// Prompt: Common Flux Queries
export async function fluxQueryExamplesPrompt() {
console.log(`=== FLUX-QUERY-EXAMPLES PROMPT CALLED ===`);
// Simple, direct approach - no dependencies
const promptResponse = {
messages: [{
role: "user",
content: {
type: "text",
text: `Here are some example Flux queries for InfluxDB:
1. Get data from the last 5 minutes:
\`\`\`flux
from(bucket: "my-bucket")
|> range(start: -5m)
|> filter(fn: (r) => r._measurement == "cpu_usage")
\`\`\`
2. Calculate the average value over time windows:
\`\`\`flux
from(bucket: "my-bucket")
|> range(start: -1h)
|> filter(fn: (r) => r._measurement == "temperature")
|> aggregateWindow(every: 5m, fn: mean)
\`\`\`
3. Find the maximum value:
\`\`\`flux
from(bucket: "my-bucket")
|> range(start: -24h)
|> filter(fn: (r) => r._measurement == "temperature" and r.sensor_id == "TLM0201")
|> max()
\`\`\`
4. Group by a tag and calculate statistics:
\`\`\`flux
from(bucket: "my-bucket")
|> range(start: -1d)
|> filter(fn: (r) => r._measurement == "network_traffic")
|> group(columns: ["host"])
|> mean()
\`\`\`
5. Join two data sources:
\`\`\`flux
cpu = from(bucket: "my-bucket")
|> range(start: -15m)
|> filter(fn: (r) => r._measurement == "cpu")
mem = from(bucket: "my-bucket")
|> range(start: -15m)
|> filter(fn: (r) => r._measurement == "mem")
join(tables: {cpu: cpu, mem: mem}, on: ["_time", "host"])
\`\`\`
Please adjust these queries to match your specific bucket names, measurements, and requirements.`,
},
}],
};
console.log(`=== FLUX-QUERY-EXAMPLES PROMPT COMPLETED SUCCESSFULLY ===`);
return promptResponse;
}