optimized quiz

clients/groq_client.py

@@ -176,8 +176,8 @@ class GroqClient:
 
     # ---------------MAIN-----------------
 
-# Non-streaming ask method for backwards compatibility
-# ============================================
+    # Non-streaming ask method for backwards compatibility
+    # ============================================
     def ask(self, raw_query: str) -> tuple[str, str | None]:
         if (
             not raw_query
@@ -256,15 +256,23 @@ class GroqClient:
                     return f"Error while calling LLM: {e}", None
 
         # If all models fail
-        return "I'm temporarily experiencing high demand. Please try again in a few minutes or rephrase your question.", None
+        return (
+            "I'm temporarily experiencing high demand. Please try again in a few minutes or rephrase your question.",
+            None,
+        )
 
-# Streaming ask method (Generator)
-# ============================================
+    # Streaming ask method (Generator)
+    # ============================================
     def ask_stream(self, raw_query: str):
         """Generator function that yields response chunks in real-time"""
 
         # Validation
-        if not raw_query or raw_query is None or raw_query == "" or len(raw_query) > 1000:
+        if (
+            not raw_query
+            or raw_query is None
+            or raw_query == ""
+            or len(raw_query) > 1000
+        ):
             yield "Please provide a valid query under 1,000 characters."
             return
 

main.py

@@ -45,7 +45,12 @@ async def chat_endpoint(request: QueryRequest):
         groq_client = get_groq_client()
         response, model = groq_client.ask(request.query)
         if response.startswith("Error while"):
-            return {"status_code": 500, "response": response, "model": model, "next_questions": []}
+            return {
+                "status_code": 500,
+                "response": response,
+                "model": model,
+                "next_questions": [],
+            }
         return {
             "status_code": 200,
             "response": response,

optimized_quiz.py

@@ -82,4 +82,14 @@ OPTIMIZED_QUESTIONS = [
     "How do construction professionals benefit from using UjenziLink?",
     "What specific problems in the construction industry does UjenziLink address?",
     "What are the different roles and permissions available in UjenziLink?",
+    "What is Falqon Jobs?",
+    "How does Falqon Jobs work?",
+    "What makes Falqon Jobs different?",
+    "How does CV matching work?",
+    "Where do the job listings come from?",
+    "Can I apply through Falqon Jobs?",
+    "Can I upload my CV?",
+    "What insights does CV matching provide?",
+    "Can I find remote jobs?",
+    "What technologies power Falqon Jobs?",
 ]