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132 lines
4.3 KiB
Python
132 lines
4.3 KiB
Python
# Copyright (c) 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 os
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from packaging.version import Version
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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 ..util import attempt_import_or_raise
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from .base import Reranker
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class CohereReranker(Reranker):
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"""
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Reranks the results using the Cohere Rerank API.
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https://docs.cohere.com/docs/rerank-guide
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Parameters
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----------
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model_name : str, default "rerank-english-v2.0"
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The name of the cross encoder model to use. Available cohere models are:
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- rerank-english-v2.0
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- rerank-multilingual-v2.0
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column : str, default "text"
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The name of the column to use as input to the cross encoder model.
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top_n : str, default None
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The number of results to return. If None, will return all results.
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"""
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def __init__(
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self,
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model_name: str = "rerank-english-v3.0",
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column: str = "text",
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top_n: Union[int, None] = None,
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return_score="relevance",
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api_key: Union[str, None] = None,
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):
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super().__init__(return_score)
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self.model_name = model_name
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self.column = column
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self.top_n = top_n
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self.api_key = api_key
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@cached_property
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def _client(self):
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cohere = attempt_import_or_raise("cohere")
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# ensure version is at least 0.5.0
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if hasattr(cohere, "__version__") and Version(cohere.__version__) < Version(
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"0.5.0"
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):
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raise ValueError(
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f"cohere version must be at least 0.5.0, found {cohere.__version__}"
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)
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if os.environ.get("COHERE_API_KEY") is None and self.api_key is None:
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raise ValueError(
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"COHERE_API_KEY not set. Either set it in your environment or \
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pass it as `api_key` argument to the CohereReranker."
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)
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return cohere.Client(os.environ.get("COHERE_API_KEY") or self.api_key)
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def _rerank(self, result_set: pa.Table, query: str):
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docs = result_set[self.column].to_pylist()
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response = self._client.rerank(
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query=query,
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documents=docs,
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top_n=self.top_n,
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model=self.model_name,
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)
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results = (
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response.results
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) # returns list (text, idx, relevance) attributes sorted descending by score
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indices, scores = list(
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zip(*[(result.index, result.relevance_score) for result in results])
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) # tuples
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result_set = result_set.take(list(indices))
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# add the scores
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result_set = result_set.append_column(
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"_relevance_score", pa.array(scores, type=pa.float32())
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)
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return result_set
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def rerank_hybrid(
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self,
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query: str,
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vector_results: pa.Table,
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fts_results: pa.Table,
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):
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combined_results = self.merge_results(vector_results, fts_results)
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combined_results = self._rerank(combined_results, query)
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if self.score == "relevance":
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combined_results = self._keep_relevance_score(combined_results)
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elif self.score == "all":
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raise NotImplementedError(
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"return_score='all' not implemented for cohere reranker"
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)
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return combined_results
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def rerank_vector(
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self,
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query: str,
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vector_results: pa.Table,
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):
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result_set = self._rerank(vector_results, query)
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if self.score == "relevance":
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result_set = result_set.drop_columns(["_distance"])
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return result_set
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def rerank_fts(
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self,
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query: str,
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fts_results: pa.Table,
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):
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result_set = self._rerank(fts_results, query)
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if self.score == "relevance":
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result_set = result_set.drop_columns(["_score"])
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return result_set
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