HumationHuman + Automation
Back to Catalog

Monitor Google Maps reviews with sentiment analysis & RAG agent using Pinecone

GtarasGtaras
46 views
2/3/2026
PostgreSQLData TransformationStock MarketDatabaseETL
Official Page

Analyze and Sync Google Maps Reviews to Pinecone

> Note: This template requires an Apify account, an OpenAI account, and a Pinecone database.

Overview

This workflow automates your reputation management by scraping Google Maps reviews, analyzing their sentiment using AI, and storing them in a Vector Database (Pinecone). It also includes a RAG (Retrieval-Augmented Generation) agent that allows you to chat with your review data via Telegram to ask specific questions about customer feedback.

How it works

  • Scrape: At a scheduled time (or manually), the workflow triggers an Apify actor to scrape the latest reviews from a specific Google Maps URL.
  • Analyze: It uses GPT-4o to analyze the sentiment of the reviews and generates a summary of complaints and praises.
  • Notify: A Telegram message is sent with the sentiment score and a summary of the latest reviews.
  • Store: The review data is embedded and upserted into a Pinecone Vector Store.
  • Chat (RAG): You can send messages to a Telegram bot to query the database (e.g., "What are people saying about our coffee?"). The AI retrieves relevant reviews from Pinecone to answer your question.
  • Cleanup: A weekly schedule cleans up the namespace to ensure data freshness (optional).

How to set up

  1. Apify: Create an account and subscribe to the Google Maps Reviews Scraper actor. Set up your Apify credentials in n8n.
  2. OpenAI: Set up your OpenAI credentials in n8n.
  3. Pinecone: Create an Index in Pinecone. Set up your Pinecone credentials in n8n.
  4. Telegram: Create a new bot via BotFather to get your Token and Chat ID. Set up your Telegram credentials in n8n.

Configure the Workflow

  1. Open the ⚠️ CONFIGURATION nodes.
  2. Paste your telegram_chat_id.
  3. Paste the Maps_url of the business you want to monitor.
  4. Define your pinecone_namespace.

HTTP Node Configuration

In the "Empty Namespace" node, update the URL to match your Pinecone Index Host and add your Pinecone API Key in the header or use credentials.

🙋 Support

If you encounter any issues during setup or have questions about customization, please reach out to our dedicated support email: foivosautomationhelp@gmail.com

Monitor Google Maps Reviews with Sentiment Analysis & RAG Agent using Pinecone

This n8n workflow automates the process of fetching Google Maps reviews, performing sentiment analysis, and storing them in a Pinecone vector database. It also includes an AI Agent that can interact with these reviews based on user prompts from Telegram.

What it does

This workflow simplifies monitoring and analyzing Google Maps reviews by:

  1. Triggering (Manual or Scheduled): The workflow can be executed manually or on a predefined schedule to fetch new reviews.
  2. Fetching Google Maps Reviews: It makes an HTTP request to a Google Maps API endpoint (or similar) to retrieve review data.
  3. Processing Review Data: The fetched review data is transformed and prepared for further analysis.
  4. Performing Sentiment Analysis: An OpenAI Chat Model is used to analyze the sentiment of each review.
  5. Generating Embeddings: OpenAI Embeddings are created for each review, converting text into numerical vectors.
  6. Storing in Pinecone: The reviews and their embeddings are stored in a Pinecone vector store, enabling efficient semantic search and retrieval.
  7. AI Agent Interaction (Telegram): A Telegram bot acts as a trigger for an AI Agent. Users can send prompts to the bot, and the AI Agent, utilizing the Pinecone vector store, will retrieve relevant reviews and respond.
  8. Notifying on Telegram: The AI Agent's responses or other relevant information can be sent back to the user via Telegram.
  9. Logging to Google Sheets: Key review data and analysis results are logged into a Google Sheet for easy tracking and reporting.

Prerequisites/Requirements

To use this workflow, you will need:

  • n8n Instance: A running n8n instance.
  • Google Maps API Key (or similar): For the "HTTP Request" node to fetch reviews.
  • OpenAI API Key: For the "OpenAI Chat Model" (for sentiment analysis) and "Embeddings OpenAI" (for generating embeddings) nodes.
  • Pinecone API Key and Environment: For the "Pinecone Vector Store" node to store and retrieve review data.
  • Telegram Bot Token: For the "Telegram Trigger" and "Telegram" nodes to interact with users.
  • Google Sheets Credential: For the "Google Sheets" node to log data.

Setup/Usage

  1. Import the workflow: Download the provided JSON and import it into your n8n instance.
  2. Configure Credentials:
    • Set up your Google Sheets credential.
    • Set up your OpenAI credential.
    • Set up your Pinecone credential, including your API Key and Environment.
    • Set up your Telegram credential with your bot token.
  3. Configure Nodes:
    • HTTP Request: Update the URL and any necessary headers/parameters to fetch Google Maps reviews from your desired source.
    • Edit Fields (Set): Adjust the fields being set or transformed if your review data structure differs.
    • OpenAI Chat Model: Ensure the correct model and parameters are selected for sentiment analysis.
    • Embeddings OpenAI: Verify the model used for generating embeddings.
    • Pinecone Vector Store: Configure your Pinecone index name and namespace.
    • AI Agent: Customize the agent's prompt and tools as needed for your specific use case.
    • Telegram Trigger: Ensure your Telegram bot is set up to receive messages.
    • Telegram (Output): Configure the chat ID where messages should be sent.
    • Google Sheets: Specify your spreadsheet ID, sheet name, and the data to be written.
  4. Activate the workflow: Once configured, activate the workflow. You can trigger it manually using the "Manual Trigger" or set up a schedule using the "Schedule Trigger."
  5. Interact via Telegram: If the Telegram trigger is enabled, send messages to your configured Telegram bot to interact with the AI Agent.

Related Templates

AI-powered code review with linting, red-marked corrections in Google Sheets & Slack

Advanced Code Review Automation (AI + Lint + Slack) Who’s it for For software engineers, QA teams, and tech leads who want to automate intelligent code reviews with both AI-driven suggestions and rule-based linting — all managed in Google Sheets with instant Slack summaries. How it works This workflow performs a two-layer review system: Lint Check: Runs a lightweight static analysis to find common issues (e.g., use of var, console.log, unbalanced braces). AI Review: Sends valid code to Gemini AI, which provides human-like review feedback with severity classification (Critical, Major, Minor) and visual highlights (red/orange tags). Formatter: Combines lint and AI results, calculating an overall score (0–10). Aggregator: Summarizes results for quick comparison. Google Sheets Writer: Appends results to your review log. Slack Notification: Posts a concise summary (e.g., number of issues and average score) to your team’s channel. How to set up Connect Google Sheets and Slack credentials in n8n. Replace placeholders (<YOURSPREADSHEETID>, <YOURSHEETGIDORNAME>, <YOURSLACKCHANNEL_ID>). Adjust the AI review prompt or lint rules as needed. Activate the workflow — reviews will start automatically whenever new code is added to the sheet. Requirements Google Sheets and Slack integrations enabled A configured AI node (Gemini, OpenAI, or compatible) Proper permissions to write to your target Google Sheet How to customize Add more linting rules (naming conventions, spacing, forbidden APIs) Extend the AI prompt for project-specific guidelines Customize the Slack message formatting Export analytics to a dashboard (e.g., Notion or Data Studio) Why it’s valuable This workflow brings realistic, team-oriented AI-assisted code review to n8n — combining the speed of automated linting with the nuance of human-style feedback. It saves time, improves code quality, and keeps your team’s review history transparent and centralized.

higashiyama By higashiyama
90

AI-powered candidate nurturing with scheduled WhatsApp & Gmail follow-ups

What This Workflow Does This workflow automates the candidate nurturing process, solving the common problem of candidates losing interest or "ghosting" after an application. It keeps them engaged and informed by sending a personalized, multi-channel (WhatsApp & Gmail) sequence of follow-up messages over their first week. The automation triggers when a new candidate is added to your ATS (e.g., via a Recrutei webhook). It then uses AI to generate a custom 3-part message (for Day 1, Day 3, and Day 7) tailored to the candidate's age and the specific job they applied for, ensuring a professional and empathetic experience that strengthens your employer brand. How it Works Trigger: A Webhook node captures the new candidate data from your Applicant Tracking System (ATS) or form. Data Preparation: Two Code nodes clean the incoming data. The first (Separating information) extracts key fields and formats the phone number. The second (Extract age) calculates the candidate's age from their birthday to be used by the AI. AI Content Generation: The workflow sends the candidate's details (name, age, job title) to an AI model (AI Recruitment Assistant). The AI has a detailed system prompt to generate three distinct messages for Day 1 (Thank You), Day 3 (Friendly Reminder), and Day 7 (Final Reinforcement), adapting its tone based on the candidate's age. Split Messages: A Code node (Separating messages per days) receives the single text block from the AI and splits it into three separate variables (day1, day3, day7). Day 1 Send: The workflow immediately sends the day1 message via both Gmail and WhatsApp (configured for Evolution API). Day 3 Send: A "Wait" node pauses the workflow for 2 days, after which it sends the day3 message. Day 7 Send: Another "Wait" node pauses for 4 more days, then sends the final day7 message, completing the 7-day nurturing sequence. Setup Instructions This workflow is plug-and-play once you configure the following 5 steps: Webhook Node: Copy the Test URL from the Webhook node and configure it in your ATS (e.g., Recrutei) or form builder to trigger whenever a new candidate is added. Run one test submission to make the data structure visible to n8n. AI Credentials: In the AI Recruitment Assistant node, select or create your OpenAI API credential. MCP Credential (Optional): If you use a Recrutei MCP, paste your endpoint URL into the MCP Recrutei node. Gmail Credentials: In all three Message Gmail nodes (Day 1, 3, 7), select or create your Gmail (OAuth2) credential. Optional:* In the same nodes, go to Options and change the Sender Name from your_company to your actual company name. WhatsApp (Evolution API): This template is pre-configured for the Evolution API. In all three Message WhatsApp nodes (Day 1, 3, 7), you must: URL: Replace {server-url} and {instance} with your Evolution API details. Headers: In the "Header Parameters" section, replace yourapikey with your actual Evolution API key.

Recrutei Automações By Recrutei Automações
54

Auto-reply & create Linear tickets from Gmail with GPT-5, gotoHuman & human review

This workflow automatically classifies every new email from your linked mailbox, drafts a personalized reply, and creates Linear tickets for bugs or feature requests. It uses a human-in-the-loop with gotoHuman and continuously improves itself by learning from approved examples. How it works The workflow triggers on every new email from your linked mailbox. Self-learning Email Classifier: an AI model categorizes the email into defined categories (e.g., Bug Report, Feature Request, Sales Opportunity, etc.). It fetches previously approved classification examples from gotoHuman to refine decisions. Self-learning Email Writer: the AI drafts a reply to the email. It learns over time by using previously approved replies from gotoHuman, with per-classification context to tailor tone and style (e.g., different style for sales vs. bug reports). Human Review in gotoHuman: review the classification and the drafted reply. Drafts can be edited or retried. Approved values are used to train the self-learning agents. Send approved Reply: the approved response is sent as a reply to the email thread. Create ticket: if the classification is Bug or Feature Request, a ticket is created by another AI agent in Linear. Human Review in gotoHuman: How to set up Most importantly, install the gotoHuman node before importing this template! (Just add the node to a blank canvas before importing) Set up credentials for gotoHuman, OpenAI, your email provider (e.g. Gmail), and Linear. In gotoHuman, select and create the pre-built review template "Support email agent" or import the ID: 6fzuCJlFYJtlu9mGYcVT. Select this template in the gotoHuman node. In the "gotoHuman: Fetch approved examples" http nodes you need to add your formId. It is the ID of the review template that you just created/imported in gotoHuman. Requirements gotoHuman (human supervision, memory for self-learning) OpenAI (classification, drafting) Gmail or your preferred email provider (for email trigger+replies) Linear (ticketing) How to customize Expand or refine the categories used by the classifier. Update the prompt to reflect your own taxonomy. Filter fetched training data from gotoHuman by reviewer so the writer adapts to their personalized tone and preferences. Add more context to the AI email writer (calendar events, FAQs, product docs) to improve reply quality.

gotoHumanBy gotoHuman
353