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Cohere retired `embed-english-v2.0`, `embed-english-light-v2.0` and `embed-multilingual-v2.0` on April 4, 2026 ([deprecations](https://docs.cohere.com/docs/deprecations), which lists `embed-english-v3.0`, `embed-multilingual-v3.0` and `embed-v4.0` as the embedding alternatives). `CohereEmbeddingFunction` still defaulted to `embed-multilingual-v2.0`, so every call without an explicit `name` fails. The default is now `embed-multilingual-v3.0`, the multilingual v3 model, and the retired models are removed from the docstring and the Cohere integration page (the page also listed `embed-english-v2.0` as the default, which did not match the code). The docs example, its generated snippet, the docs test and the slow Cohere test use the new name. This changes the default vector size: `ndims()` returns 1024 for `embed-multilingual-v3.0`, where the old default gave 768. Tables created from a schema that uses `cohere.ndims()` get 1024-dimension vectors from now on. A table that already holds 768-dimension vectors from the retired model needs a re-embed with a current model, since the old model can no longer be called. The `ndims()` entries for the v2 names are left as they are. Test: `test_cohere_default_model_is_not_a_retired_v2_model` in `python/python/tests/test_embeddings.py` checks the default name and that `ndims()` is 1024. The repository's pytest session could not be run for this change (its `conftest.py` needs a newer native module than the released wheel); the body of that test was run against the released `lancedb` 0.40.0 with this version of `cohere.py` in place, where it passes, and it returns `embed-multilingual-v2.0` / 768 without the change. ruff check and format are clean on the touched Python files. No Cohere request was made. This change was made with Claude Code. 🤖 Generated with [Claude Code](https://claude.com/claude-code) --------- Co-authored-by: Claude Sonnet 5.5 <noreply@anthropic.com> Co-authored-by: Xuanwo <github@xuanwo.io>
The Multimodal Lakehouse for AI
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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⭐ Click here ⭐ to see how fast we're growing!
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 |
Join Us and Contribute
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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