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Fixes #1540 I could not reproduce this on current `main` from Python, but I could still reproduce it from the Rust SDK. Python no longer reproduces because the current Python vector/hybrid query paths re-chunk results into a `pyarrow.Table` before returning batches. Rust still reproduced because `max_batch_length` was passed into planning/scanning, but vector search could still emit larger `RecordBatch`es later in execution (for example after KNN / TopK), so it was not enforced on the final Rust output stream. This PR enforces `max_batch_length` on the final Rust query output stream and adds Rust regression coverage. Before the fix, the Rust repro produced: `num_batches=2, max_batch=8192, min_batch=1808, all_le_100=false` After the fix, the same repro produces batches `<= 100`. ## Runnable Rust repro Before this fix, current `main` could still return batches like `[8192, 1808]` here even with `max_batch_length = 100`: ```rust use std::sync::Arc; use arrow_array::{ types::Float32Type, FixedSizeListArray, RecordBatch, RecordBatchReader, StringArray, }; use arrow_schema::{DataType, Field, Schema}; use futures::TryStreamExt; use lancedb::query::{ExecutableQuery, QueryBase, QueryExecutionOptions}; #[tokio::main] async fn main() -> Result<(), Box<dyn std::error::Error>> { let tmp = tempfile::tempdir()?; let uri = tmp.path().to_str().unwrap(); let rows = 10_000; let schema = Arc::new(Schema::new(vec![ Field::new("id", DataType::Utf8, false), Field::new( "vector", DataType::FixedSizeList(Arc::new(Field::new("item", DataType::Float32, true)), 4), false, ), ])); let ids = StringArray::from_iter_values((0..rows).map(|i| format!("row-{i}"))); let vectors = FixedSizeListArray::from_iter_primitive::<Float32Type, _, _>( (0..rows).map(|i| Some(vec![Some(i as f32), Some(1.0), Some(2.0), Some(3.0)])), 4, ); let batch = RecordBatch::try_new(schema.clone(), vec![Arc::new(ids), Arc::new(vectors)])?; let reader: Box<dyn RecordBatchReader + Send> = Box::new( arrow_array::RecordBatchIterator::new(vec![Ok(batch)].into_iter(), schema), ); let db = lancedb::connect(uri).execute().await?; let table = db.create_table("test", reader).execute().await?; let mut opts = QueryExecutionOptions::default(); opts.max_batch_length = 100; let mut stream = table .query() .nearest_to(vec![0.0, 1.0, 2.0, 3.0])? .limit(rows) .execute_with_options(opts) .await?; let mut sizes = Vec::new(); while let Some(batch) = stream.try_next().await? { sizes.push(batch.num_rows()); } println!("{sizes:?}"); Ok(()) } ``` Signed-off-by: yaommen <myanstu@163.com>
The Multimodal AI Lakehouse
How to Install ✦ Detailed Documentation ✦ Tutorials and Recipes ✦ Contributors
The ultimate multimodal data platform for AI/ML applications.
LanceDB is designed for fast, scalable, and production-ready vector search. It is built on top of the Lance columnar format. You can store, index, and search over petabytes of multimodal data and vectors with ease. LanceDB is a central location where developers can build, train and analyze their AI workloads.
Demo: Multimodal Search by Keyword, Vector or with SQL
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Key Features:
- Fast Vector Search: Search billions of vectors in milliseconds with state-of-the-art indexing.
- Comprehensive Search: Support for vector similarity search, full-text search and SQL.
- Multimodal Support: Store, query and filter vectors, metadata and multimodal data (text, images, videos, point clouds, and more).
- Advanced Features: Zero-copy, automatic versioning, manage versions of your data without needing extra infrastructure. GPU support in building vector index.
Products:
- Open Source & Local: 100% open source, runs locally or in your cloud. No vendor lock-in.
- Cloud and Enterprise: Production-scale vector search with no servers to manage. Complete data sovereignty and security.
Ecosystem:
- Columnar Storage: Built on the Lance columnar format for efficient storage and analytics.
- Seamless Integration: Python, Node.js, Rust, and REST APIs for easy integration. Native Python and Javascript/Typescript support.
- Rich Ecosystem: Integrations with LangChain 🦜️🔗, LlamaIndex 🦙, Apache-Arrow, Pandas, Polars, DuckDB and more on the way.
How to Install:
Follow the Quickstart doc to set up LanceDB locally.
API & SDK: We also support Python, Typescript and Rust SDKs
| Interface | Documentation |
|---|---|
| Python SDK | https://lancedb.github.io/lancedb/python/python/ |
| Typescript SDK | https://lancedb.github.io/lancedb/js/globals/ |
| Rust SDK | https://docs.rs/lancedb/latest/lancedb/index.html |
| REST API | https://docs.lancedb.com/api-reference/rest |
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We welcome contributions from everyone! Whether you're a developer, researcher, or just someone who wants to help out.
If you have any suggestions or feature requests, please feel free to open an issue on GitHub or discuss it on our Discord server.
Check out the GitHub Issues if you would like to work on the features that are planned for the future. If you have any suggestions or feature requests, please feel free to open an issue on GitHub.
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