mirror of
https://github.com/lancedb/lancedb.git
synced 2026-10-13 15:22:30 +00:00
Jack Ye
b21fb2a4dc
The pages describing this code lived in lancedb/docs, a repository with ten stars that neither contributing guide mentioned. A user who hit a wrong page while reading the source had to find a separate, near-invisible repository to fix it. These 102 pages now sit beside the code, and a documentation fix is a pull request in the repository the reader already had open. `docs/web` is a complete Mintlify site: `cd docs/web && mint dev` serves it with no other checkout. Enterprise pages stay private and are merged in when the published site is assembled, so links into them do not resolve here — every open-source page does. `docs/web-tests` holds the tests the code examples are extracted from. They ran against a published wheel before; here they can run against this repository's own SDK, so an example that stops compiling fails the pull request that broke it rather than being noticed later. Both directories sit beside `docs/src` and are invisible to it: mkdocs reads `docs_dir: src`, so the SDK reference builds and deploys exactly as before.
90 lines
2.6 KiB
Rust
90 lines
2.6 KiB
Rust
// SPDX-License-Identifier: Apache-2.0
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// SPDX-FileCopyrightText: Copyright The LanceDB Authors
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// --8<-- [start:imports]
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use std::{iter::once, sync::Arc};
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use arrow_array::{Float64Array, Int32Array, RecordBatch, RecordBatchIterator, StringArray};
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use arrow_schema::{DataType, Field, Schema};
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use futures::StreamExt;
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use lancedb::{
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arrow::IntoArrow,
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connect,
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embeddings::{openai::OpenAIEmbeddingFunction, EmbeddingDefinition, EmbeddingFunction},
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query::{ExecutableQuery, QueryBase},
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Result,
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};
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// --8<-- [end:imports]
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// --8<-- [start:openai_embeddings]
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#[tokio::main]
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async fn main() -> Result<()> {
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let tempdir = tempfile::tempdir().unwrap();
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let tempdir = tempdir.path().to_str().unwrap();
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let api_key = std::env::var("OPENAI_API_KEY").expect("OPENAI_API_KEY is not set");
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let embedding = Arc::new(OpenAIEmbeddingFunction::new_with_model(
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api_key,
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"text-embedding-3-large",
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)?);
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let db = connect(tempdir).execute().await?;
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db.embedding_registry()
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.register("openai", embedding.clone())?;
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let table = db
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.create_table("vectors", make_data())
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.add_embedding(EmbeddingDefinition::new(
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"text",
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"openai",
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Some("embeddings"),
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))?
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.execute()
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.await?;
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let query = Arc::new(StringArray::from_iter_values(once("something warm")));
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let query_vector = embedding.compute_query_embeddings(query)?;
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let mut results = table
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.vector_search(query_vector)?
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.limit(1)
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.execute()
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.await?;
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let rb = results.next().await.unwrap()?;
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let out = rb
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.column_by_name("text")
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.unwrap()
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.as_any()
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.downcast_ref::<StringArray>()
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.unwrap();
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let text = out.iter().next().unwrap().unwrap();
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println!("Closest match: {}", text);
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Ok(())
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}
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// --8<-- [end:openai_embeddings]
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fn make_data() -> impl IntoArrow {
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let schema = Schema::new(vec![
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Field::new("id", DataType::Int32, true),
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Field::new("text", DataType::Utf8, false),
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Field::new("price", DataType::Float64, false),
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]);
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let id = Int32Array::from(vec![1, 2, 3, 4]);
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let text = StringArray::from_iter_values(vec![
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"Black T-Shirt",
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"Leather Jacket",
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"Winter Parka",
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"Hooded Sweatshirt",
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]);
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let price = Float64Array::from(vec![10.0, 50.0, 100.0, 30.0]);
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let schema = Arc::new(schema);
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let rb = RecordBatch::try_new(
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schema.clone(),
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vec![Arc::new(id), Arc::new(text), Arc::new(price)],
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)
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.unwrap();
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Box::new(RecordBatchIterator::new(vec![Ok(rb)], schema))
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}
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