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import re
import time
from concurrent.futures import ThreadPoolExecutor
from app.llm_services import call_chat_model_openai
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="movies",
genres=None,
providers=None,
year_range=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:
yield "[[MODE:recommendation]]\n"
t0 = time.time()
retrieved_movies = 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")
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:
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)
return chat
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