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cohere.py
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131 lines (116 loc) · 4.33 KB
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# Copyright (c) 2023. LanceDB Developers
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
from packaging.version import Version
from functools import cached_property
from typing import Union
import pyarrow as pa
from ..util import attempt_import_or_raise
from .base import Reranker
class CohereReranker(Reranker):
"""
Reranks the results using the Cohere Rerank API.
https://docs.cohere.com/docs/rerank-guide
Parameters
----------
model_name : str, default "rerank-english-v2.0"
The name of the cross encoder model to use. Available cohere models are:
- rerank-english-v2.0
- rerank-multilingual-v2.0
column : str, default "text"
The name of the column to use as input to the cross encoder model.
top_n : str, default None
The number of results to return. If None, will return all results.
"""
def __init__(
self,
model_name: str = "rerank-english-v3.0",
column: str = "text",
top_n: Union[int, None] = None,
return_score="relevance",
api_key: Union[str, None] = None,
):
super().__init__(return_score)
self.model_name = model_name
self.column = column
self.top_n = top_n
self.api_key = api_key
@cached_property
def _client(self):
cohere = attempt_import_or_raise("cohere")
# ensure version is at least 0.5.0
if hasattr(cohere, "__version__") and Version(cohere.__version__) < Version(
"0.5.0"
):
raise ValueError(
f"cohere version must be at least 0.5.0, found {cohere.__version__}"
)
if os.environ.get("COHERE_API_KEY") is None and self.api_key is None:
raise ValueError(
"COHERE_API_KEY not set. Either set it in your environment or \
pass it as `api_key` argument to the CohereReranker."
)
return cohere.Client(os.environ.get("COHERE_API_KEY") or self.api_key)
def _rerank(self, result_set: pa.Table, query: str):
docs = result_set[self.column].to_pylist()
response = self._client.rerank(
query=query,
documents=docs,
top_n=self.top_n,
model=self.model_name,
)
results = (
response.results
) # returns list (text, idx, relevance) attributes sorted descending by score
indices, scores = list(
zip(*[(result.index, result.relevance_score) for result in results])
) # tuples
result_set = result_set.take(list(indices))
# add the scores
result_set = result_set.append_column(
"_relevance_score", pa.array(scores, type=pa.float32())
)
return result_set
def rerank_hybrid(
self,
query: str,
vector_results: pa.Table,
fts_results: pa.Table,
):
combined_results = self.merge_results(vector_results, fts_results)
combined_results = self._rerank(combined_results, query)
if self.score == "relevance":
combined_results = self._keep_relevance_score(combined_results)
elif self.score == "all":
raise NotImplementedError(
"return_score='all' not implemented for cohere reranker"
)
return combined_results
def rerank_vector(
self,
query: str,
vector_results: pa.Table,
):
result_set = self._rerank(vector_results, query)
if self.score == "relevance":
result_set = result_set.drop_columns(["_distance"])
return result_set
def rerank_fts(
self,
query: str,
fts_results: pa.Table,
):
result_set = self._rerank(fts_results, query)
if self.score == "relevance":
result_set = result_set.drop_columns(["_score"])
return result_set