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2.57 kB
| # | |
| # 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 os | |
| import re | |
| import tiktoken | |
| from api.utils.file_utils import get_project_base_directory | |
| def singleton(cls, *args, **kw): | |
| instances = {} | |
| def _singleton(): | |
| key = str(cls) + str(os.getpid()) | |
| if key not in instances: | |
| instances[key] = cls(*args, **kw) | |
| return instances[key] | |
| return _singleton | |
| def rmSpace(txt): | |
| txt = re.sub(r"([^a-z0-9.,\)>]) +([^ ])", r"\1\2", txt, flags=re.IGNORECASE) | |
| return re.sub(r"([^ ]) +([^a-z0-9.,\(<])", r"\1\2", txt, flags=re.IGNORECASE) | |
| def findMaxDt(fnm): | |
| m = "1970-01-01 00:00:00" | |
| try: | |
| with open(fnm, "r") as f: | |
| while True: | |
| line = f.readline() | |
| if not line: | |
| break | |
| line = line.strip("\n") | |
| if line == 'nan': | |
| continue | |
| if line > m: | |
| m = line | |
| except Exception: | |
| pass | |
| return m | |
| def findMaxTm(fnm): | |
| m = 0 | |
| try: | |
| with open(fnm, "r") as f: | |
| while True: | |
| line = f.readline() | |
| if not line: | |
| break | |
| line = line.strip("\n") | |
| if line == 'nan': | |
| continue | |
| if int(line) > m: | |
| m = int(line) | |
| except Exception: | |
| pass | |
| return m | |
| tiktoken_cache_dir = get_project_base_directory() | |
| os.environ["TIKTOKEN_CACHE_DIR"] = tiktoken_cache_dir | |
| # encoder = tiktoken.encoding_for_model("gpt-3.5-turbo") | |
| encoder = tiktoken.get_encoding("cl100k_base") | |
| def num_tokens_from_string(string: str) -> int: | |
| """Returns the number of tokens in a text string.""" | |
| try: | |
| return len(encoder.encode(string)) | |
| except Exception: | |
| return 0 | |
| def truncate(string: str, max_len: int) -> str: | |
| """Returns truncated text if the length of text exceed max_len.""" | |
| return encoder.decode(encoder.encode(string)[:max_len]) | |