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Fede Kamelhar
8be1f8e00f
Adds `OCIGenAIEmbeddings`, registered as `"oci-genai"`, embedding text with the Cohere models hosted on Oracle Cloud Infrastructure Generative AI (default `cohere.embed-v4.0`) via the `oci` SDK, alongside the existing Bedrock, Gemini and watsonx functions. - Auth delegated to the OCI SDK: `API_KEY` / `SECURITY_TOKEN` profiles from the OCI config file, or `INSTANCE_PRINCIPAL` / `RESOURCE_PRINCIPAL`. No credentials are stored on the function or in table metadata; `compartment_id` can come from `OCI_COMPARTMENT_ID` so the OCID need not be baked into metadata either. - Endpoint derived from `region` (default `us-chicago-1`) or overridden with `service_endpoint`; dedicated AI cluster endpoint OCIDs use `DedicatedServingMode`. - Asymmetric Cohere input types (`SEARCH_DOCUMENT` for the source column, `SEARCH_QUERY` for queries); `truncate` and `output_dimensions` (embed-v4.0: 256/512/1024/1536) forwarded. - Batched at the service limit of 96 inputs; empty inputs are not sent and come back as `None`. - `ndims()` uses `output_dimensions` when set, otherwise a single cached probe request, so there is no model→dims table to keep in sync with the catalog. - SDK retries disabled in favour of LanceDB's `max_retries` backoff; 401/403 service errors carry `status_code` so they surface without retrying, with an actionable message (expired session token, IAM policy, region, model). - `oci>=2.165.1` added to the `embeddings` extra (the floor where `EmbedTextDetails.output_dimensions` exists). Tests: 22 mocked unit tests (auth paths, batching, input types, config validation, retry behaviour, pickling, table-metadata round trip) and a live test skipped unless `OCI_COMPARTMENT_ID` is set, which passes against us-chicago-1 for embed-v4.0 at 1536 and 512 dims and embed-multilingual-v3.0 at 1024. Verified end to end with a real LanceDB table (`LanceModel` with `SourceField`/`VectorField`, 6 rows, two semantic queries ranked correctly). `ruff format --check`, `ruff check`, pyright and typos are clean. Docs: a page under `docs/src/embeddings/available_embedding_models/text_embedding_functions/` in the shape of the remaining `voyageai_embedding.md`; the function is exported from `lancedb.embeddings` so the API reference picks it up. The hosted docs' available-models table lives outside this repo and would need a separate update.
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:
- LanceDB Embedded (open source): 100% open source, runs in your application's process; your data can live on local disk or in object storage. No vendor lock-in.
- LanceDB Enterprise: Production-scale distributed search, indexing, and maintenance. Plus, 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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