AI-Powered Company Documents Q&A Assistant with Google Drive and GPT-4 Mini

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          "systemMessage": "You are a helpful company assistant with access to company documents stored in a knowledge base.\n\nYour task is to:\n1. Answer user questions accurately using information from the company documents\n2. Use the Vector Store tool to search for relevant information when needed\n3. Cite specific documents or sections when providing answers\n4. If you cannot find relevant information, politely say so and offer to help with something else\n5. Be professional, concise, and helpful in all responses\n\nAlways prioritize accuracy over speculation."
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          "value": "company docs"
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        "toolDescription": "Search company documents and knowledge base for relevant information. Use this when users ask questions about company policies, procedures, or any documented information."
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        "content": "## How it works\n\nThis workflow creates an AI assistant that answers questions using your company's documents. It has two independent flows:\n\n**Document Processing**: Monitors a Google Drive folder for new files. When detected, documents are downloaded, split into chunks, converted to embeddings, and stored in an in-memory vector database for semantic search.\n\n**Chat Interface**: Users send questions via webhook. The AI agent searches the knowledge base, retrieves relevant context, and generates accurate answers with source citations. Conversation history is maintained for context-aware responses.\n\n## Setup steps\n\n1. Connect your Google Drive OAuth2 credentials to the trigger node\n2. Select the folder to monitor in \"Watch Company Docs Folder\"\n3. Add your OpenAI API key to both the Chat Model and Embeddings nodes\n4. Configure Google Service Account for document downloads\n5. Test the webhook endpoint with a POST request containing `{\"data\": \"your question\", \"session_id\": \"user123\"}`"
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        "content": "## Chat Flow\nWebhook receives questions → AI agent searches vector store → GPT-4 generates answers with citations → Response sent back to user"
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        "content": "## Document Processing\nDrive trigger detects new files → Downloads document → Extracts and chunks text → Generates embeddings → Stores in vector database"
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