Google Docs and Google Gemini Chat Model integration

AI-Powered Tech Radar Advisor This project is built on top of the famous open source ThoughtWorks Tech Radar. You can use this template to build your own AI-Powered Tech Radar Advisor for your company or group of companies. Target Audience This template is perfect for: Tech Audit & Governance Leaders:** Those seeking to build a tech landscape AI platform portal. Tech Leaders & Architects:** Those aiming to provide modern AI platforms that help others understand the rationale behind strategic technology adoption. Product Managers:** Professionals looking to align product innovation with the company's current tech trends. IT & Engineering Teams:** Teams that need to aggregate, analyze, and visualize technology data from multiple sources efficiently. Digital Transformation Experts:** Innovators aiming to leverage AI for actionable insights and strategic recommendations. Data Analysts & Scientists:** Individuals who want to combine structured SQL analysis with advanced semantic search using vector databases. Developers:** Those interested in integrating RAG chatbot functionality with conversation storage. Description Tech Constellation is an AI-powered Tech Radar solution designed to help organizations visualize and steer their technology adoption strategy. It seamlessly ingests data from a Tech Radar Google Sheet—converting it into both a MySQL database and a vector index—to consolidate your tech landscape in one place. The platform integrates an interactive AI chat interface powered by four specialized agents: AI Agent Router:** Analyzes and routes user queries to the most suitable processing agent. SQL Agent:** Executes precise SQL queries on structured data. RAG Agent:** Leverages semantic, vector-based search for in-depth insights. Output Guardrail Agent:** Validates responses to ensure they remain on-topic and accurate. This powerful template is perfect for technology leaders, product managers, engineering teams, and digital transformation experts looking to make data-driven decisions aligned with strategic initiatives across groups of parent-child companies. Features Data Ingestion A Google Sheet containing tech radar data is used as the primary source. The data is ingested and converted into a MySQL database. Simultaneously, the data is indexed into a vector database for semantic (vector-based) search. Interactive AI Chat Chat Integration:** An AI-powered chat interface allows users to ask questions about the tech radar. Customizable AI Agents:** AI Agent Router: Determines the query type and routes it to the appropriate agent. SQL Agent: Processes queries using SQL on structured data. RAG Agent: Performs vector-based searches on document-like data. Output Guardrail Agent: Validates queries and ensures that the responses remain on-topic and accurate. Usage Examples Tell me, is TechnologyABC adopted or on hold, and why? List all the tools that are considered part of the strategic direction for company3 but are not adopted. Project Links & Additional Details GitHub Repository (Frontend Interface Source Code):** github.com/dragonjump/techconstellation Try It:** https://scaler.my

🤖 AI-Powered RAG Chatbot with Google Drive Integration This workflow creates a powerful RAG (Retrieval-Augmented Generation) chatbot that can process, store, and interact with documents from Google Drive using Qdrant vector storage and Google's Gemini AI. How It Works Document Processing & Storage 📚 Retrieves documents from a specified Google Drive folder Processes and splits documents into manageable chunks Extracts metadata using AI for enhanced search capabilities Stores document vectors in Qdrant for efficient retrieval Intelligent Chat Interface 💬 Provides a conversational interface powered by Google Gemini Uses RAG to retrieve relevant context from stored documents Maintains chat history in Google Docs for reference Delivers accurate, context-aware responses Vector Store Management 🗄️ Features secure delete operations with human verification Includes Telegram notifications for important operations Maintains data integrity with proper version control Supports batch processing of documents Setup Steps Configure API Credentials: Set up Google Drive & Docs access Configure Gemini AI API Set up Qdrant vector store connection Add Telegram bot for notifications Add OpenAI Api Key to the 'Delete Qdrant Points by File ID' node Configure Document Sources: Set Google Drive folder ID Define Qdrant collection name Set up document processing parameters Test and Deploy: Verify document processing Test chat functionality Confirm vector store operations Check notification system This workflow is ideal for organizations needing to create intelligent chatbots that can access and understand large document repositories while maintaining context and providing accurate responses through RAG technology.

This n8n workflow creates a financial analysis tool that generates reports on a company's quarterly earnings using the capabilities of OpenAI GPT-4o-mini, Google's Gemini AI and Pinecone's vector search. By analyzing PDFs of any company's earnings reports from their Investor Relations page, this workflow can answer complex financial questions and automatically compile findings into a structured Google Doc. How it works: Data loading and indexing Fetches links to PDF earnings document from a Google Sheet containing a list of file links. Downloads the PDFs from Google Drive. Parses the PDFs, splits the text into chunks, and generates embeddings using the Embeddings Google AI node (text-embedding-004 model). Stores the embeddings and corresponding text chunks in a Pinecone vector database for semantic search. Report generation with AI agent Utilizes an AI Agent node with a specifically crafted system prompt. The agent orchestrates the entire process. The agent uses a Vector Store Tool to access and retrieve information from the Pinecone database. Report delivery Saves the generated report as a Google Doc in a specified Google Drive location. Set up steps Google Cloud Project & Vertex AI API: Create a Google Cloud project. Enable the Vertex AI API for your project. Google AI API key: Obtain a Google AI API key from Google AI Studio. Pinecone account and API key: Create a free account on the Pinecone website. Obtain your API key from your Pinecone dashboard. Create an index named company-earnings in your Pinecone project. Google Drive - download and save financial documents: Go to a company you want to analize and download their quarterly earnings PDFs Save the PDFs in Google Drive Create a Google Sheet that stores a list of file URLs pointing to the PDFs you downloaded and saved to Google Drive Configure credentials in your n8n environment for: Google Sheets OAuth2 Google Drive OAuth2 Google Docs OAuth2 Google Gemini(PaLM) Api (using your Google AI API key) Pinecone API (using your Pinecone API key) Import and configure the workflow: Import this workflow into your n8n instance. Update the List Of Files To Load (Google Sheets) node to point to your Google Sheet. Update the Download File From Google Drive to point to the column where the file URLs are Update the Save Report to Google Docs node to point to your Google Doc where you want the report saved.