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`OllamaEmbeddings.compute_source_embeddings(np.array(["hello", "world"]))` raises a validation error before sending a request. The embedding interface accepts NumPy arrays, but the adapter passes the array directly to Ollama's string-or-sequence request field. Convert NumPy text arrays to lists before the batched request. This preserves input order, the single request, model options and `keep_alive`. The tests cover Unicode/object/sliced arrays, empty input, existing list/Arrow/query inputs and API error propagation. The added input-contract tests also cover two-dimensional arrays and byte strings, with equivalent list inputs as controls. The real Ollama SDK rejects 2-D inputs and invalid UTF-8 locally, before transport. Valid UTF-8 bytes are decoded normally. No extra adapter validation is needed for these cases. Validation on Python 3.12.14 / macOS arm64: - Built the Rust/PyO3 bindings using the repository's uv/maturin environment. - The initial regression run had 5 failures and 4 passing controls on the base; all 9 passed with the fix. With the 6 additional input-contract cases, the related embedding and trust suites now total 55 passed / 11 skipped. - All Python Ruff lint and formatting checks pass. Root checks still report the same 49 lint errors and 11 documentation formatting files reproduced on the untouched base. - Separate review exercised 13 public API cases. Another 7 groups passed through a real loopback HTTP transport, including local validation, UTF-8 serialization and empty-batch model-error propagation. The regression tests use the real Ollama 0.6.2 SDK with HTTPX MockTransport. The loopback check uses an HTTP surrogate, not an Ollama daemon. No live model inference was run. A live Ollama server was unavailable locally. [The current server handler](https://github.com/ollama/ollama/blob/2cd63f7a15039ccd790e97b31edc091fa1707c56/server/routes.go#L1212) explicitly returns HTTP 200 with empty embeddings for empty input after model lookup and runner setup. The adapter preserves the existing empty-list request and its model/setup errors. This is a source check, not a live-server test. Developed with OpenAI Codex, with separate agent review and executed local checks.
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
Star LanceDB to get updates!
⭐ 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.
Contributors
Stay in Touch With Us