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https://github.com/lancedb/lancedb.git
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eb31d95fef
- Rename safe_import -> attempt_import_or_raise (closes https://github.com/lancedb/lancedb/pull/923) - Update docs - Add Notebook example (@changhiskhan you can use it for the talk. Comes with "open in colab" button) - Latency benchmark & results comparison, sanity check on real-world data - Updates the default openai model to gpt-4
82 lines
2.7 KiB
Python
82 lines
2.7 KiB
Python
import os
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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-v2.0",
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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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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_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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docs = combined_results[self.column].to_pylist()
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results = 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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) # 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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combined_results = combined_results.take(list(indices))
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# add the scores
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combined_results = combined_results.append_column(
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"_relevance_score", pa.array(scores, type=pa.float32())
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)
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if self.score == "relevance":
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combined_results = combined_results.drop_columns(["score", "_distance"])
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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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