mirror of
https://git.mirrors.martin98.com/https://github.com/infiniflow/ragflow.git
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cc064040a2
### What problem does this PR solve? #1024 ### Type of change - [x] Bug Fix (non-breaking change which fixes an issue)
131 lines
4.8 KiB
Python
131 lines
4.8 KiB
Python
#
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# Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
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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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#
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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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#
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import re
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import requests
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import torch
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from FlagEmbedding import FlagReranker
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from huggingface_hub import snapshot_download
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import os
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from abc import ABC
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import numpy as np
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from api.utils.file_utils import get_home_cache_dir
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from rag.utils import num_tokens_from_string, truncate
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def sigmoid(x):
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return 1 / (1 + np.exp(-x))
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class Base(ABC):
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def __init__(self, key, model_name):
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pass
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def similarity(self, query: str, texts: list):
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raise NotImplementedError("Please implement encode method!")
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class DefaultRerank(Base):
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_model = None
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def __init__(self, key, model_name, **kwargs):
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"""
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If you have trouble downloading HuggingFace models, -_^ this might help!!
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For Linux:
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export HF_ENDPOINT=https://hf-mirror.com
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For Windows:
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Good luck
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^_-
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"""
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if not DefaultRerank._model:
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try:
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self._model = FlagReranker(os.path.join(get_home_cache_dir(), re.sub(r"^[a-zA-Z]+/", "", model_name)),
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use_fp16=torch.cuda.is_available())
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except Exception as e:
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self._model = snapshot_download(repo_id=model_name,
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local_dir=os.path.join(get_home_cache_dir(),
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re.sub(r"^[a-zA-Z]+/", "", model_name)),
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local_dir_use_symlinks=False)
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self._model = FlagReranker(os.path.join(get_home_cache_dir(), model_name),
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use_fp16=torch.cuda.is_available())
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def similarity(self, query: str, texts: list):
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pairs = [(query,truncate(t, 2048)) for t in texts]
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token_count = 0
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for _, t in pairs:
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token_count += num_tokens_from_string(t)
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batch_size = 32
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res = []
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for i in range(0, len(pairs), batch_size):
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scores = self._model.compute_score(pairs[i:i + batch_size], max_length=2048)
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scores = sigmoid(np.array(scores)).tolist()
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res.extend(scores)
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return np.array(res), token_count
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class JinaRerank(Base):
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def __init__(self, key, model_name="jina-reranker-v1-base-en",
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base_url="https://api.jina.ai/v1/rerank"):
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self.base_url = "https://api.jina.ai/v1/rerank"
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self.headers = {
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"Content-Type": "application/json",
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"Authorization": f"Bearer {key}"
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}
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self.model_name = model_name
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def similarity(self, query: str, texts: list):
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texts = [truncate(t, 8196) for t in texts]
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data = {
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"model": self.model_name,
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"query": query,
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"documents": texts,
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"top_n": len(texts)
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}
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res = requests.post(self.base_url, headers=self.headers, json=data).json()
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return np.array([d["relevance_score"] for d in res["results"]]), res["usage"]["total_tokens"]
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class YoudaoRerank(DefaultRerank):
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_model = None
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def __init__(self, key=None, model_name="maidalun1020/bce-reranker-base_v1", **kwargs):
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from BCEmbedding import RerankerModel
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if not YoudaoRerank._model:
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try:
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print("LOADING BCE...")
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YoudaoRerank._model = RerankerModel(model_name_or_path=os.path.join(
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get_home_cache_dir(),
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re.sub(r"^[a-zA-Z]+/", "", model_name)))
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except Exception as e:
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YoudaoRerank._model = RerankerModel(
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model_name_or_path=model_name.replace(
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"maidalun1020", "InfiniFlow"))
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def similarity(self, query: str, texts: list):
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pairs = [(query, truncate(t, self._model.max_length)) for t in texts]
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token_count = 0
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for _, t in pairs:
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token_count += num_tokens_from_string(t)
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batch_size = 32
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res = []
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for i in range(0, len(pairs), batch_size):
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scores = self._model.compute_score(pairs[i:i + batch_size], max_length=self._model.max_length)
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scores = sigmoid(np.array(scores)).tolist()
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res.extend(scores)
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return np.array(res), token_count
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