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Rig-Qdrant Integration

The rig-qdrant crate provides a vector store implementation using Qdrant as the underlying datastore. Qdrant is a highly performant vector search database built in Rust.

Key Features

  • Integration with OpenAI: Utilizes OpenAI’s embedding models to generate vector representations of data.
  • Flexible Querying: Offers methods to perform top-N searches and retrieve node IDs based on vector similarity.

Usage

Setup

Add the rig-qdrant crate to your Cargo.toml:

[dependencies]
rig-qdrant = "0.1.5"

Example Workflow

  1. Connect to Qdrant: Establish a connection using the Qdrant struct.
let qdrant_client = Qdrant::connect("http://localhost:6334").await?;
  1. Create Collection: Define and create a vector collection, then build your QdrantVectorStore.
if !qdrant_client.collection_exists(COLLECTION_NAME).await? {
    qdrant_client.create_collection(
        CreateCollectionBuilder::new(COLLECTION_NAME)
            .vectors_config(VectorParamsBuilder::new(
                COLLECTION_SIZE as u64,
                qdrant_client::qdrant::Distance::Cosine)
            ),
        ).await?;
}
 
let query_params = QueryPointsBuilder::new(COLLECTION_NAME).with_payload(true);
let vector_store = QdrantVectorStore::new(qdrant_inner, model.clone(), query_params.build());
  1. Perform Vector Search: Query the vector index for similar nodes.
let results = vector_store.top_n::<Movie>("a historical movie on quebec", 5).await?;

Example Code

const COLLECTION_NAME: &str = "MY_COLLECTION";
const COLLECTION_SIZE: usize = 1536; // vector embedding size for the collection goes here
 
#[tokio::main]
async fn main() -> Result<(), anyhow::Error> {
    let openai_api_key = env::var("OPENAI_API_KEY").expect("OPENAI_API_KEY not set");
    let openai_client = Client::new(&openai_api_key);
    let model = openai_client.embedding_model(TEXT_EMBEDDING_ADA_002);
 
    let qdrant_client = Qdrant::connect("http://localhost:6334").await?;
 
    // Create a collection with 1536 dimensions if it doesn't exist
    // Note: Make sure the dimensions match the size of the embeddings returned by the
    // model you are using
    if !qdrant_client.collection_exists(COLLECTION_NAME).await? {
        qdrant_client.create_collection(
            CreateCollectionBuilder::new(COLLECTION_NAME)
                .vectors_config(VectorParamsBuilder::new(
                    COLLECTION_SIZE as u64,
                    qdrant_client::qdrant::Distance::Cosine)
                ),
            ).await?;
    }
 
    let query_params = QueryPointsBuilder::new(COLLECTION_NAME).with_payload(true);
    let vector_store = QdrantVectorStore::new(qdrant_inner, model.clone(), query_params.build());
 
 
    let results = vector_store.top_n::<Utterance>(query, 1).await?;
    println!("{:#?}", results);
 
    Ok(())
}

Additional Resources

  • Examples: See the examples directory for detailed usage scenarios.
  • Qdrant Documentation: Refer to the Qdrant documentation for more information on setting up and using collections.

API Reference (docs.rs)
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