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a668326 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 | """Lightweight, dependency-free summary + Q&A with an optional LLM upgrade.
Base mode (no API key, no model):
* Extractive summary — ranks transcript segments by domain-keyword density
and bookmark coverage, returns the top sentences in chronological order.
* Q&A — keyword retrieval over the timeline; answers with the best-matching
transcript snippet and its timestamp ("grounded answer").
Optional mode:
* If ``LLM_API_KEY`` is set, callers may plug in a real LLM. The hook is
intentionally isolated so the base app never imports an LLM SDK.
"""
from __future__ import annotations
from typing import Dict, List
from src.config import Config, CONFIG
from src.search import VideoSearch
from src.utils import extract_keywords
def extractive_summary(
timeline_doc: Dict[str, object],
config: Config = CONFIG,
max_points: int = 6,
) -> Dict[str, object]:
"""Return a few key sentences ranked by keyword density + bookmark signal."""
entries: List[Dict[str, object]] = list(timeline_doc.get("timeline", []))
scored = []
for e in entries:
text = str(e.get("transcript", ""))
kw = extract_keywords(text, config.domain_terms)
score = len(kw) + (2 if e.get("bookmark") else 0)
if score > 0 and text:
scored.append((score, e.get("time_sec", 0.0), e.get("time_label"), text, kw))
scored.sort(key=lambda x: x[0], reverse=True)
top = scored[:max_points]
top.sort(key=lambda x: x[1]) # back to chronological order
points = [
{"time_label": t[2], "text": t[3], "keywords": t[4]} for t in top
]
# Collect the distinct topics mentioned across the whole video.
topics: List[str] = []
for e in entries:
for k in extract_keywords(str(e.get("transcript", "")), config.domain_terms):
if k not in topics:
topics.append(k)
return {
"video_id": timeline_doc.get("video_id", ""),
"mode": "extractive",
"summary_points": points,
"topics": topics,
}
def answer_question(
question: str,
timeline_doc: Dict[str, object],
config: Config = CONFIG,
top_k: int = 3,
) -> Dict[str, object]:
"""Grounded Q&A: retrieve the best timeline snippets for the question."""
engine = VideoSearch(timeline_doc, config)
found = engine.search(question, top_k=top_k)
results = found["results"]
if results:
best = results[0]
answer = (
f"At {best['time_label']}: \"{best['text']}\""
if best.get("text")
else f"See {best['time_label']}."
)
else:
answer = "No relevant moment was found in this video for that question."
return {
"question": question,
"video_id": timeline_doc.get("video_id", ""),
"mode": found["mode"],
"answer": answer,
"evidence": results,
"llm_used": False,
}
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