Spaces:
Paused
Paused
File size: 8,998 Bytes
ab2ded1 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 |
#
# 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):
if len(texts) == 0:
return np.array([]), 0
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"]
class LocalAIRerank(Base):
def __init__(self, key, model_name, base_url):
pass
def similarity(self, query: str, texts: list):
raise NotImplementedError("The LocalAIRerank has not been implement")
class NvidiaRerank(Base):
def __init__(
self, key, model_name, base_url="https://ai.api.nvidia.com/v1/retrieval/nvidia/"
):
if not base_url:
base_url = "https://ai.api.nvidia.com/v1/retrieval/nvidia/"
self.model_name = model_name
if self.model_name == "nvidia/nv-rerankqa-mistral-4b-v3":
self.base_url = os.path.join(
base_url, "nv-rerankqa-mistral-4b-v3", "reranking"
)
if self.model_name == "nvidia/rerank-qa-mistral-4b":
self.base_url = os.path.join(base_url, "reranking")
self.model_name = "nv-rerank-qa-mistral-4b:1"
self.headers = {
"accept": "application/json",
"Content-Type": "application/json",
"Authorization": f"Bearer {key}",
}
def similarity(self, query: str, texts: list):
token_count = num_tokens_from_string(query) + sum(
[num_tokens_from_string(t) for t in texts]
)
data = {
"model": self.model_name,
"query": {"text": query},
"passages": [{"text": text} for text in texts],
"truncate": "END",
"top_n": len(texts),
}
res = requests.post(self.base_url, headers=self.headers, json=data).json()
rank = np.array([d["logit"] for d in res["rankings"]])
indexs = [d["index"] for d in res["rankings"]]
return rank[indexs], token_count
class LmStudioRerank(Base):
def __init__(self, key, model_name, base_url):
pass
def similarity(self, query: str, texts: list):
raise NotImplementedError("The LmStudioRerank has not been implement")
class OpenAI_APIRerank(Base):
def __init__(self, key, model_name, base_url):
pass
def similarity(self, query: str, texts: list):
raise NotImplementedError("The api has not been implement")
class CoHereRerank(Base):
def __init__(self, key, model_name, base_url=None):
from cohere import Client
self.client = Client(api_key=key)
self.model_name = model_name
def similarity(self, query: str, texts: list):
token_count = num_tokens_from_string(query) + sum(
[num_tokens_from_string(t) for t in texts]
)
res = self.client.rerank(
model=self.model_name,
query=query,
documents=texts,
top_n=len(texts),
return_documents=False,
)
rank = np.array([d.relevance_score for d in res.results])
indexs = [d.index for d in res.results]
return rank[indexs], token_count
|