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Closes #3506 ## Problem The Bedrock embedding compute path (`rust/lancedb/src/embeddings/bedrock.rs`) panics instead of returning a typed error in several places: - `serde_json::to_vec(&request_body).unwrap()`: request serialization. - `block_in_place(...).unwrap()`: the AWS `invoke_model` send result; any API error terminates the worker instead of propagating. - `v.as_f64().unwrap() as f32`: panics on non-numeric values in the returned embedding array. - `Handle::current()` + `block_in_place` assume a multi-threaded Tokio runtime and panic when that assumption does not hold (no runtime, or a current-thread runtime). Malformed payloads, non-numeric embedding values, or an incompatible runtime should surface as typed errors and never panic. ## Fix - Serialize the request body before the blocking section so a serialization failure returns `Error::Runtime` via `?`. - Map the `invoke_model` send error to `Error::Runtime` instead of `unwrap`. - Add a `json_array_to_f32` helper that converts the response array to `Vec<f32>`, returning `Error::Runtime` for a missing/non-array field or a non-numeric element (used by both the Titan and Cohere paths). - Add `current_multi_thread_handle()` (`Handle::try_current()` + a `RuntimeFlavor::CurrentThread` guard) so an absent or incompatible runtime returns a typed error rather than panicking in `block_in_place`. Scope note: the sibling `openai.rs` provider uses the same `block_in_place` + `block_on` bridge, so the bridge pattern itself is kept; this change only removes the panic paths that are specific to the Bedrock provider. ## Testing Added 6 unit tests (no AWS credentials required): - `json_array_to_f32`: valid numbers, non-array payload, and non-numeric element. - `current_multi_thread_handle`: errors with no runtime, errors on a current-thread runtime, and succeeds on a multi-threaded runtime. All pass; `cargo fmt` and `cargo clippy` clean. Build/test with `--features bedrock,lance/protoc`.
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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