mirror of
https://github.com/lancedb/lancedb.git
synced 2026-10-14 15:52:26 +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.
96 lines
3.5 KiB
Rust
96 lines
3.5 KiB
Rust
// SPDX-License-Identifier: Apache-2.0
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// SPDX-FileCopyrightText: Copyright The LanceDB Authors
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use std::{iter::once, sync::Arc};
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use arrow_array::{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::{
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sentence_transformers::SentenceTransformersEmbeddings, EmbeddingDefinition,
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EmbeddingFunction,
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},
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query::{ExecutableQuery, QueryBase},
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Result,
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};
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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 embedding = SentenceTransformersEmbeddings::builder().build()?;
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let embedding = Arc::new(embedding);
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let db = connect(tempdir).execute().await?;
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db.embedding_registry()
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.register("sentence-transformers", 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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"facts",
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"sentence-transformers",
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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(
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"How many bones are in the human body?",
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)));
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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("facts")
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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!("Answer: {}", text);
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Ok(())
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}
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fn make_data() -> impl IntoArrow {
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let schema = Schema::new(vec![Field::new("facts", DataType::Utf8, false)]);
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let facts = StringArray::from_iter_values(vec![
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"Albert Einstein was a theoretical physicist.",
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"The capital of France is Paris.",
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"The Great Wall of China is one of the Seven Wonders of the World.",
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"Python is a popular programming language.",
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"Mount Everest is the highest mountain in the world.",
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"Leonardo da Vinci painted the Mona Lisa.",
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"Shakespeare wrote Hamlet.",
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"The human body has 206 bones.",
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"The speed of light is approximately 299,792 kilometers per second.",
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"Water boils at 100 degrees Celsius.",
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"The Earth orbits the Sun.",
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"The Pyramids of Giza are located in Egypt.",
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"Coffee is one of the most popular beverages in the world.",
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"Tokyo is the capital city of Japan.",
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"Photosynthesis is the process by which plants make their food.",
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"The Pacific Ocean is the largest ocean on Earth.",
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"Mozart was a prolific composer of classical music.",
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"The Internet is a global network of computers.",
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"Basketball is a sport played with a ball and a hoop.",
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"The first computer virus was created in 1983.",
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"Artificial neural networks are inspired by the human brain.",
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"Deep learning is a subset of machine learning.",
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"IBM's Watson won Jeopardy! in 2011.",
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"The first computer programmer was Ada Lovelace.",
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"The first chatbot was ELIZA, created in the 1960s.",
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]);
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let schema = Arc::new(schema);
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let rb = RecordBatch::try_new(schema.clone(), vec![Arc::new(facts)]).unwrap();
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Box::new(RecordBatchIterator::new(vec![Ok(rb)], schema))
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}
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