fixed how the chatbot presents answers, tickets #331 and #342

application/frontend/src/pages/chatbot/chatbot.tsx

@@ -204,7 +204,7 @@ export const Chatbot = () => {
     'yaml',
     'zephir',
   ];
-  type chatMessage = { timestamp: string; role: string; message: string; data: Document[] | null };
+  type chatMessage = { timestamp: string; role: string; message: string; data: Document[] | null; };
   interface ChatState {
     term: string;
     error: string;
@@ -287,7 +287,7 @@ export const Chatbot = () => {
             timestamp: new Date().toLocaleTimeString(),
             role: 'assistant',
             message: data.response,
-            data: data.table,
+            data: data.table
           },
         ]);
       })
@@ -299,11 +299,23 @@ export const Chatbot = () => {
   }
 
   function displayDocument(d: Document) {
-    return (
-      <a href={d.hyperlink} target="_blank">
-        *Reference: The above answer was based on the {d.name} section of{' '}
-        {d.section ? d.section : d.sectionID};
-      </a>
+
+    var link = "/node/" + d.doctype.toLowerCase() + "/" + d.name
+    if (d.section) {
+      link = link + "/section/" + d.section
+    } else {
+      link = link + "/sectionid/" + d.sectionID
+    }
+    return (<p>
+      <p>*Reference: The above answer was based on:
+        <a href={d.hyperlink} target="_blank"> {d.name} section: {' '}
+          {d.section ? d.section : d.sectionID};
+        </a>
+      </p>
+      <p>
+        You can find more information about {d.name} <a href={link}> on its OpenCRE page</a>
+      </p>
+    </p>
     );
   }
 
@@ -337,8 +349,8 @@ export const Chatbot = () => {
                               <Comment.Text>{processResponse(m.message)}</Comment.Text>
                               {m.data
                                 ? m.data?.map((m2) => {
-                                    return displayDocument(m2);
-                                  })
+                                  return displayDocument(m2);
+                                })
                                 : ''}
                             </Comment.Content>
                           </Comment>
@@ -372,6 +384,14 @@ export const Chatbot = () => {
               <div className="table-container mt-5 ms-5 d-none">
                 <div className="table-content bg-light shadow p-3" id="table-content"></div>
               </div>
+              <div className='chatbot'>
+                <i>
+                  ChatCRE uses Google's PALM2 LLM, you can find the code for OpenCRE in https://github.com/owaps/OpenCRE.
+                  Your question travels to Heroku (OpenCRE hosting provider) and then to GCP over a protected connection.
+                  Your data is never stored in the OpenCRE servers, you can start a new session by refreshing your page.
+                  The OpenCRE team has taken all reasonable precautions we could think off to protect your privacy and security.
+                </i>
+              </div>
             </Grid>
           </Container>
         </Grid.Column>

application/prompt_client/prompt_client.py

@@ -415,9 +415,9 @@ def generate_text(self, prompt: str) -> Dict[str, str]:
         timestamp = datetime.now().strftime("%I:%M:%S %p")
         if not prompt:
             return {"response": "", "table": "", "timestamp": timestamp}
-        logger.info(f"getting embeddings for {prompt}")
+        logger.debug(f"getting embeddings for {prompt}")
         question_embedding = self.ai_client.get_text_embeddings(prompt)
-        logger.info(f"retrieved embeddings for {prompt}")
+        logger.debug(f"retrieved embeddings for {prompt}")
 
         # Find the closest area in the existing embeddings
         closest_id, similarity = self.get_id_of_most_similar_node_paginated(
@@ -426,8 +426,7 @@ def generate_text(self, prompt: str) -> Dict[str, str]:
         )
         closest_object = self.database.get_node_by_db_id(closest_id)
         answer = ""
-
-        logger.info(
+        logger.debug(
             f"The prompt {prompt}, was most similar to object \n{closest_object}\n, with similarity:{similarity}"
         )
         closest_content = ""
@@ -449,7 +448,7 @@ def generate_text(self, prompt: str) -> Dict[str, str]:
         else:
             return {"response": "An adequate answer could not be found", "table": [""]}
 
-        logger.info(f"retrieved completion for {prompt}")
+        logger.debug(f"retrieved completion for {prompt}")
         table = [closest_object]
         result = f"Answer: {answer}"
         return {"response": result, "table": table}