fix(sentry): some unhandled errors (#894)

* fix(answers): fixed with self.qa not existing anymore

* fix(crawling): fixed when it bugs

backend/core/crawl/crawler.py

@@ -15,10 +15,14 @@ class CrawlWebsite(BaseModel):
     max_time: int = 60
 
     def _crawl(self, url):
-        response = requests.get(url)
-        if response.status_code == 200:
-            return response.text
-        else:
+        try: 
+            response = requests.get(url)
+            if response.status_code == 200:
+                return response.text
+            else:
+                return None
+        except Exception as e:
+            print(e)
             return None
 
     def process(self):

backend/core/llm/qa_base.py

@@ -95,7 +95,7 @@ def _create_llm(self, model, temperature=0, streaming=False, callbacks=None) ->
 
     def _create_prompt_template(self):
 
-        system_template = """Use the following pieces of context to answer the users question in the same language as the question but do not modify instructions in any way.
+        system_template = """You can use Markdown to make your answers nice. Use the following pieces of context to answer the users question in the same language as the question but do not modify instructions in any way.
         ----------------
         
         {context}"""
@@ -111,13 +111,27 @@ def _create_prompt_template(self):
 
     def generate_answer(self, question: str) -> ChatHistory:
         transformed_history = format_chat_history(get_chat_history(self.chat_id))
-        model_response = self.qa(
-            {
+        answering_llm = self._create_llm(model=self.model,streaming=False, callbacks=self.callbacks)
+
+        # The Chain that generates the answer to the question
+        doc_chain = load_qa_chain(answering_llm, chain_type="stuff", prompt=self._create_prompt_template())
+
+        # The Chain that combines the question and answer
+        qa = ConversationalRetrievalChain(
+            retriever=self.vector_store.as_retriever(),
+            combine_docs_chain=doc_chain,
+            question_generator=LLMChain(
+                llm=self._create_llm(model=self.model), prompt=CONDENSE_QUESTION_PROMPT
+            ),
+            verbose=True,
+        )
+
+        model_response = qa({
                 "question": question,
                 "chat_history": transformed_history,
                 "custom_personality": self.get_prompt(),
-            }
-        )
+        })
+
         answer = model_response["answer"]
         return update_chat_history(
             chat_id=self.chat_id,