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Build a 100% local RAG with n8n, Ollama and Qdrant. This agent uses a semantic database (Qdrant) to answer questions about PDF files. Tutorial Click here to view the YouTube Tutorial How it works Build a chatbot that answers based on documents you provide it (Retrieval Augmented Generation). You can upload as many PDF files as you want to the Qdrant database. The chatbot will use its retrieval tool to fetch the chunks and use them to answer questions. Installation 1. Install n8n + Ollama + Qdrant using the Self-hosted AI starter kit 2. Make sure to install Llama 3.2 and mxbai-embed-large as embeddings model. How to use it 1. First run the "Data Ingestion" part and upload as many PDF files as you want 2. Run the Chatbot and start asking questions about the documents you uploaded
by Thomas JanssenThis template quickly shows how to use RAG in n8n. Who is this for? This template is for everyone who wants to start giving knowledge to their Agents through RAG. Requirements Have a PDF with custom knowledge that you want to provide to your agent. Setup No setup required. Just hit Execute Workflow, upload your knowledge document and then start chatting. How to customize this to your needs 1. Add custom instructions to your Agent by changing the prompts in it. 2. Add a different way to load in knowledge to your vector store, e.g. by looking at some Google Drive files or loading knowledge from a table. 2. Exchange the Simple Vector Store nodes with your own vector store tools ready for production. 3. Add a more sophisticated way to rank files found in the vector store. For more information read our docs on RAG in n8n.
by n8n TeamRun an AI-powered degree audit for each senior student. This template reads student rows from Google Sheets, evaluates completed courses against hard-coded program requirements, and writes back an AI Degree Summary of what's still missing (major core, Gen Eds, major electives, and upper-division credits). It's designed for quick advisor/registrar review and SIS prototypes. Trigger: Manual — When clicking "Execute workflow" Core nodes: Google Sheets, OpenAI Chat Model, (optional) Structured Output Parser Programs included: Computer Science BS, Business Administration BBA, Psychology BA, Mechanical Engineering BS, Biology BS (Pre-Med), English Literature BA, Data Science BS, Nursing BSN, Economics BA, Graphic Design BFA Who's it for Registrars & advisors who need fast, consistent degree checks Student success teams building prototype dashboards SIS/EdTech builders exploring AI-assisted auditing How it works 1. Read seniors from Google Sheets (Senior_data) with: StudentID, Name, Program, Year, CompletedCourses. 2. AI Agent compares CompletedCourses to built-in requirements (per program) and computes Missing items + a short Summary. 3. Write back to the same sheet using "Append or update" by StudentID (updates AI Degree Summary; you can also map the raw Missing array to a column if desired). Example JSON (for one student): { "StudentID": "S001", "Program": "Computer Science BS", "Missing": [ "GEN-REMAIN | General Education credits remaining | 6", "CS-EL-REM | CS Major Electives (200+ level) | 6", "UPPER-DIV | Additional Upper-Division (200+ level) credits needed | 18", "FREE-EL | Free Electives to reach 120 total credits | 54" ], "Summary": "All core CS courses are complete. Still need 6 Gen Ed credits, 6 CS electives, and 66 total credits overall, including 18 upper-division credits — prioritize 200/300-level CS electives." } Setup (2 steps) 1) Connect Google Sheets (OAuth2) In n8n → Credentials → New → Google Sheets (OAuth2) and sign in. In the Google Sheets nodes, select your spreadsheet and the Senior_data tab. Ensure your input sheet has at least: StudentID, Name, Program, Year, CompletedCourses. 2) Connect OpenAI (API Key) In n8n → Credentials → New → OpenAI API, paste your key. In the OpenAI Chat Model node, select that credential and a model (e.g., gpt-4o or gpt-5). Requirements Sheet columns: StudentID, Name, Program, Year, CompletedCourses CompletedCourses format: pipe-separated IDs (e.g., GEN-101|GEN-103|CS-101). Program labels: should match the built-in list (e.g., Computer Science BS). Credits/levels: Template assumes upper-division ≥ 200-level (adjust the prompt if your policy differs). Customization Change requirements: Edit the Agent's system message to update totals, core lists, elective credit rules, or level thresholds. Store more output: Map Missing to a new column (e.g., AI Missing List) or write rows to a separate sheet for dashboards. Distribute results: Email summaries to advisors/students (Gmail/Outlook), or generate PDFs for advising folders. Add guardrails: Extend the prompt to enforce residency, capstone, minor/cognate constraints, or per-college Gen Ed variations. Best practices (per n8n guidelines) Sticky notes are mandatory: Include a yellow sticky note that contains this description and quick setup steps; add neutral sticky notes for per-step tips. Rename nodes clearly: e.g., "Get Seniors," "Degree Audit Agent," "Update Summary." No hardcoded secrets: Use credentials—not inline keys in HTTP or Code nodes. Sanitize identifiers: Don't ship personal spreadsheet IDs or private links in the published version. Use a Set node for config: Centralize user-tunable values (e.g., column names, tab names). Troubleshooting OpenAI 401/429: Verify API key/billing; slow concurrency if rate-limited. Empty summaries: Check column names and that CompletedCourses uses |. Program mismatch: Align Program labels to those in the prompt (exact naming recommended). Sheets auth errors: Reconnect Google Sheets OAuth2 and re-select spreadsheet/tab. Limitations Not an official audit: It infers gaps from the listed completions; registrar rules can be more nuanced. Catalog drift: Requirements are hard-coded in the prompt—update them each term/year. Upper-division heuristic: Adjust the level threshold if your institution defines it differently. Tags & category Category: Education / Student Information Systems Tags: degree-audit, registrar, google-sheets, openai, electives, upper-division, graduation-readiness Changelog v1.0.0 — Initial release: Senior_data in/out, 10 programs, AI Degree Summary output, append/update by StudentID. Contact Need help tailoring this to your catalog (e.g., per-college Gen Eds, capstones, minors, PDFs/email)? rbreen@ynteractive.com robert@ynteractive.com Robert Breen — https://www.linkedin.com/in/robert-breen-29429625/ ynteractive.com — https://ynteractive.com
by Robert Breen