Développement terminé - Chattez avec des PDF via Telegram

• •Token Bot Telegram
• •Clé API Google Gemini
• •Clé API Pinecone

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  "name": "Development Done - Chat with PDF via Telegram",
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        "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."
      },
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    },
    {
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      "name": "Note autocollante1",
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      ],
      "parameters": {
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        "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)."
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    },
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      "type": "n8n-nodes-base.stickyNote",
      "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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      "position": [
        1408,
        544
      ],
      "parameters": {
        "color": 3,
        "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."
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    },
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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`)."
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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."
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