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.
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](23f2287eb5/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.
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>
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>
@eddyxu added instructions for linting here:
7af213801a/python/README.md (L45-L50)
However, we had a lot of failures and weren't checking this in CI. This
PR fixes all lints and adds a check to CI to keep us in compliance with
the lints.
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.
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.
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.
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 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>
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
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"
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>
