Rodrigo Rodriguez (Pragmatismo)
037db5c381
- Add comprehensive documentation in botbook/ with 12 chapters - Add botapp/ Tauri desktop application - Add botdevice/ IoT device support - Add botlib/ shared library crate - Add botmodels/ Python ML models service - Add botplugin/ browser extension - Add botserver/ reorganized server code - Add bottemplates/ bot templates - Add bottest/ integration tests - Add botui/ web UI server - Add CI/CD workflows in .forgejo/workflows/ - Add AGENTS.md and PROD.md documentation - Add dependency management scripts (DEPENDENCIES.sh/ps1) - Remove legacy src/ structure and migrations - Clean up temporary and backup files
45 lines
1.2 KiB
QBasic
45 lines
1.2 KiB
QBasic
PARAM customer_id AS STRING
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PARAM time_period AS INTEGER DEFAULT 30
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# Gather customer communications
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emails = CALL "/storage/json", ".gbdata/communication_logs",
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"to = '${customer_id}' OR from = '${customer_id}' AND timestamp > NOW() - DAYS(${time_period})"
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support_tickets = CALL "/crm/tickets/list", {
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"customer_id": customer_id,
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"created_after": NOW() - DAYS(time_period)
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}
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meeting_notes = CALL "/crm/meetings/list", {
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"customer_id": customer_id,
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"date_after": NOW() - DAYS(time_period)
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}
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# Combine all text for analysis
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all_text = ""
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FOR EACH email IN emails
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all_text = all_text + email.subject + " " + email.body + " "
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NEXT
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FOR EACH ticket IN support_tickets
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all_text = all_text + ticket.description + " " + ticket.resolution + " "
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NEXT
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FOR EACH meeting IN meeting_notes
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all_text = all_text + meeting.notes + " "
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NEXT
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# Analyze sentiment
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sentiment = CALL "/ai/analyze/text", all_text, "sentiment"
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# Generate insights
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insights = CALL "/ai/analyze/text", all_text, "key_topics"
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RETURN {
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"customer_id": customer_id,
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"time_period": time_period + " days",
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"sentiment_score": sentiment.score,
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"sentiment_label": sentiment.label,
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"key_topics": insights.topics,
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"recommendations": insights.recommendations
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}
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