# 🤖 Model Loading Guide for VectorLite
This guide explains how to find and load real BERT models for your VectorLite project.
## 1. Finding the Right Model
### 🎯 **Best Models for VectorLite**
| `sentence-transformers/all-MiniLM-L6-v2` | 384 | 22MB | ⚡⚡⚡ | ⭐⭐⭐⭐ | **Recommended** - Fast & efficient |
| `sentence-transformers/all-mpnet-base-v2` | 768 | 420MB | ⚡⚡ | ⭐⭐⭐⭐⭐ | High quality similarity |
| `microsoft/DialoGPT-small` | 768 | 117MB | ⚡⚡ | ⭐⭐⭐ | Conversational text |
| `distilbert-base-uncased` | 768 | 250MB | ⚡⚡ | ⭐⭐⭐⭐ | General purpose |
### 🔍 **How to Research Models**
1. **Visit Hugging Face Model Hub**: https://huggingface.co/models
2. **Filter by**:
- Library: `sentence-transformers`
- Task: `sentence-similarity`
- Language: `en`
3. **Check model cards** for:
- Performance metrics (accuracy, speed)
- Model size and memory requirements
- Supported languages
- Example usage
### 📊 **Model Selection Criteria**
- **Size**: Smaller = faster download, less memory
- **Speed**: Important for real-time search
- **Quality**: Better embeddings = better search results
- **Dimensions**: 384 is more efficient than 768
- **Specialization**: Some models are better for specific domains
## 2. Loading Models in Code
### 🚀 **Step-by-Step Implementation**
#### Step 1: Add Dependencies
Add these to your `Cargo.toml`:
```toml
[dependencies]
candle-core = { version = "0.9.1", features = ["accelerate"] }
candle-nn = "0.9.1"
candle-transformers = "0.9.1"
tokenizers = "0.19"
hf-hub = "0.3"
tokio = { version = "1.0", features = ["full"] }
serde_json = "1.0"
```
#### Step 2: Download Model Files
```rust
use hf_hub::api::tokio::Api;
async fn download_model(model_id: &str) -> Result<(), Box<dyn std::error::Error>> {
let api = Api::new()?;
let repo = api.model(model_id.to_string());
// Download required files
let config_path = repo.get("config.json").await?;
let tokenizer_path = repo.get("tokenizer.json").await?;
let model_path = repo.get("pytorch_model.bin").await?;
println!("✅ Model files downloaded to: {:?}", config_path.parent().unwrap());
Ok(())
}
```
#### Step 3: Load Tokenizer
```rust
use tokenizers::Tokenizer;
fn load_tokenizer(tokenizer_path: &str) -> Result<Tokenizer, Box<dyn std::error::Error>> {
let tokenizer = Tokenizer::from_file(tokenizer_path)?;
Ok(tokenizer)
}
```
#### Step 4: Load Model Config
```rust
use candle_transformers::models::bert::Config;
use serde_json;
fn load_config(config_path: &str) -> Result<Config, Box<dyn std::error::Error>> {
let config_str = std::fs::read_to_string(config_path)?;
let config: Config = serde_json::from_str(&config_str)?;
Ok(config)
}
```
#### Step 5: Load Model Weights
```rust
use candle_core::{Device, DType};
use candle_nn::VarBuilder;
use candle_transformers::models::bert::BertModel;
fn load_model(model_path: &str, config: &Config, device: &Device) -> Result<BertModel, Box<dyn std::error::Error>> {
let weights = VarBuilder::from_pth(model_path, DType::F32, device)?;
let model = BertModel::load(&weights, config)?;
Ok(model)
}
```
#### Step 6: Generate Embeddings
```rust
use candle_core::Tensor;
fn generate_embedding(
text: &str,
tokenizer: &Tokenizer,
model: &BertModel,
device: &Device,
) -> Result<Vec<f64>, Box<dyn std::error::Error>> {
// 1. Tokenize
let encoding = tokenizer.encode(text, true)?;
let token_ids = encoding.get_ids();
// 2. Convert to tensor
let input_ids = Tensor::new(token_ids, device)?;
let input_ids = input_ids.unsqueeze(0)?; // Add batch dimension
// 3. Run through model
let outputs = model.forward(&input_ids)?;
let embeddings = outputs.hidden_states.unwrap_or(outputs.last_hidden_state);
// 4. Extract [CLS] token (first token)
let cls_embedding = embeddings.i((0, 0))?;
// 5. Convert to Vec<f64>
let embedding: Vec<f64> = cls_embedding.to_vec1()?;
// 6. L2 normalize
let norm: f64 = embedding.iter().map(|x| x * x).sum::<f64>().sqrt();
let normalized: Vec<f64> = if norm > 0.0 {
embedding.iter().map(|x| x / norm).collect()
} else {
embedding
};
Ok(normalized)
}
```
## 3. Complete Working Example
See `examples/real_model_loading.rs` for a complete implementation.
### 🏃 **Quick Start**
```bash
# Run the example
cargo run --example real_model_loading
# This will:
# 1. Download the model from Hugging Face
# 2. Load it into memory
# 3. Generate embeddings for sample texts
```
## 4. Production Considerations
### 💾 **Caching Models**
```rust
use std::path::Path;
fn get_model_cache_path(model_id: &str) -> PathBuf {
let cache_dir = std::env::var("HF_HOME")
.unwrap_or_else(|_| ".cache/huggingface".to_string());
Path::new(&cache_dir).join("hub").join(model_id)
}
```
### 🚀 **Performance Optimization**
1. **Use GPU if available**:
```rust
let device = Device::Cpu; ```
2. **Batch processing**:
```rust
fn generate_embeddings_batch(texts: &[String]) -> Result<Vec<Vec<f64>>> {
}
```
3. **Model quantization** (for smaller models):
```rust
let weights = VarBuilder::from_pth(model_path, DType::F16, device)?;
```
### 🔧 **Error Handling**
```rust
#[derive(Error, Debug)]
pub enum ModelError {
#[error("Failed to download model: {0}")]
DownloadFailed(String),
#[error("Failed to load tokenizer: {0}")]
TokenizerError(String),
#[error("Failed to load model: {0}")]
ModelLoadError(String),
#[error("Inference failed: {0}")]
InferenceError(String),
}
```
## 5. Next Steps
1. **Choose your model** based on your needs
2. **Implement the loading code** using the examples above
3. **Test with your data** to ensure quality
4. **Optimize for performance** based on your use case
5. **Add error handling** for production use
## 🎯 **Recommended Path**
For VectorLite, I recommend starting with `sentence-transformers/all-MiniLM-L6-v2` because:
- ✅ Small and fast
- ✅ Designed for similarity search
- ✅ 384 dimensions (efficient)
- ✅ Great performance on most tasks
This will give you a solid foundation that you can optimize later!