mirror of
https://github.com/lancedb/lancedb.git
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9a9a73a65d
1. Support persistent embedding function so users can just search using query string 2. Add fixed size list conversion for multiple vector columns 3. Add support for empty query (just apply select/where/limit). 4. Refactor and simplify some of the data prep code --------- Co-authored-by: Chang She <chang@lancedb.com> Co-authored-by: Weston Pace <weston.pace@gmail.com>
106 lines
3.3 KiB
Python
106 lines
3.3 KiB
Python
# Copyright 2023 LanceDB Developers
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import uuid
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from functools import cached_property
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from typing import Union
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import pyarrow as pa
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from lance import json_to_schema
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from lancedb.common import DATA, VEC, VECTOR_COLUMN_NAME
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from ..query import LanceVectorQueryBuilder
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from ..table import Query, Table, _sanitize_data
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from .arrow import to_ipc_binary
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from .client import ARROW_STREAM_CONTENT_TYPE
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from .db import RemoteDBConnection
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class RemoteTable(Table):
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def __init__(self, conn: RemoteDBConnection, name: str):
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self._conn = conn
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self._name = name
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def __repr__(self) -> str:
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return f"RemoteTable({self._conn.db_name}.{self._name})"
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@cached_property
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def schema(self) -> pa.Schema:
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"""Return the schema of the table."""
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resp = self._conn._loop.run_until_complete(
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self._conn._client.post(f"/v1/table/{self._name}/describe/")
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)
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schema = json_to_schema(resp["schema"])
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return schema
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def to_arrow(self) -> pa.Table:
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"""Return the table as an Arrow table."""
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raise NotImplementedError("to_arrow() is not supported on the LanceDB cloud")
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def to_pandas(self):
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"""Return the table as a Pandas DataFrame.
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Intercept `to_arrow()` for better error message.
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"""
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return NotImplementedError("to_pandas() is not supported on the LanceDB cloud")
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def create_index(
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self,
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metric="L2",
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num_partitions=256,
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num_sub_vectors=96,
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vector_column_name: str = VECTOR_COLUMN_NAME,
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replace: bool = True,
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):
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raise NotImplementedError
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def add(
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self,
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data: DATA,
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mode: str = "append",
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on_bad_vectors: str = "error",
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fill_value: float = 0.0,
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) -> int:
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data = _sanitize_data(
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data,
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self.schema,
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metadata=None,
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on_bad_vectors=on_bad_vectors,
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fill_value=fill_value,
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)
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payload = to_ipc_binary(data)
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request_id = uuid.uuid4().hex
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self._conn._loop.run_until_complete(
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self._conn._client.post(
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f"/v1/table/{self._name}/insert/",
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data=payload,
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params={"request_id": request_id, "mode": mode},
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content_type=ARROW_STREAM_CONTENT_TYPE,
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)
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)
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def search(
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self, query: Union[VEC, str], vector_column_name: str = VECTOR_COLUMN_NAME
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) -> LanceVectorQueryBuilder:
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return LanceVectorQueryBuilder(self, query, vector_column_name)
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def _execute_query(self, query: Query) -> pa.Table:
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result = self._conn._client.query(self._name, query)
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return self._conn._loop.run_until_complete(result).to_arrow()
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def delete(self, predicate: str):
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raise NotImplementedError
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