Automatisation de documents avec n8n : traitement et génération

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        "content": "## Step 1. Watch Folder and Import New Documents\n[Read more about Local File Trigger](https://docs.n8n.io/integrations/builtin/core-nodes/n8n-nodes-base.localfiletrigger)\n\nWith n8n's local file trigger, we're able to trigger the workflow when files are created in our target folder. We still have to import them however as the trigger will only give the file's path. The \"Extract From\" node is used to get at the file's contents."
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        "content": "## Step 2. Summarise and Vectorise Document Contents\n[Learn more about using the Qdrant VectorStore](https://docs.n8n.io/integrations/builtin/cluster-nodes/root-nodes/n8n-nodes-langchain.vectorstoreqdrant)\n\nCapturing the document into our vector store is intended for a technique we'll use later known as Retrieval Augumented Generation or \"RAG\" for short. For our scenario, this allows our LLM to retrieve context more efficiently which produces better respsonses."
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        "content": "## Step 3. Loop through Templates\n\nWe'll ask the LLM to help us generate 3 types of notes from the imported source document. These notes are intended to breakdown the content for faster study. Our templates for this demo are:\n(1) **Study guide**\n(2) **Briefing document**\n(3) **Timeline**"
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        "color": 7,
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        "height": 806.6560661824452,
        "content": "## Step 4. Use AI Agents to Query and Generate Template Documents\n[Read more about using the Question & Answer Retrieval Chain](https://docs.n8n.io/integrations/builtin/cluster-nodes/root-nodes/n8n-nodes-langchain.chainretrievalqa)\n\nn8n allows us to easily use a chain of LLMs as agents which can work together to handle any task!\nHere the agents generate questions to explore the content of the source document and use the answers to generate the template. "
      },
      "typeVersion": 1
    },
    {
      "id": "77fda269-6877-422f-b6e6-4346bde862db",
      "name": "Sticky Note4",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        2560,
        67.64523011966037
      ],
      "parameters": {
        "color": 7,
        "width": 771.8710855215123,
        "height": 384.22073222791266,
        "content": "## Step 5. Export Generated Templates To Folder\n[Learn more about writing to the local filesystem](https://docs.n8n.io/integrations/builtin/core-nodes/n8n-nodes-base.filesreadwrite)\n\nFinally, the AI generated documents can now be exported to disk. This workflow makes it easy to generate any kind of document from various source material and can be used for training and sales."
      },
      "typeVersion": 1
    },
    {
      "id": "08839972-f0f4-4144-bf27-810664cbf828",
      "name": "Qdrant Vector Store",
      "type": "@n8n/n8n-nodes-langchain.vectorStoreQdrant",
      "position": [
        1200,
        560
      ],
      "parameters": {
        "mode": "insert",
        "options": {},
        "qdrantCollection": {
          "__rl": true,
          "mode": "list",
          "value": "storynotes",
          "cachedResultName": "storynotes"
        }
      },
      "credentials": {
        "qdrantApi": {
          "id": "NyinAS3Pgfik66w5",
          "name": "QdrantApi account"
        }
      },
      "typeVersion": 1
    },
    {
      "id": "7e216411-83ee-4b82-9e00-285d4f2d3224",
      "name": "Sticky Note5",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        -360,
        80
      ],
      "parameters": {
        "width": 390.63004227317265,
        "height": 401.0080676370763,
        "content": "## Try It Out! \n\n### This workflow automates generating notes from a source document.\n* It watches a target folder to pick up new files.\n* When a new file is detected, it saves the contents of the file in a vectorstore.\n* multiple AI agents guided by a templates list, generate the predetermined notes.\n* These notes are then export alongside the original source file for the user.\n\n\n### Need Help?\nJoin the [Discord](https://discord.com/invite/XPKeKXeB7d) or ask in the [Forum](https://community.n8n.io/)!\n\nHappy Hacking!"
      },
      "typeVersion": 1
    },
    {
      "id": "f2c363d3-a2bf-4468-ad54-f26649ce6ab8",
      "name": "Interview",
      "type": "@n8n/n8n-nodes-langchain.chainLlm",
      "position": [
        2660,
        680
      ],
      "parameters": {
        "text": "=## document summary\n {{ $('Prep For AI').item.json.summary }}",
        "messages": {
          "messageValues": [
            {
              "message": "=Given the following document summary, what questions would you ask to create a {{ $('For Each Doc Type...').item.json.title }} for the document? Generate 5 questions."
            }
          ]
        },
        "promptType": "define",
        "hasOutputParser": true
      },
      "typeVersion": 1.4
    },
    {
      "id": "ce3da55d-8c22-40bb-8781-63c2e6bcb824",
      "name": "Sticky Note6",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        1960,
        380
      ],
      "parameters": {
        "width": 172.26820279743384,
        "height": 295.46359440513226,
        "content": "\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n### 💡Add your own templates here!\n"
      },
      "typeVersion": 1
    }
  ],
  "pinData": {},
  "connections": {
    "2secs": {
      "main": [
        [
          {
            "node": "For Each Doc Type...",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Merge": {
      "main": [
        [
          {
            "node": "Prep For AI",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Discover": {
      "main": [
        [
          {
            "node": "Aggregate",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Generate": {
      "main": [
        [
          {
            "node": "2secs",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Settings": {
      "main": [
        [
          {
            "node": "Import File",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Aggregate": {
      "main": [
        [
          {
            "node": "Generate",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Interview": {
      "main": [
        [
          {
            "node": "Split Out",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Split Out": {
      "main": [
        [
          {
            "node": "Discover",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "To Binary": {
      "main": [
        [
          {
            "node": "Export to Folder",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Import File": {
      "main": [
        [
          {
            "node": "Get FileType",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Prep For AI": {
      "main": [
        [
          {
            "node": "Get Doc Types",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Get FileType": {
      "main": [
        [
          {
            "node": "Extract from PDF",
            "type": "main",
            "index": 0
          }
        ],
        [
          {
            "node": "Extract from DOCX",
            "type": "main",
            "index": 0
          }
        ],
        [
          {
            "node": "Extract from TEXT",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Get Doc Types": {
      "main": [
        [
          {
            "node": "Split Out Doc Types",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Extract from PDF": {
      "main": [
        [
          {
            "node": "Prep Incoming Doc",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Extract from DOCX": {
      "main": [
        [
          {
            "node": "Prep Incoming Doc",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Extract from TEXT": {
      "main": [
        [
          {
            "node": "Prep Incoming Doc",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Prep Incoming Doc": {
      "main": [
        [
          {
            "node": "Qdrant Vector Store",
            "type": "main",
            "index": 0
          },
          {
            "node": "Summarization Chain",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Local File Trigger": {
      "main": [
        [
          {
            "node": "Settings",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Default Data Loader": {
      "ai_document": [
        [
          {
            "node": "Qdrant Vector Store",
            "type": "ai_document",
            "index": 0
          }
        ]
      ]
    },
    "Qdrant Vector Store": {
      "main": [
        [
          {
            "node": "Merge",
            "type": "main",
            "index": 1
          }
        ]
      ]
    },
    "Split Out Doc Types": {
      "main": [
        [
          {
            "node": "For Each Doc Type...",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Summarization Chain": {
      "main": [
        [
          {
            "node": "Merge",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "For Each Doc Type...": {
      "main": [
        [
          {
            "node": "Get Generated Documents",
            "type": "main",
            "index": 0
          }
        ],
        [
          {
            "node": "Interview",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Qdrant Vector Store1": {
      "ai_vectorStore": [
        [
          {
            "node": "Vector Store Retriever",
            "type": "ai_vectorStore",
            "index": 0
          }
        ]
      ]
    },
    "Vector Store Retriever": {
      "ai_retriever": [
        [
          {
            "node": "Discover",
            "type": "ai_retriever",
            "index": 0
          }
        ]
      ]
    },
    "Get Generated Documents": {
      "main": [
        [
          {
            "node": "To Binary",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Item List Output Parser": {
      "ai_outputParser": [
        [
          {
            "node": "Interview",
            "type": "ai_outputParser",
            "index": 0
          }
        ]
      ]
    },
    "Embeddings Mistral Cloud": {
      "ai_embedding": [
        [
          {
            "node": "Qdrant Vector Store",
            "type": "ai_embedding",
            "index": 0
          }
        ]
      ]
    },
    "Mistral Cloud Chat Model": {
      "ai_languageModel": [
        [
          {
            "node": "Interview",
            "type": "ai_languageModel",
            "index": 0
          }
        ]
      ]
    },
    "Embeddings Mistral Cloud1": {
      "ai_embedding": [
        [
          {
            "node": "Qdrant Vector Store1",
            "type": "ai_embedding",
            "index": 0
          }
        ]
      ]
    },
    "Mistral Cloud Chat Model1": {
      "ai_languageModel": [
        [
          {
            "node": "Summarization Chain",
            "type": "ai_languageModel",
            "index": 0
          }
        ]
      ]
    },
    "Mistral Cloud Chat Model2": {
      "ai_languageModel": [
        [
          {
            "node": "Discover",
            "type": "ai_languageModel",
            "index": 0
          }
        ]
      ]
    },
    "Mistral Cloud Chat Model3": {
      "ai_languageModel": [
        [
          {
            "node": "Generate",
            "type": "ai_languageModel",
            "index": 0
          }
        ]
      ]
    },
    "Recursive Character Text Splitter": {
      "ai_textSplitter": [
        [
          {
            "node": "Default Data Loader",
            "type": "ai_textSplitter",
            "index": 0
          }
        ]
      ]
    }
  }
}