enterprise-search

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Enterprise search: relevance tuning, query understanding, index management, search quality, ranking optimization, schema design.

AI & Automation 393 stars 36 forks Updated today MIT

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Quality Score: 95/100

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Skill Content

# Enterprise Search Engineering Search infrastructure design, relevance tuning, query understanding, index management, quality measurement, and performance optimization. Each mode loads its own reference files on demand. --- ## Mode Detection Classify into one mode before proceeding. | Mode | Signal Phrases | Reference | |------|---------------|-----------| | **RELEVANCE** | tune relevance, BM25, boost, function score, field weight, LTR, learned ranking, ranking model | `references/relevance-tuning.md` | | **QUERY** | query understanding, intent classification, entity extraction, query expansion, synonyms, spell correction, query rewriting | `references/query-understanding.md` | | **INDEX** | schema design, mapping, analyzer chain, reindex, alias, ILM, index template, field type | `references/index-management.md` | | **QUALITY** | nDCG, MRR, precision, recall, search quality, judgment, A/B test, evaluation, search funnel | `references/search-quality.md` | | **PERFORMANCE** | slow query, shard, cache, circuit breaker, scroll, search_after, query optimization, latency | `references/performance-optimization.md` | | **ARCHITECTURE** | search architecture, hybrid search, vector search, pipeline design, platform selection, migration | (cross-reference: load relevant references based on sub-topic) | If the request spans modes, pick the primary and note the secondary. ARCHITECTURE mode loads references from whichever sub-topics apply. --- ## Workflow by Mode ### RELEVANCE Mo...

Details

Author
notque
Repository
notque/vexjoy-agent
Created
2 months ago
Last Updated
today
Language
Python
License
MIT

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