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https://github.com/lancedb/lancedb.git
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91cab3b556
* Replaces Python implementation of Remote SDK with Rust one. * Drops dependency on `attrs` and `cachetools`. Makes `requests` an optional dependency used only for embeddings feature. * Adds dependency on `nest-asyncio`. This was required to get hybrid search working. * Deprecate `request_thread_pool` parameter. We now use the tokio threadpool. * Stop caching the `schema` on a remote table. Schema is mutable and there's no mechanism in place to invalidate the cache. * Removed the client-side resolution of the vector column. We should already be resolving this server-side.
137 lines
4.3 KiB
Python
137 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 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 .base import Reranker
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API_URL = "https://api.jina.ai/v1/rerank"
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class JinaReranker(Reranker):
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"""
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Reranks the results using the Jina Rerank API.
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https://jina.ai/rerank
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Parameters
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----------
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model_name : str, default "jina-reranker-v2-base-multilingual"
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The name of the cross reanker model to use
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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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api_key : str, default None
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The api key to access Jina API. If you pass None, you can set JINA_API_KEY
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environment variable
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"""
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def __init__(
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self,
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model_name: str = "jina-reranker-v2-base-multilingual",
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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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import requests
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if os.environ.get("JINA_API_KEY") is None and self.api_key is None:
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raise ValueError(
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"JINA_API_KEY not set. Either set it in your environment or \
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pass it as `api_key` argument to the JinaReranker."
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)
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self.api_key = self.api_key or os.environ.get("JINA_API_KEY")
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self._session = requests.Session()
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self._session.headers.update(
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{"Authorization": f"Bearer {self.api_key}", "Accept-Encoding": "identity"}
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)
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return self._session
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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.post( # type: ignore
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API_URL,
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json={
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"query": query,
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"documents": docs,
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"model": self.model_name,
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"top_n": self.top_n,
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},
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).json()
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if "results" not in response:
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raise RuntimeError(response["detail"])
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results = response["results"]
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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 JinaReranker"
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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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