Construire un chatbot de base de connaissances avec les embeddings vectoriels OpenAI, RAG et MongoDB

• •Clé API OpenAI
• •Chaîne de connexion MongoDB

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        "options": {
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                "name": "doc_id",
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        "jsonData": "={{ $json.content }}",
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      "name": "Google Importateur de Documents",
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        "documentURL": "https://docs.google.com/document/d/1gvgp71e9edob8WLqFIYCdzC7kUq3pLO37VKb-a-vVW4/edit?tab=t.0"
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        "color": 5,
        "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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      "name": "À la réception d'un message de chat",
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      "parameters": {
        "color": 4,
        "content": "This workflow uses retrieval-augmented generation (RAG) to answer user questions by searching the MongoDB vector store and generating AI responses with context."
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      "parameters": {
        "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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