Desarrollado - Chat con PDF a través de Telegram

• •Bot Token de Telegram
• •Clave de API de Google Gemini
• •Clave de API de Pinecone

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  "name": "Development Done - Chat with PDF via Telegram",
  "tags": [
    {
      "id": "0BJmSFs6EfUsyTWo",
      "name": "AI Internal",
      "createdAt": "2025-10-13T09:18:40.187Z",
      "updatedAt": "2025-10-13T09:18:40.187Z"
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  "nodes": [
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        "text": "=**INSTRUCTIONS:**\nYou are an expert RAG system. Answer the user's question ONLY using the provided context.\n\n1.  **Format Mode:** Your response MUST be formatted for Telegram's **HTML Parse Mode**.\n2.  **Formatting:**\n    * Use the bold tag (<b>) for all titles and key benefits.\n    * **CRITICAL LINE BREAK RULE:** For all line breaks, lists, and spacing, use the **newline character (\\n)** instead of any HTML tags like <br/> or <br>.\n    * Ensure the list is clearly structured with a single newline after each key item.\n\n**CONTEXT:** (The retrieved information follows here)\nSearch the database with the retriever for information for the answer\n\n**USER QUESTION:**\n{{ $json.message.text }}",
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          "__rl": true,
          "mode": "list",
          "value": "telegram",
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      "credentials": {
        "pineconeApi": {
          "id": "Your_Pinecone_Credential_ID",
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      "name": "Check If is a document",
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        "conditions": {
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            "caseSensitive": true,
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              "leftValue": "={{ $json.message.document }}",
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      "typeVersion": 2
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    {
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      "name": "Change to application/pdf",
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        "jsCode": "// Função para modificar os metadados do arquivo binário\nfunction modifyBinaryMetadata(items) {\n for (const item of items) {\n if (item.binary && item.binary.data) {\n // Modifica o tipo MIME\n item.binary.data.mimeType = 'application/pdf';\n \n // Garante que o nome do arquivo termine com .pdf\n if (!item.binary.data.fileName.toLowerCase().endsWith('.pdf')) {\n item.binary.data.fileName += '.pdf';\n }\n \n // Atualiza o contentType no fileType (se existir)\n if (item.binary.data.fileType) {\n item.binary.data.fileType.contentType = 'application/pdf';\n }\n }\n }\n return items;\n}\n\n// Aplica a modificação e retorna os itens atualizados\nreturn modifyBinaryMetadata($input.all());"
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    {
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      "name": "Telegram get File",
      "type": "n8n-nodes-base.telegram",
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        "fileId": "={{ $json.message.document.file_id }}",
        "resource": "file",
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      "credentials": {
        "telegramApi": {
          "id": "Your_Telegram_Credential_ID",
          "name": "Telegram account"
        }
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    {
      "id": "f9af0cdd-7ea3-4e97-b53f-b65fdf4e9355",
      "name": "Telegram Response",
      "type": "n8n-nodes-base.telegram",
      "onError": "continueErrorOutput",
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      ],
      "webhookId": "a5b324f8-649f-4a3a-b26f-57cd0eb341ec",
      "parameters": {
        "text": "={{ $json.response.text }}",
        "chatId": "={{ $('Telegram Message Trigger').item.json.message.chat.id }}",
        "additionalFields": {
          "parse_mode": "HTML",
          "appendAttribution": false
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      "webhookId": "52437171-bdad-408c-bced-5ffec75b851d",
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        "text": "={{ $json.metadata.pdf.totalPages }} pages saved on Pinecone",
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          "appendAttribution": false
        }
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          "mode": "list",
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          "cachedResultName": "telegram"
        }
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      "credentials": {
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          "id": "Your_Pinecone_Credential_ID",
          "name": "PineconeApi account"
        }
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    {
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      "name": "Modelo de chat Google Gemini",
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          "id": "Your_Gemini_Credential_ID",
          "name": "Your Google Gemini Credential Name"
        }
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    },
    {
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      "name": "Telegram Message Trigger",
      "type": "n8n-nodes-base.telegramTrigger",
      "position": [
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      ],
      "webhookId": "b178f034-9997-4832-9bb4-a43c3015506e",
      "parameters": {
        "updates": [
          "message"
        ],
        "additionalFields": {}
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      "credentials": {
        "telegramApi": {
          "id": "Your_Telegram_Credential_ID",
          "name": "Telegram account"
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      "name": "Nota adhesiva",
      "type": "n8n-nodes-base.stickyNote",
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      "parameters": {
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        "width": 368,
        "height": 272,
        "content": "## 1. **Trigger & Router**\n**Task:** This node is the starting point. It listens for *any* message on the configured Telegram bot.\n\n**Output:**\n- **Text:** User's question (goes to the RAG chain).\n- **Document:** Binary file data (goes to the indexing process).\n\n**Next Stop:** Routes the message to the **\"Check If is a document\"** node."
      },
      "typeVersion": 1
    },
    {
      "id": "246059a8-fbc0-4f33-8cfd-7b33c87c7c02",
      "name": "Nota adhesiva1",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        176,
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      ],
      "parameters": {
        "color": 6,
        "width": 384,
        "height": 256,
        "content": "## 2. **Router Node**\n**Task:** Determines the message type to route the workflow.\n\n**Condition:** Checks if `{{ $json.message.document }}` **exists**.\n- **TRUE (Top Branch):** A file was uploaded. Proceed to **Indexing** (download the file).\n- **FALSE (Bottom Branch):** It is a plain text question. Proceed to **Querying** (run the RAG Chain)."
      },
      "typeVersion": 1
    },
    {
      "id": "6e03fbfc-58e6-4cd5-9ce9-82b0942a85e2",
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      "position": [
        1824,
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      ],
      "parameters": {
        "width": 336,
        "height": 272,
        "content": "## 5. **Text Splitter**\n**Task:** Breaks the large PDF text into small, manageable chunks for indexing.\n\n**Configuration:**\n- **Chunk Size:** 3000\n- **Chunk Overlap:** 200 (Ensures context overlaps between chunks for better retrieval).\n\n**Purpose:** This configuration is key for **Retrieval-Augmented Generation (RAG)** accuracy."
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    },
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      "position": [
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      "parameters": {
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        "width": 368,
        "height": 336,
        "content": "## 4. **Embeddings (Indexing)**\n**Task:** Converts the split text chunks into high-dimensional numerical **vectors**.\n\n**Model:** `models/gemini-embedding-001`\n\n**Purpose:** These vectors are what the Pinecone database stores and uses for fast, semantic similarity searches when a user asks a question."
      },
      "typeVersion": 1
    },
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        "content": "## 6. **Vector Store: Indexing**\n**Task:** **Inserts** the Gemini embeddings and their associated text content into the configured Pinecone index (`telegram`).\n\n**Mode:** **`insert`**\n\n**Result:** The PDF is now fully indexed and searchable. The workflow then sends a confirmation to the user via **\"Telegram Response about Database\"**."
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        "content": "## 7. **Core RAG Engine**\n**Task:** Generates the final, context-based answer.\n\n**Connections:**\n- **LLM:** Google Gemini Chat Model (for generation).\n- **Retriever:** Vector Store Retriever (for context lookup).\n\n**CRITICAL PROMPT:** The prompt strictly enforces:\n1.  Answer **ONLY** using provided context.\n2.  Response must be in **Telegram HTML Parse Mode** (`<b>` and `\\n`)."
      },
      "typeVersion": 1
    },
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        "content": "## 8. **Final Telegram Response**\n**Task:** Sends the RAG result back to the user.\n\n**Text Input:** `={{ $json.response.text }}`\n\n**CRITICAL SETTING:**\n- **Parse Mode:** **HTML**\nThis is required to properly display the bold tags (`<b>`) and newline characters (`\\n`) formatted by the Question and Answer Chain."
      },
      "typeVersion": 1
    }
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  "pinData": {},
  "settings": {
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