Context ingestion pipeline

• •Point de terminaison HTTP Webhook (généré automatiquement par n8n)
• •Clé API OpenAI

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      "webhookId": "webhook-uuid-placeholder",
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            {
              "id": "d1c59fe6-0834-45bd-8cc2-1c399773d7ee",
              "name": "title",
              "type": "string",
              "value": "={{ $json.body.data.title }}"
            },
            {
              "id": "bde4d7fb-c21b-4a5e-bfbf-96aaf0ad7b6b",
              "name": "transcript",
              "type": "string",
              "value": "={{ $json.body.data.transcript }}"
            },
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              "id": "a79b01b6-e602-43b4-a3c2-7efca1cedf3a",
              "name": "timestamp",
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      "parameters": {
        "text": "={{ $json.transcript }}",
        "options": {
          "systemMessage": "=You are a **Context Extraction Agent**.\nYour role is to ingest text from the user, which will have been captured using **speech-to-text** and may therefore contain transcription errors, missing words, or imprecise phrasing.\n\n**Your tasks are:**\n\n1. **Infer intended meaning:**\n\n   * If any words appear to be obvious mistranscriptions, you may replace them with the most likely intended words based on the context.\n\n2. **Reformulate into third person:**\n\n   * Change all first-person references (\"I\", \"me\", \"my\") into \"User\" or \"their\" where appropriate.\n   * Example: `\"I really enjoy spicy food\"` → `\"User enjoys spicy food\"`.\n\n3. **Extract context data only:**\n\n   * Identify and isolate **significant, specific facts** about the user that could be useful for grounding AI inference in a Retrieval-Augmented Generation (RAG) pipeline.\n   * Omit casual musings, filler thoughts, and irrelevant narrative.\n\n4. **Format the output in plain text:**\n\n   * Keep each fact as a separate line.\n   * Optionally group facts under **all-caps headers** with one blank line before and after.\n   * Avoid any other formatting, markup, or commentary.\n\n5. **Output rules:**\n\n   * No introductory or concluding remarks.\n   * The result is a single continuous plain text document containing only the extracted facts.\n   * Keep the facts **short, precise, and formulaic**.\n\n---\n\n**Example Input:**\n\n```\nI just moved to a new city last month, and I'm still figuring out the best pizza places.  \nI think my favorite so far is Margarita pizza, though I really miss the one I used to get back home.  \nOh, and my new apartment has a great view of the downtown area.  \n```\n\n**Example Output:**\n\n```\nLOCATION  \nUser moved to a new city recently.  \n\nFOOD PREFERENCES  \nUser likes pizza.  \nUser's favorite type of pizza is Margarita.  \n\nOTHER  \nUser's apartment has a view of the downtown area.  \n"
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      "name": "Analyseur de sortie structurée",
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      "parameters": {
        "jsonSchemaExample": " {\n  \"output\": \"User moved to a new city recently.\\nUser likes pizza.\\nUser's favorite type of pizza is Margarita.\\nUser's apartment has a view of the downtown area.\"\n}"
      },
      "typeVersion": 1.3
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    {
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      "name": "Modèle de chat OpenRouter",
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      "position": [
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      "parameters": {
        "options": {}
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        "openRouterApi": {
          "id": "credential-id-placeholder",
          "name": "OpenRouter account"
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      "name": "Modifier les champs1",
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      "position": [
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        "assignments": {
          "assignments": [
            {
              "id": "5676fee9-3080-4b08-be04-b6203d2b132b",
              "name": "tite.",
              "type": "string",
              "value": "={{ $('Edit Fields').item.json.title }}"
            },
            {
              "id": "a46332e5-ba8c-4094-87ed-e04ab8462367",
              "name": "output",
              "type": "string",
              "value": "=Context data created: {{ $('Webhook').item.json.body.timestamp }}\n\nCONTEXT:\n\n{{ $json.output }}"
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          ]
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        "sourceProperty": "output"
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      "parameters": {
        "mode": "insert",
        "options": {
          "clearCollection": false
        },
        "milvusCollection": {
          "__rl": true,
          "mode": "list",
          "value": "user-context-collection",
          "cachedResultName": "user-context-collection"
        }
      },
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        "milvusApi": {
          "id": "credential-id-placeholder",
          "name": "Milvus account"
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        "dataType": "binary"
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      "name": "Embeddings OpenAI",
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        "openAiApi": {
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          "name": "OpenAI API"
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      "name": "Note adhésive",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
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      "parameters": {
        "width": 208,
        "height": 144,
        "content": "## Context data\n\nVoicenotes.com tag trigger for 'conext data'"
      },
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        "height": 80,
        "content": "## Narrow fields"
      },
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      "name": "Note adhésive2",
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      "position": [
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      "parameters": {
        "width": 272,
        "height": 144,
        "content": "## Context data extraction agent\n\nParses transcript and isolates context rich text"
      },
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      "parameters": {
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        "content": "## Context data prepared for embedding\n\nTimestamp injected into agent output"
      },
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    },
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        "width": 352,
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        "content": "## Embedding\n\nContext dasta embedded into Milvus\nvector database (hosted)"
      },
      "typeVersion": 1
    }
  ],
  "active": true,
  "pinData": {
    "Webhook": [
      {
        "json": {
          "body": {
            "data": {
              "id": "sample-note-id",
              "title": "Sample Voice Note Title",
              "transcript": "This is a sample transcript from a voice note. The user discusses their preferences and provides context that will be extracted and stored in the vector database for future reference."
            },
            "event": "tag.attached.299437",
            "timestamp": "2025-08-15T11:28:21+00:00"
          },
          "query": {},
          "params": {},
          "headers": {
            "host": "your-n8n-instance.com",
            "cf-ray": "ray-id-placeholder",
            "cdn-loop": "cloudflare; loops=1",
            "cf-visitor": "{\"scheme\":\"https\"}",
            "connection": "keep-alive",
            "user-agent": "GuzzleHttp/7",
            "cf-ipcountry": "US",
            "content-type": "application/json",
            "authorization": "Bearer",
            "cf-warp-tag-id": "warp-tag-placeholder",
            "content-length": "1481",
            "accept-encoding": "gzip, br",
            "x-forwarded-for": "xxx.xxx.xxx.xxx",
            "cf-connecting-ip": "xxx.xxx.xxx.xxx",
            "x-forwarded-proto": "https"
          },
          "webhookUrl": "https://your-n8n-instance.com/webhook-test/webhook-uuid-placeholder",
          "executionMode": "test"
        }
      }
    ],
    "Edit Fields": [
      {
        "json": {
          "title": "Sample Voice Note Title",
          "timestamp": "2025-08-15T11:28:21+00:00",
          "transcript": "This is a sample transcript from a voice note. The user discusses their preferences and provides context that will be extracted and stored in the vector database for future reference."
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