mirror of
https://github.com/lancedb/lancedb.git
synced 2026-10-13 07:12:27 +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.
347 lines
11 KiB
Rust
347 lines
11 KiB
Rust
// SPDX-License-Identifier: Apache-2.0
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// SPDX-FileCopyrightText: Copyright The LanceDB Authors
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use std::fs;
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use std::path::PathBuf;
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use std::sync::Arc;
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use serde::Deserialize;
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type BatchIter = Box<dyn arrow_array::RecordBatchReader + Send>;
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// --8<-- [start:basic_imports]
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use arrow_array::types::Float32Type;
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use arrow_array::{
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FixedSizeListArray, Int8Array, Int16Array, RecordBatch, RecordBatchIterator, StringArray,
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StructArray,
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};
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use arrow_schema::{DataType, Field, FieldRef, Schema};
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use futures_util::TryStreamExt;
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use lancedb::database::CreateTableMode;
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use lancedb::query::{ExecutableQuery, QueryBase, Select};
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use lancedb::{connect, table::NewColumnTransform};
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// --8<-- [end:basic_imports]
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#[derive(Debug, Clone, Deserialize)]
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struct Stats {
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strength: i8,
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courage: i8,
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magic: i8,
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wisdom: i8,
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}
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#[derive(Debug, Clone, Deserialize)]
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struct Character {
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id: i16,
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name: String,
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role: String,
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description: String,
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vector: [f32; 4],
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stats: Stats,
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}
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fn camelot_schema() -> Arc<Schema> {
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Arc::new(Schema::new(vec![
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Field::new("id", DataType::Int16, false),
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Field::new("name", DataType::Utf8, false),
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Field::new("role", DataType::Utf8, false),
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Field::new("description", DataType::Utf8, false),
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Field::new(
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"vector",
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DataType::FixedSizeList(Arc::new(Field::new("item", DataType::Float32, true)), 4),
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false,
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),
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Field::new(
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"stats",
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DataType::Struct(arrow_schema::Fields::from(vec![
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Arc::new(Field::new("strength", DataType::Int8, false)),
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Arc::new(Field::new("courage", DataType::Int8, false)),
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Arc::new(Field::new("magic", DataType::Int8, false)),
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Arc::new(Field::new("wisdom", DataType::Int8, false)),
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])),
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false,
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),
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]))
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}
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fn characters_to_record_batch(schema: Arc<Schema>, characters: &[Character]) -> RecordBatch {
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let ids = Int16Array::from_iter_values(characters.iter().map(|c| c.id));
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let names = StringArray::from_iter_values(characters.iter().map(|c| c.name.as_str()));
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let roles = StringArray::from_iter_values(characters.iter().map(|c| c.role.as_str()));
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let descriptions =
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StringArray::from_iter_values(characters.iter().map(|c| c.description.as_str()));
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let vectors = FixedSizeListArray::from_iter_primitive::<Float32Type, _, _>(
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characters
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.iter()
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.map(|c| Some(c.vector.iter().copied().map(Some).collect::<Vec<_>>())),
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4,
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);
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let strength = Int8Array::from_iter_values(characters.iter().map(|c| c.stats.strength));
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let courage = Int8Array::from_iter_values(characters.iter().map(|c| c.stats.courage));
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let magic = Int8Array::from_iter_values(characters.iter().map(|c| c.stats.magic));
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let wisdom = Int8Array::from_iter_values(characters.iter().map(|c| c.stats.wisdom));
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let stats_fields: Vec<FieldRef> = vec![
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Arc::new(Field::new("strength", DataType::Int8, false)),
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Arc::new(Field::new("courage", DataType::Int8, false)),
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Arc::new(Field::new("magic", DataType::Int8, false)),
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Arc::new(Field::new("wisdom", DataType::Int8, false)),
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];
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let stats = StructArray::new(
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stats_fields.into(),
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vec![
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Arc::new(strength),
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Arc::new(courage),
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Arc::new(magic),
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Arc::new(wisdom),
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],
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None,
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);
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RecordBatch::try_new(
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schema,
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vec![
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Arc::new(ids),
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Arc::new(names),
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Arc::new(roles),
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Arc::new(descriptions),
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Arc::new(vectors),
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Arc::new(stats),
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],
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)
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.unwrap()
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}
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fn characters_to_reader(schema: Arc<Schema>, characters: &[Character]) -> BatchIter {
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let batch = characters_to_record_batch(schema.clone(), characters);
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Box::new(RecordBatchIterator::new(vec![Ok(batch)].into_iter(), schema))
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}
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fn camelot_json_path() -> PathBuf {
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PathBuf::from(env!("CARGO_MANIFEST_DIR"))
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.join("..")
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.join("camelot.json")
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}
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#[tokio::main]
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async fn main() {
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let temp_dir = tempfile::tempdir().unwrap();
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let uri = temp_dir.path().to_str().unwrap();
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let db = connect(uri).execute().await.unwrap();
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// --8<-- [start:data_load]
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let data: Vec<Character> =
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serde_json::from_str(&fs::read_to_string(camelot_json_path()).unwrap()).unwrap();
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// --8<-- [end:data_load]
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let schema = camelot_schema();
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// --8<-- [start:basic_create_table]
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let mut table = db
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.create_table("camelot", characters_to_reader(schema.clone(), &data))
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.mode(CreateTableMode::Overwrite)
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.execute()
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.await
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.unwrap();
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// --8<-- [end:basic_create_table]
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assert_eq!(table.count_rows(None).await.unwrap(), 8);
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// --8<-- [start:basic_open_table]
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table = db.open_table("camelot").execute().await.unwrap();
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// --8<-- [end:basic_open_table]
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// --8<-- [start:basic_create_empty_table]
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let schema = Arc::new(Schema::new(vec![
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Field::new("id", DataType::Int16, false),
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Field::new("name", DataType::Utf8, false),
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Field::new("role", DataType::Utf8, false),
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Field::new("description", DataType::Utf8, false),
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Field::new(
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"vector",
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DataType::FixedSizeList(Arc::new(Field::new("item", DataType::Float32, true)), 4),
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false,
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),
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Field::new(
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"stats",
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DataType::Struct(arrow_schema::Fields::from(vec![
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Arc::new(Field::new("strength", DataType::Int8, false)),
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Arc::new(Field::new("courage", DataType::Int8, false)),
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Arc::new(Field::new("magic", DataType::Int8, false)),
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Arc::new(Field::new("wisdom", DataType::Int8, false)),
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])),
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false,
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),
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]));
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db.create_empty_table("camelot_empty", schema)
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.mode(CreateTableMode::Overwrite)
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.execute()
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.await
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.unwrap();
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// --8<-- [end:basic_create_empty_table]
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db.drop_table("camelot_empty", &[]).await.unwrap();
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// --8<-- [start:basic_add_data]
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let magical_characters = vec![
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Character {
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id: 9,
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name: "Morgan le Fay".to_string(),
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role: "Sorceress".to_string(),
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description: "A powerful enchantress, Arthur's half-sister, and a complex figure who oscillates between aiding and opposing Camelot.".to_string(),
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vector: [0.10, 0.84, 0.25, 0.70],
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stats: Stats {
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strength: 2,
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courage: 3,
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magic: 5,
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wisdom: 4,
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},
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},
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Character {
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id: 10,
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name: "The Lady of the Lake".to_string(),
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role: "Mystical Guardian".to_string(),
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description: "A mysterious supernatural figure associated with Avalon, known for giving Arthur the sword Excalibur.".to_string(),
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vector: [0.00, 0.90, 0.58, 0.88],
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stats: Stats {
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strength: 2,
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courage: 3,
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magic: 5,
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wisdom: 5,
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},
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},
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];
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table
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.add(characters_to_reader(camelot_schema(), &magical_characters))
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.execute()
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.await
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.unwrap();
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// --8<-- [end:basic_add_data]
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// --8<-- [start:basic_vector_search]
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let query_vector = [0.03, 0.85, 0.61, 0.90];
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let result = table
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.query()
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.nearest_to(&query_vector)
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.unwrap()
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.limit(5)
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.execute()
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.await
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.unwrap()
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.try_collect::<Vec<_>>()
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.await
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.unwrap();
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println!("{result:?}");
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// --8<-- [end:basic_vector_search]
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// --8<-- [start:basic_add_columns]
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table
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.add_columns(
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NewColumnTransform::SqlExpressions(vec![(
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"power".to_string(),
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"cast(((stats.strength + stats.courage + stats.magic + stats.wisdom) / 4.0) as float)"
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.to_string(),
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)]),
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None,
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)
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.await
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.unwrap();
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// --8<-- [end:basic_add_columns]
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// --8<-- [start:basic_vector_search_q1]
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// Who are the characters similar to "wizard"?
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let query_vector_1 = [0.03, 0.85, 0.61, 0.90];
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let r1 = table
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.query()
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.nearest_to(&query_vector_1)
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.unwrap()
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.limit(5)
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.select(Select::Columns(vec![
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"name".to_string(),
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"role".to_string(),
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"description".to_string(),
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]))
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.execute()
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.await
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.unwrap()
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.try_collect::<Vec<_>>()
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.await
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.unwrap();
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println!("{r1:?}");
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// --8<-- [end:basic_vector_search_q1]
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// --8<-- [start:basic_vector_search_q2]
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// Who are the characters similar to "wizard" with high magic stats?
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let query_vector_2 = [0.03, 0.85, 0.61, 0.90];
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let r2 = table
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.query()
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.nearest_to(&query_vector_2)
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.unwrap()
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.only_if("stats.magic > 3")
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.select(Select::Columns(vec![
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"name".to_string(),
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"role".to_string(),
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"description".to_string(),
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]))
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.limit(5)
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.execute()
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.await
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.unwrap()
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.try_collect::<Vec<_>>()
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.await
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.unwrap();
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println!("{r2:?}");
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// --8<-- [end:basic_vector_search_q2]
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// --8<-- [start:basic_vector_search_q3]
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// Who are the strongest characters?
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let r3 = table
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.query()
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.only_if("stats.strength > 3")
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.select(Select::Columns(vec![
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"name".to_string(),
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"role".to_string(),
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"description".to_string(),
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]))
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.limit(5)
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.execute()
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.await
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.unwrap()
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.try_collect::<Vec<_>>()
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.await
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.unwrap();
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println!("{r3:?}");
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// --8<-- [end:basic_vector_search_q3]
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// --8<-- [start:basic_vector_search_q4]
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// Who are the strongest characters?
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let r4 = table
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.query()
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.select(Select::Columns(vec![
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"name".to_string(),
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"role".to_string(),
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"description".to_string(),
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"power".to_string(),
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]))
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.execute()
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.await
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.unwrap()
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.try_collect::<Vec<_>>()
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.await
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.unwrap();
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println!("{r4:?}");
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// --8<-- [end:basic_vector_search_q4]
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// --8<-- [start:basic_drop_columns]
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table.drop_columns(&["power"]).await.unwrap();
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// --8<-- [end:basic_drop_columns]
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// --8<-- [start:basic_delete_rows]
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table.delete("role = 'Traitor Knight'").await.unwrap();
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// --8<-- [end:basic_delete_rows]
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// --8<-- [start:basic_drop_table]
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db.drop_table("camelot", &[]).await.unwrap();
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// --8<-- [end:basic_drop_table]
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}
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