Sone-071 Work ❲Tested & Working❳

| Step | Description | |------|-------------| | | Use a lightweight BERT‑based classifier (trained on 150k historic queries) to label intent: date_range , numeric_range , status , tag , custom_field . | | 2. Entity Detection | Run spaCy NER + custom regexes for amounts, dates (relative like “last month”, “Q1 2025”), IDs. | | 3. Filter Generation | Map intent+entities to filter JSON structures. | | 4. Scoring | Score each candidate with a logistic regression that factors: confidence from intent, entity match count, historical acceptance rate (per tenant). | | 5. Result Count Estimation | Issue a lightweight COUNT(*) query using the generated filter on the search index (cached for 30 s). | | 6. Feedback Loop | Store SUGGESTION_APPLIED or SUGGESTION_REJECTED events. Retrain the ranking model nightly. |

| Metric | Target | Alert | |--------|--------|-------| | smartsearch.suggestion.latency_ms (p95) | ≤ 300 ms | > 500 ms → PagerDuty | | smartsearch.suggestion.accuracy (acceptance / total) | ≥ 0.65 | < 0.55 → SONE-071

The innovative aspect of SONE-071 lies in its ability to combine these different energy sources into a single, cohesive system. By using advanced algorithms and machine learning techniques, the system can optimize energy production in real-time, taking into account factors such as weather conditions, energy demand, and system efficiency. | Step | Description | |------|-------------| | |

Advanced Smart‑Search with Contextual Filters Ticket: SONE‑071 Product Area: Search (frontend + backend) Target Release: v3.4.0 (Q3‑2026) Stakeholders: Scoring | Score each candidate with a logistic