mirror of
https://github.com/lancedb/lancedb.git
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Merge branch 'main' into docs_march
This commit is contained in:
ayush chaurasia
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@@ -48,11 +48,20 @@
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
import lancedb
|
||||
uri = "data/sample-lancedb"
|
||||
db = lancedb.connect(uri)
|
||||
```
|
||||
```python
|
||||
--8<-- "python/python/tests/docs/test_basic.py:imports"
|
||||
--8<-- "python/python/tests/docs/test_basic.py:connect"
|
||||
|
||||
--8<-- "python/python/tests/docs/test_basic.py:connect_async"
|
||||
```
|
||||
|
||||
!!! note "Asynchronous Python API"
|
||||
|
||||
The asynchronous Python API is new and has some slight differences compared
|
||||
to the synchronous API. Feel free to start using the asynchronous version.
|
||||
Once all features have migrated we will start to move the synchronous API to
|
||||
use the same syntax as the asynchronous API. To help with this migration we
|
||||
have created a [migration guide](migration.md) detailing the differences.
|
||||
|
||||
=== "Typescript"
|
||||
|
||||
@@ -82,15 +91,14 @@ If you need a reminder of the uri, you can call `db.uri()`.
|
||||
### Create a table from initial data
|
||||
|
||||
If you have data to insert into the table at creation time, you can simultaneously create a
|
||||
table and insert the data into it. The schema of the data will be used as the schema of the
|
||||
table and insert the data into it. The schema of the data will be used as the schema of the
|
||||
table.
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
tbl = 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}])
|
||||
--8<-- "python/python/tests/docs/test_basic.py:create_table"
|
||||
--8<-- "python/python/tests/docs/test_basic.py:create_table_async"
|
||||
```
|
||||
|
||||
If the table already exists, LanceDB will raise an error by default.
|
||||
@@ -100,10 +108,8 @@ table.
|
||||
You can also pass in a pandas DataFrame directly:
|
||||
|
||||
```python
|
||||
import pandas as pd
|
||||
df = pd.DataFrame([{"vector": [3.1, 4.1], "item": "foo", "price": 10.0},
|
||||
{"vector": [5.9, 26.5], "item": "bar", "price": 20.0}])
|
||||
tbl = db.create_table("table_from_df", data=df)
|
||||
--8<-- "python/python/tests/docs/test_basic.py:create_table_pandas"
|
||||
--8<-- "python/python/tests/docs/test_basic.py:create_table_async_pandas"
|
||||
```
|
||||
|
||||
=== "Typescript"
|
||||
@@ -138,15 +144,14 @@ table.
|
||||
|
||||
Sometimes you may not have the data to insert into the table at creation time.
|
||||
In this case, you can create an empty table and specify the schema, so that you can add
|
||||
data to the table at a later time (as long as it conforms to the schema). This is
|
||||
data to the table at a later time (as long as it conforms to the schema). This is
|
||||
similar to a `CREATE TABLE` statement in SQL.
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
import pyarrow as pa
|
||||
schema = pa.schema([pa.field("vector", pa.list_(pa.float32(), list_size=2))])
|
||||
tbl = db.create_table("empty_table", schema=schema)
|
||||
--8<-- "python/python/tests/docs/test_basic.py:create_empty_table"
|
||||
--8<-- "python/python/tests/docs/test_basic.py:create_empty_table_async"
|
||||
```
|
||||
|
||||
=== "Typescript"
|
||||
@@ -168,7 +173,8 @@ Once created, you can open a table as follows:
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
tbl = db.open_table("my_table")
|
||||
--8<-- "python/python/tests/docs/test_basic.py:open_table"
|
||||
--8<-- "python/python/tests/docs/test_basic.py:open_table_async"
|
||||
```
|
||||
|
||||
=== "Typescript"
|
||||
@@ -188,7 +194,8 @@ If you forget the name of your table, you can always get a listing of all table
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
print(db.table_names())
|
||||
--8<-- "python/python/tests/docs/test_basic.py:table_names"
|
||||
--8<-- "python/python/tests/docs/test_basic.py:table_names_async"
|
||||
```
|
||||
|
||||
=== "Javascript"
|
||||
@@ -210,15 +217,8 @@ After a table has been created, you can always add more data to it as follows:
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
|
||||
# Option 1: Add a list of dicts to a table
|
||||
data = [{"vector": [1.3, 1.4], "item": "fizz", "price": 100.0},
|
||||
{"vector": [9.5, 56.2], "item": "buzz", "price": 200.0}]
|
||||
tbl.add(data)
|
||||
|
||||
# Option 2: Add a pandas DataFrame to a table
|
||||
df = pd.DataFrame(data)
|
||||
tbl.add(data)
|
||||
--8<-- "python/python/tests/docs/test_basic.py:add_data"
|
||||
--8<-- "python/python/tests/docs/test_basic.py:add_data_async"
|
||||
```
|
||||
|
||||
=== "Typescript"
|
||||
@@ -240,7 +240,8 @@ Once you've embedded the query, you can find its nearest neighbors as follows:
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
tbl.search([100, 100]).limit(2).to_pandas()
|
||||
--8<-- "python/python/tests/docs/test_basic.py:vector_search"
|
||||
--8<-- "python/python/tests/docs/test_basic.py:vector_search_async"
|
||||
```
|
||||
|
||||
This returns a pandas DataFrame with the results.
|
||||
@@ -274,7 +275,8 @@ LanceDB allows you to create an ANN index on a table as follows:
|
||||
=== "Python"
|
||||
|
||||
```py
|
||||
tbl.create_index()
|
||||
--8<-- "python/python/tests/docs/test_basic.py:create_index"
|
||||
--8<-- "python/python/tests/docs/test_basic.py:create_index_async"
|
||||
```
|
||||
|
||||
=== "Typescript"
|
||||
@@ -286,15 +288,15 @@ LanceDB allows you to create an ANN index on a table as follows:
|
||||
=== "Rust"
|
||||
|
||||
```rust
|
||||
--8<-- "rust/lancedb/examples/simple.rs:create_index"
|
||||
--8<-- "rust/lancedb/examples/simple.rs:create_index"
|
||||
```
|
||||
|
||||
!!! note "Why do I need to create an index manually?"
|
||||
LanceDB does not automatically create the ANN index for two reasons. The first is that it's optimized
|
||||
for really fast retrievals via a disk-based index, and the second is that data and query workloads can
|
||||
be very diverse, so there's no one-size-fits-all index configuration. LanceDB provides many parameters
|
||||
to fine-tune index size, query latency and accuracy. See the section on
|
||||
[ANN indexes](ann_indexes.md) for more details.
|
||||
LanceDB does not automatically create the ANN index for two reasons. The first is that it's optimized
|
||||
for really fast retrievals via a disk-based index, and the second is that data and query workloads can
|
||||
be very diverse, so there's no one-size-fits-all index configuration. LanceDB provides many parameters
|
||||
to fine-tune index size, query latency and accuracy. See the section on
|
||||
[ANN indexes](ann_indexes.md) for more details.
|
||||
|
||||
## Delete rows from a table
|
||||
|
||||
@@ -305,7 +307,8 @@ This can delete any number of rows that match the filter.
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
tbl.delete('item = "fizz"')
|
||||
--8<-- "python/python/tests/docs/test_basic.py:delete_rows"
|
||||
--8<-- "python/python/tests/docs/test_basic.py:delete_rows_async"
|
||||
```
|
||||
|
||||
=== "Typescript"
|
||||
@@ -322,7 +325,7 @@ This can delete any number of rows that match the filter.
|
||||
|
||||
The deletion predicate is a SQL expression that supports the same expressions
|
||||
as the `where()` clause (`only_if()` in Rust) on a search. They can be as
|
||||
simple or complex as needed. To see what expressions are supported, see the
|
||||
simple or complex as needed. To see what expressions are supported, see the
|
||||
[SQL filters](sql.md) section.
|
||||
|
||||
=== "Python"
|
||||
@@ -344,7 +347,8 @@ Use the `drop_table()` method on the database to remove a table.
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
db.drop_table("my_table")
|
||||
--8<-- "python/python/tests/docs/test_basic.py:drop_table"
|
||||
--8<-- "python/python/tests/docs/test_basic.py:drop_table_async"
|
||||
```
|
||||
|
||||
This permanently removes the table and is not recoverable, unlike deleting rows.
|
||||
|
||||
@@ -19,27 +19,163 @@ Allows you to set parameters when registering a `sentence-transformers` object.
|
||||
| `normalize` | `bool` | `True` | Whether to normalize the input text before feeding it to the model |
|
||||
|
||||
|
||||
```python
|
||||
db = lancedb.connect("/tmp/db")
|
||||
registry = EmbeddingFunctionRegistry.get_instance()
|
||||
func = registry.get("sentence-transformers").create(device="cpu")
|
||||
??? "Check out available sentence-transformer models here!"
|
||||
```markdown
|
||||
- sentence-transformers/all-MiniLM-L12-v2
|
||||
- sentence-transformers/paraphrase-mpnet-base-v2
|
||||
- sentence-transformers/gtr-t5-base
|
||||
- sentence-transformers/LaBSE
|
||||
- sentence-transformers/all-MiniLM-L6-v2
|
||||
- sentence-transformers/bert-base-nli-max-tokens
|
||||
- sentence-transformers/bert-base-nli-mean-tokens
|
||||
- sentence-transformers/bert-base-nli-stsb-mean-tokens
|
||||
- sentence-transformers/bert-base-wikipedia-sections-mean-tokens
|
||||
- sentence-transformers/bert-large-nli-cls-token
|
||||
- sentence-transformers/bert-large-nli-max-tokens
|
||||
- sentence-transformers/bert-large-nli-mean-tokens
|
||||
- sentence-transformers/bert-large-nli-stsb-mean-tokens
|
||||
- sentence-transformers/distilbert-base-nli-max-tokens
|
||||
- sentence-transformers/distilbert-base-nli-mean-tokens
|
||||
- sentence-transformers/distilbert-base-nli-stsb-mean-tokens
|
||||
- sentence-transformers/distilroberta-base-msmarco-v1
|
||||
- sentence-transformers/distilroberta-base-msmarco-v2
|
||||
- sentence-transformers/nli-bert-base-cls-pooling
|
||||
- sentence-transformers/nli-bert-base-max-pooling
|
||||
- sentence-transformers/nli-bert-base
|
||||
- sentence-transformers/nli-bert-large-cls-pooling
|
||||
- sentence-transformers/nli-bert-large-max-pooling
|
||||
- sentence-transformers/nli-bert-large
|
||||
- sentence-transformers/nli-distilbert-base-max-pooling
|
||||
- sentence-transformers/nli-distilbert-base
|
||||
- sentence-transformers/nli-roberta-base
|
||||
- sentence-transformers/nli-roberta-large
|
||||
- sentence-transformers/roberta-base-nli-mean-tokens
|
||||
- sentence-transformers/roberta-base-nli-stsb-mean-tokens
|
||||
- sentence-transformers/roberta-large-nli-mean-tokens
|
||||
- sentence-transformers/roberta-large-nli-stsb-mean-tokens
|
||||
- sentence-transformers/stsb-bert-base
|
||||
- sentence-transformers/stsb-bert-large
|
||||
- sentence-transformers/stsb-distilbert-base
|
||||
- sentence-transformers/stsb-roberta-base
|
||||
- sentence-transformers/stsb-roberta-large
|
||||
- sentence-transformers/xlm-r-100langs-bert-base-nli-mean-tokens
|
||||
- sentence-transformers/xlm-r-100langs-bert-base-nli-stsb-mean-tokens
|
||||
- sentence-transformers/xlm-r-base-en-ko-nli-ststb
|
||||
- sentence-transformers/xlm-r-bert-base-nli-mean-tokens
|
||||
- sentence-transformers/xlm-r-bert-base-nli-stsb-mean-tokens
|
||||
- sentence-transformers/xlm-r-large-en-ko-nli-ststb
|
||||
- sentence-transformers/bert-base-nli-cls-token
|
||||
- sentence-transformers/all-distilroberta-v1
|
||||
- sentence-transformers/multi-qa-MiniLM-L6-dot-v1
|
||||
- sentence-transformers/multi-qa-distilbert-cos-v1
|
||||
- sentence-transformers/multi-qa-distilbert-dot-v1
|
||||
- sentence-transformers/multi-qa-mpnet-base-cos-v1
|
||||
- sentence-transformers/multi-qa-mpnet-base-dot-v1
|
||||
- sentence-transformers/nli-distilroberta-base-v2
|
||||
- sentence-transformers/all-MiniLM-L6-v1
|
||||
- sentence-transformers/all-mpnet-base-v1
|
||||
- sentence-transformers/all-mpnet-base-v2
|
||||
- sentence-transformers/all-roberta-large-v1
|
||||
- sentence-transformers/allenai-specter
|
||||
- sentence-transformers/average_word_embeddings_glove.6B.300d
|
||||
- sentence-transformers/average_word_embeddings_glove.840B.300d
|
||||
- sentence-transformers/average_word_embeddings_komninos
|
||||
- sentence-transformers/average_word_embeddings_levy_dependency
|
||||
- sentence-transformers/clip-ViT-B-32-multilingual-v1
|
||||
- sentence-transformers/clip-ViT-B-32
|
||||
- sentence-transformers/distilbert-base-nli-stsb-quora-ranking
|
||||
- sentence-transformers/distilbert-multilingual-nli-stsb-quora-ranking
|
||||
- sentence-transformers/distilroberta-base-paraphrase-v1
|
||||
- sentence-transformers/distiluse-base-multilingual-cased-v1
|
||||
- sentence-transformers/distiluse-base-multilingual-cased-v2
|
||||
- sentence-transformers/distiluse-base-multilingual-cased
|
||||
- sentence-transformers/facebook-dpr-ctx_encoder-multiset-base
|
||||
- sentence-transformers/facebook-dpr-ctx_encoder-single-nq-base
|
||||
- sentence-transformers/facebook-dpr-question_encoder-multiset-base
|
||||
- sentence-transformers/facebook-dpr-question_encoder-single-nq-base
|
||||
- sentence-transformers/gtr-t5-large
|
||||
- sentence-transformers/gtr-t5-xl
|
||||
- sentence-transformers/gtr-t5-xxl
|
||||
- sentence-transformers/msmarco-MiniLM-L-12-v3
|
||||
- sentence-transformers/msmarco-MiniLM-L-6-v3
|
||||
- sentence-transformers/msmarco-MiniLM-L12-cos-v5
|
||||
- sentence-transformers/msmarco-MiniLM-L6-cos-v5
|
||||
- sentence-transformers/msmarco-bert-base-dot-v5
|
||||
- sentence-transformers/msmarco-bert-co-condensor
|
||||
- sentence-transformers/msmarco-distilbert-base-dot-prod-v3
|
||||
- sentence-transformers/msmarco-distilbert-base-tas-b
|
||||
- sentence-transformers/msmarco-distilbert-base-v2
|
||||
- sentence-transformers/msmarco-distilbert-base-v3
|
||||
- sentence-transformers/msmarco-distilbert-base-v4
|
||||
- sentence-transformers/msmarco-distilbert-cos-v5
|
||||
- sentence-transformers/msmarco-distilbert-dot-v5
|
||||
- sentence-transformers/msmarco-distilbert-multilingual-en-de-v2-tmp-lng-aligned
|
||||
- sentence-transformers/msmarco-distilbert-multilingual-en-de-v2-tmp-trained-scratch
|
||||
- sentence-transformers/msmarco-distilroberta-base-v2
|
||||
- sentence-transformers/msmarco-roberta-base-ance-firstp
|
||||
- sentence-transformers/msmarco-roberta-base-v2
|
||||
- sentence-transformers/msmarco-roberta-base-v3
|
||||
- sentence-transformers/multi-qa-MiniLM-L6-cos-v1
|
||||
- sentence-transformers/nli-mpnet-base-v2
|
||||
- sentence-transformers/nli-roberta-base-v2
|
||||
- sentence-transformers/nq-distilbert-base-v1
|
||||
- sentence-transformers/paraphrase-MiniLM-L12-v2
|
||||
- sentence-transformers/paraphrase-MiniLM-L3-v2
|
||||
- sentence-transformers/paraphrase-MiniLM-L6-v2
|
||||
- sentence-transformers/paraphrase-TinyBERT-L6-v2
|
||||
- sentence-transformers/paraphrase-albert-base-v2
|
||||
- sentence-transformers/paraphrase-albert-small-v2
|
||||
- sentence-transformers/paraphrase-distilroberta-base-v1
|
||||
- sentence-transformers/paraphrase-distilroberta-base-v2
|
||||
- sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
|
||||
- sentence-transformers/paraphrase-multilingual-mpnet-base-v2
|
||||
- sentence-transformers/paraphrase-xlm-r-multilingual-v1
|
||||
- sentence-transformers/quora-distilbert-base
|
||||
- sentence-transformers/quora-distilbert-multilingual
|
||||
- sentence-transformers/sentence-t5-base
|
||||
- sentence-transformers/sentence-t5-large
|
||||
- sentence-transformers/sentence-t5-xxl
|
||||
- sentence-transformers/sentence-t5-xl
|
||||
- sentence-transformers/stsb-distilroberta-base-v2
|
||||
- sentence-transformers/stsb-mpnet-base-v2
|
||||
- sentence-transformers/stsb-roberta-base-v2
|
||||
- sentence-transformers/stsb-xlm-r-multilingual
|
||||
- sentence-transformers/xlm-r-distilroberta-base-paraphrase-v1
|
||||
- sentence-transformers/clip-ViT-L-14
|
||||
- sentence-transformers/clip-ViT-B-16
|
||||
- sentence-transformers/use-cmlm-multilingual
|
||||
- sentence-transformers/all-MiniLM-L12-v1
|
||||
```
|
||||
|
||||
class Words(LanceModel):
|
||||
text: str = func.SourceField()
|
||||
vector: Vector(func.ndims()) = func.VectorField()
|
||||
!!! info
|
||||
You can also load many other model architectures from the library. For example models from sources such as BAAI, nomic, salesforce research, etc.
|
||||
See this HF hub page for all [supported models](https://huggingface.co/models?library=sentence-transformers).
|
||||
|
||||
table = db.create_table("words", schema=Words)
|
||||
table.add(
|
||||
[
|
||||
{"text": "hello world"}
|
||||
{"text": "goodbye world"}
|
||||
]
|
||||
)
|
||||
!!! note "BAAI Embeddings example"
|
||||
Here is an example that uses BAAI embedding model from the HuggingFace Hub [supported models](https://huggingface.co/models?library=sentence-transformers)
|
||||
```python
|
||||
db = lancedb.connect("/tmp/db")
|
||||
registry = EmbeddingFunctionRegistry.get_instance()
|
||||
model = registry.get("sentence-transformers").create(name="BAAI/bge-small-en-v1.5", device="cpu")
|
||||
|
||||
class Words(LanceModel):
|
||||
text: str = model.SourceField()
|
||||
vector: Vector(model.ndims()) = model.VectorField()
|
||||
|
||||
table = db.create_table("words", schema=Words)
|
||||
table.add(
|
||||
[
|
||||
{"text": "hello world"}
|
||||
{"text": "goodbye world"}
|
||||
]
|
||||
)
|
||||
|
||||
query = "greetings"
|
||||
actual = table.search(query).limit(1).to_pydantic(Words)[0]
|
||||
print(actual.text)
|
||||
```
|
||||
Visit sentence-transformers [HuggingFace HUB](https://huggingface.co/sentence-transformers) page for more information on the available models.
|
||||
|
||||
query = "greetings"
|
||||
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:
|
||||
|
||||
76
docs/src/migration.md
Normal file
76
docs/src/migration.md
Normal file
@@ -0,0 +1,76 @@
|
||||
# Rust-backed Client Migration Guide
|
||||
|
||||
In an effort to ensure all clients have the same set of capabilities we have begun migrating the
|
||||
python and node clients onto a common Rust base library. In python, this new client is part of
|
||||
the same lancedb package, exposed as an asynchronous client. Once the asynchronous client has
|
||||
reached full functionality we will begin migrating the synchronous library to be a thin wrapper
|
||||
around the asynchronous client.
|
||||
|
||||
This guide describes the differences between the two APIs and will hopefully assist users
|
||||
that would like to migrate to the new API.
|
||||
|
||||
## Closeable Connections
|
||||
|
||||
The Connection now has a `close` method. You can call this when
|
||||
you are done with the connection to eagerly free resources. Currently
|
||||
this is limited to freeing/closing the HTTP connection for remote
|
||||
connections. In the future we may add caching or other resources to
|
||||
native connections so this is probably a good practice even if you
|
||||
aren't using remote connections.
|
||||
|
||||
In addition, the connection can be used as a context manager which may
|
||||
be a more convenient way to ensure the connection is closed.
|
||||
|
||||
```python
|
||||
import lancedb
|
||||
|
||||
async def my_async_fn():
|
||||
with await lancedb.connect_async("my_uri") as db:
|
||||
print(await db.table_names())
|
||||
```
|
||||
|
||||
It is not mandatory to call the `close` method. If you do not call it
|
||||
then the connection will be closed when the object is garbage collected.
|
||||
|
||||
## Closeable Table
|
||||
|
||||
The Table now also has a `close` method, similar to the connection. This
|
||||
can be used to eagerly free the cache used by a Table object. Similar to
|
||||
the connection, it can be used as a context manager and it is not mandatory
|
||||
to call the `close` method.
|
||||
|
||||
### Changes to Table APIs
|
||||
|
||||
- Previously `Table.schema` was a property. Now it is an async method.
|
||||
- The method `Table.__len__` was removed and `len(table)` will no longer
|
||||
work. Use `Table.count_rows` instead.
|
||||
|
||||
### Creating Indices
|
||||
|
||||
The `Table.create_index` method is now used for creating both vector indices
|
||||
and scalar indices. It currently requires a column name to be specified (the
|
||||
column to index). Vector index defaults are now smarter and scale better with
|
||||
the size of the data.
|
||||
|
||||
To specify index configuration details you will need to specify which kind of
|
||||
index you are using.
|
||||
|
||||
### Querying
|
||||
|
||||
The `Table.search` method has been renamed to `AsyncTable.vector_search` for
|
||||
clarity.
|
||||
|
||||
## Features not yet supported
|
||||
|
||||
The following features are not yet supported by the asynchronous API. However,
|
||||
we plan to support them soon.
|
||||
|
||||
- You cannot specify an embedding function when creating or opening a table.
|
||||
You must calculate embeddings yourself if using the asynchronous API
|
||||
- The merge insert operation is not supported in the asynchronous API
|
||||
- Cleanup / compact / optimize indices are not supported in the asynchronous API
|
||||
- add / alter columns is not supported in the asynchronous API
|
||||
- The asynchronous API does not yet support any full text search or reranking
|
||||
search
|
||||
- Remote connections to LanceDb Cloud are not yet supported.
|
||||
- The method Table.head is not yet supported.
|
||||
@@ -8,17 +8,20 @@ This section contains the API reference for the OSS Python API.
|
||||
pip install lancedb
|
||||
```
|
||||
|
||||
## Connection
|
||||
The following methods describe the synchronous API client. There
|
||||
is also an [asynchronous API client](#connections-asynchronous).
|
||||
|
||||
## Connections (Synchronous)
|
||||
|
||||
::: lancedb.connect
|
||||
|
||||
::: lancedb.db.DBConnection
|
||||
|
||||
## Table
|
||||
## Tables (Synchronous)
|
||||
|
||||
::: lancedb.table.Table
|
||||
|
||||
## Querying
|
||||
## Querying (Synchronous)
|
||||
|
||||
::: lancedb.query.Query
|
||||
|
||||
@@ -86,4 +89,42 @@ pip install lancedb
|
||||
|
||||
::: lancedb.rerankers.cross_encoder.CrossEncoderReranker
|
||||
|
||||
::: lancedb.rerankers.openai.OpenaiReranker
|
||||
::: lancedb.rerankers.openai.OpenaiReranker
|
||||
|
||||
## Connections (Asynchronous)
|
||||
|
||||
Connections represent a connection to a LanceDb database and
|
||||
can be used to create, list, or open tables.
|
||||
|
||||
::: lancedb.connect_async
|
||||
|
||||
::: lancedb.db.AsyncConnection
|
||||
|
||||
## Tables (Asynchronous)
|
||||
|
||||
Table hold your actual data as a collection of records / rows.
|
||||
|
||||
::: lancedb.table.AsyncTable
|
||||
|
||||
## Indices (Asynchronous)
|
||||
|
||||
Indices can be created on a table to speed up queries. This section
|
||||
lists the indices that LanceDb supports.
|
||||
|
||||
::: lancedb.index.BTree
|
||||
|
||||
::: lancedb.index.IvfPq
|
||||
|
||||
## Querying (Asynchronous)
|
||||
|
||||
Queries allow you to return data from your database. Basic queries can be
|
||||
created with the [AsyncTable.query][lancedb.table.AsyncTable.query] method
|
||||
to return the entire (typically filtered) table. Vector searches return the
|
||||
rows nearest to a query vector and can be created with the
|
||||
[AsyncTable.vector_search][lancedb.table.AsyncTable.vector_search] method.
|
||||
|
||||
::: lancedb.query.AsyncQueryBase
|
||||
|
||||
::: lancedb.query.AsyncQuery
|
||||
|
||||
::: lancedb.query.AsyncVectorQuery
|
||||
|
||||
Reference in New Issue
Block a user