Adds the client half of database-scoped named Secrets: a Secret is a
name and
an opaque value stored by the service, and a Function binds one to the
environment variable its library already reads. Secrets are addressed by
a
namespace path plus a name.
The UDF body is unchanged and stays portable — it reads `OPENAI_API_KEY`
the
way it always did, and the binding is what puts a value there:
```python
db.create_secret("openai-prod", os.environ["OPENAI_API_KEY"])
function = db.create_function(
analyze_caption,
secrets=[
EnvVarSecret(secret_name="openai-prod", env_variable="OPENAI_API_KEY")
],
)
function.secret_bindings # the Secret's name, never its value
```
- `create_secret` / `alter_secret` / `list_secrets` / `describe_secret`
/
`drop_secret` on sync, async and remote connections, with the pyo3
binding
and the Rust client behind them. Each takes `namespace_path`
keyword-only,
defaulting to the root.
- **There is no read API, by construction rather than by policy** — no
code
path returns a stored credential, and `describe_secret` answers with
metadata only.
- `EnvVarSecret` is a pure local constructor: it contacts no server, so
it
cannot fail on a Secret that does not exist. It exists so that a bare
string
in that position — which would be a credential — is a `TypeError` rather
than a plausible-looking mistake that reads identically in a diff.
- `create_function(..., secrets=[...])` carries the bindings as
`secret_bindings`: a list of `SecretBinding` tagged by `kind`, so a
later
delivery mode is a variant rather than a sibling field. The value never
travels — it is resolved by the service when the Function runs, which is
what
lets a rotation reach columns already pinned to an older
FunctionVersion.
- A binding names its Secret as a `SecretReference` of `{name,
namespace_path}`
rather than one joined string, so no delimiter has to be excluded from
every
name and segment forever, and `ClientConfig.id_delimiter` cannot
contradict
an identity built on a fixed separator.
- A root namespace is omitted from the request body rather than sent
empty, so
a root request is byte-identical to one from a client that predates
namespaces. Tests pin it.
This is the client surface the design's §4 describes; the service side
lives in
sophon.
**Previously split across two PRs.** Namespace addressing was #4151,
stacked on
this one; it is folded in here so the Secret identity contract — name,
namespace path, and the binding that carries both — is reviewable as one
piece
rather than as a shape introduced and then replaced.
## Identifier safety, merged from #4189
**#4189 is merged into this branch**, so the client half of Secrets and
the
guards on the identity it puts in the URL are one PR. What it added:
- Components are checked where the identifier is built, before a request
is
constructed. `create_secret("../jobs", value)` no longer resolves to
`/v1/jobs/create` and delivers a credential-bearing body to a route with
none
of this one's body suppression.
- Each component is percent-encoded and joined by the delimiter, so
nothing
inside a component can end the path segment or add one.
- A component may not be empty, a relative segment (`.`, `..`, and their
`%2e`
spellings), or the delimiter itself — the three ways a component erases
a
boundary the split has to recover. `["prod", ""]` joined to `prod$`,
which
reads back as `["prod"]`.
- `$` is the only accepted `id_delimiter`, refused at client
construction.
`ClientConfig.id_delimiter` remains, since the identifier grammar comes
from
the Lance REST catalog standard, but a value that would produce
identifiers no
service splits the caller's way is now an error where it was written.
- One `build_object_identifier` and one character set serve tables,
namespaces,
Secrets, Functions and materialized views.
Components are checked for *addressability*, not a character set: the
name's
own grammar stays each object's own, so a catalog database keeps the `/`
that
`RemoteCatalog::validate_name` allows.
## Known shortcoming
`secret_bindings` is omitted from a registration body when empty, so a
client
that binds nothing sends what a client without bindings sends. When a
client
does bind a Secret and the service does not know the field, the field is
ignored: registration succeeds, the returned version carries no
bindings, and
the Function fails at execution with the variable unset, far from the
call that
asked for it.
`ServerVersion` is how this codebase refuses a feature the service is
too old
for, and it gates five features already. It does not gate this one: it
is held
per table, and registering a Function is a database-level call. Noted at
the
field in `remote/db.rs`; wiring the gate is follow-up work.
**Tests:** lancedb lib 1340 passed, `first_class_function_slice1` 9,
`first_class_function_slice2` 3, plus Python tests across both slices.
Rebased onto `main` after #4176 (OCI Function identity), #4191 (`.`/`..`
table
names) and #4195 (remote catalogs).
🤖 Generated with [Claude Code](https://claude.com/claude-code)
https://claude.ai/code/session_01UfmeJ533rQDnPBkMtjerV6
---------
Co-authored-by: Claude Opus 5 (1M context) <noreply@anthropic.com>
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.
