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Jonathan M HsiehandClaude Opus 5
7898699208
A Secret is addressed as `POST /v1/secret/{id}/<action>`, where `{id}` is the
namespace path and the name joined by a delimiter. The client performs that
join; the service can only judge the components the split produced. So a
component carrying path or delimiter syntax does not arrive as bad input for
the service to refuse -- it arrives as a different, well-formed identity.
`create_secret("a$b", value)` read as the namespace `a` and the name `b`: a
Secret that may already exist, whose credential an alter would overwrite. The
service never refused it because it was never asked about `a$b`. `"../jobs"`
did not reach the service at all, taking a body holding a credential to
whatever route the normalized path resolved to.
`Connection` now checks each component before anything is joined:
alphanumerics, `_`, `-` and `.`, at most 255 bytes, and not periods alone --
a component of only periods is read as a relative path after decoding, so no
spelling of it addresses anything. `RemoteDatabase` adds the one rule that
depends on configuration it owns: a component may not contain the delimiter it
is about to be joined with. The default `$` is outside the character set, so
that fires only for a configured delimiter, where the readable choices -- `.`,
`-`, `_` -- are all inside it.
This is not a second opinion on whether a name is acceptable. It is what makes
the service's opinion reachable, and it belongs to whoever builds the URL:
- The join happens here, and the join is what decides identity.
- It is one definition, not two. The service already imports this crate's
`validate_table_name` and `validate_namespace`; the Secret rule is placed
beside them so it can be imported the same way rather than restated.
- It covers every binding. Python and Node wrap this crate, and today the
checks are scattered -- Secrets validated in Python, Functions at
registration, tables in exactly one method, Rust nowhere.
- `validate_table_name` is already called on the local path in
`database/listing.rs`. The remote path is the inconsistent one.
Percent-encoding stays as a second line. Every admitted character is
unreserved, so an accepted component encodes to itself and the route reads as
before -- `/v1/secret/prod$vision_2$openai-prod.v1/drop`.
Tests: an illegal component never reaches the transport; a dot-only component
is refused as a name and as a segment; a configured delimiter is refused inside
a component while the same two names as separate segments are the ordinary
case.
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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