mirror of
https://github.com/qdrant/landing_page.git
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Abdon PijpelinkandClaude Sonnet 4.6
478b96554f
* Break Inference page into several pages * Make all inference code snippets testable and clean up * Make more snippets testable * Edits * Document automatic query and passage prefix injection in Cloud Inference Qdrant Cloud Inference silently applies model-specific prefixes (e.g. "query: "/"passage: " for E5, BGE-style instruction prefix for BGE/mxbai/ Snowflake arctic-embed) so users don't need to manage them manually. Add a section explaining this behavior, the idempotency guarantee, and the scope (Qdrant-hosted models only; external providers handle their own). Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * Document short query optimization in Cloud Inference Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * Update links * Expand on external provider API key usage * Add section about external provider API keys * Default to header for external API keys --------- Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
47 lines
1.6 KiB
Rust
47 lines
1.6 KiB
Rust
use std::collections::HashMap;
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use qdrant_client::{
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Payload, Qdrant,
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qdrant::{Document, NamedVectors, PointStruct, UpsertPointsBuilder, Value},
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};
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pub async fn main() -> anyhow::Result<()> {
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let client = Qdrant::from_url("http://localhost:6334").build()?; // @hide
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client
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.with_header("openai-api-key", "<YOUR_OPENAI_API_KEY>")
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.upsert_points(
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UpsertPointsBuilder::new(
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"{collection_name}",
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vec![PointStruct::new(
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1,
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NamedVectors::default()
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.add_vector(
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"large",
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Document {
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text: "Recipe for baking chocolate chip cookies".into(),
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model: "openai/text-embedding-3-small".into(),
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options: HashMap::new(),
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},
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)
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.add_vector(
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"small",
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Document {
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text: "Recipe for baking chocolate chip cookies".into(),
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model: "openai/text-embedding-3-small".into(),
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options: HashMap::<String, Value>::from_iter(vec![(
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"mrl".into(),
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Value::from(64),
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)]),
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},
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),
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Payload::default(),
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)],
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)
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.wait(true),
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)
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.await?;
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Ok(())
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}
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