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https://github.com/lancedb/lancedb.git
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b91139d3c7
Convert contextualization and embeddings functionality. And use it with converted notebook for video search
106 lines
3.2 KiB
Python
106 lines
3.2 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 math
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import ratelimiter
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from retry import retry
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from typing import Callable, Union
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from lance.vector import vec_to_table
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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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def with_embeddings(
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func: Callable,
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data: Union[pa.Table, pd.DataFrame],
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column: str = "text",
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wrap_api: bool = True,
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show_progress: bool = False,
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batch_size: int = 1000,
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):
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func = EmbeddingFunction(func)
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if wrap_api:
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func = func.retry().rate_limit().batch_size(batch_size)
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if show_progress:
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func = func.show_progress()
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if isinstance(data, pd.DataFrame):
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data = pa.Table.from_pandas(data)
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embeddings = func(data[column].to_numpy())
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table = vec_to_table(np.array(embeddings))
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return data.append_column("vector", table["vector"])
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class EmbeddingFunction:
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def __init__(self, func: Callable):
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self.func = func
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self.rate_limiter_kwargs = {}
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self.retry_kwargs = {}
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self._batch_size = None
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self._progress = False
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def __call__(self, text):
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# Get the embedding with retry
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@retry(**self.retry_kwargs)
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def embed_func(c):
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return self.func(c.tolist())
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max_calls = self.rate_limiter_kwargs["max_calls"]
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limiter = ratelimiter.RateLimiter(
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max_calls, period=self.rate_limiter_kwargs["period"]
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)
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rate_limited = limiter(embed_func)
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batches = self.to_batches(text)
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embeds = [emb for c in batches for emb in rate_limited(c)]
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return embeds
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def __repr__(self):
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return f"EmbeddingFunction(func={self.func})"
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def rate_limit(self, max_calls=0.9, period=1.0):
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self.rate_limiter_kwargs = dict(max_calls=max_calls, period=period)
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return self
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def retry(self, tries=10, delay=1, max_delay=30, backoff=3, jitter=1):
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self.retry_kwargs = dict(
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tries=tries,
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delay=delay,
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max_delay=max_delay,
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backoff=backoff,
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jitter=jitter,
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)
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return self
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def batch_size(self, batch_size):
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self._batch_size = batch_size
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return self
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def show_progress(self):
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self._progress = True
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return self
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def to_batches(self, arr):
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length = len(arr)
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def _chunker(arr):
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for start_i in range(0, len(arr), self._batch_size):
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yield arr[start_i : start_i + self._batch_size]
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if self._progress:
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from tqdm.auto import tqdm
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yield from tqdm(_chunker(arr), total=math.ceil(length / self._batch_size))
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else:
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return _chunker(arr)
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