rig-core 0.35.0

//! Demonstrates embedding documents and querying an in-memory vector index with OpenAI.
//! Requires `OPENAI_API_KEY` and the `derive` feature.
//! Run it to compare `top_n` results with `top_n_ids`.

use rig::prelude::*;
use rig::providers::openai::client::Client;
use rig::vector_store::request::VectorSearchRequest;
use rig::{
    Embed,
    embeddings::EmbeddingsBuilder,
    providers::openai,
    vector_store::{VectorStoreIndex, in_memory_store::InMemoryVectorStore},
};
use serde::{Deserialize, Serialize};

type SearchMatch = (f64, String, String);

// Shape of data that needs to be RAG'ed.
// The definition field will be used to generate embeddings.
#[derive(Embed, Clone, Deserialize, Debug, Serialize, Eq, PartialEq, Default)]
struct WordDefinition {
    id: String,
    word: String,
    #[embed]
    definitions: Vec<String>,
}

fn sample_documents() -> Vec<WordDefinition> {
    vec![
        WordDefinition {
            id: "doc0".to_string(),
            word: "flurbo".to_string(),
            definitions: vec![
                "A green alien that lives on cold planets.".to_string(),
                "A fictional digital currency that originated in the animated series Rick and Morty.".to_string(),
            ],
        },
        WordDefinition {
            id: "doc1".to_string(),
            word: "glarb-glarb".to_string(),
            definitions: vec![
                "An ancient tool used by the ancestors of the inhabitants of planet Jiro to farm the land.".to_string(),
                "A fictional creature found in the distant, swampy marshlands of the planet Glibbo in the Andromeda galaxy.".to_string(),
            ],
        },
        WordDefinition {
            id: "doc2".to_string(),
            word: "linglingdong".to_string(),
            definitions: vec![
                "A term used by inhabitants of the sombrero galaxy to describe humans.".to_string(),
                "A rare, mystical instrument crafted by the ancient monks of the Nebulon Mountain Ranges on the planet Quarm.".to_string(),
            ],
        },
    ]
}

fn print_matches(label: &str, matches: &[SearchMatch]) {
    println!("{label}:");
    for (score, id, word) in matches {
        println!("  score={score:.4} id={id} word={word}");
    }
}

fn print_id_matches(label: &str, matches: &[(f64, String)]) {
    println!("{label}:");
    for (score, id) in matches {
        println!("  score={score:.4} id={id}");
    }
}

#[tokio::main]
async fn main() -> Result<(), anyhow::Error> {
    let openai_client = Client::from_env();
    let embedding_model = openai_client.embedding_model(openai::TEXT_EMBEDDING_ADA_002);
    let embeddings = EmbeddingsBuilder::new(embedding_model.clone())
        .documents(sample_documents())?
        .build()
        .await?;

    let vector_store =
        InMemoryVectorStore::from_documents_with_id_f(embeddings, |doc| doc.id.clone());

    let query =
        "I need to buy something in a fictional universe. What type of money can I use for this?";
    let req = VectorSearchRequest::builder()
        .query(query)
        .samples(1)
        .build()?;

    let index = vector_store.index(embedding_model);
    let results = index
        .top_n::<WordDefinition>(req.clone())
        .await?
        .into_iter()
        .map(|(score, id, doc)| (score, id, doc.word))
        .collect::<Vec<SearchMatch>>();

    let id_results = index.top_n_ids(req).await?.into_iter().collect::<Vec<_>>();

    print_matches("Top document matches", &results);
    print_id_matches("Top document ids", &id_results);

    Ok(())
}