Smarter RAG agents with enriched retrieval and modular workflows

An extendable RAG template to build powerful, explainable AI assistants — with query understanding, semantic metadata, and support for free-tier tools like Gemini, Gemma and Supabase. Description This workflow helps you build smart, production-ready RAG agents that go far beyond basic document Q&A. It includes: ✅ File ingestion and chunking ✅ Asynchronous LLM-powered enrichment ✅ Filterable metadata-based search ✅ Gemma-based query understanding and generation ✅ Cohere re-ranking ✅ Memory persistence via Postgres Everything is modular, low-cost, and designed to run even with free-tier LLMs and vector databases. Whether you want to build a chatbot, internal knowledge assistant, documentation search engine, or a filtered content explorer — this is your foundation. ⚙️ How It Works This workflow is divided into 3 pipelines: 📥 Ingestion - Upload a PDF via form - Extract text and chunk it for embedding - Store in Supabase vector store using Google Gemini embeddings 🧠 Enrichment (Async) - Scheduled task fetches new chunks - Each chunk is enriched with LLM metadata (topics, usecase, risks, audience level, summary, etc.) - Metadata is added to the vector DB for improved retrieval and filtering 🤖 Agent Chat - A user question triggers the RAG agent - Query Builder transforms it into keywords and filters - Vector DB is queried and reranked - The final answer is generated using only retrieved evidence, with references - Chat memory is managed via Postgres 🌟 Key Features - Asynchronous enrichment → Save tokens, batch process with free-tier LLMs like Gemma - Metadata-aware → Improved filtering and reranking - Explainable answers → Agent cites sources and sections - Chat memory → Persistent context with Postgres - Modular design → Swap LLMs, rerankers, vector DBs, and even enrichment schema - Free to run → Built with Gemini, Gemma, Cohere, Supabase (free tier-compatible) 🔐 Required Credentials |Tool|Use| |-|-|-| |Supabase w/ PostreSQL|Vector DB + storage| |Google Gemini/Gemma|Embeddings & LLM| |Cohere API|Re-ranking| |PostgreSQL|Chat memory| 🧰 Customization Tips - Swap extractFromFile with Notion/Google Drive integrations - Extend Metadata Obtention prompt to fit your domain (e.g., financial, legal) - Replace LLMs with OpenAI, Mistral, or Ollama - Replace Postgre Chat Memory with Simple Memory or any other - Use a webhook instead of a form to automate ingestion - Connect to Telegram/Slack UI with a few extra nodes 💡 Use Cases - Company knowledge base bot (internal docs, SOPs) - Educational assistant with smart filtering (by topic or level) - Legal or policy assistant that cites source sections - Product documentation Q&A with multi-language support - Training material assistant that highlights risks/examples - Content Generation 🧠 Who It’s For - Indie developers building smart chatbots - AI consultants prototyping Q&A assistants - Teams looking for an internal knowledge agent - Anyone building affordable, explainable AI tools 🚀 Try It Out! Deploy a modular RAG assistant using n8n, Supabase, and Gemini — fully customizable and almost free to run. 1. 📁 Prepare Your PDFs - Use any internal documents, manuals, or reports in PDF format. - Optional: Add Google Drive integration to automate ingestion. 2. 🧩 Set Up Supabase - Create a free Supabase project - Use the table creation queries included in the workflow to set up your schema. - Add your supabaseUrl and supabaseKey in your n8n credentials. > 💡 Pro Tip: Make sure you match the embedding dimensions to your model. This workflow uses Gemini text-embedding-04 (768-dim) — if switching to OpenAI, change your table vector size to 1536. 3. 🧠 Connect Gemini & Gemma - Use Gemini/Gemma for embeddings and optional metadata enrichment. - Or deploy locally for lightweight async LLM processing (via Ollama/HuggingFace). 4. ⚙️ Import the Workflow in n8n - Open n8n (self-hosted or cloud). - Import the workflow file and paste your credentials. You’re ready to ingest, enrich, and query your docume