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3c46d7f268
Sometimes LangChain would insert a single `[np.nan]` as a placeholder if the embedding function failed. This causes a problem for Lance format because then the array can't be stored as a FixedSizedListArray. Instead: 1. By default we remove rows with embedding lengths less than the maximum length in the batch 2. If `strict=True` kwargs is set to True, then a `ValueError` is raised if the embeddings aren't all the same length --------- Co-authored-by: Chang She <chang@lancedb.com>
96 lines
2.9 KiB
Python
96 lines
2.9 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 attr
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import numpy as np
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import pandas as pd
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import pyarrow as pa
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import pytest
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from aiohttp import web
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from lancedb.remote.client import RestfulLanceDBClient, VectorQuery
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@attr.define
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class MockLanceDBServer:
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runner: web.AppRunner = attr.field(init=False)
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site: web.TCPSite = attr.field(init=False)
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async def query_handler(self, request: web.Request) -> web.Response:
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table_name = request.match_info["table_name"]
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assert table_name == "test_table"
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await request.json()
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# TODO: do some matching
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vecs = pd.Series([np.random.rand(128) for x in range(10)], name="vector")
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ids = pd.Series(range(10), name="id")
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df = pd.DataFrame([vecs, ids]).T
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batch = pa.RecordBatch.from_pandas(
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df,
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schema=pa.schema(
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[
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pa.field("vector", pa.list_(pa.float32(), 128)),
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pa.field("id", pa.int64()),
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]
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),
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)
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sink = pa.BufferOutputStream()
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with pa.ipc.new_file(sink, batch.schema) as writer:
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writer.write_batch(batch)
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return web.Response(body=sink.getvalue().to_pybytes())
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async def setup(self):
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app = web.Application()
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app.add_routes([web.post("/table/{table_name}", self.query_handler)])
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self.runner = web.AppRunner(app)
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await self.runner.setup()
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self.site = web.TCPSite(self.runner, "localhost", 8111)
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async def start(self):
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await self.site.start()
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async def stop(self):
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await self.runner.cleanup()
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@pytest.mark.skip(reason="flaky somehow, fix later")
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@pytest.mark.asyncio
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async def test_e2e_with_mock_server():
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mock_server = MockLanceDBServer()
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await mock_server.setup()
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await mock_server.start()
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try:
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client = RestfulLanceDBClient("lancedb+http://localhost:8111")
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df = (
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await client.query(
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"test_table",
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VectorQuery(
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vector=np.random.rand(128).tolist(),
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k=10,
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_metric="L2",
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columns=["id", "vector"],
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),
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)
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).to_df()
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assert "vector" in df.columns
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assert "id" in df.columns
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finally:
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# make sure we don't leak resources
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await mock_server.stop()
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