Rig-Qdrant

Vector store index integration for Qdrant. This integration supports dense vector retrieval using Rig's embedding providers. It is also extensible to allow all hybrid queries supported by Qdrant.

Installation

[dependencies]
rig-qdrant = "0.2.5"
rig-core = "0.36.0"

The root rig facade also exposes this crate behind the qdrant feature.

Examples

See examples/qdrant_vector_search.rs for an end-to-end example using a Qdrant collection with a Rig embedding model.

Filtered searches use the crate-level QdrantFilter type:

use rig_core::vector_store::request::{SearchFilter, VectorSearchRequest};
use rig_qdrant::QdrantFilter;

let req = VectorSearchRequest::<QdrantFilter>::builder()
    .query("What is a linglingdong?")
    .samples(1)
    .filter(QdrantFilter::eq(
        "id",
        serde_json::json!("f9e17d59-32e5-440c-be02-b2759a654824"),
    ))
    .build();