Ai fitness coach Strava data analysis and personalized training insights
Detailed Title "Triathlon Coach AI Workflow: Strava Data Analysis and Personalized Training Insights using n8n" --- Description This n8n workflow enables you to build an AI-driven virtual triathlon coach that seamlessly integrates with Strava to analyze activity data and provide athletes with actionable training insights. The workflow processes data from activities like swimming, cycling, and running, delivers personalized feedback, and sends motivational and performance improvement advice via email or WhatsApp. --- Workflow Details Trigger: Strava Activity Updates Node: Strava Trigger Purpose: Captures updates from Strava whenever an activity is recorded or modified. The data includes metrics like distance, pace, elevation, heart rate, and more. Integration: Uses Strava API for real-time synchronization. Step 1: Data Preprocessing Node: Code Purpose: Combines and flattens the raw Strava activity data into a structured format for easier processing in subsequent nodes. Logic: A recursive function flattens JSON input to create a clean and readable structure. Step 2: AI Analysis with Google Gemini Node: Google Gemini Chat Model Purpose: Leverages Google Gemini's advanced language model to analyze the activity data. Functionality: Identifies key performance metrics. Provides feedback and insights specific to the type of activity (e.g., running, swimming, or cycling). Offers tailored recommendations and motivational advice. Step 3: Generate Structured Output Node: Structure Output Purpose: Processes the AI-generated response to create a structured format, such as headings, paragraphs, and bullet lists. Output: Formats the response for clear communication. Step 4: Convert to HTML Node: Convert to HTML Purpose: Converts the structured output into an HTML format suitable for email or other presentation methods. Output: Ensures the response is visually appealing and easy to understand. Step 5: Send Email with Training Insights Node: Send Email Purpose: Sends a detailed email to the athlete with performance insights, training recommendations, and motivational messages. Integration: Utilizes Gmail or SMTP for secure and efficient email delivery. Optional Step: WhatsApp Notifications Node: WhatsApp Business Cloud Purpose: Sends a summary of the activity analysis and key recommendations via WhatsApp for instant access. Integration: Connects to WhatsApp Business Cloud for automated messaging. --- Additional Notes Customization: You can modify the AI prompt to adapt the recommendations to the athlete's specific goals or fitness levels. The workflow is flexible and can accommodate additional nodes for more advanced analysis or output formats. Scalability: Ideal for individual athletes or coaches managing multiple athletes. Can be expanded to include additional metrics or insights based on user preferences. Performance Metrics Handled: Swimming: SWOLF, stroke count, pace. Cycling: Cadence, power zones, elevation. Running: Pacing, stride length, heart rate zones. --- Implementation Steps Set Up Strava API Key: Log in to Strava Developers to generate your API key. Integrate the API key into the Strava Trigger node. Configure Google Gemini Integration: Use your Google Gemini (PaLM) API credentials in the Google Gemini Chat Model node. Customize Email and WhatsApp Messaging: Update the Send Email and WhatsApp Business Cloud nodes with the recipient’s details. Automate Execution: Deploy the workflow and use n8n's scheduling features or cron jobs for periodic execution. --- GET n8n Now N8N COURSE n8n Book Developer Notes Author: Amjid Ali improvements. Resources: See in Action: Syncbricks Youtube PayPal: Support the Developer Courses : SyncBricks LMS By using this workflow, triathletes and coaches can elevate training to the next level with AI-powered insights and actionable recommendations.
By Amjid AliIT 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 Import the Template: Import the template into your n8n instance. Configure the Google Drive Trigger: Set the folder ID (e.g., 1RQvAHIw8cQbtwI9ZvdVV0k0x6TM6H12P) and connect your Google Drive credentials. Set Up Pinecone Nodes: Enter your Pinecone index details and credentials. Configure the Chat AI Agent: Provide your Google Gemini (or OpenRouter) API credentials. 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 returned support information. Additional Notes Ensure all credentials (Google Drive, Pinecone, and Chat AI) are correctly set up and tested before deploying the workflows in production. The template is fully customizable. Adjust the text cleaning, splitting parameters, or the number of document chunks retrieved based on your support documentation's size and structure. This template not only enhances IT support efficiency but also offers a scalable solution for managing and leveraging growing volumes of support content.
By AI Incarnation