cb5978132c
- precompute.py: Berechnet paarweise Cosine-Similarity aller Absätze,
speichert Top-10-Nachbarn pro Absatz in semantic_links Tabelle
- API: /api/similar-precomputed/{podcast}/{episode}/{idx} — liefert
vorberechnete ähnliche Stellen in <1ms
- Getestet: 728 Absätze, 7144 Links (Threshold 0.55)
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
92 lines
3.0 KiB
Python
92 lines
3.0 KiB
Python
#!/usr/bin/env python3
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"""Precompute similarity links between paragraphs and store in DB."""
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import os
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import sys
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import json
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import numpy as np
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from database import get_db, get_all_embeddings, init_db
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MIN_SCORE = float(os.environ.get("SIMILARITY_THRESHOLD", "0.55"))
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MAX_LINKS_PER_PARA = 10
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def precompute_similarities(podcast_id=None):
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"""Compute top-N similar paragraphs for each paragraph and store as JSON."""
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vectors, meta = get_all_embeddings(podcast_id)
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if vectors is None or len(meta) == 0:
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print("No embeddings found.")
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return
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# Fix NaN vectors
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nan_mask = np.isnan(vectors).any(axis=1)
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if nan_mask.any():
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print(f"Warning: {nan_mask.sum()} NaN vectors found, zeroing them out")
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vectors[nan_mask] = 0
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n = len(meta)
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print(f"Computing similarity matrix for {n} paragraphs...")
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# Compute full similarity matrix (n x n)
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sim_matrix = vectors @ vectors.T
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np.fill_diagonal(sim_matrix, 0) # No self-links
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# For each paragraph, find top-N similar from OTHER episodes
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db = get_db()
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# Create table if needed
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db.execute("""
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CREATE TABLE IF NOT EXISTS semantic_links (
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id INTEGER PRIMARY KEY AUTOINCREMENT,
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podcast_id TEXT NOT NULL,
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source_episode TEXT NOT NULL,
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source_idx INTEGER NOT NULL,
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target_podcast TEXT NOT NULL,
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target_episode TEXT NOT NULL,
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target_idx INTEGER NOT NULL,
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score REAL NOT NULL
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)
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""")
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db.execute("CREATE INDEX IF NOT EXISTS idx_semantic_source ON semantic_links(podcast_id, source_episode, source_idx)")
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# Clear existing links
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if podcast_id:
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db.execute("DELETE FROM semantic_links WHERE podcast_id = ?", (podcast_id,))
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else:
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db.execute("DELETE FROM semantic_links")
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total_links = 0
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for i in range(n):
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scores = sim_matrix[i]
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top_indices = np.argsort(scores)[::-1]
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links_added = 0
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for j in top_indices:
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if links_added >= MAX_LINKS_PER_PARA:
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break
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if scores[j] < MIN_SCORE:
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break
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# Skip same episode
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if meta[i]["episode_id"] == meta[j]["episode_id"] and meta[i]["podcast_id"] == meta[j]["podcast_id"]:
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continue
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db.execute(
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"INSERT INTO semantic_links (podcast_id, source_episode, source_idx, target_podcast, target_episode, target_idx, score) VALUES (?, ?, ?, ?, ?, ?, ?)",
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(meta[i]["podcast_id"], meta[i]["episode_id"], meta[i]["idx"],
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meta[j]["podcast_id"], meta[j]["episode_id"], meta[j]["idx"],
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float(scores[j]))
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)
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links_added += 1
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total_links += 1
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db.commit()
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db.close()
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print(f"Stored {total_links} semantic links (threshold: {MIN_SCORE})")
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if __name__ == "__main__":
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init_db()
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podcast_id = sys.argv[1] if len(sys.argv) > 1 else None
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precompute_similarities(podcast_id)
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