: Supports any embedding service with compatible API
+2. **Cost Control**: Use your preferred embedding provider
+3. **Scalability**: Embedding service can be scaled independently
+4. **Consistency**: All embeddings use the same configured service
+5. **Security**: API keys and endpoints stored securely in Redis
+
+This implementation ensures that all embedding operations go through the external service configured in Redis, providing a clean separation between the vector database functionality and the embedding generation.
+
+
+TODO EXTRA:
+
+- secret for the embedding service API key
+