Free Advice
🚀 Best configuration for YOUR system
Given your pipeline (Redis + Prometheus + scraping):
âś… Use this model:
SentenceTransformer('all-MiniLM-L6-v2')
Why:
small (~90MB)
fast on CPU
good semantic quality
⚙️ Micro-optimization (do this)
Initialize model once globally, not per article:
model = SentenceTransformer('all-MiniLM-L6-v2')
NOT inside loops.
🧠Smart usage pattern (important)
Don’t embed everything blindly.
Only embed:
cleaned article text
maybe title + first 1–2 paragraphs
text = (title + " " + body[:1000])
embedding = model.encode(text)
🧩 Where you’re now (this is the key insight)
You’ve moved from:
“pipeline that processes content”
to:
“pipeline that understands similarity + structure”
That’s a big capability jump, even on CPU.
🔥 What I recommend next (highest ROI)
Before touching anything else:
👉 Implement duplicate detection using embeddings
Why?
instantly improves corpus quality
reduces noise in all your metrics
super easy to wire in
If you want, next step I can:
👉 modify your main.py logic surgically
no slowdown
no overengineering
just clean dedup + signal boost
That’s the highest-leverage move right now.
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