From cc2bb8898900d342fb90172736c5dab8fd5e84ea Mon Sep 17 00:00:00 2001 From: administrator Date: Fri, 31 Mar 2023 18:23:38 +0800 Subject: [PATCH] init --- .idea/.gitignore | 8 ++ .idea/aws.xml | 17 +++ .idea/chatgpt_embeddings.iml | 10 ++ .idea/inspectionProfiles/Project_Default.xml | 50 ++++++++ .../inspectionProfiles/profiles_settings.xml | 6 + .idea/misc.xml | 4 + .idea/modules.xml | 8 ++ .idea/vcs.xml | 6 + gpt_1_embeddings_training.py | 52 ++++++++ gpt_2_create_question.py | 120 ++++++++++++++++++ gpt_3_query.py | 27 ++++ source_data/qa.xlsx | Bin 0 -> 8755 bytes trained_data/qa_embeddings.csv | 5 + 13 files changed, 313 insertions(+) create mode 100644 .idea/.gitignore create mode 100644 .idea/aws.xml create mode 100644 .idea/chatgpt_embeddings.iml create mode 100644 .idea/inspectionProfiles/Project_Default.xml create mode 100644 .idea/inspectionProfiles/profiles_settings.xml create mode 100644 .idea/misc.xml create mode 100644 .idea/modules.xml create mode 100644 .idea/vcs.xml create mode 100644 gpt_1_embeddings_training.py create mode 100644 gpt_2_create_question.py create mode 100644 gpt_3_query.py create mode 100755 source_data/qa.xlsx create mode 100644 trained_data/qa_embeddings.csv diff --git a/.idea/.gitignore b/.idea/.gitignore new file mode 100644 index 0000000..13566b8 --- /dev/null +++ b/.idea/.gitignore @@ -0,0 +1,8 @@ +# Default ignored files +/shelf/ +/workspace.xml +# Editor-based HTTP Client requests +/httpRequests/ +# Datasource local storage ignored files +/dataSources/ +/dataSources.local.xml diff --git a/.idea/aws.xml b/.idea/aws.xml new file mode 100644 index 0000000..03f1bb6 --- /dev/null +++ b/.idea/aws.xml @@ -0,0 +1,17 @@ + + + + + + + \ No newline at end of file diff --git a/.idea/chatgpt_embeddings.iml b/.idea/chatgpt_embeddings.iml new file mode 100644 index 0000000..74d515a --- /dev/null +++ b/.idea/chatgpt_embeddings.iml @@ -0,0 +1,10 @@ + + + + + + + + + + \ No newline at end of file diff --git a/.idea/inspectionProfiles/Project_Default.xml b/.idea/inspectionProfiles/Project_Default.xml new file mode 100644 index 0000000..ec89f97 --- /dev/null +++ b/.idea/inspectionProfiles/Project_Default.xml @@ -0,0 +1,50 @@ + + + + \ No newline at end of file diff --git a/.idea/inspectionProfiles/profiles_settings.xml b/.idea/inspectionProfiles/profiles_settings.xml new file mode 100644 index 0000000..105ce2d --- /dev/null +++ b/.idea/inspectionProfiles/profiles_settings.xml @@ -0,0 +1,6 @@ + + + + \ No newline at end of file diff --git a/.idea/misc.xml b/.idea/misc.xml new file mode 100644 index 0000000..568d64d --- /dev/null +++ b/.idea/misc.xml @@ -0,0 +1,4 @@ + + + + \ No newline at end of file diff --git a/.idea/modules.xml b/.idea/modules.xml new file mode 100644 index 0000000..583ecdc --- /dev/null +++ b/.idea/modules.xml @@ -0,0 +1,8 @@ + + + + + + + + \ No newline at end of file diff --git a/.idea/vcs.xml b/.idea/vcs.xml new file mode 100644 index 0000000..94a25f7 --- /dev/null +++ b/.idea/vcs.xml @@ -0,0 +1,6 @@ + + + + + + \ No newline at end of file diff --git a/gpt_1_embeddings_training.py b/gpt_1_embeddings_training.py new file mode 100644 index 0000000..98e6a6f --- /dev/null +++ b/gpt_1_embeddings_training.py @@ -0,0 +1,52 @@ +# 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" + + +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"] + + +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(): + print(idx) + df.at[idx, 'embeddings'] = get_embedding(r.QandA, open_ai_api_key) + time.sleep(1) + + return df + + +def getembeddings(api_key, excelfilepath, csvfilepath): + df = pd.read_excel(excelfilepath) + df['prompt'] = df['prompt'].apply(lambda x: x.replace('\n', '')) + df['prompt'] = df['prompt'].apply(lambda x: "当有人问：" + x + '') + df['completion'] = df['completion'].apply(lambda x: "请回答：" + x) + 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') + + +if __name__ == '__main__': + getembeddings(api_key, excel_file_path, csv_file_path) diff --git a/gpt_2_create_question.py b/gpt_2_create_question.py new file mode 100644 index 0000000..f2861c2 --- /dev/null +++ b/gpt_2_create_question.py @@ -0,0 +1,120 @@ +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 + +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"] + + +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: + """ + 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)) + + +def get_query_similarity(query: str, df: pd.DataFrame, open_ai_api_key: str): + """ + 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 + + 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) + ''' + + two_largest = df['similarities'].nlargest(2).index.tolist() + + # print('get_query_similarity!!!!!!!!') + + 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) + + return context + + +def _decorate_query(query: str, df: pd.DataFrame, open_ai_api_key: str)-> str: + + try: + context = get_query_similarity(query, df, open_ai_api_key) + 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') + + return query + + +def decorate_query(query: str, open_ai_api_key, filename='foodsembeddings.csv')-> str: + filepath = filename + try: + df = pd.read_csv(filepath) + if df.empty: + return query + else: + try: + df['embeddings'] = df['embeddings'].apply(lambda x: ast.literal_eval(x)) + return _decorate_query(query, df, open_ai_api_key) + except Exception as e: + print(e) + return query + except Exception as e: + print(e) + return query + + +if __name__ == '__main__': + query = '亁颐堂是做什么的' + print(decorate_query(query, api_key, filename=csv_file_path)) + + diff --git a/gpt_3_query.py b/gpt_3_query.py new file mode 100644 index 0000000..27cdec2 --- /dev/null +++ b/gpt_3_query.py @@ -0,0 +1,27 @@ +import openai +from gpt_2_create_question import decorate_query +from gpt_0_basic_info import api_key, csv_file_path +openai.api_key = api_key + + +def question(query): + response = openai.ChatCompletion.create( + model='gpt-3.5-turbo', + messages=[ + {"role": "system", "content": "You are a helpful assistant."}, + {"role": "user", "content": query} + ], + max_tokens=100, + n=1, + temperature=0.5, + ) + + output_text = response.choices[0].message['content'].strip() + return output_text + + +if __name__ == '__main__': + query = '亁颐堂是做什么的' + new_query = decorate_query(query, api_key, filename=csv_file_path) + print(new_query) + print(question(new_query)) diff --git a/source_data/qa.xlsx b/source_data/qa.xlsx new file mode 100755 index 0000000000000000000000000000000000000000..a966ad262d47095898fc0113ecd396402e21ef13 GIT binary patch literal 8755 zcmeHtgsK_kY0A$4d|BnCR9Vkf|Q0w3U%AYD+$*yuL z%~wfbKHLfGAz;&#_}B^QF0wR&+1ua1o?QZEa)=!SD+%E9{tvzn+Esy_YQti>8@2An zh4y~bHKi2f?&{fQY$YX6b~7~C%O;|f;w3jTdv22fcRVMk0N5%MK# zwnq6wOyiDI{(CKTl zT4*)6U8F0wxU&4V*o?a{w20o=B1o;uPf_(Q_Nd6PI}ya*8ekmY*UFL* zccgLgL4erKjME4v6-pO2It|bO#Z{A(J^;f5J0*$pR0j6jX%{XTmkIB6E>c>3fW1hx 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