* Sets `"useCodeBlocks": true`
* Adds a post-processing script `nodejs/typedoc_post_process.js` that
puts the parameter description on the same line as the parameter name,
like it is in our Python docs. This makes the text hierarchy clearer in
those sections and also makes the sections shorter.
Closes#1791Closes#1764Closes#1897 (Makes this unnecessary)
BREAKING CHANGE: when using azure connection string `az://...` the call
to connect will fail if the azure storage credentials are not set. this
is breaking from the previous behaviour where the call would fail after
connect, when user invokes methods on the connection.
The code to support VoyageAI embedding and rerank models was added in
the https://github.com/lancedb/lancedb/pull/1799 PR.
Some of the documentation changes was also made, here adding the
VoyageAI embedding doc link to the index page.
These are my first PRs in lancedb and while i checked the
documentation/code structure, i might missed something important. Please
let me know if any changes required!
# PR Summary
PR fixes the `migration.md` reference in `docs/src/guides/tables.md`. On
the way, it also fixes some typos found in that document.
Signed-off-by: Emmanuel Ferdman <emmanuelferdman@gmail.com>
* Test that we can insert subschemas (omit nullable columns) in Python.
* More work is needed to support this in Node. See:
https://github.com/lancedb/lancedb/issues/1832
* Test that we can insert data with nullable schema but no nulls in
non-nullable schema.
* Add `"null"` option for `on_bad_vectors` where we fill with null if
the vector is bad.
* Make null values not considered bad if the field itself is nullable.
This is done as setup for a PR that will fix the OpenAI dependency
issue.
* [x] FTS examples
* [x] Setup mock openai
* [x] Ran `npm audit fix`
* [x] sentences embeddings test
* [x] Double check formatting of docs examples
This exposes the `LANCEDB_LOG` environment variable in node, so that
users can now turn on logging.
In addition, fixes a bug where only the top-level error from Rust was
being shown. This PR makes sure the full error chain is included in the
error message. In the future, will improve this so the error chain is
set on the [cause](https://nodejs.org/api/errors.html#errorcause)
property of JS errors https://github.com/lancedb/lancedb/issues/1779Fixes#1774
BREAKING CHANGE: default tokenizer no longer does stemming or stop-word
removal. Users should explicitly turn that option on in the future.
- upgrade lance to 0.19.1
- update the FTS docs
- update the FTS API
Upstream change notes:
https://github.com/lancedb/lance/releases/tag/v0.19.1
---------
Signed-off-by: BubbleCal <bubble-cal@outlook.com>
Co-authored-by: Will Jones <willjones127@gmail.com>
Resovles #1709. Adds `trust_remote_code` as a parameter to the
`TransformersEmbeddingFunction` class with a default of False. Updated
relevant documentation with the same.
- Enforce all rerankers always return _relevance_score. This was already
loosely done in tests before but based on user feedback its better to
always have _relevance_score present in all reranked results
- Deprecate LinearCombinationReranker in docs. And also fix a case where
it would not return _relevance_score if one result set was missing
Though the markdown can be rendered well on GitHub (GFM style?), but it
seems that it's required to insert a blank line between a paragraph and
a list block to make it render well with `mkdocs`?
see also the web page:
https://lancedb.github.io/lancedb/concepts/index_hnsw/
Docs used `get_registry.get(...)` whereas what works is
`get_registry().get(...)`. Fixing the two instances I found. I tested
the open clip version by trying it locally in a Jupyter notebook.
- Both LinearCombination (the current default) and RRF are pretty fast
compared to model based rerankers. RRF is slightly faster.
- In our tests RRF has also been slightly more accurate.
This PR:
- Makes RRF the default reranker
- Removed duplicate docs for rerankers
Refine and improve the language clarity and quality across all example
pages in the documentation to ensure better understanding and
readability.
---------
Co-authored-by: Ayush Chaurasia <ayush.chaurarsia@gmail.com>
Currently, the only documented way of performing hybrid search is by
using embedding API and passing string queries that get automatically
embedded. There are use cases where users might like to pass vectors and
text manually instead.
This ticket contains more information and historical context -
https://github.com/lancedb/lancedb/issues/937
This breaks a undocumented pathway that allowed passing (vector, text)
tuple queries which was intended to be temporary, so this is marked as a
breaking change. For all practical purposes, this should not really
impact most users
### usage
```
results = table.search(query_type="hybrid")
.vector(vector_query)
.text(text_query)
.limit(5)
.to_pandas()
```
