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Xuanwo
c72931b30f
Adds `TypeSafeReranker`, which reranks vector, FTS, and hybrid results with the [TypeSafe System One API](https://docs.typesafe.ai/introduction). Each result is scored independently: TypeSafe reads `{"query", "document"}` and answers a yes/no (noul) question, and the probability of yes becomes `_relevance_score`. Unlike listwise LLM rerankers, the score is an absolute probability, so it is comparable across queries and can be thresholded. The question's `instructions` and `true`/`false` `criteria` are configurable, since domain-specific criteria are what make this kind of scoring work well ([TypeSafe's re-ranking cookbook](https://docs.typesafe.ai/cookbooks/rerank_typesafe)). The API takes one state per request, so the reranker sends one request per result on a thread pool bounded by `max_concurrency`. It deliberately does not use the background event loop: rerankers are called synchronously from inside the async query APIs, where `LOOP.run` would deadlock. TypeSafe scores for the same pair vary slightly between calls, so results with close scores can swap places when a search is repeated. The shared reranker test helper now takes `deterministic=False` for this case: it still checks result sizes and descending scores, but not that two identical searches return the same order. The SDK is imported only when the client is created and questions are sent as plain dicts, so the new tests run in CI with a fake client and without `typesafe-sdk` installed. The live-API test is skipped without `TYPESAFE_API_KEY`. Ranking quality has not been compared with other API rerankers.
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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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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