Gmail zu Vektoreinbettungen mit PGVector und Ollama

• •Google-Konto + Gmail API-Anmeldedaten
• •PostgreSQL-Datenbankverbindungsdaten

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  "name": "Gmail to Vector Embeddings with PGVector and Ollama",
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            "metadataValues": [
              {
                "name": "emails_metadata.id",
                "value": "={{ $('Extract email fields').item.json.email_id }}"
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              {
                "name": "emails_metadata.thread_id",
                "value": "={{ $('Extract email fields').item.json.thread_id }}"
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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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        "content": "## Edit this ⬇️\nAnd specify your Gmail account creation date"
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        "content": "## Activate the workflow\nAnd this trigger will check for new mail, every minute"
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              "type": "string",
              "value": "={{ $json.threadId }}"
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              "name": "email_subject",
              "type": "string",
              "value": "={{ $json.subject }}"
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              "name": "attachments",
              "type": "array",
              "value": "={{ Object.keys($binary).map(item => $binary[item].fileName).filter(item => !!item) }}"
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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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