├── .gitignore ├── LICENSE ├── README.md ├── content_embedding.py ├── embedding.py ├── excel_embedding.py ├── main.py ├── requirements.txt ├── user_query.py └── vector_db.py /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | lib/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | wheels/ 23 | pip-wheel-metadata/ 24 | share/python-wheels/ 25 | *.egg-info/ 26 | .installed.cfg 27 | *.egg 28 | MANIFEST 29 | 30 | # PyInstaller 31 | # Usually these files are written by a python script from a template 32 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 33 | *.manifest 34 | *.spec 35 | 36 | # Installer logs 37 | pip-log.txt 38 | pip-delete-this-directory.txt 39 | 40 | # Unit test / coverage reports 41 | htmlcov/ 42 | .tox/ 43 | .nox/ 44 | .coverage 45 | .coverage.* 46 | .cache 47 | nosetests.xml 48 | coverage.xml 49 | *.cover 50 | *.py,cover 51 | .hypothesis/ 52 | .pytest_cache/ 53 | 54 | # Translations 55 | *.mo 56 | *.pot 57 | 58 | # Django stuff: 59 | *.log 60 | local_settings.py 61 | db.sqlite3 62 | db.sqlite3-journal 63 | 64 | # Flask stuff: 65 | instance/ 66 | .webassets-cache 67 | 68 | # Scrapy stuff: 69 | .scrapy 70 | 71 | # Sphinx documentation 72 | docs/_build/ 73 | 74 | # PyBuilder 75 | target/ 76 | 77 | # Jupyter Notebook 78 | .ipynb_checkpoints 79 | 80 | # IPython 81 | profile_default/ 82 | ipython_config.py 83 | 84 | # pyenv 85 | .python-version 86 | 87 | # pipenv 88 | # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. 89 | # However, in case of collaboration, if having platform-specific dependencies or dependencies 90 | # having no cross-platform support, pipenv may install dependencies that don't work, or not 91 | # install all needed dependencies. 92 | #Pipfile.lock 93 | 94 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow 95 | __pypackages__/ 96 | 97 | # Celery stuff 98 | celerybeat-schedule 99 | celerybeat.pid 100 | 101 | # SageMath parsed files 102 | *.sage.py 103 | 104 | # Environments 105 | .env 106 | .venv 107 | env/ 108 | venv/ 109 | ENV/ 110 | env.bak/ 111 | venv.bak/ 112 | 113 | # Spyder project settings 114 | .spyderproject 115 | .spyproject 116 | 117 | # Rope project settings 118 | .ropeproject 119 | 120 | # mkdocs documentation 121 | /site 122 | 123 | # mypy 124 | .mypy_cache/ 125 | .dmypy.json 126 | dmypy.json 127 | 128 | # Pyre type checker 129 | .pyre/ 130 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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It is safest 630 | to attach them to the start of each source file to most effectively 631 | state the exclusion of warranty; and each file should have at least 632 | the "copyright" line and a pointer to where the full notice is found. 633 | 634 | 635 | Copyright (C) 636 | 637 | This program is free software: you can redistribute it and/or modify 638 | it under the terms of the GNU General Public License as published by 639 | the Free Software Foundation, either version 3 of the License, or 640 | (at your option) any later version. 641 | 642 | This program is distributed in the hope that it will be useful, 643 | but WITHOUT ANY WARRANTY; without even the implied warranty of 644 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 645 | GNU General Public License for more details. 646 | 647 | You should have received a copy of the GNU General Public License 648 | along with this program. If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # knowledge-base-with-gpt 2 | 3 | ## Target 4 | 5 | - 使用现有数据集建立本地知识库 6 | - 使用自然语言查询知识库内容 7 | - 使用自然语言返回查询结果 8 | 9 | ## Implementation 10 | 11 | ![9dTKpRlhtF6CkSz](https://s2.loli.net/2023/03/14/9dTKpRlhtF6CkSz.jpg) 12 | 13 | ### prefix prompt 14 | 15 | ```json 16 | [ 17 | { 18 | "role": "system", 19 | "content": "我是一个非常有帮助的QA机器人，能准确地使用现有文档回答用户的问题。我可以使用所提供的文本来形成我的答案，在可能的情况下，尽量使用自己的话而不是逐字逐句地抄袭原文。我的回答是准确、有帮助、简明、清晰的。" 20 | }, 21 | { 22 | "role": "user", 23 | "content": "我的问题是：[XXXXX]，请使用以下段落来提供问题的答案：\n1.[向量查询结果top1]\n2.[向量查询结果top2]\n..." 24 | } 25 | ] 26 | ``` 27 | 28 | ### example 29 | 30 | ![K7NHrZYxsyuzmiq](https://s2.loli.net/2023/03/14/K7NHrZYxsyuzmiq.png) 31 | 32 | ## Requirements 33 | 34 | Database: postgreSQL + pgvector 35 | 36 | Python 3.8+ 37 | 38 | ```bash 39 | pip3 install -r requirements.txt 40 | ``` 41 | 42 | 在`main.py`中使用正确的路径调用`content_to_db()`后使用`user_query_loop()` 43 | -------------------------------------------------------------------------------- /content_embedding.py: -------------------------------------------------------------------------------- 1 | """ 2 | 遍历Markdown文件，将文件内容分割为合适的大小，并通过embedding将数据存入向量数据库 3 | """ 4 | 5 | import os 6 | from typing import Generator 7 | from embedding import create_embedding 8 | from vector_db import Storage 9 | 10 | 11 | def walk_mds(top: str) -> Generator[str, None, None]: 12 | """ 13 | 该函数用于递归遍历以'top'为根目录的文件夹树，并生成发现的.md文件的文件路径。 14 | 参数: 15 | top: 开始递归搜索的根目录。 16 | 生成: 17 | 每个发现的.md文件的文件路径。 18 | """ 19 | for root, dirs, files in os.walk(top, topdown=True): 20 | for name in files: 21 | file_path = os.path.join(root, name) 22 | if file_path.endswith('.md'): 23 | yield file_path 24 | 25 | 26 | def split_string(input_string: str) -> Generator[str, None, None]: 27 | """ 28 | 将一个较长的字符串分成最大长度为600的块。 29 | 参数： 30 | input_string: 要分割的字符串。 31 | 生成： 32 | 输入字符串的每个块, 最大长度为600。 33 | """ 34 | max_length = 600 35 | while len(input_string) > max_length: 36 | yield input_string[:max_length] 37 | input_string = input_string[max_length:] 38 | yield input_string 39 | 40 | 41 | def md_files_to_string(dir_path: str) -> Generator[Generator[str, None, None], None, None]: 42 | """ 43 | 此函数用于读取给定目录下所有的 .md 文件，并将其内容分成最大长度为 600 的字符串，逐一生成这些字符串。 44 | 参数： 45 | dir_path: 包含 .md 文件的目录路径。 46 | 生成器输出： 47 | 每个 .md 文件的内容被拆分为长度最大为 600 的多个字符串，逐一生成这些字符串。 48 | """ 49 | for file_path in walk_mds(dir_path): 50 | with open(file_path, encoding="utf-8") as f: 51 | read_data = f.read() 52 | yield split_string(read_data) 53 | 54 | 55 | def content_to_db(docs_dir: str) -> None: 56 | """ 57 | 将指定目录中的md文件内容添加到数据库中。 58 | :param docs_dir: md文件所在目录 59 | """ 60 | storage = Storage() 61 | for str_list in md_files_to_string(docs_dir): 62 | for text in str_list: 63 | try: 64 | _, vector = create_embedding(str(text)) 65 | except Exception as exce: 66 | print(str(exce)) 67 | input("wait for command to retry") 68 | _, vector = create_embedding(str(text)) 69 | storage.add(text, vector) 70 | print(f"> 完成插入text: [{text[0:10]}], embedding: {vector[0:3]}") 71 | 72 | 73 | if __name__ == '__main__': 74 | content_to_db("/Users/abcd/Desktop/md-docs") 75 | -------------------------------------------------------------------------------- /embedding.py: -------------------------------------------------------------------------------- 1 | """ 2 | 利用openai的embedding api生成词向量 3 | """ 4 | import openai 5 | import os 6 | from typing import * 7 | 8 | openai.api_key = os.getenv("OPENAI_API_KEY") 9 | 10 | 11 | def create_embedding(text: str): 12 | """Create an embedding for the provided text.""" 13 | embedding = openai.Embedding.create( 14 | model="text-embedding-ada-002", input=text) 15 | return text, embedding.data[0].embedding 16 | -------------------------------------------------------------------------------- /excel_embedding.py: -------------------------------------------------------------------------------- 1 | import os 2 | from embedding import create_embedding 3 | from vector_db import Storage 4 | 5 | import openpyxl 6 | 7 | 8 | def read_excel(excel_file: str): 9 | bk = openpyxl.load_workbook(excel_file) 10 | sheet = bk.active 11 | maxrow = sheet.max_row 12 | minrow = sheet.min_row+1 # skip table header 13 | for i in range(minrow, maxrow + 1): 14 | q_value = sheet.cell(i, 1).value 15 | a_value = sheet.cell(i, 2).value 16 | yield q_value, a_value # read QA data 17 | 18 | 19 | def content_to_db(excel_file: str) -> None: 20 | storage = Storage() 21 | for q_value, a_value in read_excel(excel_file): 22 | try: 23 | _, vector = create_embedding(str(q_value)) 24 | except Exception as exce: 25 | print(str(exce)) 26 | input("wait for command to retry") 27 | _, vector = create_embedding(str(q_value)) 28 | storage.add(a_value, vector) 29 | print(f"> 完成插入text: [{a_value[0:10]}], embedding: {vector[0:3]}") 30 | 31 | 32 | if __name__ == '__main__': 33 | content_to_db('/Users/abcd/Desktop/IM知识库.xlsx') 34 | -------------------------------------------------------------------------------- /main.py: -------------------------------------------------------------------------------- 1 | from content_embedding import content_to_db 2 | from user_query import user_query_loop 3 | 4 | def main() -> None: 5 | """ 6 | main 7 | """ 8 | # 将数据向量化后存入数据库 9 | # content_to_db("") 10 | 11 | # 用户提问查询 12 | user_query_loop() 13 | 14 | if __name__ == '__main__': 15 | main() 16 | -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | openai 2 | SQLAlchemy 3 | pgvector 4 | psycopg2 5 | openpyxl 6 | -------------------------------------------------------------------------------- /user_query.py: -------------------------------------------------------------------------------- 1 | """ 2 | 命令行方式的用户提问搜索 3 | """ 4 | import openai 5 | import os 6 | from embedding import create_embedding 7 | from vector_db import Storage 8 | 9 | def limit_context_length(context, max_length=3000): 10 | """ 11 | 限制文本列表的总长度不超过指定的最大值。 12 | :param context: 文本列表。 13 | :param max_length: 最大长度限制，默认为3000。 14 | :return: 截取到的前n个文本段落。 15 | """ 16 | # 获取每个文本段落的长度。 17 | paragraph_lengths = [len(paragraph) for paragraph in context] 18 | 19 | total_length = sum(paragraph_lengths) 20 | if total_length <= max_length: 21 | # 如果总长度小于等于最大长度限制，则不需要截断文本。 22 | return context 23 | 24 | # 如果总长度超过最大长度限制，则截取到前n个文本段落。 25 | current_length = 0 26 | for index, length in enumerate(paragraph_lengths): 27 | current_length += length 28 | if current_length > max_length: 29 | # 切片复制新的列表，并返回截取到的前n个文本段落。 30 | return context[:index] 31 | 32 | # 如果所有的文本段落都被包含，则返回整个文本列表。 33 | return context 34 | 35 | 36 | def completion(query: str, context: list[str]) -> str: 37 | """ 38 | 根据query和context调用openai ChatCompletion 39 | """ 40 | context = limit_context_length(context, 3000) 41 | 42 | text = "\n".join(f"{index}. {text.strip()}" for index, 43 | text in enumerate(context)) 44 | response = openai.ChatCompletion.create( 45 | model="gpt-3.5-turbo", 46 | messages=[ 47 | {'role': 'system', 48 | 'content': '''我是一个非常有帮助的QA机器人，能准确地使用现有文档回答用户的问题。 49 | 我可以使用所提供的文本来形成我的答案，在可能的情况下，尽量使用自己的话而不是逐字逐句地抄袭原文。 50 | 我的回答是准确、有帮助、简明、清晰的。'''}, 51 | {'role': 'user', 'content': f'我的问题是：{query}\n请使用以下的知识库内容来提供问题的答案：\n{text}'}, 52 | ], 53 | ) 54 | print(f"使用的tokens: {response.usage.total_tokens}") 55 | return response.choices[0].message.content 56 | 57 | 58 | def user_query_loop() -> None: 59 | """ 60 | Loop for user queries. 61 | """ 62 | storage = Storage() 63 | limit = 35 64 | while True: 65 | query = input("请输入问题: \n> ") 66 | if query == "quit": 67 | break 68 | _, embedding = create_embedding(query) 69 | texts = storage.get_texts(embedding, limit) 70 | texts = list(set(texts)) # drop duplicated texts 71 | print(f"已找到相关片段: {len(texts)}") 72 | 73 | answer = completion(query, texts) 74 | print(">> Answer:") 75 | print(answer.strip()) 76 | print("=====================================") 77 | 78 | 79 | if __name__ == '__main__': 80 | user_query_loop() 81 | -------------------------------------------------------------------------------- /vector_db.py: -------------------------------------------------------------------------------- 1 | """ 2 | PostgreSQL + pgvector实现向量数据库 3 | """ 4 | from pgvector.sqlalchemy import Vector 5 | from sqlalchemy import create_engine, Column, Integer, String 6 | from sqlalchemy.orm import sessionmaker, declarative_base 7 | 8 | Base = declarative_base() 9 | SQL_URL = "postgresql://localhost:5432/" 10 | 11 | 12 | class EmbeddingEntity(Base): 13 | __tablename__ = '' 14 | id = Column(Integer, primary_key=True) 15 | text = Column(String) 16 | embedding = Column(Vector(1536)) 17 | 18 | 19 | class Storage: 20 | """数据库存储类""" 21 | def __init__(self): 22 | """初始化存储""" 23 | self._postgresql = SQL_URL 24 | self._engine = create_engine(self._postgresql) 25 | Base.metadata.create_all(self._engine) 26 | Session = sessionmaker(bind=self._engine) 27 | self._session = Session() 28 | 29 | def add(self, text: str, embedding: list[float]): 30 | """添加新的嵌入向量""" 31 | self._session.add(EmbeddingEntity(text=text, embedding=embedding)) 32 | self._session.commit() 33 | 34 | def add_all(self, embeddings: list[tuple[str, list[float]]]): 35 | """添加多个嵌入向量""" 36 | data = [EmbeddingEntity(text=text, embedding=embedding) 37 | for text, embedding in embeddings] 38 | self._session.add_all(data) 39 | self._session.commit() 40 | 41 | def get_texts(self, embedding: list[float], limit=30) -> list[str]: 42 | """获取给定嵌入向量对应的文本""" 43 | result = self._session.query(EmbeddingEntity).order_by( 44 | EmbeddingEntity.embedding.cosine_distance(embedding)).limit(limit).all() 45 | return [s.text for s in result] 46 | 47 | def clear(self): 48 | """清空数据库""" 49 | self._session.query(EmbeddingEntity).delete() 50 | self._session.commit() 51 | 52 | def __del__(self): 53 | """关闭session""" 54 | self._session.close() 55 | 56 | 57 | if __name__ == '__main__': 58 | storage = Storage() 59 | storage.add_all([('test', [0]*1536)]) 60 | --------------------------------------------------------------------------------