VEXA, OpenAI 및 Mem0를 사용한 회의 인텔리전스 분석 자동화

• •Redis 서버 연결 정보
• •HTTP Webhook 엔드포인트(n8n이 자동으로 생성)
• •대상 API의 인증 정보가 필요할 수 있음
• •OpenAI API Key

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        "content": "### How it works\n\nThis workflow has two distinct parts that work together to manage a meeting's entire lifecycle.\n\n* **Part 1: Bot Management.** A command is received from your CRM (Baserow) to either deploy or remove an AI bot from a Google Meet. This ensures the bot is present only when needed. The workflow instantly updates the status in your Baserow table to `In Progress` or `Stopped`.\n* **Part 2: AI-Powered Analysis.** Upon meeting completion, a separate webhook from VEXA triggers the second part of the workflow. It automatically retrieves the full transcript and uses a powerful AI model (OpenAI) to analyze the conversation. The AI extracts key details like a **summary**, **next steps**, and **potential risks**. This structured output is then logged into a memory layer (Mem0) for a persistent, queryable record of all conversations.\n\n\n### Who's it for\n\nBuilt for non-technical solopreneurs and small business owners who want to effortlessly add conversation intelligence to their workflow. Get a single source of truth for all your meeting data without manual transcription or analysis.\n\n\n### Setup Steps\n\nRapid deployment. Your action plan:\n\n1.  **Register Webhook:** First, run the manual trigger in this workflow to send your n8n webhook URL to VEXA. This is a crucial one-time setup step that tells VEXA where to send meeting transcripts after a call.\n2.  **Connect Your CRM:** Copy the `vexa-start` webhook URL from n8n and paste it into your Baserow automation. For example, trigger it when you set the \"Send Bot\" field to `Start_Bot` in your table.\n3.  **Integrate Platforms:** Set up your VEXA, Mem0, and OpenAI API credentials in n8n.\n4.  **Use the Baserow Template:** We provide a free Baserow template that serves as your bot control panel. You can access it here: [https://baserow.io/public/grid/t5kYjovKEHjNix2-6Rijk99y4SDeyQY4rmQISciC14w](https://baserow.io/public/grid/t5kYjovKEHjNix2-6Rijk99y4SDeyQY4rmQISciC14w). This template contains the necessary fields like `Meeting Link`, `Bot Name`, and the `Send Bot` command field that triggers this workflow.\n\n\n### Requirements\n\n* Active n8n instance (self-hosted/cloud).\n* **VEXA.ai** account.\n* **Mem0.ai** account.\n* **Baserow** account for bot management.\n* **OpenAI** account.\n* **Redis Account:** A Redis database is required to temporarily store user IDs.\n* Your Baserow table must include specific fields:\n    * **`Meeting Link`**: The URL of your Google Meet.\n    * **`Bot Name`**: The name you want your AI bot to have.\n    * **`Send Bot`**: A single-select field with `Start_Bot` and `Stop_Bot` as options.\n    * **`Status`**: A single-select field for the workflow to update.\n\n### Next Steps\n\nThis workflow provides a powerful foundation. Here are a few ways to extend its value:\n\n* **Automate Follow-ups:** Trigger an automated email or task in your CRM based on AI-identified next steps or red flags.\n* **Integrate Email & Engagements:** Connect your email or other communication platforms to a workflow that uses Mem0 to parse and store all engagements, building a holistic client memory.\n* **Build a Headless CRM:** Use these combined workflows to create a fully AI-powered, headless CRM that handles everything from initial contact to proactive client management without any manual data entry."
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      "position": [
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        "content": "## Register Your Webhook\n"
      },
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    },
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      "position": [
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        "content": "## BOT Management "
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        "width": 280,
        "height": 80,
        "content": "## The Notetaking Magic"
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HELPER FUNCTIONS\n// ===================================================================\n\n// Helper: Vertaalt een datumstring naar een Unix timestamp (seconden sinds de epoch).\nfunction getUnixTimestamp(dateString) {\n    if (!dateString) return null;\n    try {\n        const dateObj = new Date(dateString);\n        if (isNaN(dateObj.getTime())) return null;\n        return Math.floor(dateObj.getTime() / 1000);\n    } catch (e) {\n        return null;\n    }\n}\n\n// Helper: Zoekt naar bekende agentnamen in de tekst.\nfunction determineAgentContactPerson(text) {\n    if (!text) return '';\n    const searchableText = String(text).toLowerCase();\n    for (const agent of AGENT_CONTACT_NAMES) {\n        if (agent.keywords.some(keyword => searchableText.includes(keyword))) {\n            return agent.name;\n        }\n    }\n    return '';\n}\n\n// Helper: Escaped een string voor JSON-compatibiliteit.\nfunction escapeJsonString(str) {\n    if (typeof str !== 'string') return str;\n    return JSON.stringify(str).slice(1, -1);\n}\n\n// Helper: Haalt een waarde op uit een object met een fallback voor als de key niet bestaat.\nfunction getWithFallback(obj, key, fallback = null) {\n    if (obj && obj.hasOwnProperty(key)) {\n        return obj[key] !== null ? obj[key] : fallback;\n    }\n    return fallback;\n}\n\n// ===================================================================\n// III. MAIN EXECUTION\n// ===================================================================\nconst output_items = [];\n\n// Dit script verwerkt een enkel LLM output-item dat als input wordt gegeven.\ntry {\n    const llm_output_item = items[0]; // De LLM output is het eerste item\n    const llm_output = llm_output_item.json.output || {};\n\n    const meeting_summary = getWithFallback(llm_output, 'summary', \"No summary provided.\");\n    const meeting_sentiment = getWithFallback(llm_output, 'overallSentiment', \"Neutral\");\n    const meeting_red_flags = getWithFallback(llm_output, 'potential red flags', \"None\");\n    const meeting_next_steps = getWithFallback(llm_output, 'nextSteps', \"None\");\n    const social_chatter = getWithFallback(llm_output, 'socialChatter', \"None\");\n    const client_id = getWithFallback(llm_output, 'user_id', \"unknown_client@example.com\");\n\n    const agent_contact_person_name = determineAgentContactPerson(meeting_summary);\n\n    // --- Samenstellen van de 'content' voor Mem0 ---\n    const mem0_content_raw = `Klant had een vergadering met ${agent_contact_person_name || 'een agent'}.\n\nSamenvatting: ${meeting_summary}\nVolgende Stappen: ${meeting_next_steps}`;\n\n    const mem0_content = escapeJsonString(mem0_content_raw);\n\n    // --- Samenstellen van de 'metadata' ---\n    const mem0_metadata = {\n        \"type\": \"meeting\",\n        \"summary\": escapeJsonString(meeting_summary),\n        \"sentiment\": escapeJsonString(meeting_sentiment),\n        \"red_flags\": escapeJsonString(meeting_red_flags),\n        \"next_steps\": escapeJsonString(meeting_next_steps),\n        \"social_chatter\": escapeJsonString(social_chatter),\n        \"agent_contact_person_name\": escapeJsonString(agent_contact_person_name),\n        \"native_meeting_id\": getWithFallback(llm_output, 'native_meeting_id', 'no_id')\n    };\n\n    const mem0_categories = [\"meeting\", \"summary\"];\n    if (meeting_red_flags.toLowerCase() !== \"none\" && meeting_red_flags.length > 2) {\n        mem0_categories.push(\"red_flag\");\n    }\n    if (meeting_next_steps.toLowerCase() !== \"none\") {\n        mem0_categories.push(\"action_items\");\n    }\n\n    // --- Definitieve Mem0 Payload ---\n    const mem0_payload = {\n        \"messages\": [\n            {\n                \"role\": \"user\",\n                \"content\": mem0_content\n            }\n        ],\n        \"user_id\": client_id,\n        \"agent_id\": AGENT_ID,\n        \"app_id\": APP_ID,\n        \"run_id\": `meeting_${client_id}_${Date.now()}`,\n        \"metadata\": mem0_metadata,\n        \"infer\": true,\n        \"categories\": mem0_categories,\n        \"version\": \"v2\",\n        \"output_format\": \"v1.1\",\n        \"timestamp\": getUnixTimestamp(new Date().toISOString())\n    };\n    \n    output_items.push({\"json\": {\"mem0Payload\": mem0_payload}});\n\n} catch (error) {\n    console.error(`Fout bij verwerken van LLM output:`, error.message, error);\n    output_items.push({\"json\": {\"error\": true, \"message\": String(error), \"originalItem\": items[0].json}});\n}\n\nreturn output_items;"
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