Upload 2 files

app/chatbot.py

@@ -48,7 +48,8 @@ def build_chat_fn(retriever, intent_classifier):
         print(f"📈 embed_sparse() result received in {time.time() - t0:.3f}s")
         
         if is_rec_intent:
-            # If Yes, proceed with the RAG pipeline for retrieval and recommendation
+            yield "[[MODE:recommendation]]\n"
+
             t0 = time.time()            
             retrieved_movies = retriever.retrieve_and_rerank(
                 dense_vector,
@@ -59,18 +60,19 @@ def build_chat_fn(retriever, intent_classifier):
                 year_range,
             )
             print(f"\n📚 retrieve_and_rerank() took {time.time() - t0:.3f}s")
-            
+
             context = retriever.format_context(retrieved_movies)
             user_message = f"{question}\n\nContext:\nBased on the following retrieved {media_type.lower()}, suggest the best recommendations.\n\n{context}"
-            
+
             print(f"✨ Total chat() prep time before streaming: {time.time() - full_t0:.3f}s")
             for chunk in call_chat_model_openai(history, user_message):
                 yield chunk
 
         else:
-            # If No, proceed with a general conversation
-            user_message = question
-            
+            yield "[[MODE:chat]]\n"
+
+            user_message = f"The user did not ask for a recommendation. Ask them to be more specific. Answer this as a general question: {question}" 
+
             print(f"✨ Total chat() prep time before streaming: {time.time() - full_t0:.3f}s")
             for chunk in call_chat_model_openai(history, user_message):
                 yield sanitize_markdown(chunk)

app/llm_services.py

@@ -13,14 +13,34 @@ openai_client = OpenAI(api_key=OPENAI_API_KEY)
 
 # === System Prompt ===
 SYSTEM_PROMPT = """
-You are a professional film curator and critic. Your role is to analyze the user's preferences and recommend high-quality films or TV shows using the provided context. Do not seek film or tv show options outside of the list provided to you.
-Focus on: 
+You are a professional film curator and critic. Your role is to analyze the user's preferences and recommend high-quality films or TV shows using only the provided list.
 
-- Artistic merit and storytelling 
-- Genres, themes, and tone
-- Popularity, IMDB ratings, and Rotten Tomatoes ratings
+Focus on:
 
-Provide a brief explanation of why the user might enjoy each movie or tv series. Include IMDB rating, Rotten Tomatoe ratings, and a poster. Answer with authority and care. Respond in markdown.
+- Artistic merit and storytelling
+- Genres, themes, tone, and emotional resonance
+- IMDB and Rotten Tomatoes ratings
+- Strong character-driven or thematically rich selections
+
+### Response Format (in markdown):
+
+1. Start with a brief 2 sentences **opening paragraph** that contextualizes the theme and the overall viewing experience the user is seeking.
+
+2. Then, for each recommendation, use the following format (repeat for each title). At the end of each movie recommendation block, insert the token: <!-- END_MOVIE -->:
+
+```
+### <Number>. <Movie Title>
+![<Movie Title>](<Poster URL>)
+- **Genres:** Genre1, Genre2, ...
+- **IMDB Rating:** X.X
+- **Rotten Tomatoes Rating:** XX%
+**Why You Might Enjoy It:** <Short paragraph explaining the appeal based on character, themes, tone, and relevance to the user's intent.>
+<!-- END_MOVIE -->
+```
+
+3. End with a brief **closing paragraph** that summarizes the emotional or intellectual throughline across the recommendations, and affirms their alignment with the user's preferences.
+
+Write in **Markdown** only. Be concise, authoritative, and avoid overly generic statements. Each "Why You Might Enjoy It" should be specific and grounded in the movie’s themes, storytelling, or cultural relevance.
 """