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#
# Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
#
# 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 re
import threading
import requests
import torch
from FlagEmbedding import FlagReranker
from huggingface_hub import snapshot_download
import os
from abc import ABC
import numpy as np
from api.utils.file_utils import get_home_cache_dir
from rag.utils import num_tokens_from_string, truncate
def sigmoid(x):
return 1 / (1 + np.exp(-x))
class Base(ABC):
def __init__(self, key, model_name):
pass
def similarity(self, query: str, texts: list):
raise NotImplementedError("Please implement encode method!")
class DefaultRerank(Base):
_model = None
_model_lock = threading.Lock()
def __init__(self, key, model_name, **kwargs):
"""
If you have trouble downloading HuggingFace models, -_^ this might help!!
For Linux:
export HF_ENDPOINT=https://hf-mirror.com
For Windows:
Good luck
^_-
"""
if not DefaultRerank._model:
with DefaultRerank._model_lock:
if not DefaultRerank._model:
try:
DefaultRerank._model = FlagReranker(os.path.join(get_home_cache_dir(), re.sub(r"^[a-zA-Z]+/", "", model_name)), use_fp16=torch.cuda.is_available())
except Exception as e:
model_dir = snapshot_download(repo_id= model_name,
local_dir=os.path.join(get_home_cache_dir(), re.sub(r"^[a-zA-Z]+/", "", model_name)),
local_dir_use_symlinks=False)
DefaultRerank._model = FlagReranker(model_dir, use_fp16=torch.cuda.is_available())
self._model = DefaultRerank._model
def similarity(self, query: str, texts: list):
pairs = [(query,truncate(t, 2048)) for t in texts]
token_count = 0
for _, t in pairs:
token_count += num_tokens_from_string(t)
batch_size = 4096
res = []
for i in range(0, len(pairs), batch_size):
scores = self._model.compute_score(pairs[i:i + batch_size], max_length=2048)
scores = sigmoid(np.array(scores)).tolist()
if isinstance(scores, float): res.append(scores)
else: res.extend(scores)
return np.array(res), token_count
class JinaRerank(Base):
def __init__(self, key, model_name="jina-reranker-v1-base-en",
base_url="https://api.jina.ai/v1/rerank"):
self.base_url = "https://api.jina.ai/v1/rerank"
self.headers = {
"Content-Type": "application/json",
"Authorization": f"Bearer {key}"
}
self.model_name = model_name
def similarity(self, query: str, texts: list):
texts = [truncate(t, 8196) for t in texts]
data = {
"model": self.model_name,
"query": query,
"documents": texts,
"top_n": len(texts)
}
res = requests.post(self.base_url, headers=self.headers, json=data).json()
return np.array([d["relevance_score"] for d in res["results"]]), res["usage"]["total_tokens"]
class YoudaoRerank(DefaultRerank):
_model = None
_model_lock = threading.Lock()
def __init__(self, key=None, model_name="maidalun1020/bce-reranker-base_v1", **kwargs):
from BCEmbedding import RerankerModel
if not YoudaoRerank._model:
with YoudaoRerank._model_lock:
if not YoudaoRerank._model:
try:
print("LOADING BCE...")
YoudaoRerank._model = RerankerModel(model_name_or_path=os.path.join(
get_home_cache_dir(),
re.sub(r"^[a-zA-Z]+/", "", model_name)))
except Exception as e:
YoudaoRerank._model = RerankerModel(
model_name_or_path=model_name.replace(
"maidalun1020", "InfiniFlow"))
self._model = YoudaoRerank._model
def similarity(self, query: str, texts: list):
pairs = [(query, truncate(t, self._model.max_length)) for t in texts]
token_count = 0
for _, t in pairs:
token_count += num_tokens_from_string(t)
batch_size = 32
res = []
for i in range(0, len(pairs), batch_size):
scores = self._model.compute_score(pairs[i:i + batch_size], max_length=self._model.max_length)
scores = sigmoid(np.array(scores)).tolist()
if isinstance(scores, float): res.append(scores)
else: res.extend(scores)
return np.array(res), token_count
class XInferenceRerank(Base):
def __init__(self, key="xxxxxxx", model_name="", base_url=""):
self.model_name = model_name
self.base_url = base_url
self.headers = {
"Content-Type": "application/json",
"accept": "application/json"
}
def similarity(self, query: str, texts: list):
data = {
"model": self.model_name,
"query": query,
"return_documents": "true",
"return_len": "true",
"documents": texts
}
res = requests.post(self.base_url, headers=self.headers, json=data).json()
return np.array([d["relevance_score"] for d in res["results"]]), res["meta"]["tokens"]["input_tokens"]+res["meta"]["tokens"]["output_tokens"]
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