OpenAI
6 Ergebnisse — ausschließlich quellenverknüpfte n8n-Referenzen
Multi-Source RAG System with GPT-4 Turbo, News & Academic Papers Integration
Multi-Source RAG System with GPT-4 Turbo, News & Academic Papers Integration This workflow provides an enterprise-grade RAG (Retrieval-Augmented Generation) system that intelligently searches multiple sources and generates AI-powered responses using GPT-4 Turbo. How it works This workflow provides an enterprise-grade RAG (Retrieval-Augmented Generation) system that intelligently searches multiple sources and generates AI-powered responses using GPT-4 Turbo. Key Steps Form Input - Collects user queries with customizable search scope, response style, and language preferences Intelligent Search - Routes queries to appropriate sources (web, academic papers, news, internal documents) Data Aggregation - Unifies and processes information from multiple sources with quality scoring AI Processing - Uses GPT-4 Turbo to generate context-aware, source-grounded responses Response Enhancement - Formats outputs in various styles (comprehensive, concise, technical, etc.) Multi-Channel Delivery - Delivers results via webhook, email, Slack, and optional PDF generation Data Sources & AI Models Search Sources Web Search**: Google, Bing, DuckDuckGo integration Academic Papers**: arXiv, PubMed, Google Sc
Auto document your n8n workflows
Who is this for? This workflow is designed for n8n users and developers who need to automate the documentation process of their n8n workflows. It's particularly useful for teams looking to streamline their documentation efforts and ensure consistency across their workflow documentation. What problem is this workflow solving? / Use case The primary problem this workflow addresses is the manual and time-consuming process of creating documentation for n8n workflows. It automates the generation of concise, clear, and comprehensive documentation directly from the workflow's JSON, making it easier for both technical and non-technical users to understand what the workflow does and how it operates. What this workflow does Upon receiving a form submission with the workflow title and JSON, this workflow automatically generates documentation that includes: A brief introduction to the workflow. The trigger mechanism (webhook URLs for test and production environments, or cron schedules). Setup requirements, including necessary credentials and external dependencies. Setup Credentials Setup: Ensure you have OpenAI API credentials configured in n8n to use the GPT model for generating documentation
Qualify replies from Pipedrive persons with AI
About the workflow The workflow reads every reply that is received from a cold email campaign and qualifies if the lead is interested in a meeting. If the lead is interested, a deal is made in pipedrive. You can add as many email inboxes as you need! Setup: Add credentials to the Gmail, OpenAI and Pipedrive Nodes. Add a in_campaign field in Pipedrive for persons. In Pipedrive click on your credentials at the top right, go to company settings > Data fields > Person and click on add custom field. Single option [TRUE/FALSE]. If you have only one email inbox, you can delete one of the Gmail nodes. If you have more than two email inboxes, you can duplicate a Gmail node as many times as you like. Just connect it to the Get email node, and you are good to go! In the Gmail inbox nodes, select Inbox under label names and uncheck Simplify.
Prepare CSV files with GPT-4
This workflow generates CSV files containing a list of 10 random users with specific characteristics using OpenAI's GPT-4 model. It then splits this data into batches, converts it to CSV format, and saves it to disk for further use. The execution of the workflow begins from here when triggered manually. "OpenAI" Node. This uses the OpenAI API to generate random user data. The input to the OpenAI API is a fixed string, which asks for a list of 10 random users with some specific attributes. The attributes include a name and surname starting with the same letter, a subscription status, and a subscription date (if they are subscribed). There is also a short example of the JSON object structure. This technique is called one-shot prompting. "Split In Batches" Node. This node is used to handle the OpenAI responses one by one. "Parse JSON" Node. This node converts the content of the message received from the OpenAI node (which is in string format) into a JSON object. "Make JSON Table" Node. This node is used to convert the JSON data into a tabular format, which is easier to handle for further data processing. "Convert to CSV" Node. This node converts the table format data received from the
Telegram AI Chatbot
The workflow starts by listening for messages from Telegram users. The message is then processed, and based on its content, different actions are taken. If it's a regular chat message, the workflow generates a response using the OpenAI API and sends it back to the user. If it's a command to create an image, the workflow generates an image using the OpenAI API and sends the image to the user. If the command is unsupported, an error message is sent. Throughout the workflow, there are additional nodes for displaying notes and simulating typing actions.
Reddit AI digest
This workflow digests mentions of n8n on Reddit that can be sent as an single email or Slack summary each week. We use OpenAI to classify if a specific Reddit post is really about n8n or not, and then the summarise it into a bullet point sentence. How it works Get posts from Reddit that might be about n8n; Filter for the most relevant posts (posted in last 7 days and more than 5 upvotes and is original content); Check if the post is actually about n8n; If it is, categorise with OpenAI. Bear in mind: Workflow only considers first 500 characters of each reddit post. So if n8n is mentioned after this amount, it won't register as being a post about n8n.io. Next steps Improve OpenAI Summary node prompt to return cleaner summaries; Extend to more platforms/sources - e.g. it would be really cool to monitor larger Slack communities in this way; Do some classification on type of user to highlight users likely to be in our ICP; Separate a list of data sources (reddit, twitter, slack, discord etc.), extract messages from there and have them go to a sub workflow for classification and summarisation.