Floke
23d0c695d6
Completed the GTM engine setup:\n\n- Implemented 'Dual Opener' generation (Primary/Secondary) in ClassificationService.\n- Migrated DB to support two opener fields.\n- Updated API and Frontend to handle and display both openers.\n- Fixed bug creating duplicate website_scrape entries.\n- Hardened metric extraction by improving the LLM prompt and adding content length checks.
57 lines
2.2 KiB
Python
57 lines
2.2 KiB
Python
import sqlite3
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import json
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DB_PATH = "/app/companies_v3_fixed_2.db"
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def inspect(name_part):
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try:
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conn = sqlite3.connect(DB_PATH)
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cursor = conn.cursor()
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print(f"Searching for '{name_part}' in {DB_PATH}...")
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cursor.execute("SELECT id, name, website, industry_ai, calculated_metric_value, standardized_metric_value FROM companies WHERE name LIKE ?", (f'%{name_part}%',))
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companies = cursor.fetchall()
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if not companies:
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print("No hits.")
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return
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for c in companies:
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cid, name, website, industry, metric, std_metric = c
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print("\n" + "="*40)
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print(f"🏢 {name} (ID: {cid})")
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print(f" Vertical: {industry}")
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print(f" Website: {website}")
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print(f" Metric: {metric} (Std: {std_metric})")
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# Fetch Enrichment Data
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cursor.execute("SELECT source_type, content FROM enrichment_data WHERE company_id = ?", (cid,))
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rows = cursor.fetchall()
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print("\n 📚 Enrichment Data:")
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for r in rows:
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stype, content_raw = r
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print(f" - {stype}")
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try:
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content = json.loads(content_raw)
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if stype == "website_scrape":
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summary = content.get("summary", "")
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raw = content.get("raw_text", "")
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print(f" > Summary: {summary[:150]}...")
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print(f" > Raw Length: {len(raw)}")
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if len(raw) > 500:
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print(f" > Raw Snippet: {raw[:300]}...")
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elif stype == "wikipedia":
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print(f" > URL: {content.get('url')}")
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intro = content.get("intro_text", "") or content.get("full_text", "")
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print(f" > Intro: {str(intro)[:150]}...")
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except:
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print(" > (Content not valid JSON)")
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except Exception as e:
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print(f"Error: {e}")
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finally:
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if conn: conn.close()
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if __name__ == "__main__":
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inspect("Therme Erding")
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