Semantic search implement karein — users natural language mein search karein aur accurate results payein.
شروع از
PKR 70,000
AI-powered search blends keyword matching with semantic embeddings so users find relevant items even when their wording differs from your catalog titles. We design indexing pipelines, facet filters, and permission scopes so each user sees only allowed records. Zero-result states suggest refinements, popular alternatives, or contact paths instead of empty pages. Relevance tuning uses real query logs rather than generic defaults.
1. Query analysis & schema design
Real user queries clustered by intent. Index fields chosen to support both exact filters and semantic recall.
2. Index & embedding pipeline
Records normalized, embeddings generated on change events, and keyword analyzers configured per language.
3. Hybrid ranking tuning
Weight between BM25 and vector scores adjusted using golden queries. Boost rules applied for freshness or business priority where agreed.
4. Permissions & zero-result UX
Auth filters verified in test accounts. Empty states wired to suggestions and logging for continuous improvement.
| فیصلہ عنصر | یہ طریقہ | متبادل | نوٹس |
|---|---|---|---|
| Hybrid ranking | BM25 + vector fusion tuned on your logged queries | Install vector plugin with default cosine similarity only | Default vector search misses exact SKU matches buyers still type. |
| Permission-aware queries | Auth claims compiled into index filters on every search | Search all records then filter in application memory | Post-filter leaks titles via timing attacks and wastes compute. |
| Zero-result handling | Logged failures with suggestion rules and content gap exports | Blank state saying no results | Blank states increase bounce; logged failures drive catalog fixes. |
| Relevance iteration | Golden query set and periodic tuning sessions included in scope | Ship once with factory default boosts | Factory defaults ignore your catalog's synonym and freshness patterns. |
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