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Add application file
Browse files- Document_QA.py +149 -0
- app.py +59 -0
Document_QA.py
ADDED
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import openai
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import faiss
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import numpy as np
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import pickle
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from tqdm import tqdm
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import argparse
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import os
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def create_embeddings(input):
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"""Create embeddings for the provided input."""
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# input = ['ddd','aaa','ccccccccccccccc','ddddd']
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result = []
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# limit about 1000 tokens per request
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# 记录文章每行的长度
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# 0 [100]
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# 1 [200]
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# 2 [4100]
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# 3 [999]
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lens = [len(text) for text in input]
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query_len = 0
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start_index = 0
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tokens = 0
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def get_embedding(input_slice):
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embedding = openai.Embedding.create(model="text-embedding-ada-002", input=input_slice)
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#返回了(文字,embedding)和文字的token
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return [(text, data.embedding) for text, data in zip(input_slice, embedding.data)], embedding.usage.total_tokens
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#将文字的数量按照4096切分成多块,每一块去计算一次embedding,如果不足4096则一次计算所有文本的embedding
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for index, l in tqdm(enumerate(lens)):
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query_len += l
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if query_len > 4096:
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ebd, tk = get_embedding(input[start_index:index + 1])
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query_len = 0
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start_index = index + 1
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tokens += tk
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result.extend(ebd)
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if query_len > 0:
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ebd, tk = get_embedding(input[start_index:])
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tokens += tk
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result.extend(ebd)
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return result, tokens
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def create_embedding(text):
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"""Create an embedding for the provided text."""
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embedding = openai.Embedding.create(model="text-embedding-ada-002", input=text)
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return text, embedding.data[0].embedding
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class QA():
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def __init__(self,data_embe) -> None:
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d = 1536
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index = faiss.IndexFlatL2(d)
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embe = np.array([emm[1] for emm in data_embe])
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data = [emm[0] for emm in data_embe]
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index.add(embe)
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#所有emdding
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self.index = index
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#所有文字
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self.data = data
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def __call__(self, query):
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embedding = create_embedding(query)
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#输出与用户的问题相关的文字
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context = self.get_texts(embedding[1], limit)
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#将用户的问题和涉及的文字告诉gpt,并将答案返回
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answer = self.completion(query,context)
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return answer,context
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def get_texts(self,embeding,limit):
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_,text_index = self.index.search(np.array([embeding]),limit)
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context = []
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for i in list(text_index[0]):
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context.extend(self.data[i:i+5])
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# context = [self.data[i] for i in list(text_index[0])]
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#输出与用户的问题相关的文字
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return context
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def completion(self,query, context):
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"""Create a completion."""
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lens = [len(text) for text in context]
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maximum = 3000
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for index, l in enumerate(lens):
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maximum -= l
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if maximum < 0:
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context = context[:index + 1]
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print("超过最大长度,截断到前", index + 1, "个片段")
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break
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text = "\n".join(f"{index}. {text}" for index, text in enumerate(context))
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response = openai.ChatCompletion.create(
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model="gpt-3.5-turbo",
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messages=[
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{'role': 'system',
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'content': f'你是一个有帮助的AI文章助手,从下文中提取有用的内容进行回答,不能回答不在下文提到的内容,相关性从高到底排序:\n\n{text}'},
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{'role': 'user', 'content': query},
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],
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)
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print("使用的tokens:", response.usage.total_tokens)
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return response.choices[0].message.content
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description="Document QA")
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parser.add_argument("--input_file", default="input.txt", dest="input_file", type=str,help="输入文件路径")
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parser.add_argument("--file_embeding", default="input_embed.pkl", dest="file_embeding", type=str,help="文件embeding文件路径")
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parser.add_argument("--print_context", action='store_true',help="是否打印上下文")
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args = parser.parse_args()
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if os.path.isfile(args.file_embeding):
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data_embe = pickle.load(open(args.file_embeding,'rb'))
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else:
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with open(args.input_file,'r',encoding='utf-8') as f:
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texts = f.readlines()
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#按照行对文章进行切割
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texts = [text.strip() for text in texts if text.strip()]
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data_embe,tokens = create_embeddings(texts)
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pickle.dump(data_embe,open(args.file_embeding,'wb'))
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print("文本消耗 {} tokens".format(tokens))
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qa =QA(data_embe)
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limit = 10
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while True:
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query = input("请输入查询(help可查看指令):")
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if query == "quit":
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break
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elif query.startswith("limit"):
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try:
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limit = int(query.split(" ")[1])
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print("已设置limit为", limit)
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except Exception as e:
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print("设置limit失败", e)
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continue
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elif query == "help":
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print("输入limit [数字]设置limit")
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print("输入quit退出")
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continue
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answer,context = qa(query)
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if args.print_context:
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print("已找到相关片段:")
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for text in context:
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print('\t', text)
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print("=====================================")
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print("回答如下\n\n")
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print(answer.strip())
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print("=====================================")
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app.py
ADDED
@@ -0,0 +1,59 @@
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import gradio as gr
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import openai
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# from gpt_reader.pdf_reader import PaperReader
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# from gpt_reader.prompt import BASE_POINTS
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from Document_QA import QA
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from Document_QA import create_embeddings
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class GUI:
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def __init__(self):
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self.api_key = ""
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self.session = ""
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self.all_embedding =None
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self.tokens = 0
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#load pdf and create all embedings
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def pdf_init(self, api_key, pdf_path):
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openai.api_key = api_key
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with open(pdf_path,'r',encoding='utf-8') as f:
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texts = f.readlines()
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#按照行对文章进行切割
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texts = [text.strip() for text in texts if text.strip()]
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self.all_embedding,self.tokens = create_embeddings(texts)
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def get_answer(self, question):
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qa = QA(self.all_embedding)
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answer,context = qa(question)
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return answer.strip()
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# def analyse(self, api_key, pdf_file):
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# self.session = PaperReader(api_key, points_to_focus=BASE_POINTS)
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# return self.session.read_pdf_and_summarize(pdf_file)
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# def ask_question(self, question):
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# if self.session == "":
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# return "Please upload PDF file first!"
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# return self.session.question(question)
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with gr.Blocks() as demo:
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gr.Markdown(
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"""
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# CHATGPT-PAPER-READER
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""")
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with gr.Tab("Upload PDF File"):
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pdf_input = gr.File(label="PDF File")
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api_input = gr.Textbox(label="OpenAI API Key")
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#result = gr.Textbox(label="PDF Summary")
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upload_button = gr.Button("Start Analyse")
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with gr.Tab("Ask question about your PDF"):
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question_input = gr.Textbox(label="Your Question", placeholder="Authors of this paper?")
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answer = gr.Textbox(label="Answer")
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ask_button = gr.Button("Ask")
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app = GUI()
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upload_button.click(fn=app.pdf_init, inputs=[api_input, pdf_input])
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ask_button.click(app.get_answer, inputs=question_input, outputs=answer)
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if __name__ == "__main__":
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demo.title = "CHATGPT-PAPER-READER"
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demo.launch() # add "share=True" to share CHATGPT-PAPER-READER app on Internet.
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