From 0487ede436c28828baa950ef82e8e3704d387cd1 Mon Sep 17 00:00:00 2001 From: jackylee-ch Date: Fri, 7 Aug 2026 21:35:24 +0800 Subject: [PATCH] docs: use indices instead of indexes in guide and quickstart `AGENTS.md` asks for `indices` (not `indexes`) consistently in APIs and docs. These five occurrences are all prose about index objects. Leaves `maintained_indexes` in `dataset.py` alone: it matches the field name in `python/src/dataset.rs`, so renaming it on the Python side only would break the cross-language naming rule. --- docs/src/guide/data_types.md | 2 +- docs/src/quickstart/index.md | 2 +- docs/src/quickstart/vector-search.md | 6 +++--- 3 files changed, 5 insertions(+), 5 deletions(-) diff --git a/docs/src/guide/data_types.md b/docs/src/guide/data_types.md index 06f26da1db3..c3f222d36ba 100644 --- a/docs/src/guide/data_types.md +++ b/docs/src/guide/data_types.md @@ -408,7 +408,7 @@ This maps to Lance's `FixedSizeList(Float32, 384)` type, which is optimized for: 3. **Align dimensions for SIMD**: Vector dimensions divisible by 8 enable optimal SIMD acceleration. Common dimensions: 128, 256, 384, 512, 768, 1024, 1536. -4. **Create indexes for large datasets**: For datasets with more than ~10,000 vectors, create an ANN index for fast search: +4. **Create indices for large datasets**: For datasets with more than ~10,000 vectors, create an ANN index for fast search: ```python # IVF_PQ is recommended for most use cases diff --git a/docs/src/quickstart/index.md b/docs/src/quickstart/index.md index 34367c7177f..f10c5749014 100644 --- a/docs/src/quickstart/index.md +++ b/docs/src/quickstart/index.md @@ -105,4 +105,4 @@ dataset.to_table().to_pandas() Now that you've mastered the basics of creating Lance datasets, here's what you can explore next: - **[Versioning Your Datasets with Lance](versioning.md)** - Learn how to track changes over time with native versioning -- **[Vector Indexing and Vector Search With Lance](vector-search.md)** - Build high-performance vector search capabilities with ANN indexes +- **[Vector Indexing and Vector Search With Lance](vector-search.md)** - Build high-performance vector search capabilities with ANN indices diff --git a/docs/src/quickstart/vector-search.md b/docs/src/quickstart/vector-search.md index 6b1f6a5e516..c21bc6d9f37 100644 --- a/docs/src/quickstart/vector-search.md +++ b/docs/src/quickstart/vector-search.md @@ -1,13 +1,13 @@ --- title: Vector Search -description: High-performance vector search with ANN indexes, including IVF_PQ, IVF_HNSW_PQ, and IVF_HNSW_SQ +description: High-performance vector search with ANN indices, including IVF_PQ, IVF_HNSW_PQ, and IVF_HNSW_SQ --- # Vector Indexing and Vector Search With Lance -Lance provides high-performance vector search capabilities with ANN (Approximate Nearest Neighbor) indexes. +Lance provides high-performance vector search capabilities with ANN (Approximate Nearest Neighbor) indices. -By the end of this tutorial, you'll be able to build and use ANN indexes to dramatically speed up vector search operations while maintaining high accuracy. You'll also learn how to tune search parameters for optimal performance and combine vector search with metadata queries in a single operation. +By the end of this tutorial, you'll be able to build and use ANN indices to dramatically speed up vector search operations while maintaining high accuracy. You'll also learn how to tune search parameters for optimal performance and combine vector search with metadata queries in a single operation. ## Install the Python SDK