Construire un chatbot de base de connaissances avec Jotform, RAG sur Supabase, Together AI et Gemini

• •URL et Clé API Supabase
• •Peut nécessiter les informations d'identification d'authentification de l'API cible
• •Clé API Google Gemini

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        "content": "### Part 1: Feeding the AI Knowledge (The \"Librarian\" part)\n\nThis part of the workflow runs whenever someone uploads a new PDF contract using your Jotform form. Its only job is to read, understand, and store the information from that document.\n\n* A user uploads a PDF contract through a JotForm, which is then downloaded.\n* The system extracts the raw text and splits it into smaller, more manageable chunks.\n* Each text chunk is converted into a numerical representation, called an embedding, that captures its semantic meaning.\n* These embeddings and their original text are stored in a Supabase vector database, effectively creating a searchable knowledge library.\n"
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        "content": "---\n\n### Part 2: Asking the AI a Question (The \"Researcher\" part)\n\nThis part of the workflow runs whenever a user sends a message in a chat interface. Its job is to find the right information from the library and generate an answer.\n\n* A user asks a question, which the system converts into a numerical embedding to understand its meaning.\n* This embedding is used to search a vector database, retrieving the most relevant chunks of text from the stored documents.\n* The retrieved text chunks are then provided to an AI agent as the sole context for answering the question.\n* The AI generates a precise and accurate answer based only on the provided context, ensuring it doesn't invent information."
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