Smarter RAG Agents with Enriched Retrieval and Modular Workflows
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Alejandro Scuncia
Description
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, use_case, 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 document base.
💬 Have Feedback or Ideas? I’d Love to Hear
This project is open, modular, and evolving — just like great workflows should be :).
If you’ve tried it, built on top of it, or have suggestions for improvement, I’d genuinely love to hear from you. Let’s share ideas, collaborate, or just connect as part of the n8n builder community.
📧 [email protected]
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