Postgres full-text search over counselling reports, ranked by relevance. Filter by school, grade, or report kind.
Harsh Bhatt explore the recommended track for one quarter; revisit at term-end. Action: introduce Harsh Bhatt to alumni
Harsh Bhatt. Top RIASEC dimensions: Realistic (88), Investigative (59), Artistic (84). The pattern suggests strong fit for analytical
Harsh Bhatt. Top RIASEC dimensions: Realistic (25), Investigative (62), Artistic (72). The pattern suggests strong fit for analytical
Harsh Bhatt. Top RIASEC dimensions: Realistic (88), Investigative (59), Artistic (84). The pattern suggests strong fit for analytical
Harsh Bhatt. Top RIASEC dimensions: Realistic (25), Investigative (62), Artistic (72). The pattern suggests strong fit for analytical
Harsh Bhatt. Top RIASEC dimensions: Realistic (25), Investigative (62), Artistic (72). The pattern suggests strong fit for analytical
Harsh Bhatt (Grade 12). Discussion focused on engineering as a pathway and the family's view on balancing
Harsh Bhatt (Grade 10). Discussion focused on architecture as a pathway and the family's view on balancing
Harsh Bhatt (Grade 10). Discussion focused on finance as a pathway and the family's view on balancing
Harsh Bhatt completed the mock CET aptitude battery. Performance: Maths 89/100, Logical Reasoning 95/100, English 95/100. Time management
Harsh Bhatt completed the mock CET aptitude battery. Performance: Maths 72/100, Logical Reasoning 63/100, English 56/100. Time management
Harsh Bhatt completed the mock CET aptitude battery. Performance: Maths 76/100, Logical Reasoning 84/100, English 87/100. Time management
Harsh Bhatt completed the mock CET aptitude battery. Performance: Maths 81/100, Logical Reasoning 79/100, English 91/100. Time management
Harsh Bhatt completed the mock CET aptitude battery. Performance: Maths 84/100, Logical Reasoning 85/100, English 57/100. Time management
Harsh Bhatt based on assessment battery + 3 counselling sessions. Top pathway: finance (consistent across RIASEC, DAT V, and self
Harsh Bhatt was engaged today. We explored their interest in civil services, discussing pathway options at the intersection
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.