rag 0.1.5

A Rust library and CLI for Retrieval-Augmented Generation
Documentation 
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use rag::QdrantVectorStore;
use rag::vector_store::{Document, VectorStore};

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    println!("=== Qdrant Vector Store Example ===\n");

    // Assumes Qdrant is running locally with a collection named "rag_docs".
    // Create the collection first via Qdrant's dashboard or API with
    // the correct vector size and distance metric.
    let store = QdrantVectorStore::new("http://localhost:6333", "rag_docs");

    let docs = vec![
        Document::new("Rust is a systems programming language".to_string())
            .with_embedding(vec![1.0, 0.0, 0.0]),
        Document::new("Python is great for scripting".to_string())
            .with_embedding(vec![0.0, 1.0, 0.0]),
        Document::new("Go is built for concurrency".to_string())
            .with_embedding(vec![0.0, 0.0, 1.0]),
    ];

    store.add_batch(docs).await?;
    println!("Added 3 documents to Qdrant");

    let results = store.search(&[1.0, 0.0, 0.0], 2).await?;
    println!("\nTop 2 results for query [1.0, 0.0, 0.0]:");
    for (i, sim) in results.iter().enumerate() {
        println!(
            "  {}. {} (score: {:.4})",
            i + 1,
            sim.document.content,
            sim.score
        );
    }

    println!("\nDocument count: {}", store.count().await?);

    Ok(())
}