DIGITAL BIZ TECH This workflow corresponds to n8n.io template #10096 — we link there as the canonical source.
This workflow follows the Agent → Chat Trigger recipe pattern — see all workflows that pair these two integrations.
The workflow JSON
Copy or download the full n8n JSON below. Paste it into a new n8n workflow, add your credentials, activate. Full import guide →
{
"meta": {
"templateCredsSetupCompleted": true
},
"nodes": [
{
"id": "7935a827-516b-4e64-b370-f17d23c70857",
"name": "Embeddings OpenAI1",
"type": "@n8n/n8n-nodes-langchain.embeddingsOpenAi",
"position": [
1488,
432
],
"parameters": {
"options": {}
},
"credentials": {
"openAiApi": {
"name": "<your credential>"
}
},
"typeVersion": 1.2
},
{
"id": "32429618-1bed-4134-953f-fa5f0eed6981",
"name": "OpenAI Chat Model1",
"type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
"position": [
528,
432
],
"parameters": {
"model": {
"__rl": true,
"mode": "list",
"value": "gpt-4.1-mini",
"cachedResultName": "gpt-4.1-mini"
},
"options": {
"temperature": 0.5
}
},
"credentials": {
"openAiApi": {
"name": "<your credential>"
}
},
"typeVersion": 1.2
},
{
"id": "c770feb0-c01a-4e94-832a-3dafc88fb28f",
"name": "Web Search ",
"type": "@n8n/n8n-nodes-langchain.toolHttpRequest",
"position": [
1200,
480
],
"parameters": {
"url": "https://api.tavily.com/search",
"method": "POST",
"jsonBody": "={\n \"query\": \"{query}\",\n \"topic\": \"general\",\n \"search_depth\": \"advanced\",\n \"max_results\": 20,\n \"include_answer\": true,\n \"include_raw_content\": false,\n \"include_images\": false,\n \"include_image_descriptions\": false,\n \"include_domains\": [],\n \"exclude_domains\": [\"\"]\n}",
"sendBody": true,
"sendHeaders": true,
"specifyBody": "json",
"toolDescription": "Web Search tool ",
"optimizeResponse": true,
"parametersHeaders": {
"values": [
{
"name": "Authorization",
"value": "add tavily token",
"valueProvider": "fieldValue"
},
{
"name": "Content-Type",
"value": "application/json",
"valueProvider": "fieldValue"
}
]
},
"placeholderDefinitions": {
"values": [
{
"name": "query",
"type": "string",
"description": "Search Querry"
}
]
}
},
"typeVersion": 1.1
},
{
"id": "5b31d3c8-3fe5-4cfd-bb09-81193e6d973f",
"name": "Simple Memory1",
"type": "@n8n/n8n-nodes-langchain.memoryBufferWindow",
"position": [
688,
432
],
"parameters": {},
"typeVersion": 1.3
},
{
"id": "42ff3014-5b83-474f-a35c-5384828b49fd",
"name": "Qdrant Vector Store",
"type": "@n8n/n8n-nodes-langchain.vectorStoreQdrant",
"position": [
1488,
304
],
"parameters": {
"mode": "retrieve-as-tool",
"topK": 3,
"options": {},
"toolName": "add_name",
"toolDescription": "Use RAG to look up information in the knowledgebase.",
"qdrantCollection": {
"__rl": true,
"mode": "list",
"value": "docaiauto",
"cachedResultName": "docaiauto"
}
},
"credentials": {
"qdrantApi": {
"name": "<your credential>"
}
},
"typeVersion": 1.1
},
{
"id": "c405636b-4957-4de1-9cd4-8733647af3a9",
"name": "When chat message received",
"type": "@n8n/n8n-nodes-langchain.chatTrigger",
"position": [
496,
112
],
"parameters": {
"public": true,
"options": {}
},
"typeVersion": 1.1
},
{
"id": "8f2950f4-0c96-4d70-9aa0-2729dd0b560e",
"name": "Default Data Loader",
"type": "@n8n/n8n-nodes-langchain.documentDefaultDataLoader",
"position": [
3344,
1328
],
"parameters": {
"options": {
"metadata": {
"metadataValues": [
{
"name": "source",
"value": "={{ $('set all metadata').item.json[\"Document name\"] }}"
},
{
"name": "blobType",
"value": "application/jsonb"
},
{
"name": "loc",
"value": "={{ $('Mistral DOC OCR').item.json.usage_info }}"
},
{
"name": "source_metadata_id",
"value": "={{ $('Google Drive1').item.json.file_url }}"
},
{
"name": "department",
"value": "ai_automation"
},
{
"name": "PROJECT",
"value": "={{ $('set all metadata').item.json.PROJECT }}"
},
{
"name": "=DOCUMNENT_TYPE",
"value": "={{ $('set all metadata').item.json.DOCUMENT_TYPE }}"
},
{
"name": "ASSIGNED_TO",
"value": "={{ $('set all metadata').item.json.ASSIGNEDTO }}"
}
]
}
},
"jsonData": "={{ $json.content }}",
"jsonMode": "expressionData"
},
"typeVersion": 1
},
{
"id": "b008bae1-bc63-47da-8afa-b8a1b17ea412",
"name": "Character Text Splitter",
"type": "@n8n/n8n-nodes-langchain.textSplitterCharacterTextSplitter",
"position": [
3408,
1440
],
"parameters": {},
"typeVersion": 1
},
{
"id": "fb42d3ab-5c20-4efc-920a-87dc96890cab",
"name": "Google Drive",
"type": "n8n-nodes-base.googleDrive",
"position": [
368,
1136
],
"parameters": {
"filter": {
"folderId": {
"__rl": true,
"mode": "url",
"value": "="
}
},
"options": {
"fields": [
"id",
"name",
"webViewLink",
"mimeType",
"*"
]
},
"resource": "fileFolder",
"searchMethod": "query"
},
"credentials": {
"googleDriveOAuth2Api": {
"name": "<your credential>"
}
},
"typeVersion": 3
},
{
"id": "393465a4-4b14-42ef-b2ca-608a161c6914",
"name": "Google Drive1",
"type": "n8n-nodes-base.googleDrive",
"position": [
1088,
1152
],
"parameters": {
"fileId": {
"__rl": true,
"mode": "url",
"value": "={{ $('add metadata').item.json.file_url }}"
},
"options": {},
"operation": "download"
},
"credentials": {
"googleDriveOAuth2Api": {
"name": "<your credential>"
}
},
"typeVersion": 3
},
{
"id": "3e38082e-4619-47cf-98e0-f7f66a8541c1",
"name": "Mistral Upload",
"type": "n8n-nodes-base.httpRequest",
"position": [
1328,
1152
],
"parameters": {
"url": "https://api.mistral.ai/v1/files",
"method": "POST",
"options": {},
"sendBody": true,
"contentType": "multipart-form-data",
"authentication": "predefinedCredentialType",
"bodyParameters": {
"parameters": [
{
"name": "purpose",
"value": "ocr"
},
{
"name": "file",
"parameterType": "formBinaryData",
"inputDataFieldName": "data"
}
]
},
"nodeCredentialType": "mistralCloudApi"
},
"credentials": {
"mistralCloudApi": {
"name": "<your credential>"
}
},
"typeVersion": 4.2
},
{
"id": "255683ca-712a-4386-ae19-b3fbb5f37e30",
"name": "Mistral Signed URL",
"type": "n8n-nodes-base.httpRequest",
"position": [
1552,
1152
],
"parameters": {
"url": "=https://api.mistral.ai/v1/files/{{ $json.id }}/url",
"options": {},
"sendQuery": true,
"sendHeaders": true,
"authentication": "predefinedCredentialType",
"queryParameters": {
"parameters": [
{
"name": "expiry",
"value": "24"
}
]
},
"headerParameters": {
"parameters": [
{
"name": "Accept",
"value": "application/json"
}
]
},
"nodeCredentialType": "mistralCloudApi"
},
"credentials": {
"mistralCloudApi": {
"name": "<your credential>"
}
},
"typeVersion": 4.2
},
{
"id": "2e846e38-ae17-4717-a39b-b3d0e0c567f9",
"name": "Mistral DOC OCR",
"type": "n8n-nodes-base.httpRequest",
"position": [
1776,
1152
],
"parameters": {
"url": "https://api.mistral.ai/v1/ocr",
"method": "POST",
"options": {},
"jsonBody": "={\n \"model\": \"mistral-ocr-latest\",\n \"document\": {\n \"type\": \"document_url\",\n \"document_url\": \"{{ $json.url }}\"\n },\n \"include_image_base64\": true\n}",
"sendBody": true,
"specifyBody": "json",
"authentication": "predefinedCredentialType",
"nodeCredentialType": "mistralCloudApi"
},
"credentials": {
"mistralCloudApi": {
"name": "<your credential>"
}
},
"typeVersion": 4.2
},
{
"id": "a1c139ec-5ec2-4142-aacc-5df387b677fd",
"name": "When clicking \u2018Test workflow\u2019",
"type": "n8n-nodes-base.manualTrigger",
"position": [
96,
1136
],
"parameters": {},
"typeVersion": 1
},
{
"id": "6ceb4cbc-b4b0-4ac6-befe-a92649279bf4",
"name": "Embeddings OpenAI",
"type": "@n8n/n8n-nodes-langchain.embeddingsOpenAi",
"position": [
3168,
1456
],
"parameters": {
"model": "text-embedding-3-small",
"options": {}
},
"credentials": {
"openAiApi": {
"name": "<your credential>"
}
},
"typeVersion": 1
},
{
"id": "ee2ce006-d94c-49a7-849f-8155c95ee118",
"name": "Qdrant Vector Store1",
"type": "@n8n/n8n-nodes-langchain.vectorStoreQdrant",
"position": [
3184,
1152
],
"parameters": {
"mode": "insert",
"options": {},
"qdrantCollection": {
"__rl": true,
"mode": "list",
"value": "docaiauto",
"cachedResultName": "docaiauto"
},
"embeddingBatchSize": "=200"
},
"credentials": {
"qdrantApi": {
"name": "<your credential>"
}
},
"typeVersion": 1.1
},
{
"id": "b4a1a6a6-6f2e-4ac6-8fba-f5752ac66259",
"name": "Mistral Cloud Chat Model",
"type": "@n8n/n8n-nodes-langchain.lmChatMistralCloud",
"position": [
2208,
1456
],
"parameters": {
"model": "mistral-small-latest",
"options": {}
},
"credentials": {
"mistralCloudApi": {
"name": "<your credential>"
}
},
"typeVersion": 1
},
{
"id": "85a90fcf-b75c-4ecc-90e2-611c8db61073",
"name": "ai chat agent",
"type": "@n8n/n8n-nodes-langchain.agent",
"position": [
992,
112
],
"parameters": {
"options": {
"systemMessage": "=You are a helpful, intelligent AI Knowledge Bot and proactive chat agent. \nLead conversations by asking open-ended questions and offering suggestions. \nAnticipate needs, follow up thoughtfully, and make the user feel supported throughout.\n\n---\n\n## **Knowledge Access Workflow**\n\n1. **Primary Source: qdrant vector store (`add_table_name` table)** \n - For **every** user query, first search **only** in qdrant. \n - If relevant information is found:\n - Respond in a **clear, structured, and informative** manner. \n - **Always include multiple images** if available \u2014 aim for **maximum number of relevant images**, not just one. \n - Place each image **inline**, immediately after the sentence/section it illustrates. \n - Never group all images at the end.\n - Always **cite the internal document name** and **provide a clickable URL** to the source document.\n - Use `list doc` tool with `source_metadata_id` to retrieve the document URL.\n \n **Example Citation:** \n > This information is based on the internal AI documentation related to automation projects (Automation Timesheet Blogs). \n > [View Full Document](document_url_here)\n\n2. **If No Match Found:** \n - Reply: \n `\"The information you requested is not available in our internal documentation. Would you like me to do a Web search?\"`\n\n3. **Web Search Rules:** \n - Only search if user explicitly agrees (e.g., \"Yes\", \"Okay\"). \n - use 'web search 1'tool\n - Clearly label as **External Source** and provide clickable URLs. \n Example: \n > This information comes from an external source ([website_name](source_url)).\n\n4. **If No Data in Qdrant or Web:** \n - Reply: \n `\"At the moment, there is no information available to fully address this request based on current internal and external sources.\"`\n\n---\n\n## **Formatting & Style**\n- **Language:** All qdrant queries in English; all responses in English. \n- **Tone:** Professional, clear, structured, and helpful. \n- **Image Usage:** \n - Always include **all available relevant images** MARKDOWN \u2014 even if it means showing several per section. \n - Place images **exactly after the point they illustrate**. \n - Use different images for different subtopics, comparisons, or examples. \n - Do **not** skip images if they exist in the source.\n- **For Guide/Instruction Responses:** \n 1. **Step-by-Step Guide** (detailed instructions) \n 2. **Things to Consider** (common pitfalls, important details) \n 3. **Further Understanding** \n - Suggest 2 related topics the user might explore next, relevant to their query.\n- Never skip key details. Always check if more images can be added to improve clarity.\n\n---\n\n## **Core Principles**\n- Be proactive \u2014 never wait for the user to guide the entire conversation. \n- Ask open-ended follow-ups. \n- Anticipate related needs based on the query. \n- Maintain accuracy, cite sources, and **use as many relevant images as possible**. \n- ALWAYS SHOW IMAGE AND Keep images inline and contextually placed.\n",
"returnIntermediateSteps": false
}
},
"typeVersion": 1.7
},
{
"id": "3e71d64a-3a04-4f5e-b009-8eddbef94e8c",
"name": "Sticky Note",
"type": "n8n-nodes-base.stickyNote",
"position": [
1008,
416
],
"parameters": {
"color": 5,
"width": 400,
"height": 208,
"content": "## WEB SEARCH using tavily (http node)\n [Tavily setup Guide](https://docs.tavily.com/welcome)"
},
"typeVersion": 1
},
{
"id": "3c335f41-3d05-4436-ad0f-ce6957967a0c",
"name": "Sticky Note1",
"type": "n8n-nodes-base.stickyNote",
"position": [
320,
928
],
"parameters": {
"color": 5,
"width": 224,
"height": 400,
"content": "## GET ALL FILE DATA FROM SELECTED GOOGLE DRIVE FOLDER"
},
"typeVersion": 1
},
{
"id": "a404df40-0f40-46b5-a65b-edc182d06d9b",
"name": "Sticky Note2",
"type": "n8n-nodes-base.stickyNote",
"position": [
1936,
1040
],
"parameters": {
"color": 5,
"width": 192,
"height": 256,
"content": "## Remove empty data fields "
},
"typeVersion": 1
},
{
"id": "4776e7ea-7243-474f-9ec0-47afc46d8479",
"name": "Sticky Note3",
"type": "n8n-nodes-base.stickyNote",
"position": [
1024,
976
],
"parameters": {
"color": 5,
"width": 208,
"height": 320,
"content": "## GET individual files from selected gdrive"
},
"typeVersion": 1
},
{
"id": "3a593a87-2d91-48dc-85da-98a5bc562ff6",
"name": "Sticky Note4",
"type": "n8n-nodes-base.stickyNote",
"position": [
1280,
1008
],
"parameters": {
"color": 5,
"width": 608,
"height": 272,
"content": "## MISTRAL OCR\n [OCR Guide](https://mistral.ai/news/mistral-ocr)\n1. UPLOAD FILE\n2. GET SIGNED URL\n3. GET EXTRACT DATA AFTER USING MISTRAL OCR"
},
"typeVersion": 1
},
{
"id": "65106e47-a303-4f46-be3b-207d6e6c04fa",
"name": "Sticky Note5",
"type": "n8n-nodes-base.stickyNote",
"position": [
352,
16
],
"parameters": {
"color": 5,
"width": 368,
"height": 288,
"content": "## Hosted Chat interface \n"
},
"typeVersion": 1
},
{
"id": "78103cfe-e101-4d70-8dea-08b4ca2bbdb6",
"name": "Sticky Note6",
"type": "n8n-nodes-base.stickyNote",
"position": [
1472,
96
],
"parameters": {
"color": 5,
"width": 272,
"height": 496,
"content": "## QDRANT VCETOR AND OPEN API EMBEDDING \n [QDRANT Guide](https://qdrant.tech/documentation/)"
},
"typeVersion": 1
},
{
"id": "716859d0-f3eb-431c-a6fc-37301508c968",
"name": "Sticky Note7",
"type": "n8n-nodes-base.stickyNote",
"position": [
928,
0
],
"parameters": {
"color": 5,
"width": 432,
"height": 304,
"content": "## AI chat agent\n interact with user and process user input and provide appropriate response using different tools. "
},
"typeVersion": 1
},
{
"id": "aa7c86ee-3229-415e-96eb-92fbd530aa44",
"name": "Sticky Note10",
"type": "n8n-nodes-base.stickyNote",
"position": [
2672,
1008
],
"parameters": {
"color": 5,
"width": 320,
"height": 288,
"content": "## clean all extracted data and convert them to smaller chunks"
},
"typeVersion": 1
},
{
"id": "ccd381e0-1cd2-406d-9aec-7b03c18ee435",
"name": "Sticky Note11",
"type": "n8n-nodes-base.stickyNote",
"position": [
2176,
928
],
"parameters": {
"color": 5,
"width": 288,
"height": 368,
"content": "## assignment agent\n\nfor any given file this node assign which type documents it is ,which project its related too and who are working on it"
},
"typeVersion": 1
},
{
"id": "87f7a493-d65c-447b-a465-3175c1bcbea7",
"name": "Sticky Note12",
"type": "n8n-nodes-base.stickyNote",
"position": [
3088,
976
],
"parameters": {
"color": 5,
"width": 512,
"height": 608,
"content": "## load all chunks into qdrant vector database"
},
"typeVersion": 1
},
{
"id": "66f3dfcf-9fbe-4d63-888e-dda7c422c282",
"name": "Sticky Note9",
"type": "n8n-nodes-base.stickyNote",
"position": [
576,
992
],
"parameters": {
"color": 5,
"width": 256,
"height": 320,
"content": "## loop over google drive folder items"
},
"typeVersion": 1
},
{
"id": "d21fe18b-7f9b-46b5-a8b5-2725c849db7f",
"name": "Loop Over each file in gdrive folder",
"type": "n8n-nodes-base.splitInBatches",
"position": [
608,
1136
],
"parameters": {
"options": {}
},
"typeVersion": 3
},
{
"id": "4300ad47-a92a-408b-aa8b-b56249d341e9",
"name": "add metadata",
"type": "n8n-nodes-base.set",
"position": [
896,
1152
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "10646eae-ae46-4327-a4dc-9987c2d76173",
"name": "file_id",
"type": "string",
"value": "={{ $json.id }}"
},
{
"id": "f4536df5-d0b1-4392-bf17-b8137fb31a44",
"name": "file_type",
"type": "string",
"value": "={{ $json.mimeType }}"
},
{
"id": "77d782de-169d-4a46-8a8e-a3831c04d90f",
"name": "file_title",
"type": "string",
"value": "={{ $json.name }}"
},
{
"id": "9bde4d7f-e4f3-4ebd-9338-dce1350f9eab",
"name": "file_url",
"type": "string",
"value": "={{ $json.webViewLink }}"
},
{
"id": "fae402c8-c486-4b57-8d28-bf669db6b442",
"name": "last_modified_date",
"type": "string",
"value": "={{ $json.modifiedTime }}"
}
]
}
},
"typeVersion": 3.4
},
{
"id": "efc3f354-0a6d-46dc-8ecf-793873e19466",
"name": "If NODE",
"type": "n8n-nodes-base.if",
"position": [
1984,
1152
],
"parameters": {
"options": {},
"conditions": {
"options": {
"version": 2,
"leftValue": "",
"caseSensitive": true,
"typeValidation": "loose"
},
"combinator": "and",
"conditions": [
{
"id": "d15e917b-43d2-40b8-8b49-af467ff63961",
"operator": {
"type": "string",
"operation": "notExists",
"singleValue": true
},
"leftValue": "={{ $json.data[0].parseJson().skipped }}",
"rightValue": ""
}
]
},
"looseTypeValidation": true
},
"typeVersion": 2.2
},
{
"id": "33e574c3-47a2-454b-85e9-e3e9983e3d63",
"name": "based file name it assign differ metadata",
"type": "@n8n/n8n-nodes-langchain.informationExtractor",
"position": [
2208,
1152
],
"parameters": {
"text": "=\n{{ $json.pages[0].markdown }}",
"options": {
"systemPromptTemplate": "You are an expert extraction algorithm.\nOnly extract relevant information from the text.\ngive data is "
},
"attributes": {
"attributes": [
{
"name": "document_type",
"description": "type of document this can be blog, user documentation, technical documentation, manual/guide, educational"
},
{
"name": "project",
"description": " from give project this can any one ,social/digimarketing , chatbot, knowledge bot if nothing that its default"
},
{
"name": "assigned_to",
"description": "who are handling give projects mention all name given for a give project\n\nif project LINKED/DIGIMARRKETING/SOCIAL = [employee name]\nif project KB BOT = [employee name]\nif project CHAT BOT = [employee name]\nif project TIMESHEET = [employee name]"
}
]
}
},
"typeVersion": 1
},
{
"id": "c4b2a5cb-b849-4d7e-9f24-7ef67ee4031a",
"name": "set all metadata",
"type": "n8n-nodes-base.set",
"position": [
2512,
1152
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "5132d92c-41da-4a55-ad79-0c329ca7e626",
"name": "Document name",
"type": "string",
"value": "={{ $('Google Drive1').item.json.file_title }}"
},
{
"id": "c8160701-2be7-43c6-bcfa-295fbebe0e23",
"name": "Document data",
"type": "string",
"value": "={{ $('If NODE').item.json.pages[0].markdown }}"
},
{
"id": "1087ab34-5643-4755-b545-cf34d0ae2cd2",
"name": " source",
"type": "string",
"value": "={{ $('Google Drive1').item.json.file_id }}"
},
{
"id": "4317aa31-d9fd-4adc-ab26-b48c208041b3",
"name": "ASSIGNEDTO",
"type": "string",
"value": "={{ $json.output.assigned_to.split(\",\") }}"
},
{
"id": "7a61d775-06b1-42e2-a82a-d8e756fa5586",
"name": "PROJECT",
"type": "string",
"value": "={{ $json.output.project }}"
},
{
"id": "300f49d6-8851-458d-a296-8ceb68ebdd5f",
"name": "DOCUMENT_TYPE",
"type": "string",
"value": "={{ $json.output.document_type }}"
}
]
}
},
"typeVersion": 3.4
},
{
"id": "2a4026cf-3034-49a8-8dc8-ad67ae571213",
"name": "clean output",
"type": "n8n-nodes-base.code",
"position": [
2704,
1152
],
"parameters": {
"jsCode": "// Get incoming data (first item)\nconst data = items[0].json;\n\n// If wrapped in an array, unwrap it\nconst input = Array.isArray(data) ? data[0] : data;\n\nlet output = [\n {\n content: input[\"Document data\"], // main text for embedding\n metadata: {\n document_name: input[\"Document name\"],\n source_id: input[\" source\"], // note: there is a space before \"source\" in your key\n }\n }\n];\n\nreturn output.map(o => ({ json: o }));\n"
},
"typeVersion": 2
},
{
"id": "f9172d1d-060c-46f7-8e48-b235a2610185",
"name": "convert data into smaller chunks",
"type": "n8n-nodes-base.code",
"position": [
2864,
1152
],
"parameters": {
"jsCode": "const chunkSize = 1000; // characters\nconst chunkOverlap = 100; // characters\n\n\nlet newItems = [];\n\nfor (const item of items) {\n let text = item.json.content; // <-- use item.json\n if (!text || typeof text !== \"string\") continue;\n\n const sourceId = item.json.metadata.source_id;\n\n \n // Chunking\n for (let i = 0; i < updatedText.length; i += chunkSize - chunkOverlap) {\n let chunk = updatedText.slice(i, i + chunkSize);\n newItems.push({\n json: {\n content: chunk,\n metadata: item.json.metadata\n }\n });\n }\n}\n\nreturn newItems;\n"
},
"typeVersion": 2
},
{
"id": "2a9539e7-e8dc-4c2a-a016-daab8c7d5339",
"name": "Sticky Note8",
"type": "n8n-nodes-base.stickyNote",
"position": [
3696,
736
],
"parameters": {
"color": 5,
"width": 800,
"height": 1248,
"content": "### **Node List & Descriptions**\n\n1. **Manual Trigger** \n - **What:** Kicks off the workflow when the user clicks **\u201cTest workflow.\u201d** \n - **Why:** Allows for on-demand execution and testing of the data pipeline. \n\n2. **Google Drive (List Files)** \n - **What:** Lists all files within a predefined Google Drive folder (`knowledgebaseforaibot`). \n - **Why:** To retrieve the initial list of all documents that need to be processed. \n\n3. **Loop Over Files** \n - **What:** Iterates through each file retrieved from Google Drive, processing them one by one. \n - **Why:** Ensures each document is handled individually in the following steps. \n\n4. **Google Drive (Download File)** \n - **What:** Downloads the binary content of the current file being processed in the loop. \n - **Why:** Provides the actual file data needed for OCR processing. \n\n5. **Mistral OCR (via HTTP Requests)** \n - **What:** \n - Uploads the file to the Mistral API. \n - Retrieves a temporary signed URL for access. \n - Calls the Mistral OCR endpoint to extract all text content from the document. \n - **Why:** Converts scanned or image-based documents (like PDFs, JPGs) into machine-readable text. \n\n6. **If Node (Check for Success)** \n - **What:** Verifies that the Mistral OCR operation completed successfully without errors. \n - **Why:** Ensures only valid, readable documents continue in the workflow, avoiding process failures. \n\n7. **Information Extractor (LangChain)** \n - **What:** Uses the Mistral AI chat model to analyze the extracted text and assign metadata such as: \n - `document_type` \n - `project` \n - `assigned_to` \n - **Why:** Automatically categorizes and tags documents based on their content for better organization and searchability. \n\n8. **Code Node (Clean & Chunk Data)** \n - **What:** \n - Formats the extracted text and metadata into a clean JSON structure. \n - Splits the document text into smaller, overlapping chunks (\u22481000 characters each). \n - **Why:** Prepares data for embedding and ensures compatibility with language model context limits. \n\n9. **OpenAI Embeddings (LangChain)** \n - **What:** Converts each text chunk into a numerical vector using OpenAI\u2019s `text-embedding-3-small` model. \n - **Why:** Encodes the semantic meaning of the text for similarity-based search and retrieval. \n\n10. **Qdrant Vector Store (LangChain)** \n - **What:** Inserts the text chunks and their corresponding embeddings into a Qdrant vector database collection named `docaiauto`. \n - **Why:** Creates a searchable, semantic knowledge base for AI-driven document retrieval. \n\n---\n\n### **flow of data**\n\n**Trigger \u2192 List Files (Google Drive) \u2192 Loop \u2192 Download File \u2192 OCR (Mistral) \u2192 Validate \u2192 Categorize (AI) \u2192 Clean & Chunk \u2192 Embed (OpenAI) \u2192 Store (Qdrant)**\n"
},
"typeVersion": 1
},
{
"id": "83726285-3bfd-4d22-ad35-39a5ff3ef414",
"name": "Sticky Note14",
"type": "n8n-nodes-base.stickyNote",
"position": [
-1232,
-176
],
"parameters": {
"color": 5,
"width": 864,
"height": 1856,
"content": "# Konowledge RAG and AI chat agent \n## by DIGITAL BIZ TECH\n\n## **Overview**\n\nThis workflow automates the process of transforming all files stored in Google Drive into a **searchable, intelligent knowledge base** \u2014 fully integrated with a **chat-enabled AI assistant**. \nIt also supports **web search augmentation**, allowing the agent to explain, compare, or enrich document insights using the latest online data.\n\n---\n\n## **Key Objectives**\n\n1. **Ingest and Process All Files**\n - Retrieve all files from a specific Google Drive folder.\n - Automatically extract their text content using OCR and AI-based parsing.\n - Generate structured metadata for each document (e.g., project, assignment, employee, and category).\n - Convert the text into **vector embeddings** and store them in a **Qdrant vector database** for semantic retrieval.\n\n2. **Enrich Metadata Intelligence**\n - Enhance each document with contextual metadata fields:\n - `project` \u2014 identifies which project the document belongs to.\n - `assignment` \u2014 links the file to a specific task or deliverable.\n - `employee` \u2014 indicates the team member responsible or related to the document.\n - Improves the **contextual accuracy** of RAG (Retrieval-Augmented Generation) queries by enabling more targeted retrieval.\n\n3. **Integrate with Chat-Based AI Agent**\n - The agent uses a **ai agent with different tools** to:\n - Query internal knowledge (Qdrant collection) for relevant chunks.\n - Optionally trigger a **web search (Tavily API)** when internal data is insufficient.\n - Combine results intelligently to deliver accurate and explainable answers.\n - Users can interact naturally through a chat interface to explore, summarize, or cross-reference different documents.\n\n4. **Maintain a Robust RAG Pipeline for Unstructured Data**\n - Designed to handle **unclean, inconsistent, or multi-format data sources** (PDFs, DOCX, images, etc.).\n - The cleaning and chunking logic ensures uniform embeddings, even from noisy input.\n - The RAG system automatically improves over time as new documents are added or updated in Google Drive.\n\n---\n\n## **Functional Flow**\n\n**Google Drive \u2192 File Extraction \u2192 Metadata Enrichment \u2192 Text Chunking \u2192 Embedding (OpenAI) \u2192 Storage (Qdrant) \u2192 AI Chat Agent \u2192 Web Search (Optional)**\n\n---\n\n## **Use Cases**\n\n- \ud83d\udcc1 **Automated Knowledge Management:** Build a live, self-updating internal document knowledge base. \n- \ud83e\udde0 **Smart AI Assistance:** Enable employees to query project or task documents conversationally. \n- \ud83c\udf10 **Hybrid Knowledge Retrieval:** Combine private document insights with external web data. \n- \ud83e\udde9 **Flexible Integration:** Can be connected to any folder or department for scalable RAG deployment.\n\n---\n\n## **Benefits**\n\n- Fully automated ingestion from Google Drive. \n- Rich, context-aware metadata for intelligent document relationships. \n- Chat interface for easy access to organizational knowledge. \n- Optional web search for real-time, external context expansion. \n- Maintains a clean, high-quality **RAG (Retrieval-Augmented Generation)** pipeline even from unclean data sources.\n\n---\n\n### **Quick Reference: Flow Logic**\n\n**Google Drive \u2192 OCR/Text Extraction \u2192 Metadata Enrichment \u2192 Embeddings \u2192 Qdrant (RAG Store) \u2192 Chat AI Agent \u2192 Optional Web Search**"
},
"typeVersion": 1
}
],
"connections": {
"If NODE": {
"main": [
[
{
"node": "based file name it assign differ metadata",
"type": "main",
"index": 0
}
],
[
{
"node": "Loop Over each file in gdrive folder",
"type": "main",
"index": 0
}
]
]
},
"Web Search ": {
"ai_tool": [
[
{
"node": "ai chat agent",
"type": "ai_tool",
"index": 0
}
]
]
},
"Google Drive": {
"main": [
[
{
"node": "Loop Over each file in gdrive folder",
"type": "main",
"index": 0
}
]
]
},
"add metadata": {
"main": [
[
{
"node": "Google Drive1",
"type": "main",
"index": 0
}
]
]
},
"clean output": {
"main": [
[
{
"node": "convert data into smaller chunks",
"type": "main",
"index": 0
}
]
]
},
"Google Drive1": {
"main": [
[
{
"node": "Mistral Upload",
"type": "main",
"index": 0
}
]
]
},
"Mistral Upload": {
"main": [
[
{
"node": "Mistral Signed URL",
"type": "main",
"index": 0
}
]
]
},
"Simple Memory1": {
"ai_memory": [
[
{
"node": "ai chat agent",
"type": "ai_memory",
"index": 0
}
]
]
},
"Mistral DOC OCR": {
"main": [
[
{
"node": "If NODE",
"type": "main",
"index": 0
}
]
]
},
"set all metadata": {
"main": [
[
{
"node": "clean output",
"type": "main",
"index": 0
}
]
]
},
"Embeddings OpenAI": {
"ai_embedding": [
[
{
"node": "Qdrant Vector Store1",
"type": "ai_embedding",
"index": 0
}
]
]
},
"Embeddings OpenAI1": {
"ai_embedding": [
[
{
"node": "Qdrant Vector Store",
"type": "ai_embedding",
"index": 0
}
]
]
},
"Mistral Signed URL": {
"main": [
[
{
"node": "Mistral DOC OCR",
"type": "main",
"index": 0
}
]
]
},
"OpenAI Chat Model1": {
"ai_languageModel": [
[
{
"node": "ai chat agent",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"Default Data Loader": {
"ai_document": [
[
{
"node": "Qdrant Vector Store1",
"type": "ai_document",
"index": 0
}
]
]
},
"Qdrant Vector Store": {
"ai_tool": [
[
{
"node": "ai chat agent",
"type": "ai_tool",
"index": 0
}
]
]
},
"Qdrant Vector Store1": {
"main": [
[
{
"node": "Loop Over each file in gdrive folder",
"type": "main",
"index": 0
}
]
]
},
"Character Text Splitter": {
"ai_textSplitter": [
[
{
"node": "Default Data Loader",
"type": "ai_textSplitter",
"index": 0
}
]
]
},
"Mistral Cloud Chat Model": {
"ai_languageModel": [
[
{
"node": "based file name it assign differ metadata",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"When chat message received": {
"main": [
[
{
"node": "ai chat agent",
"type": "main",
"index": 0
}
]
]
},
"convert data into smaller chunks": {
"main": [
[
{
"node": "Qdrant Vector Store1",
"type": "main",
"index": 0
}
]
]
},
"When clicking \u2018Test workflow\u2019": {
"main": [
[
{
"node": "Google Drive",
"type": "main",
"index": 0
}
]
]
},
"Loop Over each file in gdrive folder": {
"main": [
[],
[
{
"node": "add metadata",
"type": "main",
"index": 0
}
]
]
},
"based file name it assign differ metadata": {
"main": [
[
{
"node": "set all metadata",
"type": "main",
"index": 0
}
]
]
}
}
}
Credentials you'll need
Each integration node will prompt for credentials when you import. We strip credential IDs before publishing — you'll add your own.
googleDriveOAuth2ApimistralCloudApiopenAiApiqdrantApi
Pro
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About this workflow
This workflow transforms a Google Drive folder into an intelligent, searchable knowledge base and provides a chat agent to query it. It’s composed of two distinct flows: An ingestion pipeline to process documents. A live chat agent that uses RAG (Retrieval-Augmented Generation)…
Source: https://n8n.io/workflows/10096/ — original creator credit. Request a take-down →
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