Construir un chatbot de base de conocimiento con incrustaciones de vectores OpenAI, RAG y MongoDB

• •Clave de API de OpenAI
• •Cadena de conexión de MongoDB

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      "parameters": {
        "options": {
          "metadata": {
            "metadataValues": [
              {
                "name": "doc_id",
                "value": "={{ $json.documentId }}"
              }
            ]
          }
        },
        "jsonData": "={{ $json.content }}",
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        "content": "Run this workflow manually to import and index Google Docs product documentation into MongoDB with vector embeddings for fast search."
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        "height": 520,
        "content": "Search Index Example \n\n{\n  \"mappings\": {\n    \"dynamic\": false,\n    \"fields\": {\n      \"_id\": {\n        \"type\": \"string\"\n      },\n      \"text\": {\n        \"type\": \"string\"\n      },\n      \"embedding\": {\n        \"type\": \"knnVector\",\n        \"dimensions\": 1536,\n        \"similarity\": \"cosine\"\n      },\n      \"source\": {\n        \"type\": \"string\"\n      },\n      \"doc_id\": {\n        \"type\": \"string\"\n      }\n    }\n  }\n}\n"
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