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4323ca0147
Closes #3000 The hybrid search `explain_plan` now shows the reranker as the top-level node with the vector and FTS sub-plans indented underneath, instead of just listing them separately with no reranker context. **Before:** ``` Vector Search Plan: ProjectionExec: ... FTS Search Plan: ProjectionExec: ... ``` **After:** ``` RRFReranker(K=60) Vector Search Plan: ProjectionExec: ... FTS Search Plan: ProjectionExec: ... ``` Other rerankers display similarly ; e.g. `LinearCombinationReranker(weight=0.7, fill=1.0)`, `MRRReranker(weight_vector=0.5, weight_fts=0.5)`, `CohereReranker(model_name=name)`. --------- Signed-off-by: dask-58 <googldhruv@gmail.com> Co-authored-by: Will Jones <willjones127@gmail.com>
104 lines
3.7 KiB
Python
104 lines
3.7 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright The LanceDB Authors
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import pyarrow as pa
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from .base import Reranker
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from ..util import attempt_import_or_raise
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class AnswerdotaiRerankers(Reranker):
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"""
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Reranks the results using the Answerdotai Rerank API.
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All supported reranker model types can be found here:
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- https://github.com/AnswerDotAI/rerankers
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Parameters
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----------
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model_type : str, default "colbert"
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The type of the model to use.
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model_name : str, default "rerank-english-v2.0"
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The name of the model to use from the given model type.
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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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return_score : str, default "relevance"
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options are "relevance" or "all". Only "relevance" is supported for now.
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**kwargs
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Additional keyword arguments to pass to the model. For example, 'device'.
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See AnswerDotAI/rerankers for more information.
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"""
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def __init__(
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self,
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model_type="colbert",
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model_name: str = "answerdotai/answerai-colbert-small-v1",
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column: str = "text",
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return_score="relevance",
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**kwargs,
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):
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super().__init__(return_score)
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self.column = column
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rerankers = attempt_import_or_raise(
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"rerankers"
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) # import here for faster ops later
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self.model_name = model_name
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self.model_type = model_type
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self.reranker = rerankers.Reranker(
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model_name=model_name, model_type=model_type, **kwargs
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)
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def __str__(self):
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return (
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f"AnswerdotaiRerankers(model_type={self.model_type}, "
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f"model_name={self.model_name})"
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)
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def _rerank(self, result_set: pa.Table, query: str):
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result_set = self._handle_empty_results(result_set)
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if len(result_set) == 0:
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return result_set
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docs = result_set[self.column].to_pylist()
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doc_ids = list(range(len(docs)))
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result = self.reranker.rank(query, docs, doc_ids=doc_ids)
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# get the scores of each document in the same order as the input
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scores = [result.get_result_by_docid(i).score for i in doc_ids]
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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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combined_results = self._merge_and_keep_scores(vector_results, fts_results)
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combined_results = combined_results.sort_by(
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[("_relevance_score", "descending")]
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)
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return combined_results
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def rerank_vector(self, query: str, vector_results: pa.Table):
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vector_results = self._rerank(vector_results, query)
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if self.score == "relevance":
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vector_results = vector_results.drop_columns(["_distance"])
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vector_results = vector_results.sort_by([("_relevance_score", "descending")])
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return vector_results
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def rerank_fts(self, query: str, fts_results: pa.Table):
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fts_results = self._rerank(fts_results, query)
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if self.score == "relevance":
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fts_results = fts_results.drop_columns(["_score"])
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fts_results = fts_results.sort_by([("_relevance_score", "descending")])
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return fts_results
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