Build a PDF-based RAG system with OpenAI, Pinecone and Cohere reranking
This workflow contains community nodes that are only compatible with the self-hosted version of n8n. This workflow provides a complete, ready-to-use template for a Retrieval-Augmented Generation (RAG) system. It allows you to build a powerful AI chatbot that can answer questions based on the content of PDF documents you provide, using a modern and powerful stack for optimal performance. Good to know Costs: This workflow uses paid services (OpenAI, Pinecone, Cohere). Costs will be incurred based on your usage. Please review the pricing pages for each service to understand the potential expenses. Video Tutorial (Bahasa Indonesia): For a step-by-step guide on how this workflow functions, you can watch the accompanying video tutorial here: N8N Tutorial: Membangun Chatbot RAG dengan Pinecone, OpenAI, & Cohere How it works This workflow operates in two distinct stages: Data Ingestion & Indexing: It begins when a .pdf file is uploaded via the n8n Form Trigger. The Default Data Loader node processes the PDF, and the Recursive Character Text Splitter breaks down the content into smaller, manageable chunks. The Embeddings OpenAI node converts these text chunks into vector embeddings (numerical representations). Finally, the Pinecone Vector Store node takes these embeddings and stores (upserts) them into your specified Pinecone index, creating a searchable knowledge base. Conversational AI Agent: A user sends a message through the Chat Trigger. The AI Agent receives the message and uses its VectorDB tool to search the Pinecone index for relevant information. The Reranker Cohere node refines these search results, ensuring only the most relevant context is selected. The user's original question and the refined context are sent to the OpenAI Chat Model (gpt-4.1), which generates a helpful, context-aware answer. The Simple Memory node maintains conversation history, allowing for natural, multi-turn dialogues. How to use Using this workflow is a two-step process: Populate the Knowledge Base: First, you need to add documents. Trigger the workflow by using the Form Trigger and uploading a PDF file. Wait for the execution to complete. You can do this for multiple documents. Start Chatting: Once your data has been ingested, open the Chat Trigger's interface and start asking questions related to the content of your uploaded documents. The Form Trigger is just an example. Feel free to replace it with other triggers, such as a node that watches a Google Drive or Dropbox folder for new files. Requirements To run this workflow, you will need active accounts and API keys for the following services. OpenAI Account & API Key: Function: Powers text embedding and the final chat generation. Required for the Embeddings OpenAI and OpenAI Chat Model nodes. Pinecone Account & API Key: Function: Used to store and retrieve your vector knowledge base. Required for the Pinecone Vector Store and VectorDB nodes. You also need to provide your Pinecone Environment. Cohere Account & API Key: Function: Improves the accuracy of your chatbot by re-ranking search results for relevance. Required for the Reranker Cohere node. Customising this workflow This template is a great starting point. Here are a few ways you can customize it: Change the AI Personality: Edit the System Message in the AI Agent node to change the bot's behavior, tone, or instructions. Use Different Models: You can easily swap the OpenAI model for another one (e.g., gpt-3.5-turbo for lower costs) in the OpenAI Chat Model node. Adjust Retrieval: In the VectorDB tool node, you can modify the Top K parameter to retrieve more or fewer document chunks to use as context. Automate Ingestion: Replace the manual Form Trigger with an automated one, like a node that triggers whenever a new file is added to a specific cloud storage folder.
By Aji PrakosoScrape LinkedIn jobs with Gemini AI and store in Google Sheets using RSS
What Problem Does it Solve This workflow automates the process of finding and collecting job postings from LinkedIn, eliminating the need for manual job searching. It’s designed to save time and ensure you don’t miss out on new opportunities by automatically populating a spreadsheet with key job details. Key Features Automated Data Collection: The workflow pulls job posts from a LinkedIn search via an RSS feed. Intelligent Data Extraction: It scrapes the full job description and uses AI to summarize the key benefits and job responsibilities into a concise format. Centralized Database: All collected and processed information is automatically saved to a Google Sheet, providing a single source of truth for your job search. How It Works The workflow starts when manually triggered. It reads the job posts from a given RSS feed, processing each one individually. For each job, it fetches the full webpage content to extract structured data. This data is then cleaned and passed to an AI model, which generates a brief summary of the job and its benefits. Finally, a new row is either added or updated in a Google Sheet with all the collected details, including the job title, company name, and AI-generated summary. Configuration & Customization This workflow is highly customizable to fit your specific needs. RSS Feed: To get started, you'll need to provide the RSS feed URL for your desired LinkedIn job search. We can help you set this up. AI Model: The workflow uses Google Gemini by default, but it can be adjusted to work with other AI platforms. Data Destination: The output is configured to a Google Sheet, but it can easily be changed to a different platform like Notion or a CRM. AI Prompting: The AI's instructions are customizable, so you can tailor the output to extract different information or match a specific tone. If you need any help Get In Touch
By Abdullah Alshiekh