//! This example demonstrates how to perform a vector search on a Neo4j database.
//! It is based on the [Neo4j Embeddings & Vector Index Tutorial](https://neo4j.com/docs/genai/tutorials/embeddings-vector-indexes/).
//! The tutorial uses the `recommendations` dataset and the `moviePlots` index, which is created in the tutorial.
//! They both need to be configured and the database running before running this example.
//!
//! Neo4j provides a demo database for the `recommendations` dataset (see [Github Neo4j-Graph-Examples/recommendations](https://github.com/neo4j-graph-examples/recommendations/tree/main?tab=readme-ov-file#setup)).
//!
//!     const NEO4J_URI: &str = "neo4j+s://demo.neo4jlabs.com:7687";
//!     const NEO4J_DB: &str = "recommendations";
//!     const NEO4J_USERNAME: &str = "recommendations";
//!     const NEO4J_PASSWORD: &str = "recommendations";
//!
//! [examples/vector_search_simple.rs](examples/vector_search_simple.rs) provides an example starting from an empty database.
//! [examples/vector_search_movies_add_embeddings.rs](examples/vector_search_movies_add_embeddings.rs) provides an example of
//! how to add embeddings to an existing `recommendations` database.
use neo4rs::ConfigBuilder;
use rig_core::providers::openai;
use rig_core::vector_store::request::{SearchFilter, VectorSearchRequest};
use rig_neo4j::Neo4jClient;

use std::env;

use rig_core::client::EmbeddingsClient;
use rig_core::{providers::openai::Client, vector_store::VectorStoreIndex};
use serde::{Deserialize, Serialize};

#[path = "./display/lib.rs"]
mod display;

#[tokio::main]
async fn main() -> Result<(), anyhow::Error> {
    tracing_subscriber::fmt()
        .with_max_level(tracing::Level::DEBUG)
        .with_target(false)
        .init();

    const INDEX_NAME: &str = "moviePlotsEmbedding";

    // Initialize OpenAI client
    let openai_api_key = env::var("OPENAI_API_KEY")?;
    let openai_client: Client = Client::new(&openai_api_key)?;

    let neo4j_uri = "neo4j+s://demo.neo4jlabs.com:7687";
    let neo4j_username = "recommendations";
    let neo4j_password = "recommendations";

    let neo4j_client = Neo4jClient::from_config(
        ConfigBuilder::default()
            .uri(neo4j_uri)
            .user(neo4j_username)
            .password(neo4j_password)
            .db("recommendations")
            .build()?,
    )
    .await?;

    // // Select the embedding model and generate our embeddings
    let model = openai_client.embedding_model(openai::TEXT_EMBEDDING_ADA_002);

    // Define the properties that will be retrieved from querying the graph nodes
    #[derive(Debug, Deserialize, Serialize)]
    struct Movie {
        title: String,
        plot: String,
    }

    // Create a vector index on our vector store
    // ❗IMPORTANT: Reuse the same model that was used to generate the embeddings
    let index = neo4j_client.get_index(model, INDEX_NAME).await?;

    let query = "a historical movie on quebec";
    let req = VectorSearchRequest::builder()
        .query(query)
        .samples(5)
        .filter(SearchFilter::gt("node.year", 1990.into()))
        .build();

    // Query the index
    let results = index
        .top_n::<Movie>(req)
        .await?
        .into_iter()
        .map(|(score, id, doc)| display::SearchResult {
            title: doc.title,
            id,
            description: doc.plot,
            score,
        })
        .collect::<Vec<_>>();

    println!("{:#}", display::SearchResults(&results));

    let query = "A movie where the bad guy wins";
    let req = VectorSearchRequest::builder()
        .query(query)
        .samples(1)
        .build();

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

    println!("ID results: {id_results:?}");

    Ok(())
}
