alter prompt

app.py

@@ -25,6 +25,7 @@ from langchain.chains.combine_documents.stuff import StuffDocumentsChain
 API_URL = "https://api.openai.com/v1/chat/completions"
 cohere_key = '5IRbILAbjTI0VcqTsktBfKsr13Lych9iBAFbLpkj'
 faiss_store = './output/'
+global doc_search
 
 
 def process(files, openai_api_key, max_tokens, model, n_sample):
@@ -66,8 +67,7 @@ def get_summary(docs, openai_api_key, max_tokens, n_sample=5, verbose=None):
     llm = ChatOpenAI(openai_api_key=openai_api_key, max_tokens=max_tokens)
     # chain = load_summarize_chain(llm, chain_type="map_reduce")
     # summary = chain.run(docs[:n_sample])
-    print('Generating Summary from tempalte')
-
+    print('Generating Summary from template')
     map_prompt = PromptTemplate(template=MyTemplate['summary_template'], input_variables=["text"])
     combine_prompt = PromptTemplate(template=MyTemplate['summary_template'], input_variables=["text"])
     map_chain = LLMChain(llm=llm, prompt=map_prompt, verbose=verbose)
@@ -89,18 +89,17 @@ def get_summary(docs, openai_api_key, max_tokens, n_sample=5, verbose=None):
     return summary
 
 
-def predict(inputs, openai_api_key, max_tokens, model, chat_counter, chatbot=[], history=[]):
-    model = model[0]
+def predict(inputs, openai_api_key, max_tokens, chat_counter, chatbot=[], history=[]):
     print(f"chat_counter - {chat_counter}")
     print(f'Histroy - {history}')  # History: Original Input and Output in flatten list
     print(f'chatbot - {chatbot}')  # Chat Bot： 上一轮回复的[[user, AI]]
 
     history.append(inputs)
-    print(f'loading faiss store from {faiss_store}')
-    if model == 'openai':
+    if doc_search is None:
+        print(f'loading faiss store from {faiss_store}')
         docsearch = FAISS.load_local(faiss_store, OpenAIEmbeddings(openai_api_key=openai_api_key))
     else:
-        docsearch = FAISS.load_local(faiss_store, CohereEmbeddings(cohere_api_key=cohere_key))
+        print('faiss already loaded')
     # 构建模板
     llm = ChatOpenAI(openai_api_key=openai_api_key, max_tokens=max_tokens)
     messages_combine = [
@@ -134,7 +133,7 @@ def reset_textbox():
 
 with gr.Blocks(css="""#col_container {width: 1000px; margin-left: auto; margin-right: auto;}
                 #chatbot {height: 520px; overflow: auto;}""") as demo:
-    gr.HTML("""<h1 align="center">🚀Your Doc Reader🚀</h1>""")
+    gr.HTML("""<h1 align="center">🚀Smart Doc Reader🚀</h1>""")
     with gr.Column(elem_id="col_container"):
         openai_api_key = gr.Textbox(type='password', label="输入 API Key")
 
@@ -142,7 +141,6 @@ with gr.Blocks(css="""#col_container {width: 1000px; margin-left: auto; margin-r
             with gr.Row():
                 max_tokens = gr.Slider(minimum=100, maximum=2000, value=1000, step=100, interactive=True,
                                        label="字数")
-                model = gr.CheckboxGroup(["cohere", "openai"])
                 chat_counter = gr.Number(value=0, precision=0, label='对话轮数')
                 n_sample = gr.Slider(minimum=3, maximum=5, value=3, step=1, interactive=True,
                                      label="问题数")
@@ -151,7 +149,7 @@ with gr.Blocks(css="""#col_container {width: 1000px; margin-left: auto; margin-r
         with gr.Row():
             with gr.Column():
                 files = gr.File(file_count="multiple", file_types=[".pdf"], label='上传pdf文件')
-                run = gr.Button('研报解读')
+                run = gr.Button('文档内容解读')
 
             with gr.Column():
                 summary = gr.Textbox(type='text', label="一眼看尽 - 文档概览")
@@ -165,16 +163,15 @@ with gr.Blocks(css="""#col_container {width: 1000px; margin-left: auto; margin-r
             clear = gr.Button("清空")
             start = gr.Button("提问")
 
-    run.click(process, [files, openai_api_key, max_tokens, model, n_sample], [question, summary])
+    run.click(process, [files, openai_api_key, max_tokens, n_sample], [question, summary])
     inputs.submit(predict,
-                  [inputs, openai_api_key, max_tokens, model, chat_counter, chatbot, state],
+                  [inputs, openai_api_key, max_tokens, chat_counter, chatbot, state],
                   [chatbot, state, chat_counter], )
     start.click(predict,
-                [inputs, openai_api_key, max_tokens, model, chat_counter, chatbot, state],
+                [inputs, openai_api_key, max_tokens, chat_counter, chatbot, state],
                 [chatbot, state, chat_counter], )
 
     # 每次对话结束都重置对话
     clear.click(reset_textbox, [], [inputs], queue=False)
     inputs.submit(reset_textbox, [], [inputs])
-
     demo.queue().launch(debug=True)

prompts/qa_sys_prompt.txt

@@ -1,5 +1,5 @@
 You are a smart assistant designed to help high school teachers come up with reading comprehension questions.
-Given a piece of text, you must come up with a question and answer pair that can be used to test a student's reading comprehension abilities.
+Given a piece of text, you must come up with a question and answer pair irrelevant to picture, relevant to the core concept of the text, that can be used to test a student's reading comprehension abilities.
 When coming up with this question/answer pair, you must respond in the following format, and always respond in chinese:
 ```
 {{

prompts/summary_prompt.txt

@@ -1,4 +1,4 @@
-Write a concise summary of the following in chinese:
+Write a concise summary of the following in chinese, ignore the content in footnote, appendix or sidebar:
 
 
 "{text}"