# -------------------------------
# 4. Answerer
# -------------------------------
from typing import List
from pydantic import BaseModel
from openai import OpenAI
from modules.retriever import retrieve_videos
# -------------------------------
# Structured Output Classes
# -------------------------------
class VideoItem(BaseModel):
video_id: str
title: str
channel: str
description: str
class LLMAnswer(BaseModel):
answer_text: str
top_videos: List[VideoItem]
# -------------------------------
# Main Function
# -------------------------------
def answer_query(query: str, collection, top_k: int = 5) -> LLMAnswer:
"""
Answer a user query using YouTube video metadata.
Returns an LLMAnswer object with textual answer + list of videos.
"""
results = retrieve_videos(query, collection, top_k=top_k)
if not results:
return LLMAnswer(answer_text="No relevant videos found.", top_videos=[])
# Build context lines for the LLM
context_lines = []
for r in results:
if not isinstance(r, dict):
continue
vid_id = r.get("video_id", "")
title = r.get("video_title") or r.get("title", "")
channel = r.get("channel") or r.get("channel_title", "")
description = r.get("description", "")
context_lines.append(
f"- {title} ({channel}) (https://youtube.com/watch?v={vid_id})\n description: {description}"
)
context_text = "\n".join(context_lines)
# Call LLM with structured output
client = OpenAI()
response = client.chat.completions.parse(
model="gpt-4o-mini",
messages=[
{
"role": "system",
"content": (
"You are a helpful assistant that answers questions using YouTube video metadata. "
"Return your response strictly as the LLMAnswer class, including 'answer_text' and a list of **only the most relevant** 'top_videos'.\n"
"- `answer_text` MUST be very short and concise in natural language (max 1–2 sentences).\n"
"- Use `top_videos` to include only the 2–3 most relevant items from context.\n"
"- Do not include all items unless all are clearly relevant.\n"
),
},
{
"role": "user",
"content": f"Question: {query}\n\nCandidate videos:\n{context_text}\n\nPick only the relevant ones.",
},
],
response_format=LLMAnswer,
)
llm_answer = response.choices[0].message.parsed
answer_text = llm_answer.answer_text
video_html = build_video_html(llm_answer.top_videos)
return answer_text, video_html
def build_video_html(videos: list[VideoItem]) -> str:
"""Build a clean HTML table from top_videos."""
if not videos:
return "