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| import re | |
| import time | |
| from concurrent.futures import ThreadPoolExecutor | |
| from app.llm.llm_completion import call_chat_model_openai | |
| from app.services.usage_logger import log_query_and_results | |
| def sanitize_markdown(md_text: str) -> str: | |
| return re.sub(r"!\[.*?\]\(.*?\)", "", md_text) | |
| def build_chat_fn(retriever, intent_classifier): | |
| def chat( | |
| question, | |
| history, | |
| media_type="movie", | |
| genres=None, | |
| providers=None, | |
| year_range=None, | |
| session_id=None, | |
| query_id=None, | |
| device_info=None | |
| ): | |
| full_t0 = time.time() | |
| with ThreadPoolExecutor() as executor: | |
| # Classify user intent to determine if it is a recommendation ask | |
| t0 = time.time() | |
| intent_future = executor.submit( | |
| lambda q: intent_classifier(q)[0]["label"] == "recommendation", question | |
| ) | |
| print(f"\n🧠 executor.submit(classify_intent) took {time.time() - t0:.3f}s") | |
| # Embed user query as dense vector asynchronously | |
| t0 = time.time() | |
| query_vector_future = executor.submit(retriever.embed_dense, question) | |
| print(f"🧵 executor.submit(embed_text) took {time.time() - t0:.3f}s") | |
| # Wait for results | |
| t0 = time.time() | |
| is_rec_intent = intent_future.result() | |
| print(f"✅ classify_intent() result received in {time.time() - t0:.3f}s") | |
| t0 = time.time() | |
| dense_vector = query_vector_future.result() | |
| print(f"📈 embed_text() result received in {time.time() - t0:.3f}s") | |
| # Embed user query as sparse vector for hybrid retrieval | |
| t0 = time.time() | |
| sparse_vector = retriever.embed_sparse(question, media_type) | |
| print(f"📈 embed_sparse() result received in {time.time() - t0:.3f}s") | |
| if is_rec_intent: | |
| t0 = time.time() | |
| retrieved_movies, scored_lookup = retriever.retrieve_and_rerank( | |
| dense_vector, | |
| sparse_vector, | |
| media_type.lower(), | |
| genres, | |
| providers, | |
| year_range, | |
| ) | |
| print(f"\n📚 retrieve_and_rerank() took {time.time() - t0:.3f}s") | |
| query_entry = { | |
| "query_id": query_id, | |
| "session_id": session_id, | |
| "question": question, | |
| "intent": "recommendation", | |
| "media_type": media_type, | |
| "genres": genres, | |
| "providers": providers, | |
| "year_start": year_range[0], | |
| "year_end": year_range[1], | |
| "device_type": device_info.device_type, | |
| "platform" : device_info.platform, | |
| "user_agent": device_info.user_agent | |
| } | |
| result_entries = [] | |
| for rank, p in enumerate (retrieved_movies): | |
| s = scored_lookup[p.id] | |
| result_entries.append({ | |
| "query_id": query_id, | |
| "media_type": media_type, | |
| "media_id": p.payload["media_id"], | |
| "title": p.payload["title"], | |
| "rank": rank + 1, | |
| "dense_score": s["dense_score"], | |
| "sparse_score": s["sparse_score"], | |
| "reranked_score": s["reranked_score"], | |
| "is_final_rec": False | |
| }) | |
| try: | |
| log_query_and_results(query_entry, result_entries) | |
| except Exception as e: | |
| print("⚠️ Failed to log to Supabase:", e) | |
| yield "[[MODE:recommendation]]\n" | |
| 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: | |
| log_query_and_results( | |
| query_entry={ | |
| "query_id": query_id, | |
| "session_id": session_id, | |
| "question": question, | |
| "intent": "chat", | |
| "media_type": media_type, | |
| }, | |
| result_entries=[] | |
| ) | |
| 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" | |
| ) | |
| yield "[[MODE:chat]]\n" | |
| for chunk in call_chat_model_openai(history, user_message): | |
| yield sanitize_markdown(chunk) | |
| return chat | |