68 lines
2.2 KiB
Rust
68 lines
2.2 KiB
Rust
use std::time::Duration;
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use stratum_embeddings::{
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EmbeddingOptions, EmbeddingService, FastEmbedProvider, LanceDbStore, MemoryCache, VectorStore,
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VectorStoreConfig,
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};
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use tempfile::tempdir;
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use tracing::info;
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#[tokio::main]
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async fn main() -> Result<(), Box<dyn std::error::Error>> {
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tracing_subscriber::fmt::init();
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info!("Initializing embedding service...");
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let provider = FastEmbedProvider::small()?;
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let cache = MemoryCache::new(1000, Duration::from_secs(300));
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let service = EmbeddingService::new(provider).with_cache(cache);
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let dir = tempdir()?;
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let db_path = dir.path().to_str().unwrap();
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info!("Creating LanceDB store at: {}", db_path);
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let config = VectorStoreConfig::new(384);
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let store = LanceDbStore::new(db_path, "embeddings", config).await?;
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let documents = vec![
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(
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"doc1",
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"Rust provides memory safety without garbage collection",
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),
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("doc2", "Knowledge graphs represent structured information"),
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("doc3", "Vector databases enable semantic similarity search"),
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("doc4", "Machine learning models learn from data patterns"),
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("doc5", "Embeddings capture semantic meaning in vectors"),
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];
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info!("Embedding and storing {} documents...", documents.len());
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let options = EmbeddingOptions::default_with_cache();
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for (id, text) in &documents {
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let embedding = service.embed(text, &options).await?;
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let metadata = serde_json::json!({
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"text": text,
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"source": "demo"
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});
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store.upsert(id, &embedding, metadata).await?;
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}
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info!("Documents stored successfully");
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info!("Performing semantic search...");
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let query = "How do databases support similarity matching?";
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let query_embedding = service.embed(query, &options).await?;
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let results = store.search(&query_embedding, 3, None).await?;
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info!("Search results for: '{}'", query);
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for (i, result) in results.iter().enumerate() {
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let text = result.metadata["text"].as_str().unwrap_or("N/A");
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info!(" {}. [score: {:.4}] {}", i + 1, result.score, text);
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}
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let count = store.count().await?;
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info!("Total documents in store: {}", count);
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Ok(())
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}
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