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AI Agents & Assistants

Tech Radar Chat Agent with Claude, Gemini and DeepSeek

On a schedule or chat request, it syncs a Tech Radar from Google Sheets and Docs into MySQL and lets an AI agent answer questions about it.

What it does

## Chatting Stage : CHAT ENDPOINT ### Purpose This endpoint api allows you to chat with the ai agent. The ai agent input router will determine if the nature of question best answered with RAG or SQL agent. Then it invokes the workflow rag or workflow db agent accordingly. When the answer comes back , there is an AI agent output guardrail that uses the answer and validates before responding. ### How to integrate 1. Connect your frontend interface to this api below. You may change the base endpoint to webhook or webhook-test depending on your environment. You can also change the based the endpoint '{{ $env.WEBHOOK_URL }}' to your own hosted domain like '{{ $env.WEBHOOK_URL }}' curl -X POST '{{ $env.WEBHOOK_URL }}' -H 'Content-Type: application/json' -d '{ "chatInput": "i wanna write agentic ai code, which strategic direction " }' curl -X POST '{{ $env.WEBHOOK_URL }}' -H 'Content-Type: application/json' -d '{ "chatInput": "is backstage used by company2 " }' 2. You will see a sample output response: [ { "output": "Based on the researched answer provided, the tools that are considered strategic directions but are not used by company3 are:\n\n* Automatically generate Backstage entity descriptors (technique)\n* Arm in the cloud (platform)\n* Azure OpenAI Service (platform)\n* Infrastructure orchestration platforms (platform)\n* Rancher Desktop (platform)\n* Conan (tool)\n* Karpenter (tool)\n* GitHub Copilot (tool)\n* Renovate (tool)\n* Velero (tool)\n* Continue (tool)\n* LLaVA (tool)\n* Ollama (tool)\n* DataComPy (language and framework)\n* Ray (language and framework)\n* Concrete ML (language and framework)\n* Crux (language and framework)\n* Electric (language and framework)\n* Mojo (language and framework)\n\nThese tools and technologies are considered strategically important for the organization, but for various reasons, they haven't been adopted by company3 yet." } ] [ { "output": "Based on the researched answer provided, the answer to your question is:\n\nAccording to our records, the \"Automatically generate Backstage entity descriptors\" feature, which is related to Backstage, is not currently being used by Company 2. However, it does not necessarily mean that Backstage as a whole platform is not used by Company 2 in some other capacity. If you need more detailed information about Backstage's overall usage in Company 2, please feel free to ask." } ]

The steps

  1. Download Doc File From Google Drive
  2. Doc File Data Loader
  3. Cron
  4. MySQL -delete all data
  5. MySQL - insert all from sheets
  6. Google Sheets - Tech Radar
  7. Code - Transform table into rows
  8. Google Docs - Update GDoc
  9. Google Drive - Doc File Updated
  10. Content - Recursive Character Text Splitter
  11. Google Sheets - Read TechRadar
  12. Code - Simplify Mapping to Original Query
  13. Codes - Simplify Mapping to Original Query
  14. Execute Workflow - Sql Agent

64 steps in total.

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