## Summary Closes #3406 Add a regression matrix in `python/python/tests/test_nested_fields.py` that exercises the full nested field index lifecycle for both the sync and async Python table APIs. The tests will fail if any implementation regresses to leaf-only field names in `list_indices`, `index_stats`, search, or filter results. ## Test scenarios covered **Index types:** BTree scalar, IvfPq vector, FTS **Field-name edge cases (per acceptance criteria):** - `rowId` — camelCase top-level field - `` `row-id` `` — hyphenated top-level field (escaped) - `parent.`\``leaf.name`\`` ` — struct leaf whose name contains a literal dot - `MetaData.userId` — mixed-case nested path - `` `meta-data`.`user-id` `` — hyphenated struct with hyphenated leaf **Lifecycle operations per index type:** - `create_index` / `create_scalar_index` / `create_fts_index` - `list_indices` → verify canonical full dotted path (not leaf name) - `index_stats` → verify row count and index type - Filtered scan (`WHERE nested.field = value`) - Vector search via nested embedding column - FTS search via nested text column - `add` (append) then re-check index listing - `optimize` then re-check index listing **Both sync and async APIs** are covered in parallel test classes. ## Notes Lance forbids top-level field names that contain a literal `.`, so the `` `a.b` `` acceptance-criterion variant is exercised as a *struct leaf* field (`parent.`\``leaf.name`\``) rather than a top-level column.
The Multimodal AI Lakehouse
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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.
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Key Features:
- Fast Vector Search: Search billions of vectors in milliseconds with state-of-the-art indexing.
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Products:
- Open Source & Local: 100% open source, runs locally or in your cloud. No vendor lock-in.
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Ecosystem:
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- 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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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.
