OpenAI Chat Model workflow'ları
480 sonuç — tümü kaynağa bağlı n8n referansı
Effortless Email Management with AI-Powered Summarization & Review
How it Works This workflow automates the handling of incoming emails, summarizes their content, generates appropriate responses using a retrieval-augmented generation (RAG) approach, and obtains approval or suggestions before sending replies. Below is an explanation of its functionality divided into two main sections: Email Handling and Summarization: The process begins with the Email Trigger (IMAP) node which listens for new emails in a specified inbox. Once an email is received, the Markdown node converts its HTML content into plain text if necessary, followed by the Email Summarization Chain that uses AI to create a concise summary of up to 100 words. Response Generation and Approval: A Write email node generates a professional response based on the summarized content, ensuring brevity and professionalism while keeping within the word limit. Before sending out any automated replies, the system sends these drafts via Gmail for human review and approval through the Gmail node configured with free-text response options. If approved, the finalized email is sent back to the original sender using the Send Email node; otherwise, it loops back for further edits or manual intervention. A
AI-powered email processing autoresponder and response approval (Yes/No)
How it Works This workflow is designed to automate the process of handling incoming emails, summarizing their content, generating appropriate responses, and obtaining approval before sending replies. Below are the key operational steps: Email Reception and Summarization: The workflow starts with an Email Trigger (IMAP) node that listens for new emails in a specified inbox. Once an email is received, its HTML content is processed by a Markdown node to convert it into plain text if necessary, followed by an Email Summarization Chain node which uses AI to create a concise summary of the email's content using prompts tailored for this purpose. Response Generation and Approval: A Write email node generates a professional response based on the summarized content, utilizing predefined templates and guidelines such as keeping responses under 100 words and ensuring they're formatted correctly in HTML. Before sending out any automated replies, the system sends these drafts via Gmail for human review and approval through a Gmail node configured with double-approval settings. If approved (Approve?), the finalized email is sent back to the original sender using the Send Email node; otherwise, i
AI Automated HR Workflow for CV Analysis and Candidate Evaluation
How it Works This workflow automates the process of handling job applications by extracting relevant information from submitted CVs, analyzing the candidate's qualifications against a predefined profile, and storing the results in a Google Sheet. Here’s how it operates: Data Collection and Extraction: The workflow begins with a form submission (On form submission node), which triggers the extraction of data from the uploaded CV file using the Extract from File node. Two informationExtractor nodes (Qualifications and Personal Data) are used to parse specific details such as educational background, work history, skills, city, birthdate, and telephone number from the text content of the CV. Processing and Evaluation: A Merge node combines the extracted personal and qualification data into a single output. This merged data is then passed through a Summarization Chain that generates a concise summary of the candidate’s profile. An HR Expert chain evaluates the candidate against a desired profile (Profile Wanted), assigning a score and providing considerations for hiring. Finally, all collected and processed data including the evaluation results are appended to a Google Sheets document v
SChat with Postgresql Database
Who is this template for? This workflow template is designed for any professionals seeking relevent data from database using natural language. How it works Each time user ask's question using the n8n chat interface, the workflow runs. Then the message is processed by AI Agent using relevent tools - Execute SQL Query, Get DB Schema and Tables List and Get Table Definition, if required. Agent uses these tool to form and run sql query which are necessary to answer the questions. Once AI Agent has the data, it uses it to form answer and returns it to the user. Set up instructions Complete the Set up credentials step when you first open the workflow. You'll need a Postgresql Credentials, and OpenAI api key. Template was created in n8n v1.77.0
AI-Powered Email Automation for Business: Summarize & Respond with RAG
This workflow is ideal for businesses looking to automate their email responses, especially for handling inquiries about company information. It leverages AI to ensure accurate and professional communication. How It Works Email Trigger: The workflow starts with the Email Trigger (IMAP) node, which monitors an email inbox for new messages. When a new email arrives, it triggers the workflow. Email Preprocessing: The Markdown node converts the email's HTML content into plain text for easier processing by the AI models. Email Summarization: The Email Summarization Chain node uses an AI model (DeepSeek R1) to generate a concise summary of the email. The summary is limited to 100 words and is written in Italian. Email Classification: The Email Classifier node categorizes the email into predefined categories (e.g., "Company info request"). If the email does not fit any category, it is classified as "other". Email Response Generation: The Write email node uses an AI model (OpenAI) to draft a professional response to the email. The response is based on the email content and is limited to 100 words. The Review email node uses another AI model (DeepSeek) to review and format the drafted respo
Modular & Customizable AI-Powered Email Routing: Text Classifier for eCommerce
How It Works Form Submission: The workflow starts with the On form submission node, which triggers when a user submits a contact form. The form collects the user's name, email, and message. Text Classification: The Text Classifier node uses an AI model (GPT-4) to classify the submitted message into one of the predefined categories: Request Quote: For quote requests. Product info: For general product inquiries. General problem: For issues or problems related to products. Order: For questions about placed orders. Other: For any messages that don’t fit the above categories. Email Routing: Based on the classification, the workflow routes the message to the appropriate department via email: Prod. Dep.: For product-related inquiries. Quote Dep.: For quote requests. Gen. Dep.: For general problems. Order Dep.: For order-related questions. Other Dep.: For all other inquiries. Each email includes the user's name, email, message, and the classified category. Data Logging: The workflow logs the form submission and classification results into a Google Sheets document. Each department has its own sheet where the data is appended, including: User’s name, email, and message. Submission date and t
BBambooHR AI-Powered Company Policies and Benefits Chatbot
How it works This workflow enables companies to provide instant HR support by automating responses to employee queries about policies and benefits: Retrieves company policies, benefits, and HR documents from BambooHR. Uses AI to analyze and answer employee questions based on company records. Identifies the most relevant contact person for escalations. Seamlessly integrates with company systems to provide real-time HR assistance. Set up steps: Estimated time: ~20 minutes Connect your BambooHR account to allow policy retrieval. Configure AI parameters and access control settings. (Optional) Set up the employee lookup tool for personalized responses. Test the chatbot to ensure accurate responses and seamless integration. Benefits This workflow is perfect for HR teams looking to enhance employee support while reducing manual inquiries. Outperform BambooHR's "Ask BambooHR" Chatbot #1. Superior specificity of replies to general inquiries #2. More appropriate escalations when responding to sensitive employee concerns
AI Voice Chatbot with ElevenLabs & OpenAI for Customer Service and Restaurants
The "Voice RAG Chatbot with ElevenLabs and OpenAI" workflow in n8n is designed to create an interactive voice-based chatbot system that leverages both text and voice inputs for providing information. Ideal for shops, commercial activities and restaurants How it works: Here's how it operates: Webhook Activation: The process begins when a user interacts with the voice agent set up on ElevenLabs, triggering a webhook in n8n. This webhook sends a question from the user to the AI Agent node. AI Agent Processing: Upon receiving the query, the AI Agent node processes the input using predefined prompts and tools. It extracts relevant information from the knowledge base stored within the Qdrant vector database. Knowledge Base Retrieval: The Vector Store Tool node interfaces with the Qdrant Vector Store to retrieve pertinent documents or data segments matching the user’s query. Text Generation: Using the retrieved information, the OpenAI Chat Model generates a coherent response tailored to the user’s question. Response Delivery: The generated response is sent back through another webhook to ElevenLabs, where it is converted into speech and delivered audibly to the user. Continuous Interactio
Complete business WhatsApp AI-Powered RAG Chatbot using OpenAI
The provided workflow in n8n is designed to create a Business WhatsApp AI RAG (Retrieval-Augmented Generation) Chatbot. How it works: Webhook Setup: The workflow begins by setting up webhooks for verification and response. The Verify webhook receives GET requests and sends back a verification code, while the Respond webhook handles incoming POST requests from Meta regarding WhatsApp messages. Message Handling: Once a message is received, the workflow checks if the incoming JSON contains a user message. If it does, the message is processed further; otherwise, a generic response is sent. AI Agent Interaction: The user's message is passed to the AI Agent node, which uses a conversational agent with a predefined system message tailored for an electronics store. This ensures that the AI provides accurate and professional responses based on the knowledge base. Knowledge Base Utilization: The AI Agent references a knowledge base stored in Qdrant, a vector database. Documents from Google Drive are downloaded, vectorized using OpenAI embeddings, and stored in Qdrant for retrieval during conversations. Response Generation: The AI Agent generates a response using the OpenAI chat model (gpt-4o
Automate SIEM Alert Enrichment with MITRE ATT&CK, Qdrant & Zendesk in n8n
n8n Workflow: Automate SIEM Alert Enrichment with MITRE ATT&CK & Qdrant Who is this for? This workflow is ideal for: Cybersecurity teams & SOC analysts* who want to automate *SIEM alert enrichment**. IT security professionals* looking to integrate *MITRE ATT&CK intelligence** into their ticketing system. Organizations using Zendesk for security incidents* who need enhanced *contextual threat data**. Anyone using n8n and Qdrant* to build *AI-powered security workflows**. What problem does this workflow solve? Security teams receive large volumes of raw SIEM alerts that lack actionable context. Investigating every alert manually is time-consuming and can lead to delayed response times. This workflow solves this problem by: ✔ Automatically enriching SIEM alerts with MITRE ATT&CK TTPs. ✔ Tagging & classifying alerts based on known attack techniques. ✔ Providing remediation steps to guide the response team. ✔ Enhancing security tickets in Zendesk with relevant threat intelligence. What this workflow does 1️⃣ Ingests SIEM alerts (via chatbot or ticketing system like Zendesk). 2️⃣ Queries a Qdrant vector store containing MITRE ATT&CK techniques. 3️⃣ Extracts relevant TTPs (Tactics, Techni
WSpot Workplace Discrimination Patterns with AI
How It Works: • Scrapes company review data from Glassdoor using ScrapingBee. • Extracts demographic-based ratings using AI-powered text analysis. • Calculates workplace disparities with statistical measures like z-scores, effect sizes, and p-values. • Generates visualizations (scatter plots, bar charts) to highlight patterns of discrimination or bias. Example Visualizations: Set Up Steps: Estimated time: ~20 minutes. • Replace ScrapingBee and OpenAI credentials with your own. • Input the company name you want to analyze (best results with large U.S.-based organizations). • Run the workflow and review the AI-generated insights and visual reports. This workflow empowers users to identify potential workplace discrimination trends, helping advocate for greater equity and accountability. Additional Credit: Wes Medford For algorithms and inspiration
WSocial Media Analysis and Automated Email Generation
Social Media Analysis and Automated Email Generation > by Thomas Vie [email protected] Who is this for? This template is ideal for marketers, lead generation specialists, and business professionals seeking to analyze social media profiles of potential leads and automate personalized email outreach efficiently. What problem is this workflow solving? Manually analyzing social media profiles and crafting personalized emails can be time-consuming and prone to errors. This workflow streamlines the process by integrating social media APIs with AI to generate tailored communication, saving time and increasing outreach effectiveness. What this workflow does: Google Sheets Integration: Start with a Google Sheet containing lead information such as LinkedIn URL, Twitter handle, name, and email. Social Media Data Extraction: Automatically fetch profile and activity data from Twitter and LinkedIn using RapidAPI integrations. AI-Powered Content Generation: Use OpenAI's Chat Model to analyze the extracted data and generate personalized email subject lines and cover letters. Automated Email Dispatch: Send the generated email directly to the lead, with a copy sent to yourself for tracking purpos
WAI-Generated Summary Block for WordPress Posts
What is this workflow? This n8n template automates the process of adding an AI-generated summary at the top of your WordPress posts. It retrieves, processes, and updates your posts dynamically, ensuring efficiency and flexibility without relying on a heavy WordPress plugin. Example of AI Summary Section How It Works Triggers → Runs on a scheduled interval or via a webhook when a new post is published. Retrieves posts → Fetches content from WordPress and converts HTML to Markdown for AI processing. AI Summary Generation → Uses OpenAI to create a concise summary. Post Update → Inserts the summary at the top of the post while keeping the original excerpt intact. Data Logging & Notifications → Saves processed posts to Google Sheets and notifies a Slack channel. Why use this workflow? ✅ No need for a WordPress plugin → Keeps your site lightweight. ✅ Highly flexible → Easily connect with Google Sheets, Slack, or other services. ✅ Customizable → Adapt AI prompts, formatting, and integrations to your needs. ✅ Smart filtering → Ensures posts are not reprocessed unnecessarily. 💡 Check the detailed sticky notes for setup instructions and customization options!
Simple Expense Tracker with n8n Chat, AI Agent and Google Sheets
Use Case It is very convenient to add expenses via simple chat message. This workflow attempts to do exactly this using AI-powered n8n magic! Send message to a chat, something like "car wash; 59.3 usd; 25 jan 2024" And get a response: Your expense saved, here is the output of save sub-workflow:{"cost":59.3,"descr":"car wash","date":"2024-01-25","msg":"car wash; 59.3 usd; 25 jan 2024"} LLM will smartly parse your message to structured JSON and save the expense as a new row into Google Sheet! Installation 1. Set up Google Sheets: Clone this Sheet: https://docs.google.com/spreadsheets/d/1D0r3tun7LF7Ypb21CmbTKEtn76WE-kaHvBCM5NdgiPU/edit?gid=0#gid=0 (File -> Make a copy) Choose this sheet into "Save expense into Google Sheets" node. 2. Fix sub-workflow dropdown: open "Parse msg and save to Sheets" node (which is an n8n sub-workflow executor tool) and make sure the SAME workflow is chosen in the dropdown. it will allow n8n to locate and call "Workflow Input Trigger" properly when needed. 3. Activate the workflow to make chat work properly. Sent message to chat, something like "car wash; 59.3 usd; 25 jan 2024" you should get a response: Your expense saved, here is the output of save su
SAI Social Media Caption Creator creates social media post captions in Airtable
Welcome to my AI Social Media Caption Creator Workflow! What this workflow does This workflow automatically creates a social media post caption in an editorial plan in Airtable. It also uses background information on the target group, tonality, etc. stored in Airtable. This workflow has the following sequence: Airtable trigger (scan for new records every minute) Wait 1 Minute so the Airtable record creator has time to write the Briefing field retrieval of Airtable record data AI Agent to write a caption for a social media post. The agent is instructed to use background information stored in Airtable (such as target group, tonality, etc.) to create the post. Format the output and assign it to the correct field in Airtable. Post the caption into Airtable record. Requirements Airtable Database: Documentation AI API access (e.g. via OpenAI, Anthropic, Google or Ollama) Example of an editorial plan in Airtable: Editorial Plan example in Airtable For this workflow you need the Airtable fields "created_at", "Briefing" and "SoMe_Text_AI" Feel free to contact me via LinkedIn, if you have any questions!
BCWAuto-Tag Blog Posts in WordPress with AI
How it works: This workflow automates tagging for WordPress posts using AI: Fetch blog post content and metadata. Generate contextually relevant tags using AI. Verify existing tags in WordPress and create new ones if necessary. Automatically update posts with accurate and optimized tags. Set up steps: Estimated time: ~15 minutes. Configure the workflow with your WordPress API credentials. Connect your content source (e.g., RSS feed or manual input). Adjust tag formatting preferences in the workflow settings. Run the workflow to ensure proper tag creation and assignment. This workflow is perfect for marketers and content managers looking to streamline their content categorization and improve SEO efficiency.
Microsoft Outlook AI Email Assistant with contact support from Monday and Airtable
Microsoft Outlook AI Email Assistant Prerequisites 1. Microsoft 365 Login Credentials Provide your Office 365 credentials to connect Outlook. 2. Monday.com Generate an API token and have a board with your contact details. 3. Airtable Obtain an API key (or personal access token) and set up a base to store: Contacts (populated by the Monday.com sync). Rules & Categories (used by the AI Email Assistant). Use this Airtable base as the template: Airtable AI Email Assistant Template. Define your own rules, categories, and delete rules. 4. OpenAI API Key Sign up for OpenAI if you don’t already have an account. Generate a new API key at OpenAI API Keys. What the System Does 1. Daily Contact Sync (Monday.com → Airtable) Runs once a day to pull the latest contacts from Monday.com and store or update them in Airtable. 2. AI Email Categorisation & Prioritisation Fetches Outlook emails with filters. Cleans and processes email content. Matches emails with known contacts from Airtable. Uses an AI agent to classify, categorise, and prioritise emails. Updates Outlook categories and importance based on AI results. Runs in parallel with Airtable rules & categories retrieval for real-time decision-mak
WAI-Powered Information Monitoring with OpenAI, Google Sheets, Jina AI and Slack
Check Legal Regulations: This workflow involves scraping, so ensure you comply with the legal regulations in your country before getting started. Better safe than sorry! 📌 Purpose This workflow enables automated and AI-driven topic monitoring, delivering concise article summaries directly to a Slack channel in a structured and easy-to-read format. It allows users to stay informed on specific topics of interest effortlessly, without manually checking multiple sources, ensuring a time-efficient and focused monitoring experience. To get started, copy the Google Sheets template required for this workflow from here. 🎯 Target Audience This workflow is designed for: Industry professionals** looking to track key developments in their field. Research teams** who need up-to-date insights on specific topics. Companies** aiming to keep their teams informed with relevant content. ⚙️ How It Works Trigger: A Scheduler initiates the workflow at regular intervals (default: every hour). Data Retrieval: RSS feeds are fetched using the RSS Read node. Previously monitored articles are checked in Google Sheets to avoid duplicates. Content Processing: The article relevance is assessed using OpenAI (GPT
Workflow Results to Markdown Notes in Your Obsidian Vault, via Google Drive
This workflow converts any n8n workflow outputs into Markdown notes that are accessible in your Obsidian Vault through Google Drive synchronization. Setup Requirements Create a designated folder in Google Drive (Desktop). Create a symbolic link between this folder and a new target folder in your Obsidian Vault. Configure Google Drive n8n node settings. Send the output of any workflow to the trigger, and the notes will appear in your Vault folder. Optional Features You can use AI agents to: Write notes in your preferred format (e.g., Zettelkasten). Compose YAML front matter. Suggest tags. Use Cases Convert RSS feed items to notes. Create notes from YouTube video transcripts. Transform tasks in Slack messages into Obsidian tasks. (Requires setting up a corresponding workflow, e.g., RSS trigger, YouTube transcriber, or Slack bot.)
WScrape Trustpilot Reviews with DeepSeek, Analyze Sentiment with OpenAI
Workflow Overview This workflow automates the process of scraping Trustpilot reviews, extracting key details, analyzing sentiment, and saving the results to Google Sheets. It uses OpenAI for sentiment analysis and HTML parsing for review extraction. How It Works 1. Scrape Trustpilot Reviews HTTP Request**: Fetches review pages from Trustpilot (https://it.trustpilot.com/review/{{company_id}}). Paginates through pages (up to max_page limit). HTML Parsing**: Extracts review URLs using CSS selectors Splits the URLs into individual review links. 2. Extract Review Details Information Extractor**: Uses DeepSeek to extract structured data from the review: Author: Name of the reviewer. Rating: Numeric rating (1-5). Date: Review date in YYYY-MM-DD format. Title: Review title. Text: Full review text. Total Reviews: Number of reviews by the user. Country: Reviewer’s country (2-letter code). 3. Sentiment Analysis Sentiment Analysis Node**: Uses OpenAI to classify the review text as Positive, Neutral, or Negative. Example output: { "category": "Positive", "confidence": 0.95 } 4. Save to Google Sheets Google Sheets Node**: Appends or updates the extracted data to a Google Sheet Set Up Steps 1. Co
CWCreate a Branded AI-Powered Website Chatbot
Create a Branded AI Website Chatbot Engage website visitors with an intelligent chat widget powered by OpenAI. This template includes: 💬 Natural conversation handling 📅 Microsoft Outlook calendar integration 📝 Lead capture and information gathering 🔄 Human handoff capabilities Simply add a JavaScript snippet to your website and configure the workflow to match your needs. Follow our detailed setup guide to get started in minutes. > Note: Widget includes a "Powered By" affiliate link
DPersonal Shopper Chatbot for WooCommerce with RAG using Google Drive and openAI
This workflow combines OpenAI, Retrieval-Augmented Generation (RAG), and WooCommerce to create an intelligent personal shopping assistant. It handles two scenarios: Product Search: Extracts user intent (keywords, price ranges, SKUs) and fetches matching products from WooCommerce. General Inquiries: Answers store-related questions (e.g., opening hours, policies) using RAG and documents stored in Google Drive. How It Works 1. Chat Interaction & Intent Detection Chat Trigger**: Starts when a user sends a message ("When chat message received"). Information Extractor**: Uses OpenAI to analyze the message and determine if the user is searching for a product or asking a general question. Extracts: search (true/false). keyword, priceRange, SKU, category (if product-related). Example: { "search": true, "keyword": "red handbags", "priceRange": { "min": 50, "max": 100 }, "SKU": "BAG123", "category": "women's accessories" } 2. Product Search (WooCommerce Integration) AI Agent**: If search: true, routes the request to the personal_shopper tool. WooCommerce Node: Queries the WooCommerce store using extracted parameters (keyword, priceRange, SKU). Filters products in stock (stockStatus: "instock"
CAI marketing report (Google Analytics & Ads, Meta Ads), sent via email/Telegram
What this workflow does This workflow retrieves Online Marketing data (Google Analytics for several domains, Google Ads, Meta Ads) from the last 7 days and the same period in the previous year. The data is then prepared by AI as a table, analyzed and provided with a small summary. The summary is then sent by email to a desired address and, shortened and summarized again, sent to a Telegram account. This workflow has the following sequence: time trigger (e.g. every Monday at 7 a.m.) retrieval of Online Marketing data from the last 7 days (via sub workflows) assignment and summary of the data retrieval of Online Marketing data from the same time period of the previous year allocation and summary of the data preparation in tabular form and brief analysis by AI. sending the report as an email preparation in short form by AI for Telegram (optional) sending as Telegram message. Requirements The following accesses are required for the workflow: Google Analytics (via Google Analytics API): Documentation Google Ads (via HTTP Request -> Google Ads API):Documentation Meta Ads (via Facebook Graph API): Documentation AI API access (e.g. via OpenAI, Anthropic, Google or Ollama) SMTP access da
WAutomated End-to-End Fine-Tuning of OpenAI Models with Google Drive Integration
1. How it Works This n8n workflow automates fine-tuning OpenAI models through these key steps: Manual Trigger**: Starts with the "When clicking ‘Test workflow’" event to initiate the process. Downloads a .jsonl file from Google Drive Upload to OpenAI**: Uploads the .jsonl file to OpenAI via the "Upload File" node (with purpose "fine-tune"). Create Fine-tuning Job**: Sends a POST request to the endpoint https://api.openai.com/v1/fine_tuning/jobs with: { "training_file": "{{ $json.id }}", "model": "gpt-4o-mini-2024-07-18" } OpenAI automatically starts training the model based on the provided file. Interaction with the Trained Model**: An "AI Agent" uses the custom model (e.g., ft:gpt-4o-mini-2024-07-18:n3w-italia::XXXX7B) to respond to chat messages. 2. Set up Steps To configure the workflow: Prepare the Training File: Create a .jsonl file following the specified syntax (e.g., travel assistant Q/A examples). Upload it to Google Drive and update the ID in the "Google Drive" node. Configure Credentials: Google Drive: Connect an account via OAuth2 (googleDriveOAuth2Api). OpenAI: Add your API key in the "OpenAI Chat Model" and "Upload File" nodes. Customize the Model: In the "OpenAI Chat