IT support chatbot with Google Drive, Pinecone & Gemini | AI doc processing

This n8n template empowers IT support teams by automating document ingestion and instant query resolution through a conversational AI. It integrates Google Drive, Pinecone, and a Chat AI agent (using Google Gemini/OpenRouter) to transform static support documents into an interactive, searchable knowledge base. With two interlinked workflows—one for processing support documents and one for handling chat queries—employees receive fast, context-aware answers directly from your support documentation. Overview Document Ingestion Workflow - Google Drive Trigger: Monitors a specified folder for new file uploads (e.g., updated support documents). - File Download & Extraction: Automatically downloads new files and extracts text content. - Data Cleaning & Text Splitting: Utilizes a Code node to remove line breaks, trim extra spaces, and strip special characters, while a text splitter segments the content into manageable chunks. - Embedding & Storage: Generates text embeddings using Google Gemini and stores them in a Pinecone vector store for rapid similarity search. Chat Query Workflow - Chat Trigger: Initiates when an employee sends a support query. - Vector Search & Context Retrieval: Retrieves the top relevant document segments from Pinecone based on similarity scores. - Prompt Construction: A Code node combines the retrieved document snippets with the user’s query into a detailed prompt. - AI Agent Response: The constructed prompt is sent to an AI agent (using OpenRouter Chat Model) to generate a clear, step-by-step solution. Key Benefits & Use Case Imagine a large organization where every IT support document—from troubleshooting guides to system configurations—is stored in a single Google Drive folder. When an employee encounters an issue (e.g., “How do I reset my VPN credentials?”), they simply type the query into a chat interface. Instantly, the workflow retrieves the most relevant context from the ingested documents and provides a detailed, actionable answer. This process reduces resolution times, enhances support consistency, and significantly lightens the load on IT staff. Prerequisites - A valid Google Drive account with access to the designated folder. - A Pinecone account for storing and retrieving text embeddings. - Google Gemini (or OpenRouter) credentials to power the Chat AI agent. - An operational n8n instance configured with the necessary nodes and credentials. Workflow Details 1 Document Ingestion Workflow - Google Drive Trigger Node: - Listens for file creation events in the specified folder. - Google Drive Download Node: - Downloads the newly added file. - Extract from File Node: - Extracts text content from the downloaded file. - Code Node (Data Cleaning): - Cleans the extracted text by removing line breaks, trimming spaces, and eliminating special characters. - Recursive Text Splitter Node: - Segments the cleaned text into manageable chunks. - Pinecone Vector Store Node: - Generates embeddings (via Google Gemini) and uploads the chunks to Pinecone. 2 Chat Query Workflow - Chat Trigger Node: - Receives incoming user queries. - Pinecone Vector Store Node (Query): - Searches for relevant document chunks based on the query. - Code Node (Context Builder): - Sorts the retrieved documents by relevance and constructs a prompt merging the context with the query. - AI Agent Node: - Sends the prompt to the Chat AI agent, which returns a detailed answer. How to Use 1. Import the Template: - Import the template into your n8n instance. 2. Configure the Google Drive Trigger: - Set the folder ID (e.g., 1RQvAHIw8cQbtwI9ZvdVV0k0x6TM6H12P) and connect your Google Drive credentials. 3. Set Up Pinecone Nodes: - Enter your Pinecone index details and credentials. 4. Configure the Chat AI Agent: - Provide your Google Gemini (or OpenRouter) API credentials. 5. Test the Workflows: - Validate the document ingestion workflow by uploading a sample support document. - Validate the chat query workflow by sending a test query and verifying the