Gmailからベクトル埋込みとPGVector、Ollamaへの変換

• •Googleアカウント + Gmail API認証情報
• •PostgreSQLデータベース接続情報

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  "name": "Gmail to Vector Embeddings with PGVector and Ollama",
  "tags": [],
  "nodes": [
    {
      "id": "162b1a8b-2471-4880-9fcb-7f2dcfe175a8",
      "name": "Embeddings Ollama",
      "type": "@n8n/n8n-nodes-langchain.embeddingsOllama",
      "position": [
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      "parameters": {
        "model": "nomic-embed-text:latest"
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      "parameters": {
        "options": {
          "metadata": {
            "metadataValues": [
              {
                "name": "emails_metadata.id",
                "value": "={{ $('Extract email fields').item.json.email_id }}"
              },
              {
                "name": "emails_metadata.thread_id",
                "value": "={{ $('Extract email fields').item.json.thread_id }}"
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        "options": {}
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      "position": [
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      "parameters": {
        "options": {
          "destinationFieldName": "after"
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        "fieldToSplitOut": "weeks"
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      "typeVersion": 1
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      "id": "2163d5ec-416f-4299-8a9d-10c26eaef32f",
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        "content": "## Bulk e-mail import\n\nPress the `Test workflow` button to run this once, and bulk import of all your e-mail\n\n### IMPORTANT\nSpecify your Gmail account creation date by editing the code node"
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      "typeVersion": 1
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      "position": [
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      "parameters": {
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        "height": 300,
        "content": "## Edit this ⬇️\nAnd specify your Gmail account creation date"
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      "parameters": {
        "color": 4,
        "width": 640,
        "height": 180,
        "content": "## Activate the workflow\nAnd this trigger will check for new mail, every minute"
      },
      "typeVersion": 1
    },
    {
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      "name": "前後の日付を設定",
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              "name": "before",
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      "name": "メールフィールドを抽出",
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        "options": {},
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              "type": "string",
              "value": "={{ $json.text }}"
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              "type": "string",
              "value": "={{ $json.from?.text ?? '' }}"
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              "name": "email_to",
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              "type": "string",
              "value": "={{ $json.id }}"
            },
            {
              "id": "146e8e72-3c2c-4320-b93a-b109d2e46139",
              "name": "thread_id",
              "type": "string",
              "value": "={{ $json.threadId }}"
            },
            {
              "id": "a49333a5-c565-4d46-8398-d423072b1e4d",
              "name": "email_subject",
              "type": "string",
              "value": "={{ $json.subject }}"
            },
            {
              "id": "806cf930-450e-4221-8061-a71ec8bf9bbe",
              "name": "attachments",
              "type": "array",
              "value": "={{ Object.keys($binary).map(item => $binary[item].fileName).filter(item => !!item) }}"
            },
            {
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              "name": "email_cc",
              "type": "string",
              "value": "={{ $json.cc?.text ?? ''}}"
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        "content": "## Magic here 🪄\n#### (not really, just statistics)\nE-mail is stored in a `emails_metadata` structured table, and also fed to the [`nomic-embed-text`](https://ollama.com/library/nomic-embed-text) model to be stored in a `emails_embeddings` table as [vector embeddings](https://www.pinecone.io/learn/vector-embeddings/) so similarity searches are possible.\n\nThe `email_id` field can be used to make the relation between the structured records and the vector embeddings, as it's stored in their metadata as `emails_metadata.id`.\nThis is also the case for `thread_id`."
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      "typeVersion": 1
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          "__rl": true,
          "mode": "name",
          "value": "emails_metadata"
        },
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          "__rl": true,
          "mode": "list",
          "value": "public"
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          "schema": [
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              "id": "email_id",
              "type": "string",
              "display": true,
              "removed": false,
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              "required": false,
              "displayName": "email_to",
              "defaultMatch": false,
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              "removed": false,
              "required": false,
              "displayName": "email_cc",
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              "canBeUsedToMatch": false
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              "displayName": "email_text",
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              "canBeUsedToMatch": false
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          "mappingMode": "autoMapInputData",
          "matchingColumns": [
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          ],
          "attemptToConvertTypes": false,
          "convertFieldsToString": false
        },
        "options": {
          "outputColumns": [
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          ]
        },
        "operation": "upsert"
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      "typeVersion": 2.6
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      "onError": "continueRegularOutput",
      "position": [
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        "options": {},
        "tableName": "emails_embeddings"
      },
      "credentials": {},
      "typeVersion": 1.1
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        "jsCode": "// Edit this\nlet whenDidICreateMyGmailAccount = DateTime.fromISO('2013-11-01')\n\n// (don't edit further down)\nwhenDidICreateMyGmailAccount = whenDidICreateMyGmailAccount.set({day: 1})\nlet now = $now.set({day: 1})\nconst weeks = []\nwhile (Math.floor(Interval.fromDateTimes(whenDidICreateMyGmailAccount, now).length('weeks')) > -1) {\n  weeks.push(now.toISODate())\n  now = now.minus({weeks: 1})\n}\n\nreturn {json: { weeks }};"
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      "webhookId": "bace3678-df5b-4a9c-a1ef-1c219e3fd07b",
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        "filters": {
          "receivedAfter": "={{ $json.after }}",
          "receivedBefore": "={{ $json.before }}"
        },
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          "downloadAttachments": true
        },
        "operation": "getAll",
        "returnAll": true
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  "active": false,
  "pinData": {
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