Extract personal data with self-hosted LLM Mistral NeMo

Task: Create a simple API endpoint using the Webhook and Respond to Webhook nodes Why: You can prototype or replace a backend process with a single workflow Main use cases: Replace backend logic with a workflow

Want to learn the basics of n8n? Our comprehensive quick quickstart tutorial is here to guide you through the basics of n8n, step by step. Designed with beginners in mind, this tutorial provides a hands-on approach to learning n8n's basic functionalities.

You still can use the app in a workflow even if we don’t have a node for that or the existing operation for that. With the HTTP Request node, it is possible to call any API point and use the incoming data in your workflow Main use cases: Connect with apps and services that n8n doesn’t have integration with Web scraping How it works This workflow can be divided into three branches, each serving a distinct purpose: 1.Splitting into Items (HTTP Request - Get Mock Albums): The workflow initiates with a manual trigger (On clicking 'execute'). It performs an HTTP request to retrieve mock albums data from "https://jsonplaceholder.typicode.com/albums." The obtained data is split into items using the Item Lists node, facilitating easier management. 2.Data Scraping (HTTP Request - Get Wikipedia Page and HTML Extract): Another branch of the workflow involves fetching a random Wikipedia page using an HTTP request to "https://en.wikipedia.org/wiki/Special:Random." The HTML Extract node extracts the article title from the fetched Wikipedia page. 3.Handling Pagination (The final branch deals with handling pagination for a GitHub API request): It sends an HTTP request to "https://api.github.com/users/that-one-tom/starred," with parameters like the page number and items per page dynamically set by the Set node. The workflow uses conditions (If - Are we finished?) to check if there are more pages to retrieve and increments the page number accordingly (Set - Increment Page). This process repeats until all pages are fetched, allowing for comprehensive data retrieval.

This n8n template builds a simple WhatsApp chabot acting as a Sales Agent. The Agent is backed by a product catalog vector store to better answer user's questions. This template is intended to help introduce n8n users interested in building with WhatsApp. How it works This template is in 2 parts: creating the product catalog vector store and building the WhatsApp AI chatbot. A product brochure is imported via HTTP request node and its text contents extracted. The text contents are then uploaded to the in-memory vector store to build a knowledgebase for the chatbot. A WhatsApp trigger is used to capture messages from customers where non-text messages are filtered out. The customer's message is sent to the AI Agent which queries the product catalogue using the vector store tool. The Agent's response is sent back to the user via the WhatsApp node. How to use Once you've setup and configured your WhatsApp account and credentials First, populate the vector store by clicking the "Test Workflow" button. Next, activate the workflow to enable the WhatsApp chatbot. Message your designated WhatsApp number and you should receive a message from the AI sales agent. Tweak datasource and behaviour as required. Requirements WhatsApp Business Account OpenAI for LLM Customising this workflow Upgrade the vector store to Qdrant for persistance and production use-cases. Handle different WhatsApp message types for a more rich and engaging experience for customers.

Task: Merge two datasets into one based on matching rules Why: A powerful capability of n8n is to easily branch out the workflow in order to process different datasets. Even more powerful is the ability to join them back together with SQL-like joining logic. Main use cases: Appending data sets Keep only new items Keep only existing items

This workflow will backup your workflows to Github. It uses the public api to export all of the workflow data using the n8n node. It then loops over the data checks in Github to see if a file exists that uses the workflow name. Once checked it will then update the file on Github if it exists, Create a new file if it doesn't exist and if it's the same it will ignore the file. Config Options repo_owner - Github owner repo_name - Github repository name repo_path - Path within the Github repository >This workflow has been updated to use the n8n node and the code node so requires at least version 0.198.0 of n8n

This workflow employs OpenAI's language models and SerpAPI to create a responsive, intelligent conversational agent. It comes equipped with manual chat triggers and memory buffer capabilities to ensure seamless interactions. To use this template, you need to be on n8n version 1.50.0 or later.

This workflow integrates both web scraping and NLP functionalities. It uses HTML parsing to extract links, HTTP requests to fetch essay content, and AI-based summarization using GPT-4o. It's an excellent example of an end-to-end automated task that is not only efficient but also provides real value by summarizing valuable content. Note that to use this template, you need to be on n8n version 1.50.0 or later.

⚙️🛠️🚀🤖🦾 This template is a PoC of a ReAct AI Agent capable of fetching random pages (not only Wikipedia or Google search results). On the top part there's a manual chat node connected to a LangChain ReAct Agent. The agent has access to a workflow tool for getting page content. The page content extraction starts with converting query parameters into a JSON object. There are 3 pre-defined parameters: url** – an address of the page to fetch method** = full / simplified maxlimit** - maximum length for the final page. For longer pages an error message is returned back to the agent Page content fetching is a multistep process: An HTTP Request mode tries to get the page content. If the page content was successfuly retrieved, a series of post-processing begin: Extract HTML BODY; content Remove all unnecessary tags to recude the page size Further eliminate external URLs and IMG scr values (based on the method query parameter) Remaining HTML is converted to Markdown, thus recuding the page lengh even more while preserving the basic page structure The remaining content is sent back to an Agent if it's not too long (maxlimit = 70000 by default, see CONFIG node). NB: You can isolate the HTTP Request part into a separate workflow. Check the Workflow Tool description, it guides the agent to provide a query string with several parameters instead of a JSON object. Please reach out to Eduard is you need further assistance with you n8n workflows and automations! Note that to use this template, you need to be on n8n version 1.19.4 or later.

This n8n template builds a simple WhatsApp chabot acting as a Sales Agent. The Agent is backed by a product catalog vector store to better answer user's questions. This template is intended to help introduce n8n users interested in building with WhatsApp. How it works This template is in 2 parts: creating the product catalog vector store and building the WhatsApp AI chatbot. A product brochure is imported via HTTP request node and its text contents extracted. The text contents are then uploaded to the in-memory vector store to build a knowledgebase for the chatbot. A WhatsApp trigger is used to capture messages from customers where non-text messages are filtered out. The customer's message is sent to the AI Agent which queries the product catalogue using the vector store tool. The Agent's response is sent back to the user via the WhatsApp node. How to use Once you've setup and configured your WhatsApp account and credentials First, populate the vector store by clicking the "Test Workflow" button. Next, activate the workflow to enable the WhatsApp chatbot. Message your designated WhatsApp number and you should receive a message from the AI sales agent. Tweak datasource and behaviour as required. Requirements WhatsApp Business Account OpenAI for LLM Customising this workflow Upgrade the vector store to Qdrant for persistance and production use-cases. Handle different WhatsApp message types for a more rich and engaging experience for customers.

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.

The workflow first populates a Pinecone index with vectors from a Bitcoin whitepaper. Then, it waits for a manual chat message. When received, the chat message is turned into a vector and compared to the vectors in Pinecone. The most similar vectors are retrieved and passed to OpenAI for generating a chat response. Note that to use this template, you need to be on n8n version 1.19.4 or later.