Upload app.py

app.py

@@ -205,46 +205,51 @@ def deepl_memory(ss: SessionState) -> (SessionState):
 # DEEPL_API_KEY = os.getenv("DEEPL_API_KEY")
 
 def web_search(ss: SessionState, query) -> (SessionState, str):
-  search =  DuckDuckGoSearchRun(verbose=True)
-  web_result = search(query)
 
-  # 人名の抽出
-  names = []
-  names.extend(name_detector(query))
-  names.extend(name_detector(web_result))
-  if len(names)==0:
-    names = ""
-  elif len(names)==1:
-    names = names[0]
-  else:
-    names = ", ".join(names)
-
-  if ss.current_model == "gpt-3.5-turbo":
-    text = [query, web_result]
-    params = {
-        "auth_key": DEEPL_API_KEY,
-        "text": text,
-        "target_lang": "EN",
-        "source_lang": "JA",
-        "tag_handling": "xml",
-        "igonere_tags": "x",
-    }
-    request = requests.post(DEEPL_API_ENDPOINT, data=params)
-    response = request.json()
+  search =  DuckDuckGoSearchRun(verbose=True)
 
-    query = response["translations"][0]["text"]
-    web_result = response["translations"][1]["text"]
-    web_result = ss.web_summary_chain({'query': query, 'context': web_result})['text']
+  for i in range(3):
+    web_result = search(query)
+
+    # 人名の抽出
+    names = []
+    names.extend(name_detector(query))
+    names.extend(name_detector(web_result))
+    if len(names)==0:
+      names = ""
+    elif len(names)==1:
+      names = names[0]
+    else:
+      names = ", ".join(names)
+
+    if ss.current_model == "gpt-3.5-turbo":
+      text = [query, web_result]
+      params = {
+          "auth_key": DEEPL_API_KEY,
+          "text": text,
+          "target_lang": "EN",
+          "source_lang": "JA",
+          "tag_handling": "xml",
+          "ignore_tags": "x",
+      }
+      request = requests.post(DEEPL_API_ENDPOINT, data=params)
+      response = request.json()
+
+      query = response["translations"][0]["text"]
+      web_result = response["translations"][1]["text"]
+      web_result = ss.web_summary_chain({'query': query, 'context': web_result})['text']
+      if web_result != "NO INFO":
+        break
 
   if names != "":
     web_query = f"""
     {query}
-    Use the following information as a reference to answer the question above in Japanese. When translating names of Japanese people, refer to Japanese Names as a translation guide.
-    Reference: {web_result}
-    Japanese Names: {names}
+    Use the following Suggested Answer Source as a reliable reference to answer the question above in Japanese. When translating names of people, refer to Names as a translation guide.
+    Suggested Answer Source: {web_result}
+    Names: {names}
     """.strip()
   else:
-    web_query = query + "\nUse the following information as a reference to answer the question above in the Japanese.\n===\nReference: " + web_result + "\n==="
+    web_query = query + "\nUse the following Suggested Answer Source as a reliable reference to answer the question above in the Japanese.\n===\nSuggested Answer Source: " + web_result + "\n"
 
 
   return ss, web_query
@@ -263,16 +268,17 @@ def web_search(ss: SessionState, query) -> (SessionState, str):
 
 # Tokens: OpenAI 104/ Llama 105 <- In Japanese: Tokens: OpenAI 191/ Llama 162
 sys_chat_message = """
-You are an outstanding AI concierge. You understand your customers' needs from their questions and answer
-them with many specific and detailed information in Japanese. If you do not know the answer to a question,
-do make up an answer and says "誠に申し訳ございませんが、その点についてはわかりかねます". Ignore Conversation History.
+You are an outstanding AI concierge. Understand the intent of the customer's questions based on
+the conversation history. Then, answer them with many specific and detailed information in Japanese.
+If you do not know the answer to a question, do make up an answer and says
+"誠に申し訳ございませんが、その点についてはわかりかねます".
 """.replace("\n", "")
 
 chat_common_format = """
 ===
 Question: {query}
 ===
-Conversation History(Ignore):
+Conversation History:
 {chat_history}
 ===
 日本語の回答: """
@@ -286,10 +292,9 @@ chat_template_llama2 = f"<s>[INST] <<SYS>>{sys_chat_message}<</SYS>>{chat_common
 # Tokens: OpenAI 113/ Llama 111 <- In Japanese: Tokens: OpenAI 256/ Llama 225
 sys_qa_message = """
 You are an AI concierge who carefully answers questions from customers based on references.
-You understand what the customer wants to know from Question, and give a specific answer in
-Japanese using sentences extracted from the following references. If you do not know the answer,
-do not make up an answer and reply, "誠に申し訳ございませんが、その点についてはわかりかねます".
-Ignore Conversation History.
+Understand the intent of the customer's questions based on the conversation history. Then, give
+a specific answer in Japanese using sentences extracted from the following references. If you do
+not know the answer, do not make up an answer and reply, "誠に申し訳ございませんが、その点についてはわかりかねます".
 """.replace("\n", "")
 
 qa_common_format = """
@@ -297,7 +302,7 @@ qa_common_format = """
 Question: {query}
 References: {context}
 ===
-Conversation History(Ignore):
+Conversation History:
 {chat_history}
 ===
 日本語の回答: """
@@ -332,7 +337,8 @@ query_generator_template_llama2 = f"<s>[INST] <<SYS>>{query_generator_message}<<
 question_prompt_message = """
 From the following references, extract key information relevant to the question
 and summarize it in a natural English sentence with clear subject, verb, object,
-and complement.
+and complement. If there is no information in the reference that answers the question,
+do not summarize and simply answer "NO INFO"
 """.replace("\n", "")
 
 question_prompt_common_format = """
@@ -1058,8 +1064,8 @@ with gr.Blocks() as demo:
           )
         with gr.Column(scale=5):
           with gr.Row():
-            qa_flag = gr.Checkbox(label="QA mode", value=True, min_width=60, interactive=True)
-            web_flag = gr.Checkbox(label="Web Search", value=False, min_width=60, interactive=True)
+            qa_flag = gr.Checkbox(label="QA mode", value=False, min_width=60, interactive=True)
+            web_flag = gr.Checkbox(label="Web Search", value=True, min_width=60, interactive=True)
           with gr.Row():
             query_send_btn = gr.Button(value="▶")
 
@@ -1082,5 +1088,5 @@ with gr.Blocks() as demo:
 
 if __name__ == "__main__":
     demo.queue(concurrency_count=5)
-    demo.launch(debug=True,)
+    demo.launch(debug=True)