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Jonathan M HsiehandClaude Opus 5
404b91d4d6
`secret_env_bindings` took the general noun for one delivery mode. A binding is
the concept; how it arrives is a property of one. `secret_bindings` is a list of
`SecretBinding`, tagged by `kind`, so a later mode is a variant rather than a
sibling field -- and the rules that are per-Function, like how many Secrets it
may bind, stay answerable from one place.
The cost of one field is that an unknown kind is a decode error unless it is
caught. It is caught, the way `PythonRuntimeSpec` catches an unknown runtime:
`Unrecognized { kind }`, `#[non_exhaustive]`, and the payload dropped rather
than retained because the client does not proxy catalog values. A test pins it
-- a `file` binding from a newer server decodes, reports its kind, and
round-trips as its discriminator without failing the version around it.
A list has no key order to inherit, and the list is in the version hash, so
`bind_secrets` sorts it: a caller's argument order is not part of what a
Function is.
The Secret is named under `secret_ref`, not `secret`: the service scans Job
payloads for credential-shaped keys and refuses one called `secret` whatever it
holds. That guard is worth more blunt than argued with.
`create_secret` and `alter_secret` also stop sharing a request shape. They are
different operations to the service -- one refuses an existing name, the other
requires it -- and either may grow a field the other has no meaning for. What
they share is posting a body that must not be logged, which is a function.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01UfmeJ533rQDnPBkMtjerV6
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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