added configurable prompt

code/OpenAI_Queries.py

@@ -22,7 +22,7 @@ def get_languages():
     if 'question' not in st.session_state:
         st.session_state['question'] = default_question
     if 'prompt' not in st.session_state:
-        st.session_state['prompt'] = default_prompt        
+        st.session_state['prompt'] = os.getenv("QUESTION_PROMPT", "Please reply to the question using only the information present in the text above. If you can't find it, reply 'Not in the text'.\nQuestion: _QUESTION_\nAnswer:").replace(r'\n', '\n')
     if 'response' not in st.session_state:
         st.session_state['response'] = {
             "choices" :[{
@@ -31,6 +31,8 @@ def get_languages():
         }    
     if 'limit_response' not in st.session_state:
         st.session_state['limit_response'] = True
+    if 'full_prompt' not in st.session_state:
+        st.session_state['full_prompt'] = ""
 
     # Set page layout to wide screen and menu item
     menu_items = {
@@ -57,6 +59,7 @@ def get_languages():
                 "OpenAI GPT-3 Model",
                 (os.environ['OPENAI_ENGINES'].split(','))
             )
+            st.text_area("Prompt",height=100, key='prompt')
             st.tokens_response = st.slider("Tokens response length", 100, 500, 400)
             st.temperature = st.slider("Temperature", 0.0, 1.0, 0.1)
             st.selectbox("Language", [None] + list(available_languages.keys()), key='translation_language')
@@ -67,16 +70,16 @@ def get_languages():
     if question != '':
         if question != st.session_state['question']:
             st.session_state['question'] = question
-            st.session_state['prompt'], st.session_state['response'] = utils.get_semantic_answer(df, question, model=model, engine='davinci', limit_response=st.session_state['limit_response'], tokens_response=st.tokens_response, temperature=st.temperature)
+            st.session_state['full_prompt'], st.session_state['response'] = utils.get_semantic_answer(df, question, st.session_state['prompt'] ,model=model, engine='davinci', limit_response=st.session_state['limit_response'], tokens_response=st.tokens_response, temperature=st.temperature)
             st.write(f"Q: {question}")  
             st.write(st.session_state['response']['choices'][0]['text'])
             with st.expander("Question and Answer Context"):
-                st.text(st.session_state['prompt'])
+                st.text(st.session_state['full_prompt'])
         else:
             st.write(f"Q: {st.session_state['question']}")  
             st.write(f"{st.session_state['response']['choices'][0]['text']}")
             with st.expander("Question and Answer Context"):
-                st.text(st.session_state['prompt'].encode().decode())
+                st.text(st.session_state['full_prompt'].encode().decode())
 
     if st.session_state['translation_language'] is not None:
         st.write(f"Translation to other languages, 翻译成其他语言, النص باللغة العربية")

code/pages/01_Add_Document.py

@@ -91,7 +91,7 @@ def token_count():
             st.session_state['doc_text'] = st.text_area(" or Add a new text content and the click on 'Compute Embeddings'", height=600)
 
         with col2:
-            st.session_state['embeddings_model'] = st.selectbox('Embeddings models', (utils.get_embeddings_model()['doc']), disabled=True)
+            st.session_state['embeddings_model'] = st.selectbox('Embeddings models', [utils.get_embeddings_model()['doc']], disabled=True)
             st.button("Compute Embeddings", on_click=embeddings)
 
     with st.expander("View documents in the knowledge base", expanded=False):

code/utilities/utils.py

@@ -44,21 +44,18 @@ def get_semantic_answer(df, question, explicit_prompt="", model="DaVinci-text",
     restart_sequence = "\n\n"
     question += "\n"
 
-    if explicit_prompt == "":
-        res = search_semantic_redis(df, question, n=3, pprint=False, engine=engine)
-
-        if len(res) == 0:
-            prompt = f"{question}"
-        elif limit_response:
-            res_text = "\n".join(res['text'][0:int(os.getenv("NUMBER_OF_EMBEDDINGS_FOR_QNA",1))])
-            question_prompt = os.getenv("QUESTION_PROMPT", "Please reply to the question using only the information present in the text above. If you can't find it, reply 'Not in the text'.\nQuestion: _QUESTION_\nAnswer:").replace(r'\n', '\n')
-            question_prompt = question_prompt.replace("_QUESTION_", question)
-            prompt = f"{res_text}{restart_sequence}{question_prompt}"
-        else:
-            prompt = f"{res_text}{restart_sequence}{question}"
-
+    res = search_semantic_redis(df, question, n=3, pprint=False, engine=engine)
+
+    if len(res) == 0:
+        prompt = f"{question}"
+    elif limit_response:
+        res_text = "\n".join(res['text'][0:int(os.getenv("NUMBER_OF_EMBEDDINGS_FOR_QNA",1))])
+        question_prompt = explicit_prompt.replace(r'\n', '\n')
+        question_prompt = question_prompt.replace("_QUESTION_", question)
+        prompt = f"{res_text}{restart_sequence}{question_prompt}"
     else:
-        prompt = f"{explicit_prompt}{restart_sequence}{question}"
+        prompt = f"{res_text}{restart_sequence}{question}"
+
 
     response = openai.Completion.create(
         engine=model,