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](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.
- 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>
