8511edaaab
I tried to do a stable release and it failed with:
```
Traceback (most recent call last):
File "/home/runner/work/lancedb/lancedb/ci/check_breaking_changes.py", line 20, in <module>
commits = repo.compare(args.base, args.head).commits
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/github/Repository.py", line 1133, in compare
headers, data = self._requester.requestJsonAndCheck("GET", f"{self.url}/compare/{base}...{head}", params)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/github/Requester.py", line 548, in requestJsonAndCheck
return self.__check(*self.requestJson(verb, url, parameters, headers, input, self.__customConnection(url)))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/github/Requester.py", line 609, in __check
raise self.createException(status, responseHeaders, data)
github.GithubException.UnknownObjectException: 404 {"message": "Not Found", "documentation_url": "https://docs.github.com/rest/commits/commits#compare-two-commits"}
```
I believe the problem is that we are calculating the
`LAST_STABLE_RELEASE` after we have run bump version and so the newly
created tag is in the list of tags we search and it is the most recent
one and so it gets included as `LAST_STABLE_RELEASE`. Then, the call to
github fails because we haven't pushed the tag yet. This changes the
logic to grab `LAST_STABLE_RELEASE` before we create any new tags.
Developer-friendly, database for multimodal AI
LanceDB is an open-source database for vector-search built with persistent storage, which greatly simplifies retrieval, filtering and management of embeddings.
The key features of LanceDB include:
-
Production-scale vector search with no servers to manage.
-
Store, query and filter vectors, metadata and multi-modal data (text, images, videos, point clouds, and more).
-
Support for vector similarity search, full-text search and SQL.
-
Native Python and Javascript/Typescript support.
-
Zero-copy, automatic versioning, manage versions of your data without needing extra infrastructure.
-
GPU support in building vector index(*).
-
Ecosystem integrations with LangChain 🦜️🔗, LlamaIndex 🦙, Apache-Arrow, Pandas, Polars, DuckDB and more on the way.
LanceDB's core is written in Rust 🦀 and is built using Lance, an open-source columnar format designed for performant ML workloads.
Quick Start
Javascript
npm install vectordb
const lancedb = require('vectordb');
const db = await lancedb.connect('data/sample-lancedb');
const table = await db.createTable({
name: 'vectors',
data: [
{ id: 1, vector: [0.1, 0.2], item: "foo", price: 10 },
{ id: 2, vector: [1.1, 1.2], item: "bar", price: 50 }
]
})
const query = table.search([0.1, 0.3]).limit(2);
const results = await query.execute();
// You can also search for rows by specific criteria without involving a vector search.
const rowsByCriteria = await table.search(undefined).where("price >= 10").execute();
Python
pip install lancedb
import lancedb
uri = "data/sample-lancedb"
db = lancedb.connect(uri)
table = db.create_table("my_table",
data=[{"vector": [3.1, 4.1], "item": "foo", "price": 10.0},
{"vector": [5.9, 26.5], "item": "bar", "price": 20.0}])
result = table.search([100, 100]).limit(2).to_pandas()