landing_page/qdrant-landing/content/documentation/frameworks/rig-rs.md

title, short_description, description

title	short_description	description
Rig-rs	Build scalable LLM apps in Rust with Rig and use Qdrant as the vector store for semantic ingestion, retrieval, and RAG workflows.	Use Rig with Qdrant in Rust to build modular LLM applications with semantic retrieval, ingesting documents and querying them through the Qdrant vector store.

Rig-rs

Rig is a Rust library for building scalable, modular, and ergonomic LLM-powered applications. It has full support for LLM completion and embedding workflows with minimal boiler plate.

Rig supports Qdrant as a vectorstore to ingest and search for documents semantically.

Installation

cargo add rig-core rig-qdrant qdrant-client

Usage

Here's an example ingest and retrieve flow using Rig and Qdrant.

use qdrant_client::{
    qdrant::{PointStruct, QueryPointsBuilder, UpsertPointsBuilder},
    Payload, Qdrant,
};
use rig::{
    embeddings::EmbeddingsBuilder,
    providers::openai::{Client, TEXT_EMBEDDING_3_SMALL},
    vector_store::VectorStoreIndex,
};
use rig_qdrant::QdrantVectorStore;
use serde_json::json;

const COLLECTION_NAME: &str = "rig-collection";

// Initialize Qdrant client.
let client = Qdrant::from_url("http://localhost:6334").build()?;
// Initialize OpenAI client.
let openai_client = Client::new("<OPENAI_API_KEY>");
let model = openai_client.embedding_model(TEXT_EMBEDDING_3_SMALL);

let documents = EmbeddingsBuilder::new(model.clone())
    .simple_document("0981d983-a5f8-49eb-89ea-f7d3b2196d2e", "Definition of a *flurbo*: A flurbo is a green alien that lives on cold planets")
    .simple_document("62a36d43-80b6-4fd6-990c-f75bb02287d1", "Definition of a *glarb-glarb*: A glarb-glarb is a ancient tool used by the ancestors of the inhabitants of planet Jiro to farm the land.")
    .simple_document("f9e17d59-32e5-440c-be02-b2759a654824", "Definition of a *linglingdong*: A term used by inhabitants of the far side of the moon to describe humans.")
    .build()
    .await?;

let points: Vec<PointStruct> = documents
    .into_iter()
    .map(|d| {
        let vec: Vec<f32> = d.embeddings[0].vec.iter().map(|&x| x as f32).collect();
        PointStruct::new(
            d.id,
            vec,
            Payload::try_from(json!({
                "document": d.document,
            }))
            .unwrap(),
        )
    })
    .collect();

client
    .upsert_points(UpsertPointsBuilder::new(COLLECTION_NAME, points))
    .await?;

let query_params = QueryPointsBuilder::new(COLLECTION_NAME).with_payload(true);
let vector_store = QdrantVectorStore::new(client, model, query_params.build());

let results = vector_store
    .top_n::<serde_json::Value>("Define a glarb-glarb?", 1)
    .await?;

println!("Results: {:?}", results);

Further reading

• Rig-rs Documentation
• Source Code