Files
landing_page/qdrant-landing/content/documentation/headless/snippets/inference/mrl/rust.rs
T
2025-12-09 15:15:31 +01:00

52 lines
1.9 KiB
Rust

use std::collections::HashMap;
use qdrant_client::{
Payload, Qdrant,
qdrant::{Document, NamedVectors, PointStruct, UpsertPointsBuilder, Value},
};
pub async fn main() -> anyhow::Result<()> {
let client = Qdrant::from_url("http://localhost:6334").build()?;
client
.upsert_points(
UpsertPointsBuilder::new(
"{collection_name}",
vec![PointStruct::new(
1,
NamedVectors::default()
.add_vector(
"large",
Document {
text: "Recipe for baking chocolate chip cookies".into(),
model: "openai/text-embedding-3-small".into(),
options: HashMap::<String, Value>::from_iter(vec![(
"openai-api-key".into(),
"<YOUR_OPENAI_API_KEY>".into(),
)]),
},
)
.add_vector(
"small",
Document {
text: "Recipe for baking chocolate chip cookies".into(),
model: "openai/text-embedding-3-small".into(),
options: HashMap::<String, Value>::from_iter(vec![
(
"openai-api-key".into(),
Value::from("<YOUR_OPENAI_API_KEY>"),
),
("mrl".into(), Value::from(64)),
]),
},
),
Payload::default(),
)],
)
.wait(true),
)
.await?;
Ok(())
}