This prevents an issue where users can do something like:
```js
db.createTable('my-table#123123')
```
The server has logic to determine that '#' character is not allowed in
the table name, but currently this is being returned as 404 error
because it routes to `/v1/my-table#123123/create` and `#123123/create`
will not be parsed as part of path
We aren't yet ready to switch over the examples since almost all JS
examples rely on embeddings and we haven't yet ported those over.
However, this makes it possible for those that are interested to start
using `@lancedb/lancedb`
SDK
Python
Description
Exposes pyarrow batch api during query execution - relevant when there
is no vector search query, dataset is large and the filtered result is
larger than memory.
---------
Co-authored-by: Ishani Ghose <isghose@amazon.com>
Co-authored-by: Chang She <759245+changhiskhan@users.noreply.github.com>
In
https://github.com/lancedb/lancedb/commit/2de226220b78c51b22319fb4fbc4678f4d269d65
I added a new `IntoArrow` trait for adding data into a table.
Unfortunately, it seems my approach for implementing the trait for
"things that are already record batch readers" was flawed. This PR
corrects that flaw and, conveniently, removes the need to box readers at
all (though it is ok if you do).
This PR adds support for passing through a set of ordering fields at
index time (unsigned ints that tantivity can use as fast_fields) that at
query time you can sort your results on. This is useful for cases where
you want to get related hits, i.e by keyword, but order those hits by
some other score, such as popularity.
I.e search for songs descriptions that match on "sad AND jazz AND 1920"
and then order those by number of times played. Example usage can be
seen in the fts tests.
---------
Co-authored-by: Nat Roth <natroth@Nats-MacBook-Pro.local>
Co-authored-by: Chang She <759245+changhiskhan@users.noreply.github.com>
HuggingFace Dataset is written as arrow batches.
For DatasetDict, all splits are written with a "split" column appended.
- [x] what if the dataset schema already has a `split` column
- [x] add unit tests
solves https://github.com/lancedb/lancedb/issues/1086
Usage Reranking with FTS:
```
retriever = db.create_table("fine-tuning", schema=Schema, mode="overwrite")
pylist = [{"text": "Carson City is the capital city of the American state of Nevada. At the 2010 United States Census, Carson City had a population of 55,274."},
{"text": "The Commonwealth of the Northern Mariana Islands is a group of islands in the Pacific Ocean that are a political division controlled by the United States. Its capital is Saipan."},
{"text": "Charlotte Amalie is the capital and largest city of the United States Virgin Islands. It has about 20,000 people. The city is on the island of Saint Thomas."},
{"text": "Washington, D.C. (also known as simply Washington or D.C., and officially as the District of Columbia) is the capital of the United States. It is a federal district. "},
{"text": "Capital punishment (the death penalty) has existed in the United States since before the United States was a country. As of 2017, capital punishment is legal in 30 of the 50 states."},
{"text": "North Dakota is a state in the United States. 672,591 people lived in North Dakota in the year 2010. The capital and seat of government is Bismarck."},
]
retriever.add(pylist)
retriever.create_fts_index("text", replace=True)
query = "What is the capital of the United States?"
reranker = CohereReranker(return_score="all")
print(retriever.search(query, query_type="fts").limit(10).to_pandas())
print(retriever.search(query, query_type="fts").rerank(reranker=reranker).limit(10).to_pandas())
```
Result
```
text vector score
0 Capital punishment (the death penalty) has exi... [0.099975586, 0.047943115, -0.16723633, -0.183... 0.729602
1 Charlotte Amalie is the capital and largest ci... [-0.021255493, 0.03363037, -0.027450562, -0.17... 0.678046
2 The Commonwealth of the Northern Mariana Islan... [0.3684082, 0.30493164, 0.004600525, -0.049407... 0.671521
3 Carson City is the capital city of the America... [0.13989258, 0.14990234, 0.14172363, 0.0546569... 0.667898
4 Washington, D.C. (also known as simply Washing... [-0.0090408325, 0.42578125, 0.3798828, -0.3574... 0.653422
5 North Dakota is a state in the United States. ... [0.55859375, -0.2109375, 0.14526367, 0.1634521... 0.639346
text vector score _relevance_score
0 Washington, D.C. (also known as simply Washing... [-0.0090408325, 0.42578125, 0.3798828, -0.3574... 0.653422 0.979977
1 The Commonwealth of the Northern Mariana Islan... [0.3684082, 0.30493164, 0.004600525, -0.049407... 0.671521 0.299105
2 Capital punishment (the death penalty) has exi... [0.099975586, 0.047943115, -0.16723633, -0.183... 0.729602 0.284874
3 Carson City is the capital city of the America... [0.13989258, 0.14990234, 0.14172363, 0.0546569... 0.667898 0.089614
4 North Dakota is a state in the United States. ... [0.55859375, -0.2109375, 0.14526367, 0.1634521... 0.639346 0.063832
5 Charlotte Amalie is the capital and largest ci... [-0.021255493, 0.03363037, -0.027450562, -0.17... 0.678046 0.041462
```
## Vector Search usage:
```
query = "What is the capital of the United States?"
reranker = CohereReranker(return_score="all")
print(retriever.search(query).limit(10).to_pandas())
print(retriever.search(query).rerank(reranker=reranker, query=query).limit(10).to_pandas()) # <-- Note: passing extra string query here
```
Results
```
text vector _distance
0 Capital punishment (the death penalty) has exi... [0.099975586, 0.047943115, -0.16723633, -0.183... 39.728973
1 Washington, D.C. (also known as simply Washing... [-0.0090408325, 0.42578125, 0.3798828, -0.3574... 41.384884
2 Carson City is the capital city of the America... [0.13989258, 0.14990234, 0.14172363, 0.0546569... 55.220200
3 Charlotte Amalie is the capital and largest ci... [-0.021255493, 0.03363037, -0.027450562, -0.17... 58.345654
4 The Commonwealth of the Northern Mariana Islan... [0.3684082, 0.30493164, 0.004600525, -0.049407... 60.060867
5 North Dakota is a state in the United States. ... [0.55859375, -0.2109375, 0.14526367, 0.1634521... 64.260544
text vector _distance _relevance_score
0 Washington, D.C. (also known as simply Washing... [-0.0090408325, 0.42578125, 0.3798828, -0.3574... 41.384884 0.979977
1 The Commonwealth of the Northern Mariana Islan... [0.3684082, 0.30493164, 0.004600525, -0.049407... 60.060867 0.299105
2 Capital punishment (the death penalty) has exi... [0.099975586, 0.047943115, -0.16723633, -0.183... 39.728973 0.284874
3 Carson City is the capital city of the America... [0.13989258, 0.14990234, 0.14172363, 0.0546569... 55.220200 0.089614
4 North Dakota is a state in the United States. ... [0.55859375, -0.2109375, 0.14526367, 0.1634521... 64.260544 0.063832
5 Charlotte Amalie is the capital and largest ci... [-0.021255493, 0.03363037, -0.027450562, -0.17... 58.345654 0.041462
```
This PR originated from a request to add `Serialize` / `Deserialize` to
`lance_linalg::distance::DistanceType`. However, that is a strange
request for `lance_linalg` which shouldn't really have to worry about
`Serialize` / `Deserialize`. The problem is that `lancedb` is re-using
`DistanceType` and things in `lancedb` do need to worry about
`Serialize`/`Deserialize` (because `lancedb` needs to support remote
client).
On the bright side, separating the two types allows us to independently
document distance type and allows `lance_linalg` to make changes to
`DistanceType` in the future without having to worry about backwards
compatibility concerns.
This will make it easier for 3rd party integrations. They simply need to
implement `IntoArrow` for their types in order for those types to be
used in ingestion.
I know there's a larger effort to have the python client based on the
core rust implementation, but in the meantime there have been several
issues (#1072 and #485) with some of the azure blob storage calls due to
pyarrow not natively supporting an azure backend. To this end, I've
added an optional import of the fsspec implementation of azure blob
storage [`adlfs`](https://pypi.org/project/adlfs/) and passed it to
`pyarrow.fs`. I've modified the existing test and manually verified it
with some real credentials to make sure it behaves as expected.
It should be now as simple as:
```python
import lancedb
db = lancedb.connect("az://blob_name/path")
table = db.open_table("test")
table.search(...)
```
Thank you for this cool project and we're excited to start using this
for real shortly! 🎉 And thanks to @dwhitena for bringing it to my
attention with his prediction guard posts.
Co-authored-by: christiandilorenzo <christian.dilorenzo@infiniaml.com>
The LanceDB embeddings registry allows users to annotate the pydantic
model used as table schema with the desired embedding function, e.g.:
```python
class Schema(LanceModel):
id: str
vector: Vector(openai.ndims()) = openai.VectorField()
text: str = openai.SourceField()
```
Tables created like this does not require embeddings to be calculated by
the user explicitly, e.g. this works:
```python
table.add([{"id": "foo", "text": "rust all the things"}])
```
However, trying to construct pydantic model instances without vector
doesn't because it's a required field.
Instead, you need add a default value:
```python
class Schema(LanceModel):
id: str
vector: Vector(openai.ndims()) = openai.VectorField(default=None)
text: str = openai.SourceField()
```
then this completes without errors:
```python
table.add([Schema(id="foo", text="rust all the things")])
```
However, all of the vectors are filled with zeros. Instead in
add_vector_col we have to add an additional check so that the embedding
generation is called.
In Rust and Node, we have been swallowing filter validation errors. If
there was an error in parsing the filter, then the filter was silently
ignored, returning unfiltered results.
Fixes#1081
Added a small bit of documentation for the `dim` feature which is
provided by the new `text-embedding-3` model series that allows users to
shorten an embedding.
Happy to discuss a bit on the phrasing but I struggled quite a bit with
getting it to work so wanted to help others who might want to use the
newer model too
#1002 accidentally changed `checkout_latest` to do nothing if the table
was already in latest mode. This PR makes sure it forces a reload of the
table (if there is a newer version).
The synchronous table_names function in python lancedb relies on arrow's
filesystem which behaves slightly differently than object_store. As a
result, the function would not work properly in GCS.
However, the async table_names function uses object_store directly and
thus is accurate. In most cases we can fallback to using the async
table_names function and so this PR does so. The one case we cannot is
if the user is already in an async context (we can't start a new async
event loop). Soon, we can just redirect those users to use the async API
instead of the sync API and so that case will eventually go away. For
now, we fallback to the old behavior.
The fact that we convert errors to strings makes them really hard to
work with. For example, in SaaS we want to know whether the underlying
`lance::Error` was the `InvalidInput` variant, so we can return a 400
instead of a 500.
1. filtering with fts mutated the schema, which caused schema mistmatch
problems with hybrid search as it combines fts and vector search tables.
2. fts with filter failed with `with_row_id`. This was because row_id
was calculated before filtering which caused size mismatch on attaching
it after.
3. The fix for 1 meant that now row_id is attached before filtering but
passing a filter to `to_lance` on a dataset that already contains
`_rowid` raises a panic from lance. So temporarily, in case where fts is
used with a filter AND `with_row_id`, we just force user to using the
duckdb pathway.
---------
Co-authored-by: Chang She <759245+changhiskhan@users.noreply.github.com>
Arrow-js uses brittle `instanceof` checks throughout the code base.
These fail unless the library instance that produced the object matches
exactly the same instance the vectordb is using. At a minimum, this
means that a user using arrow version 15 (or any version that doesn't
match exactly the version that vectordb is using) will get strange
errors when they try and use vectordb.
However, there are even cases where the versions can be perfectly
identical, and the instanceof check still fails. One such example is
when using `vite` (e.g. https://github.com/vitejs/vite/issues/3910)
This PR solves the problem in a rather brute force, but workable,
fashion. If we encounter a schema that does not pass the `instanceof`
check then we will attempt to sanitize that schema by traversing the
object and, if it has all the correct properties, constructing an
appropriate `Schema` instance via deep cloning.
The eslint rules specify some formatting requirements that are rather
strict and conflict with vscode's default formatter. I was unable to get
auto-formatting to setup correctly. Also, eslint has quite recently
[given up on
formatting](https://eslint.org/blog/2023/10/deprecating-formatting-rules/)
and recommends using a 3rd party formatter.
This PR adds prettier as the formatter. It restores the eslint rules to
their defaults. This does mean we now have the "no explicit any" check
back on. I know that rule is pedantic but it did help me catch a few
corner cases in type testing that weren't covered in the current code.
Leaving in draft as this is dependent on other PRs.
In order to add support for `add` we needed to migrate the rust `Table`
trait to a `Table` struct and `TableInternal` trait (similar to the way
the connection is designed).
While doing this we also cleaned up some inconsistencies between the
SDKs:
* Python and Node are garbage collected languages and it can be
difficult to trigger something to be freed. The convention for these
languages is to have some kind of close method. I added a close method
to both the table and connection which will drop the underlying rust
object.
* We made significant improvements to table creation in
https://github.com/lancedb/lancedb/commit/cc5f2136a61234a9cdbe20e19c56459db8ca1051
for the `node` SDK. I copied these changes to the `nodejs` SDK.
* The nodejs tables were using fs to create tmp directories and these
were not getting cleaned up. This is mostly harmless but annoying and so
I changed it up a bit to ensure we cleanup tmp directories.
* ~~countRows in the node SDK was returning `bigint`. I changed it to
return `number`~~ (this actually happened in a previous PR)
* Tables and connections now implement `std::fmt::Display` which is
hooked into python's `__repr__`. Node has no concept of a regular "to
string" function and so I added a `display` method.
* Python method signatures are changing so that optional parameters are
always `Optional[foo] = None` instead of something like `foo = False`.
This is because we want those defaults to be in rust whenever possible
(though we still need to mention the default in documentation).
* I changed the python `AsyncConnection/AsyncTable` classes from
abstract classes with a single implementation to just classes because we
no longer have the remote implementation in python.
Note: this does NOT add the `add` function to the remote table. This PR
was already large enough, and the remote implementation is unique
enough, that I am going to do all the remote stuff at a later date (we
should have the structure in place and correct so there shouldn't be any
refactor concerns)
---------
Co-authored-by: Will Jones <willjones127@gmail.com>
The renaming of `vectordb` to `lancedb` broke the [quick start
docs](https://lancedb.github.io/lancedb/basic/#__tabbed_5_3) (it's
pointing to a non-existent directory). This PR fixes the code snippets
and the paths in the docs page.
Additionally, more fixes related to indexing docs below 👇🏽.
This changes `lancedb` from a "pure python" setuptools project to a
maturin project and adds a rust lancedb dependency.
The async python client is extremely minimal (only `connect` and
`Connection.table_names` are supported). The purpose of this PR is to
get the infrastructure in place for building out the rest of the async
client.
Although this is not technically a breaking change (no APIs are
changing) it is still a considerable change in the way the wheels are
built because they now include the native shared library.
This also renames the new experimental node package to lancedb. The
classic node package remains named vectordb.
The goal here is to avoid introducing piecemeal breaking changes to the
vectordb crate. Instead, once the new API is stabilized, we will
officially release the lancedb crate and deprecate the vectordb crate.
The same pattern will eventually happen with the npm package vectordb.
This PR adds the same consistency semantics as was added in #828. It
*does not* add the same lazy-loading of tables, since that breaks some
existing tests.
This closes#998.
---------
Co-authored-by: Weston Pace <weston.pace@gmail.com>
This is the start of a more comprehensive refactor and stabilization of
the Rust API. The `Connection` trait is cleaned up to not require
`lance` and to match the `Connection` trait in other APIs. In addition,
the concrete implementation `Database` is hidden.
BREAKING CHANGE: The struct `crate::connection::Database` is now gone.
Several examples opened a connection using `Database::connect` or
`Database::connect_with_params`. Users should now use
`vectordb::connect`.
BREAKING CHANGE: The `connect`, `create_table`, and `open_table` methods
now all return a builder object. This means that a call like
`conn.open_table(..., opt1, opt2)` will now become
`conn.open_table(...).opt1(opt1).opt2(opt2).execute()` In addition, the
structure of options has changed slightly. However, no options
capability has been removed.
---------
Co-authored-by: Will Jones <willjones127@gmail.com>
I think this should work. Need to deploy it to be sure as it can be
tested locally. Can be tested here.
2 things about this solution:
* All pages have a same meta tag, i.e, lancedb banner
* If needed, we can automatically use the first image of each page and
generate meta tags using the ultralytics mkdocs plugin that we did for
this purpose - https://github.com/ultralytics/mkdocs
Got some user feedback that the `implicit` / `explicit` distinction is
confusing.
Instead I was thinking we would just deprecate the `with_embeddings` API
and then organize working with embeddings into 3 buckets:
1. manually generate embeddings
2. use a provided embedding function
3. define your own custom embedding function
When we turned on fat LTO builds, we made the release build job **much**
more compute and memory intensive. The ARM runners have particularly low
memory per core, which makes them susceptible to OOM errors. To avoid
issues, I have enabled memory swap on ARM and bumped the side of the
runner.
This PR also reworks the table creation utilities significantly so that
they are more consistent, built on top of each other, and thoroughly
documented.
- Fixed typos and added some clarity to the hybrid search docs
- Changed "Airbnb" case to be as per the [official company
name](https://en.wikipedia.org/wiki/Airbnb) (the "bnb" shouldn't be
capitalized", and the text in the document aligns with this
- Fixed headers in nav bar
- Rename safe_import -> attempt_import_or_raise (closes
https://github.com/lancedb/lancedb/pull/923)
- Update docs
- Add Notebook example (@changhiskhan you can use it for the talk. Comes
with "open in colab" button)
- Latency benchmark & results comparison, sanity check on real-world
data
- Updates the default openai model to gpt-4
A `count_rows` method that takes a filter was recently added to
`LanceTable`. This PR adds it everywhere else except `RemoteTable` (that
will come soon).
This PR refactors how we handle read consistency: does the `LanceTable`
class always pick up modifications to the table made by other instance
or processes. Users have three options they can set at the connection
level:
1. (Default) `read_consistency_interval=None` means it will not check at
all. Users can call `table.checkout_latest()` to manually check for
updates.
2. `read_consistency_interval=timedelta(0)` means **always** check for
updates, giving strong read consistency.
3. `read_consistency_interval=timedelta(seconds=20)` means check for
updates every 20 seconds. This is eventual consistency, a compromise
between the two options above.
## Table reference state
There is now an explicit difference between a `LanceTable` that tracks
the current version and one that is fixed at a historical version. We
now enforce that users cannot write if they have checked out an old
version. They are instructed to call `checkout_latest()` before calling
the write methods.
Since `conn.open_table()` doesn't have a parameter for version, users
will only get fixed references if they call `table.checkout()`.
The difference between these two can be seen in the repr: Table that are
fixed at a particular version will have a `version` displayed in the
repr. Otherwise, the version will not be shown.
```python
>>> table
LanceTable(connection=..., name="my_table")
>>> table.checkout(1)
>>> table
LanceTable(connection=..., name="my_table", version=1)
```
I decided to not create different classes for these states, because I
think we already have enough complexity with the Cloud vs OSS table
references.
Based on #812
Adds capability to the remote python SDK to retry requests (fixes#911)
This can be configured through environment:
- `LANCE_CLIENT_MAX_RETRIES`= total number of retries. Set to 0 to
disable retries. default = 3
- `LANCE_CLIENT_CONNECT_RETRIES` = number of times to retry request in
case of TCP connect failure. default = 3
- `LANCE_CLIENT_READ_RETRIES` = number of times to retry request in case
of HTTP request failure. default = 3
- `LANCE_CLIENT_RETRY_STATUSES` = http statuses for which the request
will be retried. passed as comma separated list of ints. default `500,
502, 503`
- `LANCE_CLIENT_RETRY_BACKOFF_FACTOR` = controls time between retry
requests. see
[here](https://github.com/urllib3/urllib3/blob/23f2287eb526d9384dddeedb6f6345e263bb9b86/src/urllib3/util/retry.py#L141-L146).
default = 0.25
Only read requests will be retried:
- list table names
- query
- describe table
- list table indices
This does not add retry capabilities for writes as it could possibly
cause issues in the case where the retried write isn't idempotent. For
example, in the case where the LB times-out the request but the server
completes the request anyway, we might not want to blindly retry an
insert request.
- The JS/TS library actually expects named parameters via an object in
`.createTable()` rather than individual arguments
- Added example on how to search rows by criteria without a vector
search. TS type of `.search()` currently has the `query` parameter as
non-optional so we have to pass undefined for now.
based on https://github.com/lancedb/lancedb/pull/713
- The Reranker api can be plugged into vector only or fts only search
but this PR doesn't do that (see example -
https://txt.cohere.com/rerank/)
### Default reranker -- `LinearCombinationReranker(weight=0.7,
fill=1.0)`
```
table.search("hello", query_type="hybrid").rerank(normalize="score").to_pandas()
```
### Available rerankers
LinearCombinationReranker
```
from lancedb.rerankers import LinearCombinationReranker
# Same as default
table.search("hello", query_type="hybrid").rerank(
normalize="score",
reranker=LinearCombinationReranker()
).to_pandas()
# with custom params
reranker = LinearCombinationReranker(weight=0.3, fill=1.0)
table.search("hello", query_type="hybrid").rerank(
normalize="score",
reranker=reranker
).to_pandas()
```
Cohere Reranker
```
from lancedb.rerankers import CohereReranker
# default model.. English and multi-lingual supported. See docstring for available custom params
table.search("hello", query_type="hybrid").rerank(
normalize="rank", # score or rank
reranker=CohereReranker()
).to_pandas()
```
CrossEncoderReranker
```
from lancedb.rerankers import CrossEncoderReranker
table.search("hello", query_type="hybrid").rerank(
normalize="rank",
reranker=CrossEncoderReranker()
).to_pandas()
```
## Using custom Reranker
```
from lancedb.reranker import Reranker
class CustomReranker(Reranker):
def rerank_hybrid(self, vector_result, fts_result):
combined_res = self.merge_results(vector_results, fts_results) # or use custom combination logic
# Custom rerank logic here
return combined_res
```
- [x] Expand testing
- [x] Make sure usage makes sense
- [x] Run simple benchmarks for correctness (Seeing weird result from
cohere reranker in the toy example)
- Support diverse rerankers by default:
- [x] Cross encoding
- [x] Cohere
- [x] Reciprocal Rank Fusion
---------
Co-authored-by: Chang She <759245+changhiskhan@users.noreply.github.com>
Co-authored-by: Prashanth Rao <35005448+prrao87@users.noreply.github.com>
Minor fix to change the background color for an image in the docs. It's
now readable in both light and dark modes (earlier version made it
impossible to read in dark mode).
have added testing and an example in the docstring, will be pushing a
separate PR in recipe repo for rag example
---------
Co-authored-by: Ayush Chaurasia <ayush.chaurarsia@gmail.com>
* Bring the feature parity of Rust connect methods.
* A global connect method that can connect to local and remote / cloud
table, as the same as in js/python today.
* Easy to type
* Handle `String, &str, [String] and [&str]` well without manual
conversion
* Fix function name to be verb
* Improve docstring of Rust.
* Promote `query` and `search()` to public `Table` trait
Allow passing API key as env var:
```shell
export LANCEDB_API_KEY=sh_123...
```
with this set, apiKey argument can omitted from `connect`
```js
const db = await vectordb.connect({
uri: "db://test-proj-01-ae8343",
region: "us-east-1",
})
```
```py
db = lancedb.connect(
uri="db://test-proj-01-ae8343",
region="us-east-1",
)
```
This PR makes the following aesthetic and content updates to the docs.
- [x] Fix max width issue on mobile: Content should now render more
cleanly and be more readable on smaller devices
- [x] Improve image quality of flowchart in data management page
- [x] Fix syntax highlighting in text at the bottom of the IVF-PQ
concepts page
- [x] Add example of Polars LazyFrames to docs (Integrations)
- [x] Add example of adding data to tables using Polars (guides)
This PR makes incremental changes to the documentation.
* Closes#697
* Closes#698
## Chores
- [x] Add dark mode
- [x] Fix headers in navbar
- [x] Add `extra.css` to customize navbar styles
- [x] Customize fonts for prose/code blocks, navbar and admonitions
- [x] Inspect all admonition boxes (remove redundant dropdowns) and
improve clarity and readability
- [x] Ensure that all images in the docs have white background (not
transparent) to be viewable in dark mode
- [x] Improve code formatting in code blocks to make them consistent
with autoformatters (eslint/ruff)
- [x] Add bolder weight to h1 headers
- [x] Add diagram showing the difference between embedded (OSS) and
serverless (Cloud)
- [x] Fix [Creating an empty
table](https://lancedb.github.io/lancedb/guides/tables/#creating-empty-table)
section: right now, the subheaders are not clickable.
- [x] In critical data ingestion methods like `table.add` (among
others), the type signature often does not match the actual code
- [x] Proof-read each documentation section and rewrite as necessary to
provide more context, use cases, and explanations so it reads less like
reference documentation. This is especially important for CRUD and
search sections since those are so central to the user experience.
## Restructure/new content
- [x] The section for [Adding
data](https://lancedb.github.io/lancedb/guides/tables/#adding-to-a-table)
only shows examples for pandas and iterables. We should include pydantic
models, arrow tables, etc.
- [x] Add conceptual tutorial for IVF-PQ index
- [x] Clearly separate vector search, FTS and filtering sections so that
these are easier to find
- [x] Add docs on refine factor to explain its importance for recall.
Closes#716
- [x] Add an FAQ page showing answers to commonly asked questions about
LanceDB. Closes#746
- [x] Add simple polars example to the integrations section. Closes#756
and closes#153
- [ ] Add basic docs for the Rust API (more detailed API docs can come
later). Closes#781
- [x] Add a section on the various storage options on local vs. cloud
(S3, EBS, EFS, local disk, etc.) and the tradeoffs involved. Closes#782
- [x] Revamp filtering docs: add pre-filtering examples and redo headers
and update content for SQL filters. Closes#783 and closes#784.
- [x] Add docs for data management: compaction, cleaning up old versions
and incremental indexing. Closes#785
- [ ] Add a benchmark section that also discusses some best practices.
Closes#787
---------
Co-authored-by: Ayush Chaurasia <ayush.chaurarsia@gmail.com>
Co-authored-by: Will Jones <willjones127@gmail.com>
This mimics CREATE TABLE IF NOT EXISTS behavior.
We add `db.create_table(..., exist_ok=True)` parameter.
By default it is set to False, so trying to create
a table with the same name will raise an exception.
If set to True, then it only opens the table if it
already exists. If you pass in a schema, it will
be checked against the existing table to make sure
you get what you want. If you pass in data, it will
NOT be added to the existing table.
This pull request adds check for the presence of an environment variable
`OPENAI_API_KEY` and removes an unused parameter in
`retry_with_exponential_backoff` function.
Named it Gemini-text for now. Not sure how complicated it will be to
support both text and multimodal embeddings under the same class
"gemini"..But its not something to worry about for now I guess.
addresses #797
Problem: tantivy does not expose option to explicitly
Proposed solution here:
1. Add a `.phrase_query()` option
2. Under the hood, LanceDB takes care of wrapping the input in quotes
and replace nested double quotes with single quotes
I've also filed an upstream issue, if they support phrase queries
natively then we can get rid of our manual custom processing here.
Close#773
we pass an empty table over IPC so we don't need to manually deal with
serde. Then we just return the schema attribute from the empty table.
---------
Co-authored-by: albertlockett <albert.lockett@gmail.com>
By default tantivy-py uses 128MB heapsize. We change the default to 1GB
and we allow the user to customize this
locally this makes `test_fts.py` run 10x faster
If the input text is None, Tantivy raises an error
complaining it cannot add a NoneType. We handle this
upstream so None's are not added to the document.
If all of the indexed fields are None then we skip
this document.
I found that it was quite incoherent to have to read through the
documentation and having to search which submodule that each class
should be imported from.
For example, it is cumbersome to have to navigate to another
documentation page to find out that `EmbeddingFunctionRegistry` is from
`lancedb.embeddings`
If you add timezone information in the Field annotation for a datetime
then that will now be passed to the pyarrow data type.
I'm not sure how pyarrow enforces timezones, right now, it silently
coerces to the timezone given in the column regardless of whether the
input had the matching timezone or not. This is probably not the right
behavior. Though we could just make it so the user has to make the
pydantic model do the validation instead of doing that at the pyarrow
conversion layer.
Closes#721
fts will return results as a pyarrow table. Pyarrow tables has a
`filter` method but it does not take sql filter strings (only pyarrow
compute expressions). Instead, we do one of two things to support
`tbl.search("keywords").where("foo=5").limit(10).to_arrow()`:
Default path: If duckdb is available then use duckdb to execute the sql
filter string on the pyarrow table.
Backup path: Otherwise, write the pyarrow table to a lance dataset and
then do `to_table(filter=<filter>)`
Neither is ideal.
Default path has two issues:
1. requires installing an extra library (duckdb)
2. duckdb mangles some fields (like fixed size list => list)
Backup path incurs a latency penalty (~20ms on ssd) to write the
resultset to disk.
In the short term, once #676 is addressed, we can write the dataset to
"memory://" instead of disk, this makes the post filter evaluate much
quicker (ETA next week).
In the longer term, we'd like to be able to evaluate the filter string
on the pyarrow Table directly, one possibility being that we use
Substrait to generate pyarrow compute expressions from sql string. Or if
there's enough progress on pyarrow, it could support Substrait
expressions directly (no ETA)
---------
Co-authored-by: Will Jones <willjones127@gmail.com>
For object detection, each row may correspond to an image and each image
can have multiple bounding boxes of x-y coordinates. This means that a
`bbox` field is potentially "list of list of float". This adds support
in our pydantic-pyarrow conversion for nested lists.
addresses #797
Problem: tantivy does not expose option to explicitly
Proposed solution here:
1. Add a `.phrase_query()` option
2. Under the hood, LanceDB takes care of wrapping the input in quotes
and replace nested double quotes with single quotes
I've also filed an upstream issue, if they support phrase queries
natively then we can get rid of our manual custom processing here.
Close#773
we pass an empty table over IPC so we don't need to manually deal with
serde. Then we just return the schema attribute from the empty table.
---------
Co-authored-by: albertlockett <albert.lockett@gmail.com>
By default tantivy-py uses 128MB heapsize. We change the default to 1GB
and we allow the user to customize this
locally this makes `test_fts.py` run 10x faster
If the input text is None, Tantivy raises an error
complaining it cannot add a NoneType. We handle this
upstream so None's are not added to the document.
If all of the indexed fields are None then we skip
this document.
I found that it was quite incoherent to have to read through the
documentation and having to search which submodule that each class
should be imported from.
For example, it is cumbersome to have to navigate to another
documentation page to find out that `EmbeddingFunctionRegistry` is from
`lancedb.embeddings`
If you add timezone information in the Field annotation for a datetime
then that will now be passed to the pyarrow data type.
I'm not sure how pyarrow enforces timezones, right now, it silently
coerces to the timezone given in the column regardless of whether the
input had the matching timezone or not. This is probably not the right
behavior. Though we could just make it so the user has to make the
pydantic model do the validation instead of doing that at the pyarrow
conversion layer.
Closes#721
fts will return results as a pyarrow table. Pyarrow tables has a
`filter` method but it does not take sql filter strings (only pyarrow
compute expressions). Instead, we do one of two things to support
`tbl.search("keywords").where("foo=5").limit(10).to_arrow()`:
Default path: If duckdb is available then use duckdb to execute the sql
filter string on the pyarrow table.
Backup path: Otherwise, write the pyarrow table to a lance dataset and
then do `to_table(filter=<filter>)`
Neither is ideal.
Default path has two issues:
1. requires installing an extra library (duckdb)
2. duckdb mangles some fields (like fixed size list => list)
Backup path incurs a latency penalty (~20ms on ssd) to write the
resultset to disk.
In the short term, once #676 is addressed, we can write the dataset to
"memory://" instead of disk, this makes the post filter evaluate much
quicker (ETA next week).
In the longer term, we'd like to be able to evaluate the filter string
on the pyarrow Table directly, one possibility being that we use
Substrait to generate pyarrow compute expressions from sql string. Or if
there's enough progress on pyarrow, it could support Substrait
expressions directly (no ETA)
---------
Co-authored-by: Will Jones <willjones127@gmail.com>
For object detection, each row may correspond to an image and each image
can have multiple bounding boxes of x-y coordinates. This means that a
`bbox` field is potentially "list of list of float". This adds support
in our pydantic-pyarrow conversion for nested lists.
Use pathlib for local paths so that pathlib
can handle the correct separator on windows.
Closes#703
---------
Co-authored-by: Will Jones <willjones127@gmail.com>
This PR adds issue templates, which help two recurring issues:
* Users forget to tell us whether they are using the Node or Python SDK
* Issues don't get appropriate tags
This doesn't force the use of the templates. Because we set
`blank_issues_enabled: true`, users can still create a custom issue.
This forces the user to replace the whole FTS directory when re-creating
the index, prevent duplicate data from being created. Previously, the
whole dataset was re-added to the existing index, duplicating existing
rows in the index.
This (in combination with lancedb/lance#1707) caused #726, since the
duplicate data emitted duplicate indices for `take()` and an upstream
issue caused those queries to fail.
This solution isn't ideal, since it makes the FTS index temporarily
unavailable while the index is built. In the future, we should have
multiple FTS index directories, which would allow atomic commits of new
indexes (as well as multiple indexes for different columns).
Fixes#498.
Fixes#726.
---------
Co-authored-by: Chang She <759245+changhiskhan@users.noreply.github.com>
Closes https://github.com/lancedb/lance/issues/1738
We add a `flatten` parameter to the signature of `to_pandas`. By default
this is None and does nothing.
If set to True or -1, then LanceDB will flatten structs before
converting to a pandas dataframe. All nested structs are also flattened.
If set to any positive integer, then LanceDB will flatten structs up to
the specified level of nesting.
---------
Co-authored-by: Weston Pace <weston.pace@gmail.com>
Most recent release failed because `release` depends on `node-macos`,
but we renamed `node-macos` to `node-macos-{x86,arm64}`. This fixes that
by consolidating them back to a single `node-macos` job, which also has
the side effect of making the file shorter.
Note this currently the filter/where is only implemented for LocalTable
so that it requires an explicit cast to "enable" (see new unit test).
The alternative is to add it to the Table interface, but since it's not
available on RemoteTable this may cause some user experience issues.
- Register open_table as event
- Because we're dropping 'seach' event currently, changed the name to
'search_table' and introduced throttling
- Throttled events will be counted once per time batch so that the user
is registered but event count doesn't go up by a lot
We had some build issues with npm publish for cross-compiling arm64
macos on an x86 macos runner. Switching to m1 runner for now until
someone has time to deal with the feature flags.
follow-up tracked here: #688
there's build failure for the rust artifact but the macos arm64 build
for npm publish still passed. So we had a silent failure for 2 releases.
By setting error to immediate this should cause fail immediately.
https://github.com/lancedb/lancedb/issues/623
Fixes issue trying to print response status when using remote client. If
the error is not an HTTP error (e.g. dns/network failure), there won't
be a response.
Closes#594
The embedding functions are pydantic models so multiple instances with
the same parameters are considered ==, which means that if you have
multiple embedding columns it's possible for the embeddings to get
overwritten. Instead we use `is` instead of == to avoid this problem.
testing: modified unit test to include this case
Prior to this PR, to get a new version of a table, we need to re-open
the table. This has a few downsides w.r.t. performance:
* Object store is recreated, which takes time and throws away existing
warm connections
* Commit handler is thrown aways as well, which also may contain warm
connections
* Filename typo
* Remove rick_morty csv as users won't really be able to use it.. We can
create a an executable colab and download it from a bucket or smth.
Sets things up for this -> https://github.com/lancedb/lancedb/issues/579
- Just separates out the registry/ingestion code from the function
implementation code
- adds a `get_registry` util
- package name "open-clip" -> "open-clip-torch"
Fix broken link to embedding functions
testing: broken link was verified after local docs build to have been
repaired
---------
Co-authored-by: Chang She <chang@lancedb.com>
This PR adds an overview of embeddings docs:
- 2 ways to vectorize your data using lancedb - explicit & implicit
- explicit - manually vectorize your data using `wit_embedding` function
- Implicit - automatically vectorize your data as it comes by ingesting
your embedding function details as table metadata
- Multi-modal example w/ disappearing embedding function
Add `to_list` to return query results as list of python dict (so we're
not too pandas-centric). Closes#555
Add `to_pandas` API and add deprecation warning on `to_df`. Closes#545
Co-authored-by: Chang She <chang@lancedb.com>
Once lanceDB is installed, you can access the CLI using `lancedb` command on the console.
```
lancedb
```
This lists out all the various command-line options available. You can get the usage or help for a particular command.
```
lancedb {command} --help
```
## LanceDB config
LanceDB uses a global config file to store certain settings. These settings are configurable using the lanceDB cli.
To view your config settings, you can use:
```
lancedb config
```
These config parameters can be tuned using the cli.
```
lancedb {config_name} --{argument}
```
## LanceDB Opt-in Diagnostics
When enabled, LanceDB will send anonymous events to help us improve LanceDB. These diagnostics are used only for error reporting and no data is collected. Error & stats allow us to automate certain aspects of bug reporting, prioritization of fixes and feature requests.
These diagnostics are opt-in and can be enabled or disabled using the `lancedb diagnostics` command. These are enabled by default.
@@ -177,32 +177,6 @@ Allows you to set parameters when registering a `sentence-transformers` object.
Visit sentence-transformers [HuggingFace HUB](https://huggingface.co/sentence-transformers) page for more information on the available models.
### Huggingface embedding models
We offer support for all huggingface models (which can be loaded via [transformers](https://huggingface.co/docs/transformers/en/index) library). The default model is `colbert-ir/colbertv2.0` which also has its own special callout - `registry.get("colbert")`
Example usage -
```python
import lancedb
import pandas as pd
from lancedb.embeddings import get_registry
from lancedb.pydantic import LanceModel, Vector
model = get_registry().get("huggingface").create(name='facebook/bart-base')
actual = table.search(query).limit(1).to_pydantic(Words)[0]
print(actual.text)
```
### OpenAI embeddings
LanceDB registers the OpenAI embeddings function in the registry by default, as `openai`. Below are the parameters that you can customize when creating the instances:
@@ -55,139 +55,18 @@ LanceDB OSS supports object stores such as AWS S3 (and compatible stores), Azure
const db = await lancedb.connect("az://bucket/path");
```
In most cases, when running in the respective cloud and permissions are set up correctly, no additional configuration is required. When running outside of the respective cloud, authentication credentials must be provided. Credentials and other configuration options can be set in two ways: first, by setting environment variables. And second, by passing a `storage_options` object to the `connect` function. For example, to increase the request timeout to 60 seconds, you can set the `TIMEOUT` environment variable to `60s`:
In most cases, when running in the respective cloud and permissions are set up correctly, no additional configuration is required. When running outside of the respective cloud, authentication credentials must be provided using environment variables. In general, these environment variables are the same as those used by the respective cloud SDKs. The sections below describe the environment variables that can be used to configure each object store.
```bash
export TIMEOUT=60s
```
!!! note "`storage_options` availability"
The `storage_options` parameter is only available in Python *async* API and JavaScript API.
It is not yet supported in the Python synchronous API.
If you only want this to apply to one particular connection, you can pass the `storage_options` argument when opening the connection:
=== "Python"
```python
import lancedb
db = await lancedb.connect_async(
"s3://bucket/path",
storage_options={"timeout": "60s"}
)
```
=== "JavaScript"
```javascript
const lancedb = require("lancedb");
const db = await lancedb.connect("s3://bucket/path",
{storageOptions: {timeout: "60s"}});
```
Getting even more specific, you can set the `timeout` for only a particular table:
=== "Python"
<!-- skip-test -->
```python
import lancedb
db = await lancedb.connect_async("s3://bucket/path")
table = await db.create_table(
"table",
[{"a": 1, "b": 2}],
storage_options={"timeout": "60s"}
)
```
=== "JavaScript"
<!-- skip-test -->
```javascript
const lancedb = require("lancedb");
const db = await lancedb.connect("s3://bucket/path");
const table = db.createTable(
"table",
[{ a: 1, b: 2}],
{storageOptions: {timeout: "60s"}}
);
```
!!! info "Storage option casing"
The storage option keys are case-insensitive. So `connect_timeout` and `CONNECT_TIMEOUT` are the same setting. Usually lowercase is used in the `storage_options` argument and uppercase is used for environment variables. In the `lancedb` Node package, the keys can also be provided in `camelCase` capitalization. For example, `connectTimeout` is equivalent to `connect_timeout`.
### General configuration
There are several options that can be set for all object stores, mostly related to network client configuration.
<!-- from here: https://docs.rs/object_store/latest/object_store/enum.ClientConfigKey.html -->
| `connect_timeout` | Timeout for only the connect phase of a Client. Default: `5s`. |
| `timeout` | Timeout for the entire request, from connection until the response body has finished. Default: `30s`. |
| `user_agent` | User agent string to use in requests. |
| `proxy_url` | URL of a proxy server to use for requests. Default: `None`. |
| `proxy_ca_certificate` | PEM-formatted CA certificate for proxy connections. |
| `proxy_excludes` | List of hosts that bypass the proxy. This is a comma-separated list of domains and IP masks. Any subdomain of the provided domain will be bypassed. For example, `example.com, 192.168.1.0/24` would bypass `https://api.example.com`, `https://www.example.com`, and any IP in the range `192.168.1.0/24`. |
LanceDB OSS uses the [object-store](https://docs.rs/object_store/latest/object_store/) Rust crate for object store access. There are general environment variables that can be used to configure the object store, such as the request timeout and proxy configuration. See the [object_store ClientConfigKey](https://docs.rs/object_store/latest/object_store/enum.ClientConfigKey.html) doc for available configuration options. The environment variables that can be set are the snake-cased versions of these variable names. For example, to set `ProxyUrl` use the environment variable `PROXY_URL`. (Don't let the Rust docs intimidate you! We link to them so you can see an up-to-date list of the available options.)
### AWS S3
To configure credentials for AWS S3, you can use the `AWS_ACCESS_KEY_ID`, `AWS_SECRET_ACCESS_KEY`, and `AWS_SESSION_TOKEN` keys. Region can also be set, but it is not mandatory when using AWS.
These can be set as environment variables or passed in the `storage_options` parameter:
=== "Python"
```python
import lancedb
db = await lancedb.connect_async(
"s3://bucket/path",
storage_options={
"aws_access_key_id": "my-access-key",
"aws_secret_access_key": "my-secret-key",
"aws_session_token": "my-session-token",
}
)
```
=== "JavaScript"
```javascript
const lancedb = require("lancedb");
const db = await lancedb.connect(
"s3://bucket/path",
{
storageOptions: {
awsAccessKeyId: "my-access-key",
awsSecretAccessKey: "my-secret-key",
awsSessionToken: "my-session-token",
}
}
);
```
To configure credentials for AWS S3, you can use the `AWS_ACCESS_KEY_ID`, `AWS_SECRET_ACCESS_KEY`, and `AWS_SESSION_TOKEN` environment variables.
Alternatively, if you are using AWS SSO, you can use the `AWS_PROFILE` and `AWS_DEFAULT_REGION` environment variables.
The following keys can be used as both environment variables or keys in the `storage_options` parameter:
| `aws_region` / `region` | The AWS region the bucket is in. This can be automatically detected when using AWS S3, but must be specified for S3-compatible stores. |
| `aws_access_key_id` / `access_key_id` | The AWS access key ID to use. |
| `aws_secret_access_key` / `secret_access_key` | The AWS secret access key to use. |
| `aws_session_token` / `session_token` | The AWS session token to use. |
| `aws_endpoint` / `endpoint` | The endpoint to use for S3-compatible stores. |
| `aws_virtual_hosted_style_request` / `virtual_hosted_style_request` | Whether to use virtual hosted-style requests, where the bucket name is part of the endpoint. Meant to be used with `aws_endpoint`. Default: `False`. |
| `aws_s3_express` / `s3_express` | Whether to use S3 Express One Zone endpoints. Default: `False`. See more details below. |
| `aws_server_side_encryption` | The server-side encryption algorithm to use. Must be one of `"AES256"`, `"aws:kms"`, or `"aws:kms:dsse"`. Default: `None`. |
| `aws_sse_kms_key_id` | The KMS key ID to use for server-side encryption. If set, `aws_server_side_encryption` must be `"aws:kms"` or `"aws:kms:dsse"`. |
| `aws_sse_bucket_key_enabled` | Whether to use bucket keys for server-side encryption. |
You can see a full list of environment variables [here](https://docs.rs/object_store/latest/object_store/aws/struct.AmazonS3Builder.html#method.from_env).
!!! tip "Automatic cleanup for failed writes"
@@ -267,174 +146,22 @@ For **read-only access**, LanceDB will need a policy such as:
#### S3-compatible stores
LanceDB can also connect to S3-compatible stores, such as MinIO. To do so, you must specify both region and endpoint:
=== "Python"
```python
import lancedb
db = await lancedb.connect_async(
"s3://bucket/path",
storage_options={
"region": "us-east-1",
"endpoint": "http://minio:9000",
}
)
```
=== "JavaScript"
```javascript
const lancedb = require("lancedb");
const db = await lancedb.connect(
"s3://bucket/path",
{
storageOptions: {
region: "us-east-1",
endpoint: "http://minio:9000",
}
}
);
```
This can also be done with the ``AWS_ENDPOINT`` and ``AWS_DEFAULT_REGION`` environment variables.
#### S3 Express
LanceDB supports [S3 Express One Zone](https://aws.amazon.com/s3/storage-classes/express-one-zone/) endpoints, but requires additional configuration. Also, S3 Express endpoints only support connecting from an EC2 instance within the same region.
To configure LanceDB to use an S3 Express endpoint, you must set the storage option `s3_express`. The bucket name in your table URI should **include the suffix**.
=== "Python"
```python
import lancedb
db = await lancedb.connect_async(
"s3://my-bucket--use1-az4--x-s3/path",
storage_options={
"region": "us-east-1",
"s3_express": "true",
}
)
```
=== "JavaScript"
```javascript
const lancedb = require("lancedb");
const db = await lancedb.connect(
"s3://my-bucket--use1-az4--x-s3/path",
{
storageOptions: {
region: "us-east-1",
s3Express: "true",
}
}
);
```
LanceDB can also connect to S3-compatible stores, such as MinIO. To do so, you must specify two environment variables: `AWS_ENDPOINT` and `AWS_DEFAULT_REGION`. `AWS_ENDPOINT` should be the URL of the S3-compatible store, and `AWS_DEFAULT_REGION` should be the region to use.
<!-- TODO: we should also document the use of S3 Express once we fully support it -->
### Google Cloud Storage
GCS credentials are configured by setting the `GOOGLE_SERVICE_ACCOUNT` environment variable to the path of a JSON file containing the service account credentials. Alternatively, you can pass the path to the JSON file in the `storage_options`:
GCS credentials are configured by setting the `GOOGLE_SERVICE_ACCOUNT` environment variable to the path of a JSON file containing the service account credentials. There are several aliases for this environment variable, documented [here](https://docs.rs/object_store/latest/object_store/gcp/struct.GoogleCloudStorageBuilder.html#method.from_env).
!!! info "HTTP/2 support"
By default, GCS uses HTTP/1 for communication, as opposed to HTTP/2. This improves maximum throughput significantly. However, if you wish to use HTTP/2 for some reason, you can set the environment variable `HTTP1_ONLY` to `false`.
The following keys can be used as both environment variables or keys in the `storage_options` parameter:
| ``google_service_account`` / `service_account` | Path to the service account JSON file. |
| ``google_service_account_key`` | The serialized service account key. |
| ``google_application_credentials`` | Path to the application credentials. |
### Azure Blob Storage
Azure Blob Storage credentials can be configured by setting the `AZURE_STORAGE_ACCOUNT_NAME`and `AZURE_STORAGE_ACCOUNT_KEY` environment variables. Alternatively, you can pass the account name and key in the `storage_options` parameter:
Azure Blob Storage credentials can be configured by setting the `AZURE_STORAGE_ACCOUNT_NAME`and ``AZURE_STORAGE_ACCOUNT_KEY`` environment variables. The full list of environment variables that can be set are documented [here](https://docs.rs/object_store/latest/object_store/azure/struct.MicrosoftAzureBuilder.html#method.from_env).
=== "Python"
<!-- skip-test -->
```python
import lancedb
db = await lancedb.connect_async(
"az://my-container/my-database",
storage_options={
account_name: "some-account",
account_key: "some-key",
}
)
```
=== "JavaScript"
```javascript
const lancedb = require("lancedb");
const db = await lancedb.connect(
"az://my-container/my-database",
{
storageOptions: {
accountName: "some-account",
accountKey: "some-key",
}
}
);
```
These keys can be used as both environment variables or keys in the `storage_options` parameter:
| ``azure_storage_account_name`` | The name of the azure storage account. |
| ``azure_storage_account_key`` | The serialized service account key. |
| ``azure_client_id`` | Service principal client id for authorizing requests. |
| ``azure_client_secret`` | Service principal client secret for authorizing requests. |
| ``azure_tenant_id`` | Tenant id used in oauth flows. |
| ``azure_storage_sas_key`` | Shared access signature. The signature is expected to be percent-encoded, much like they are provided in the azure storage explorer or azure portal. |
| ``azure_storage_token`` | Bearer token. |
| ``azure_storage_use_emulator`` | Use object store with azurite storage emulator. |
| ``azure_endpoint`` | Override the endpoint used to communicate with blob storage. |
| ``azure_use_fabric_endpoint`` | Use object store with url scheme account.dfs.fabric.microsoft.com. |
| ``azure_msi_endpoint`` | Endpoint to request a imds managed identity token. |
| ``azure_object_id`` | Object id for use with managed identity authentication. |
| ``azure_msi_resource_id`` | Msi resource id for use with managed identity authentication. |
| ``azure_federated_token_file`` | File containing token for Azure AD workload identity federation. |
| ``azure_use_azure_cli`` | Use azure cli for acquiring access token. |
| ``azure_disable_tagging`` | Disables tagging objects. This can be desirable if not supported by the backing store. |
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