PHPMem v2.0.1
Version
1.6.45
Uptime
16 days 23 hours 20 minutes 42 seconds
Memory
Total
512MB
Used
12,72MB (2.48%)
Free
499,28MB
Keys
Current
14 060
Total (since start)
40 994
Evictions
0
Reclaimed
760
Expired Unfetched
0
Evicted Unfetched
0
Connections
Current
14 / 1 024 max
Total
193 420
Rejected
0
llm:d5c5f732ff065d3a69093968c02353e088b5bb99830c697f5c6269d4ecef241d
Edit
{
"action": "tool_call",
"reasoning": "No gold coverage for this; compute exact distinct counts of categorical columns (Company Name, Location, Sector, Stage, Investor) for all five tables in one UNION ALL query.",
"tool": "query_sql",
"args": {
"sql": "SELECT 'January' AS month, COUNT(*) AS row_count, COUNT(DISTINCT \"Company Name\") AS company_name, COUNT(DISTINCT Location) AS location, COUNT(DISTINCT Sector) AS sector, COUNT(DISTINCT Stage) AS stage, COUNT(DISTINCT Investor) AS investor FROM raw.\"Indian Startups _ Funding Investors Data January 2022\" UNION ALL SELECT 'February', COUNT(*), COUNT(DISTINCT \"Company Name\"), COUNT(DISTINCT Location), COUNT(DISTINCT Sector), COUNT(DISTINCT Stage), COUNT(DISTINCT Investor) FROM raw.\"Indian Startups _ Funding Investors Data February 2022\" UNION ALL SELECT 'March', COUNT(*), COUNT(DISTINCT \"Company Name\"), COUNT(DISTINCT Location), COUNT(DISTINCT Sector), COUNT(DISTINCT Stage), COUNT(DISTINCT Investor) FROM raw.\"Indian Startups _ Funding Investors Data March 2022\" UNION ALL SELECT 'April', COUNT(*), COUNT(DISTINCT \"Company Name\"), COUNT(DISTINCT Location), COUNT(DISTINCT Sector), COUNT(DISTINCT Stage), COUNT(DISTINCT Investor) FROM raw.\"Indian Startups _ Funding Investors Data April 2022\" UNION ALL SELECT 'May', COUNT(*), COUNT(DISTINCT \"Company Name\"), COUNT(DISTINCT Location), COUNT(DISTINCT Sector), COUNT(DISTINCT Stage), COUNT(DISTINCT Investor) FROM raw.\"Indian Startups _ Funding Investors Data May 2022\"",
"purpose": "Exact row and distinct counts of key categorical columns per monthly table",
"source": "raw"
}
}