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a8259ca935
### Changes 🏗️ Adds `autogpt_platform/analytics/` — 14 SQL view definitions that expose production data safely through a locked-down `analytics` schema. **Security model:** - Views use `security_invoker = false` (PostgreSQL 15+), so they execute as their owner (`postgres`), not the caller - `analytics_readonly` role only has access to `analytics.*` — cannot touch `platform` or `auth` tables directly **Files:** - `backend/generate_views.py` — does everything; auto-reads credentials from `backend/.env` - `analytics/queries/*.sql` — 14 documented view definitions (auth, user activity, executions, onboarding funnel, cohort retention) --- ### Running locally (dev) ```bash cd autogpt_platform/backend # First time only — creates analytics schema, role, grants poetry run analytics-setup # Create / refresh views (auto-reads backend/.env) poetry run analytics-views ``` ### Running in production (Supabase) ```bash cd autogpt_platform/backend # Step 1 — first time only (run in Supabase SQL Editor as postgres superuser) poetry run analytics-setup --dry-run # Paste the output into Supabase SQL Editor and run # Step 2 — apply views (use direct connection host, not pooler) poetry run analytics-views --db-url "postgresql://postgres:PASSWORD@db.<ref>.supabase.co:5432/postgres" # Step 3 — set password for analytics_readonly so external tools can connect # Run in Supabase SQL Editor: # ALTER ROLE analytics_readonly WITH PASSWORD 'your-password'; ``` --- ### Checklist 📋 #### For code changes: - [x] I have clearly listed my changes in the PR description - [x] I have made a test plan - [x] I have tested my changes according to the test plan: - [x] Setup + views applied cleanly on local Postgres 15 - [x] `analytics_readonly` can `SELECT` from all 14 `analytics.*` views - [x] `analytics_readonly` gets `permission denied` on `platform.*` and `auth.*` directly --------- Co-authored-by: Otto (AGPT) <otto@agpt.co>
98 lines
5.0 KiB
SQL
98 lines
5.0 KiB
SQL
-- =============================================================
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-- View: analytics.retention_agent
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-- Looker source alias: ds35 | Charts: 2
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-- =============================================================
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-- DESCRIPTION
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-- Weekly cohort retention broken down per individual agent.
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-- Cohort = week of a user's first use of THAT specific agent.
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-- Tells you which agents keep users coming back vs. one-shot
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-- use. Only includes cohorts from the last 180 days.
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--
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-- SOURCE TABLES
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-- platform.AgentGraphExecution — Execution records (user × agent × time)
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-- platform.AgentGraph — Agent names
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--
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-- OUTPUT COLUMNS
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-- agent_id TEXT Agent graph UUID
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-- agent_label TEXT 'AgentName [first8chars]'
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-- agent_label_n TEXT 'AgentName [first8chars] (n=total_users)'
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-- cohort_week_start DATE Week users first ran this agent
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-- cohort_label TEXT ISO week label
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-- cohort_label_n TEXT ISO week label with cohort size
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-- user_lifetime_week INT Weeks since first use of this agent
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-- cohort_users BIGINT Users in this cohort for this agent
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-- active_users BIGINT Users who ran the agent again in week k
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-- retention_rate FLOAT active_users / cohort_users
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-- cohort_users_w0 BIGINT cohort_users only at week 0 (safe to SUM)
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-- agent_total_users BIGINT Total users across all cohorts for this agent
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--
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-- EXAMPLE QUERIES
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-- -- Best-retained agents at week 2
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-- SELECT agent_label, AVG(retention_rate) AS w2_retention
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-- FROM analytics.retention_agent
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-- WHERE user_lifetime_week = 2 AND cohort_users >= 10
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-- GROUP BY 1 ORDER BY w2_retention DESC LIMIT 10;
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--
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-- -- Agents with most unique users
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-- SELECT DISTINCT agent_label, agent_total_users
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-- FROM analytics.retention_agent
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-- ORDER BY agent_total_users DESC LIMIT 20;
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-- =============================================================
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WITH params AS (SELECT 12::int AS max_weeks, (CURRENT_DATE - INTERVAL '180 days') AS cohort_start),
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events AS (
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SELECT e."userId"::text AS user_id, e."agentGraphId" AS agent_id,
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e."createdAt"::timestamptz AS created_at,
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DATE_TRUNC('week', e."createdAt")::date AS week_start
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FROM platform."AgentGraphExecution" e
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),
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first_use AS (
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SELECT user_id, agent_id, MIN(created_at) AS first_use_at,
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DATE_TRUNC('week', MIN(created_at))::date AS cohort_week_start
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FROM events GROUP BY 1,2
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HAVING MIN(created_at) >= (SELECT cohort_start FROM params)
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),
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activity_weeks AS (SELECT DISTINCT user_id, agent_id, week_start FROM events),
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user_week_age AS (
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SELECT aw.user_id, aw.agent_id, fu.cohort_week_start,
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((aw.week_start - DATE_TRUNC('week',fu.first_use_at)::date)/7)::int AS user_lifetime_week
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FROM activity_weeks aw JOIN first_use fu USING (user_id, agent_id)
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WHERE aw.week_start >= DATE_TRUNC('week',fu.first_use_at)::date
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),
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active_counts AS (
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SELECT agent_id, cohort_week_start, user_lifetime_week, COUNT(DISTINCT user_id) AS active_users
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FROM user_week_age WHERE user_lifetime_week >= 0 GROUP BY 1,2,3
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),
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cohort_sizes AS (
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SELECT agent_id, cohort_week_start, COUNT(DISTINCT user_id) AS cohort_users FROM first_use GROUP BY 1,2
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),
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cohort_caps AS (
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SELECT cs.agent_id, cs.cohort_week_start, cs.cohort_users,
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LEAST((SELECT max_weeks FROM params),
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GREATEST(0,((DATE_TRUNC('week',CURRENT_DATE)::date-cs.cohort_week_start)/7)::int)) AS cap_weeks
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FROM cohort_sizes cs
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),
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grid AS (
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SELECT cc.agent_id, cc.cohort_week_start, gs AS user_lifetime_week, cc.cohort_users
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FROM cohort_caps cc CROSS JOIN LATERAL generate_series(0, cc.cap_weeks) gs
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),
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agent_names AS (SELECT DISTINCT ON (g."id") g."id" AS agent_id, g."name" AS agent_name FROM platform."AgentGraph" g ORDER BY g."id", g."version" DESC),
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agent_total_users AS (SELECT agent_id, SUM(cohort_users) AS agent_total_users FROM cohort_sizes GROUP BY 1)
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SELECT
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g.agent_id,
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COALESCE(an.agent_name,'(unnamed)')||' ['||LEFT(g.agent_id::text,8)||']' AS agent_label,
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COALESCE(an.agent_name,'(unnamed)')||' ['||LEFT(g.agent_id::text,8)||'] (n='||COALESCE(atu.agent_total_users,0)||')' AS agent_label_n,
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g.cohort_week_start,
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TO_CHAR(g.cohort_week_start,'IYYY-"W"IW') AS cohort_label,
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TO_CHAR(g.cohort_week_start,'IYYY-"W"IW')||' (n='||g.cohort_users||')' AS cohort_label_n,
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g.user_lifetime_week, g.cohort_users,
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COALESCE(ac.active_users,0) AS active_users,
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COALESCE(ac.active_users,0)::float / NULLIF(g.cohort_users,0) AS retention_rate,
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CASE WHEN g.user_lifetime_week=0 THEN g.cohort_users ELSE 0 END AS cohort_users_w0,
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COALESCE(atu.agent_total_users,0) AS agent_total_users
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FROM grid g
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LEFT JOIN active_counts ac ON ac.agent_id=g.agent_id AND ac.cohort_week_start=g.cohort_week_start AND ac.user_lifetime_week=g.user_lifetime_week
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LEFT JOIN agent_names an ON an.agent_id=g.agent_id
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LEFT JOIN agent_total_users atu ON atu.agent_id=g.agent_id
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ORDER BY agent_label, g.cohort_week_start, g.user_lifetime_week;
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