Postgres full-text search over counselling reports, ranked by relevance. Filter by school, grade, or report kind.
Sneha Verma explore the recommended track for one quarter; revisit at term-end. Action: introduce Sneha Verma to alumni
Sneha Verma explore the recommended track for one quarter; revisit at term-end. Action: introduce Sneha Verma to alumni
Sneha Verma based on assessment battery + 3 counselling sessions. Top pathway: medicine (consistent across RIASEC, DAT V, and self
Sneha Verma (Grade 9). Discussion focused on civil services as a pathway and the family's view on balancing
Sneha Verma completed the mock CET aptitude battery. Performance: Maths 61/100, Logical Reasoning 79/100, English 91/100. Time management
Sneha Verma completed the mock CET aptitude battery. Performance: Maths 55/100, Logical Reasoning 81/100, English 89/100. Time management
Sneha Verma completed the mock CET aptitude battery. Performance: Maths 86/100, Logical Reasoning 95/100, English 76/100. Time management
Sneha Verma completed the mock CET aptitude battery. Performance: Maths 62/100, Logical Reasoning 59/100, English 87/100. Time management
Sneha Verma based on assessment battery + 3 counselling sessions. Top pathway: engineering (consistent across RIASEC, DAT V, and self
Every reports.text and counsellor_note is indexed by Postgres tsvector (english config). One GIN index, refreshed on insert.
Search box runs plainto_tsquery + ts_rank_cd. Filter chips (school, grade, RIASEC, kind) compose into the same SQL — no client-side filtering.
ts_headline wraps matches in <mark> tags so counsellors see the evidence in context.
Top 30 sorted by rank × recency. The aim is < 200 ms server-side on the full corpus, no Elasticsearch needed.