From 73472dfd718e811c9c6514a065f1f562813e447b Mon Sep 17 00:00:00 2001 From: administrator Date: Fri, 31 Mar 2023 22:39:43 +0800 Subject: [PATCH] =?UTF-8?q?=E6=9C=80=E7=BB=88=E6=B3=A8=E9=87=8A?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- gpt_1_embeddings_training.py | 76 ++++++++++++------ gpt_2_create_question.py | 143 +++++++++++++++++---------------- gpt_3_query.py | 12 +-- trained_data/qa_embeddings.csv | 8 +- 4 files changed, 132 insertions(+), 107 deletions(-) diff --git a/gpt_1_embeddings_training.py b/gpt_1_embeddings_training.py index 98e6a6f..905cc0a 100644 --- a/gpt_1_embeddings_training.py +++ b/gpt_1_embeddings_training.py @@ -1,52 +1,76 @@ # 参考文章 # https://github.com/openai/openai-cookbook/blob/main/examples/Question_answering_using_embeddings.ipynb - - import openai import pandas as pd -import time from gpt_0_basic_info import api_key, excel_file_path, csv_file_path -COMPLETIONS_MODEL = "text-davinci-003" EMBEDDING_MODEL = "text-embedding-ada-002" +openai.api_key = api_key -def get_embedding(text: str, open_ai_api_key: str, model: str = EMBEDDING_MODEL) -> list[float]: - openai.api_key = open_ai_api_key +""" +We preprocess the document sections by creating an embedding vector for each section. An embedding is a vector of +numbers that helps us understand how semantically similar or different the texts are. The closer two embeddings are to +each other, the more similar are their contents. + +翻译: +我们通过为每个部分创建嵌入向量来预处理文档部分。嵌入是一组数字，帮助我们理解文本的语义相似性或差异。两个嵌入越接近，它们的内容就越相似。 +""" + + +def get_embedding(text: str, model: str = EMBEDDING_MODEL) -> list[float]: + # 计算嵌入向量 result = openai.Embedding.create( - model=model, - input=text + model=model, + input=text ) - return result["data"][0]["embedding"] + return_data_embedding = result["data"][0]["embedding"] + # 具体数据如下 + # [-0.008970350958406925, -0.014719498343765736, ~~~~很多很多~~~~] + return return_data_embedding -def compute_doc_embeddings(df: pd.DataFrame, open_ai_api_key: str): +def compute_doc_embeddings(df: pd.DataFrame): """ Create an embedding for each row in the dataframe using the OpenAI Embeddings API. - Return a datafram with embedding - } + Return a dataframe with embedding """ - df['embeddings'] = '' - df['embeddings'] = df['embeddings'].astype('object') - - for idx, r in df.iterrows(): - print(idx) - df.at[idx, 'embeddings'] = get_embedding(r.QandA, open_ai_api_key) - time.sleep(1) - + # print(df) + """ + prompt ... QandA + 0 当有人问：公司名称 ... 当有人问：公司名称, 请回答：亁颐堂科技有限责任公司 + 1 当有人问：亁颐堂是做什么的 ... 当有人问：亁颐堂是做什么的, 请回答：亁颐堂是一个网络培训公司 + 2 当有人问：你们公司有多少人 ... 当有人问：你们公司有多少人, 请回答：亁颐堂有三十多个人 + 3 当有人问：你们公司有多少个分部 ... 当有人问：你们公司有多少个分部, 请回答：亁颐堂有北京 上海和南京三个分部 + """ + # 添加新的列'embeddings', 值为'QandA'这个列计算的向量数据 + df['embeddings'] = df['QandA'].apply(lambda x: get_embedding(x)) return df -def getembeddings(api_key, excelfilepath, csvfilepath): - df = pd.read_excel(excelfilepath) +def get_embeddings(openai_api_key, excel_file_path, csv_file_path): + df = pd.read_excel(excel_file_path) + # 删除换行"\n" df['prompt'] = df['prompt'].apply(lambda x: x.replace('\n', '')) + # 给问题加上"当有人问："的前缀 df['prompt'] = df['prompt'].apply(lambda x: "当有人问：" + x + '') - df['completion'] = df['completion'].apply(lambda x: "请回答：" + x) + # 给答案加上", 请回答："的前缀 + df['completion'] = df['completion'].apply(lambda x: ", 请回答：" + x) + # 将问题和答案合并 df['QandA'] = df['prompt'] + df['completion'] - df = compute_doc_embeddings(df, api_key)[['QandA', 'embeddings']] - df.to_csv(csvfilepath, index=False, encoding='utf-8_sig') + # 只取'QandA'和'embeddings'两列 + df = compute_doc_embeddings(df)[['QandA', 'embeddings']] + # print(df) + """ + QandA embeddings + 0 当有人问：公司名称, 请回答：亁颐堂科技有限责任公司 [-0.009215764701366425, -0.022858258336782455,... + 1 当有人问：亁颐堂是做什么的, 请回答：亁颐堂是一个网络培训公司 [-0.014166537672281265, -0.01877765916287899, ... + 2 当有人问：你们公司有多少人, 请回答：亁颐堂有三十多个人 [-0.004638118669390678, -0.011072063818573952,... + 3 当有人问：你们公司有多少个分部, 请回答：亁颐堂有北京 上海和南京三个分部 [0.0038256149273365736, -0.0033990885131061077... + """ + df.to_csv(csv_file_path, index=False, encoding='utf-8_sig') if __name__ == '__main__': - getembeddings(api_key, excel_file_path, csv_file_path) + get_embeddings(api_key, excel_file_path, csv_file_path) diff --git a/gpt_2_create_question.py b/gpt_2_create_question.py index 3a7a900..2090897 100644 --- a/gpt_2_create_question.py +++ b/gpt_2_create_question.py @@ -2,120 +2,121 @@ import numpy as np import openai import pandas as pd import ast -from gpt_0_basic_info import api_key, excel_file_path, csv_file_path +from gpt_0_basic_info import api_key, csv_file_path +from gpt_1_embeddings_training import get_embedding +# 最大的内容长度 +max_context_len = 1500 -MAXCONTEXTLEN = 1500 - -COMPLETIONS_MODEL = "text-davinci-003" EMBEDDING_MODEL = "text-embedding-ada-002" - -def get_embedding(text: str, open_ai_api_key: str, model: str=EMBEDDING_MODEL) -> list[float]: - - openai.api_key = open_ai_api_key - result = openai.Embedding.create( - model=model, - input=text - ) - return result["data"][0]["embedding"] +openai.api_key = api_key -def compute_doc_embeddings(df: pd.DataFrame, open_ai_api_key: str) : - """ - Create an embedding for each row in the dataframe using the OpenAI Embeddings API. - - Return a datafram with embedding - } - """ - df['embeddings'] = '' - df['embeddings'] = df['embeddings'].astype('object') - - for idx, r in df.iterrows(): - df.at[idx, 'embeddings'] = get_embedding(r.content, open_ai_api_key) - - return df - - -def vector_similarity(x: list[float], y: list[float]) -> float: +def vector_similarity(x: list[float], y: list[float]): """ + 计算并且返回两个向量的相似度 Returns the similarity between two vectors. Because OpenAI Embeddings are normalized to length 1, the cosine similarity is the same as the dot product. """ - return np.dot(np.array(x), np.array(y)) + return_np_dot_result = np.dot(np.array(x), np.array(y)) + return return_np_dot_result -def get_query_similarity(query: str, df: pd.DataFrame, open_ai_api_key: str): +def get_query_similarity(input_query: str, df: pd.DataFrame): """ - Find the query embedding for the supplied query, and compare it against all of the pre-calculated document embeddings - to find the most relevant sections. + Find the query embedding for the supplied query, and compare it against all of the pre-calculated document + embeddings to find the most relevant sections. Return the list of document sections, sorted by relevance in descending order. """ - openai.api_key = open_ai_api_key - query_embedding = get_embedding(query, open_ai_api_key) - - #df['similarities'] = 0 + query_embedding = get_embedding(input_query) - df['similarities'] = df['embeddings'].apply(lambda x:vector_similarity(query_embedding, x)) - - #print(df['similarities']) - ''' - for idx, r in df.iterrows(): - df.loc[idx, 'similarities'] = vector_similarity(query_embedding, r.embeddings) - ''' - + df['similarities'] = df['embeddings'].apply(lambda x: vector_similarity(query_embedding, x)) + # print(df) + """ + QandA ... similarities + 0 当有人问：公司名称, 请回答：亁颐堂科技有限责任公司 ... 0.809908 + 1 当有人问：亁颐堂是做什么的, 请回答：亁颐堂是一个网络培训公司 ... 0.877552 + 2 当有人问：你们公司有多少人, 请回答：亁颐堂有三十多个人 ... 0.808605 + 3 当有人问：你们公司有多少个分部, 请回答：亁颐堂有北京 上海和南京三个分部 ... 0.783896 + """ + + # 找到最相似的两个 two_largest = df['similarities'].nlargest(2).index.tolist() - # print('get_query_similarity!!!!!!!!') + # print(two_largest) + # [1, 0] 行的索引 - context = '' if df.loc[two_largest[0]]['similarities'] < 0.8 else df.loc[two_largest[0]]['QandA'] if (df.loc[two_largest[1]]['similarities'] < 0.8 or (len(df.loc[two_largest[1]]['QandA'] + '\n' + df.loc[two_largest[0]]['QandA'])>=MAXCONTEXTLEN)) else (df.loc[two_largest[1]]['QandA'] + '\n' + df.loc[two_largest[0]]['QandA']) - # print(two_largest[0], df.loc[two_largest[0]]['similarities'], df.loc[two_largest[0]]['QandA']) - # print(two_largest[1], df.loc[two_largest[1]]['similarities'], df.loc[two_largest[1]]['QandA']) - # print(len(df.loc[two_largest[1]]['QandA'] + '\n' + df.loc[two_largest[0]]['QandA'])) - # print(context) + # 如果最相似的df.loc[two_largest[0]]['similarities']都小于0.8，那么就返回空字符串 + # 如果第二相似的df.loc[two_largest[1]]['similarities']小于0.8，并且拼接后长度大于1500，那么就返回df.loc[two_largest[0]]['QandA'] + # 如果第二个相似的df.loc[two_largest[1]]['similarities']大于0.8，那么就返回两个拼接后的字符串 + context = '' if df.loc[two_largest[0]]['similarities'] < 0.8 else df.loc[two_largest[0]]['QandA'] \ + if (df.loc[two_largest[1]]['similarities'] < 0.8 or (len(df.loc[two_largest[1]]['QandA'] + '\n' + + df.loc[two_largest[0]]['QandA']) >= max_context_len)) \ + else (df.loc[two_largest[1]]['QandA'] + '\n' + df.loc[two_largest[0]]['QandA']) return context -def _decorate_query(query: str, df: pd.DataFrame, open_ai_api_key: str)-> str: - +def _decorate_query(input_query: str, df: pd.DataFrame) -> str: try: - context = get_query_similarity(query, df, open_ai_api_key) + context = get_query_similarity(input_query, df) if context != '': header = """请使用上下文尽可能真实、自然地回答问题，如果答案未包含在上下文中，请不要编造回答，并且不要在回答中包含”根据上下文”这个短语。\n\n上下文：\n""" - #header = "上下文：\n" - query = header + context + "\n\n 问题: " + query + "\n 回答:？" - # print(query) - return query - except: - # print('ERROR 444444') + input_query = header + context + "\n\n 问题: " + input_query + "\n 回答:？" + """ + 请使用上下文尽可能真实、自然地回答问题，如果答案未包含在上下文中，请不要编造回答，并且不要在回答中包含”根据上下文”这个短语。 - return query + 上下文： + 当有人问：公司名称, 请回答：亁颐堂科技有限责任公司 + 当有人问：亁颐堂是做什么的, 请回答：亁颐堂是一个网络培训公司 + + 问题: 亁颐堂是做什么的 + 回答:？ + """ + return input_query + except Exception as e: + print(e) + return input_query -def decorate_query(query: str, open_ai_api_key, filename='foodsembeddings.csv')-> str: - filepath = filename +def decorate_query(input_query: str, filepath) -> str: try: df = pd.read_csv(filepath) if df.empty: - return query + return input_query else: try: + # 使用.apply()方法对'embeddings'列中的每个元素进行操作。 + # 用lambda函数定义了一个匿名函数，这个匿名函数接受一个参数x，并将ast.literal_eval(x)的结果返回。 + # ast.literal_eval(x)是Python中ast模块（Abstract Syntax Trees，抽象语法树）的literal_eval()函数， + # 它安全地解析一个字符串形式的字面量表达式（如字符串形式的数字、列表、元组、字典等），并返回该表达式的对应Python对象。 + # 这里，它将字符串形式的x解析成一个Python对象。 + + # print(df) + """ + QandA embeddings + 0 当有人问：公司名称, 请回答：亁颐堂科技有限责任公司 [-0.009181897155940533, -0.022621875628829002,... + 1 当有人问：亁颐堂是做什么的, 请回答：亁颐堂是一个网络培训公司 [-0.014206966385245323, -0.018791578710079193,... + 2 当有人问：你们公司有多少人, 请回答：亁颐堂有三十多个人 [-0.004695456940680742, -0.011140977963805199,... + 3 当有人问：你们公司有多少个分部, 请回答：亁颐堂有北京 上海和南京三个分部 [0.0038718082942068577, -0.003343536052852869,... + """ df['embeddings'] = df['embeddings'].apply(lambda x: ast.literal_eval(x)) - return _decorate_query(query, df, open_ai_api_key) + + return _decorate_query(input_query, df) except Exception as e: print(e) - return query + return input_query except Exception as e: print(e) - return query + return input_query if __name__ == '__main__': - query = '谁发现了牛顿三大定律' # 不相关的就直接返回问题 - # query = '亁颐堂是做什么的' # 找到相关内容, 就添加上下文 + # query = '谁发现了牛顿三大定律' # 不相关的就直接返回问题 + query = '亁颐堂是做什么的' # 找到相关内容, 就添加上下文 # 如果内容相关就添加如下上下文 """ 请使用上下文尽可能真实、自然地回答问题，如果答案未包含在上下文中，请不要编造回答，并且不要在回答中包含”根据上下文”这个短语。 @@ -127,7 +128,7 @@ if __name__ == '__main__': 问题: 亁颐堂是做什么的 回答:？ """ - print(decorate_query(query, api_key, filename=csv_file_path))\ + print(decorate_query(query, filepath=csv_file_path))\ diff --git a/gpt_3_query.py b/gpt_3_query.py index 2152260..629ceb0 100644 --- a/gpt_3_query.py +++ b/gpt_3_query.py @@ -4,12 +4,12 @@ from gpt_0_basic_info import api_key, csv_file_path openai.api_key = api_key -def question(query): +def question(input_query): response = openai.ChatCompletion.create( model='gpt-3.5-turbo', messages=[ {"role": "system", "content": "You are a helpful assistant."}, - {"role": "user", "content": query} + {"role": "user", "content": input_query} ], max_tokens=100, n=1, @@ -22,15 +22,15 @@ def question(query): if __name__ == '__main__': query = '亁颐堂是做什么的' - new_query = decorate_query(query, api_key, filename=csv_file_path) + new_query = decorate_query(query, filepath=csv_file_path) print(new_query) # 产生如下的问题： """ 请使用上下文尽可能真实、自然地回答问题，如果答案未包含在上下文中，请不要编造回答，并且不要在回答中包含”根据上下文”这个短语。 - + 上下文： - 当有人问：公司名称请回答：亁颐堂科技有限责任公司 - 当有人问：亁颐堂是做什么的请回答：亁颐堂是一个网络培训公司 + 当有人问：公司名称, 请回答：亁颐堂科技有限责任公司 + 当有人问：亁颐堂是做什么的, 请回答：亁颐堂是一个网络培训公司 问题: 亁颐堂是做什么的 回答:？ diff --git a/trained_data/qa_embeddings.csv b/trained_data/qa_embeddings.csv index 4e13eb1..45a1219 100644 --- a/trained_data/qa_embeddings.csv +++ b/trained_data/qa_embeddings.csv @@ -1,5 +1,5 @@ QandA,embeddings -当有人问：公司名称请回答：亁颐堂科技有限责任公司,"[-0.009886950254440308, 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请回答：亁颐堂有三十多个人","[-0.004701198544353247, -0.011165346950292587, 0.005232724826782942, -0.020257413387298584, -0.025394396856427193, 0.010881425812840462, -0.021762851625680923, 0.01589955948293209, -0.014526176266372204, 0.0028276522643864155, 0.012954708188772202, 0.015463774092495441, -0.010221145115792751, -0.013469726778566837, -0.006206638645380735, 0.010855015367269516, 0.02119501121342182, -0.00613730913028121, 0.02511707879602909, 0.003803216852247715, 0.008761925622820854, -0.004562539979815483, -0.01552980300039053, 0.021683618426322937, -0.01665228046476841, 0.023598432540893555, 0.020442290231585503, -0.011719983071088791, 0.014697848819196224, -0.007315910421311855, 0.01578070968389511, -0.0017266341019421816, -0.007553611416369677, -0.01671830751001835, -0.019055701792240143, -0.011277594603598118, 0.00022036868904251605, 0.008993023075163364, 0.010003252886235714, 0.013865895569324493, 0.025196311995387077, 0.007903560064733028, 0.008279920555651188, 0.01521286740899086, 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