Extends the Secret component rules to the other two objects that join a name
into a route, because the failures are the same and were reachable from the
public Rust API for all three.
drop_table("../jobs", &[]) -> /v1/jobs/drop/
drop_table("a$b", &[]) -> /v1/table/a$b/drop/
drop_table("b", &["a"]) -> /v1/table/a$b/drop/ the same URL
The first leaves the table route entirely. The second and third are one URL
for two identities -- and `a$b` is not a legal table name, so what the caller
asked for silently becomes a different table that may exist. Neither is an
error the service could return: it is asked about the wrong object, or not
asked at all.
The dot-segment rule moves into `validate_table_name` and
`validate_namespace_name`, so the local path in `database/listing.rs` and the
service -- which imports both -- inherit it with the client. `.` and `..` pass
every character test and still cannot address anything: they are resolved as
relative path segments, and after percent-decoding, so `%2E%2E` is resolved
exactly as `..` is.
`build_table_identifier` and `build_namespace_identifier` become fallible and
validate each component before joining. That covers every remote table route
from one place rather than each call site. Both also refuse a component holding
the configured delimiter, which is the rule that cannot live in a shared
validator: the default `$` is outside every charset here, but a configured
delimiter may be any string and the readable choices -- `.`, `-`, `_` -- are
all inside them.
Functions need only the dot-segment rule. Their identifier is percent-encoded,
which covers the rest, and they have no namespace to join -- so there is no
boundary a delimiter could erase. `..` is unreserved, so it survives encoding
untouched and would post a registration body to `/v1/create`.
One test changed rather than being added to. `test_table_names_with_nested_namespace`
had its mock return `ns1$ns2$table1` from a namespace listing; the service
returns bare names there, as the sibling test and the service's own tests both
show. The client joins the namespace back on to build each table's identifier,
so the joined form was being joined twice into `ns1$ns2$ns1$ns2$table1`. Nothing
caught it because nothing validated the result.
Tests: a dot-only or delimiter-bearing table name, namespace segment and
Function name are each refused before a request is built.
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
Star LanceDB to get updates!
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 |
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.
