Workflow: Extractfromfile HTTP Automation

Workflow Details

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        {
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            ],
            "parameters": {
                "name": "knowledge_base",
                "topK": 8,
                "description": "Retrieve data about user request"
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                "rules": {
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                        {
                            "outputKey": "txt",
                            "conditions": {
                                "options": {
                                    "version": 1,
                                    "leftValue": "",
                                    "caseSensitive": true,
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                                "combinator": "and",
                                "conditions": [
                                    {
                                        "operator": {
                                            "type": "boolean",
                                            "operation": "true",
                                            "singleValue": true
                                        },
                                        "leftValue": "={{$binary.data?.fileExtension == undefined }}",
                                        "rightValue": "txt"
                                    }
                                ]
                            },
                            "renameOutput": true
                        },
                        {
                            "outputKey": "pdf",
                            "conditions": {
                                "options": {
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                                    "leftValue": "",
                                    "caseSensitive": true,
                                    "typeValidation": "strict"
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                                        "leftValue": "={{$binary.data.fileExtension }}",
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                "mode": "insert",
                "options": {
                    "queryName": "match_documents"
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                "tableName": {
                    "__rl": true,
                    "mode": "list",
                    "value": "documents",
                    "cachedResultName": "documents"
                }
            },
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                "supabaseApi": {
                    "id": "t8AQJzvZvrOMDLec",
                    "name": "Supabase account My Airtable Gen"
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            "type": "n8n-nodes-base.merge",
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                1380,
                -100
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            "parameters": [],
            "typeVersion": 3
        },
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            "id": "3afd552e-4995-493e-9cd5-ef496dfe359f",
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            "type": "@n8n\/n8n-nodes-langchain.agent",
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            "name": "Supabase Vector Store",
            "type": "@n8n\/n8n-nodes-langchain.vectorStoreSupabase",
            "position": [
                1020,
                -660
            ],
            "parameters": {
                "options": {
                    "metadata": {
                        "metadataValues": [
                            {
                                "name": "file_id",
                                "value": "300b0128-0955-4058-b0d3-a9aefe728432"
                            }
                        ]
                    }
                },
                "tableName": {
                    "__rl": true,
                    "mode": "list",
                    "value": "documents",
                    "cachedResultName": "documents"
                }
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            "credentials": {
                "supabaseApi": {
                    "id": "t8AQJzvZvrOMDLec",
                    "name": "Supabase account My Airtable Gen"
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            },
            "typeVersion": 1
        },
        {
            "id": "66df007c-0418-4551-950e-32e7d79840bd",
            "name": "Sticky Note",
            "type": "n8n-nodes-base.stickyNote",
            "position": [
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                -220
            ],
            "parameters": {
                "height": 89.3775420487803984315178240649402141571044921875,
                "content": "### Replace Storage name, database ID and credentials."
            },
            "typeVersion": 1
        },
        {
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            "name": "Sticky Note1",
            "type": "n8n-nodes-base.stickyNote",
            "position": [
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            ],
            "parameters": {
                "height": 80,
                "content": "### Replace credentials."
            },
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            "id": "8688c219-5af4-4e54-9fd1-91851829445b",
            "name": "Sticky Note2",
            "type": "n8n-nodes-base.stickyNote",
            "position": [
                1540,
                -220
            ],
            "parameters": {
                "height": 80,
                "content": "### Replace credentials."
            },
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            ],
            "parameters": {
                "height": 89.3775420487803984315178240649402141571044921875,
                "content": "### Replace Storage name, database ID and credentials."
            },
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        },
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            "id": "2ca07cb0-b5f4-4761-b954-faf2131872d9",
            "name": "Sticky Note4",
            "type": "n8n-nodes-base.stickyNote",
            "position": [
                1500,
                220
            ],
            "parameters": {
                "height": 80,
                "content": "### Replace credentials."
            },
            "typeVersion": 1
        },
        {
            "id": "8d682dae-6f88-42f0-a717-affffd37d882",
            "name": "Sticky Note5",
            "type": "n8n-nodes-base.stickyNote",
            "position": [
                1140,
                -520
            ],
            "parameters": {
                "height": 80,
                "content": "### Replace credentials."
            },
            "typeVersion": 1
        },
        {
            "id": "796b5dca-d60e-43a9-afe8-194244643557",
            "name": "Sticky Note9",
            "type": "n8n-nodes-base.stickyNote",
            "position": [
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            "parameters": {
                "color": 7,
                "width": 330.5152611046424908636254258453845977783203125,
                "height": 239.5888196628349078309838660061359405517578125,
                "content": "### ... or watch set up video [10 min]\n[![Youtube Thumbnail](https:\/\/res.cloudinary.com\/de9jgixzm\/image\/upload\/v1739773273\/Youtube%20Thumbs\/Chat%20With%20FIles.png)](https:\/\/www.youtube.com\/watch?v=glWUkdZe_3w)\n"
            },
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        {
            "id": "eba121de-a3f7-4ba5-8396-f7d64e648322",
            "name": "Sticky Note7",
            "type": "n8n-nodes-base.stickyNote",
            "position": [
                -820,
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            ],
            "parameters": {
                "color": 7,
                "width": 636.2128494576580806096899323165416717529296875,
                "height": 497.15326899309212649313849397003650665283203125,
                "content": "![5min Logo](https:\/\/res.cloudinary.com\/de9jgixzm\/image\/upload\/v1739773200\/Skool%20Assets\/ejm3hqnvhgwpnu2fv92s.png)\n## AI Agent To Chat With Files In Supabase Storage\n**Made by [Mark Shcherbakov](https:\/\/www.linkedin.com\/in\/marklowcoding\/) from community [5minAI](https:\/\/www.skool.com\/5minai-2861)**\n\nManually retrieving and analyzing specific information from large document repositories is time-consuming and inefficient. This workflow automates the process by vectorizing documents and enabling AI-powered interactions, making it easy to query and retrieve context-based information from uploaded files.\n\nThe workflow integrates Supabase with an AI-powered chatbot to process, store, and query text and PDF files. The steps include:\n- Fetching and comparing files to avoid duplicate processing.\n- Handling file downloads and extracting content based on the file type.\n- Converting documents into vectorized data for contextual information retrieval.\n- Storing and querying vectorized data from a Supabase vector store.\n\n"
            },
            "typeVersion": 1
        },
        {
            "id": "df054036-d6b9-4f53-86cb-85ad96f07d0e",
            "name": "Sticky Note6",
            "type": "n8n-nodes-base.stickyNote",
            "position": [
                -820,
                -940
            ],
            "parameters": {
                "color": 7,
                "width": 280.24621203176178596550016663968563079833984375,
                "height": 545.908788507776307596941478550434112548828125,
                "content": "### Set up steps\n\n1. **Fetch File List from Supabase**:\n - Use Supabase to retrieve the stored file list from a specified bucket.\n - Add logic to manage empty folder placeholders returned by Supabase, avoiding incorrect processing.\n\n2. **Compare and Filter Files**:\n - Aggregate the files retrieved from storage and compare them to the existing list in the Supabase `files` table.\n - Exclude duplicates and skip placeholder files to ensure only unprocessed files are handled.\n\n3. **Handle File Downloads**:\n - Download new files using detailed storage configurations for public\/private access.\n - Adjust the storage settings and GET requests to match your Supabase setup.\n\n4. **File Type Processing**:\n - Use a Switch node to target specific file types (e.g., PDFs or text files).\n - Employ relevant tools to process the content:\n - For PDFs, extract embedded content.\n - For text files, directly process the text data.\n\n5. **Content Chunking**:\n - Break large text data into smaller chunks using the Text Splitter node.\n - Define chunk size (default: 500 tokens) and overlap to retain necessary context across chunks.\n\n6. **Vector Embedding Creation**:\n - Generate vectorized embeddings for the processed content using OpenAI's embedding tools.\n - Ensure metadata, such as file ID, is included for easy data retrieval.\n\n7. **Store Vectorized Data**:\n - Save the vectorized information into a dedicated Supabase vector store.\n - Use the default schema and table provided by Supabase for seamless setup.\n\n8. **AI Chatbot Integration**:\n - Add a chatbot node to handle user input and retrieve relevant document chunks.\n - Use metadata like file ID for targeted queries, especially when multiple documents are involved."
            },
            "typeVersion": 1
        },
        {
            "id": "450a1e49-4be9-451a-9d05-2860e29c3695",
            "name": "Sticky Note8",
            "type": "n8n-nodes-base.stickyNote",
            "position": [
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            "parameters": {
                "color": 5,
                "width": 951.742164539440409498638473451137542724609375,
                "height": 809.7437181509876609197817742824554443359375,
                "content": "## Scenario 2 - AI agent"
            },
            "typeVersion": 1
        },
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            "id": "c3814c5d-8881-4598-897e-268019bee1bc",
            "name": "Sticky Note10",
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                "height": 739.2522526116407561858068220317363739013671875,
                "content": "## Scenario 1 - Flow for adding new files from Supabase storage"
            },
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    ],
    "pinData": [],
    "connections": {
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                        "node": "Loop Over Items",
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        "Merge": {
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        "Switch": {
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                ]
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        "Download": {
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        "Aggregate": {
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                        "node": "Get All files",
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                        "index": 0
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                ]
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        },
        "Get All Files": {
            "main": [
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        },
        "Loop Over Items": {
            "main": [
                null,
                [
                    {
                        "node": "If",
                        "type": "main",
                        "index": 0
                    }
                ]
            ]
        },
        "Embeddings OpenAI": {
            "ai_embedding": [
                [
                    {
                        "node": "Insert into Supabase Vectorstore",
                        "type": "ai_embedding",
                        "index": 0
                    }
                ]
            ]
        },
        "Embeddings OpenAI2": {
            "ai_embedding": [
                [
                    {
                        "node": "Supabase Vector Store",
                        "type": "ai_embedding",
                        "index": 0
                    }
                ]
            ]
        },
        "OpenAI Chat Model1": {
            "ai_languageModel": [
                [
                    {
                        "node": "AI Agent",
                        "type": "ai_languageModel",
                        "index": 0
                    }
                ]
            ]
        },
        "OpenAI Chat Model2": {
            "ai_languageModel": [
                [
                    {
                        "node": "Vector Store Tool1",
                        "type": "ai_languageModel",
                        "index": 0
                    }
                ]
            ]
        },
        "Vector Store Tool1": {
            "ai_tool": [
                [
                    {
                        "node": "AI Agent",
                        "type": "ai_tool",
                        "index": 0
                    }
                ]
            ]
        },
        "Create File record2": {
            "main": [
                [
                    {
                        "node": "Insert into Supabase Vectorstore",
                        "type": "main",
                        "index": 0
                    }
                ]
            ]
        },
        "Default Data Loader": {
            "ai_document": [
                [
                    {
                        "node": "Insert into Supabase Vectorstore",
                        "type": "ai_document",
                        "index": 0
                    }
                ]
            ]
        },
        "Extract Document PDF": {
            "main": [
                [
                    {
                        "node": "Merge",
                        "type": "main",
                        "index": 1
                    }
                ]
            ]
        },
        "Supabase Vector Store": {
            "ai_vectorStore": [
                [
                    {
                        "node": "Vector Store Tool1",
                        "type": "ai_vectorStore",
                        "index": 0
                    }
                ]
            ]
        },
        "When chat message received": {
            "main": [
                [
                    {
                        "node": "AI Agent",
                        "type": "main",
                        "index": 0
                    }
                ]
            ]
        },
        "Insert into Supabase Vectorstore": {
            "main": [
                [
                    {
                        "node": "Loop Over Items",
                        "type": "main",
                        "index": 0
                    }
                ]
            ]
        },
        "Recursive Character Text Splitter": {
            "ai_textSplitter": [
                [
                    {
                        "node": "Default Data Loader",
                        "type": "ai_textSplitter",
                        "index": 0
                    }
                ]
            ]
        },
        "When clicking \u2018Test workflow\u2019": {
            "main": [
                [
                    {
                        "node": "Get All Files",
                        "type": "main",
                        "index": 0
                    }
                ]
            ]
        }
    }
}
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