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Taylor
a065b4b9c6
Adds a Python `nvidia` embedding provider for Nemotron 3.5 Embed 1B and 8B. It works with NVIDIA's hosted API and self-hosted NIM `/v1` endpoints, and uses the existing `openai` client. The OpenAI provider can't send NVIDIA's passage/query input types or pick Nemotron's dimensions. - Documents are embedded as `passage`, searches as `query`. - Default dimensions are 2048 (1B) and 4096 (8B), and `dim` is checked against each model's supported range. Self-hosted model IDs need an explicit `dim`. - Response count, order, dimensions and values are checked. Null and empty text get a null vector without a request. - Each request is retried on its own with LanceDB's retry policy. Invalid input or responses fail immediately. - `NVIDIA_API_KEY` is only sent to the hosted API. Custom endpoints need an explicit `api_key` (a `$var:` reference), and endpoints restored from table metadata must be allowed with `allow_endpoint`. - Request dimensions are sent through `extra_body`, preserving the NVIDIA wire contract while supporting the declared minimum `openai==1.6.1`. Python and text only. Docs are in `integrations/embedding/nvidia`. Originally authored by Taylor (@tsmithv11). ### Testing - `test_nvidia_embeddings.py`: 45 passed, 2 deselected with both the locked `openai==2.44.0` and minimum `openai==1.6.1`. These use the real OpenAI client with mock HTTP transport and real local tables. - Embedding, registry, field-metadata and trust suites: 163 passed, 4 skipped, 10 deselected. - Historical live experiments: hosted 8B passed a five-request smoke test; hosted 1B returned HTTP 500. No live request was made in this stage, so current hosted 1B availability was not verified. Enterprise coverage uses the repository mock HTTP server, not a live deployment.
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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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 |
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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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