Knowledge Base RAG

Knowledge Base RAG System Generator

Build a production-grade knowledge base — similar to Microsoft Copilot Studio's "Knowledge" feature — where users add files, URLs, and text, and an AI agent uses all of it as grounded context.

This skill supports two output modes:

• n8n Mode → Generates importable n8n workflow JSONs + SQL schema
• Code Mode → Generates application code (TypeScript or Python) + SQL schema

Both modes share the same RAG theory, database schema, and architectural principles. The difference is in the implementation layer.

When to Use This Skill

Use this skill when the user wants to:

• Build a knowledge base where users upload documents and an AI answers from them
• Implement RAG (Retrieval-Augmented Generation) in any application
• Create a document Q&A system, FAQ bot, or support assistant grounded in custom data
• Add semantic search or vector search to an existing project
• Build something like Copilot Studio's Knowledge feature with their own stack
• Set up a document ingestion pipeline with chunking, embedding, and indexing
• Integrate hybrid search (vector + keyword) into their AI agent or chatbot

This skill covers the full lifecycle: ingestion, chunking, embedding, storage, retrieval, reranking, and context formatting — for both n8n workflow and application code approaches.

Core Components Overview

Before diving into references, here's what a complete RAG system needs:

Quick Reference: What to Read and When

What This System Does

Users can add three types of knowledge sources:

1. Files — PDF, DOCX, TXT, MD
2. URLs — Websites scraped and indexed
3. Text — Plain text or HTML pasted directly

All content is processed through the same pipeline:

Source → Extract Text → Contextual Chunking → Hybrid Indexing → Ready for AI
                              ↓                      ↓
                    (context-enriched chunks)  (vector + BM25 indexes)

The AI queries this knowledge base using:

Question → Query Expansion → Hybrid Search (vector + BM25)
    → Rerank Results → Format Context → AI Response

Step-by-Step Generation Process

Step 1: Determine Output Mode

This is the first and most important question. Ask the user:

"How do you want to build this system? I can generate:

A) n8n Workflows — Importable JSON workflows for n8n. Best if you're building automations with n8n and want to plug RAG into your AI Agent nodes.

B) Application Code — TypeScript or Python services/APIs. Best if you're building a custom application (Next.js, Express, FastAPI, etc.) and want to integrate RAG directly into your codebase.

Which approach fits your project?"

How to infer the mode if not explicitly stated:

• User mentions n8n, workflows, AI Agent node, webhooks → n8n Mode
• User mentions Next.js, Express, FastAPI, React, API routes, their app → Code Mode
• User is building a SaaS product → likely Code Mode
• User wants quick automation / chatbot → likely n8n Mode
• If ambiguous → ask

Step 2: Gather Requirements

Ask (skip already answered):

Common to both modes:

1. Database — Supabase Cloud or self-hosted PostgreSQL?
2. Source types — Files / URLs / Text? Default: all three
3. File types — PDF, DOCX, TXT/MD? Default: all
4. Multi-tenant — Multiple clients/tenants? Default: no
5. Embedding model — Default: OpenAI text-embedding-3-small (1536d)
6. RAG level — Basic (vector-only) or Advanced (hybrid + contextual)? Default: Advanced
7. Content language — For BM25 config. Default: Portuguese

n8n Mode specific: 8. n8n environment — Self-hosted or cloud? 9. Trigger type — Webhook only, or also scheduled/event-driven?

Code Mode specific: 8. Language — TypeScript or Python? 9. Framework — Express, Next.js API Routes, FastAPI, NestJS, etc.? 10. Existing project — Adding to existing codebase or greenfield? 11. ORM/DB client — Prisma, Drizzle, Knex, pg, asyncpg, SQLAlchemy, etc.?

Step 3: Read Theory

Always read references/rag-theory.md first, regardless of mode. This ensures the system uses modern RAG techniques. Key concepts covered:

• Naive RAG → Advanced RAG → Modern RAG evolution
• Contextual chunking (Anthropic's technique, 49-67% retrieval improvement)
• Hybrid search (vector + BM25)
• Reranking with cross-encoders
• Query expansion and HyDE
• Common failure modes and fixes
• Decision framework: prototype vs production vs mission-critical

Step 4: Generate SQL Schema

Same for both modes. Read references/sql-schema.md, then generate:

• knowledge_sources table (unified for files, URLs, text)
• document_chunks table (embedding + tsvector for hybrid search)
• match_documents() and hybrid_search() PostgreSQL functions
• HNSW + GIN indexes
• Optional: RLS, multi-tenant variant

Step 5: Generate Implementation (mode-dependent)

If n8n Mode:

Read these references:

• references/workflow-ingestion.md
• references/workflow-rag-query.md
• references/n8n-patterns.md

Generate:

1. schema.sql — Database setup
2. workflow-file-upload.json — File ingestion
3. workflow-url-scrape.json — URL ingestion
4. workflow-text-input.json — Text ingestion
5. workflow-rag-query.json — AI agent query tool

If Code Mode:

Read these references:

• references/code-ingestion.md
• references/code-rag-query.md
• references/code-patterns.md

Generate project files based on user's stack. Typical output:

TypeScript (Express/Next.js):

schema.sql
src/
  lib/
    db.ts                  — Database connection
    embeddings.ts          — OpenAI embedding client
    chunker.ts             — Text chunking with sentence boundaries
    contextualizer.ts      — Contextual chunking via LLM
    text-extractors.ts     — PDF/DOCX/TXT extraction
    url-scraper.ts         — URL content extraction
  services/
    ingestion.service.ts   — Unified ingestion pipeline
    rag-query.service.ts   — Hybrid search + formatting
  routes/
    knowledge.routes.ts    — REST API endpoints
  types/
    knowledge.types.ts     — TypeScript interfaces

Python (FastAPI):

schema.sql
app/
  core/
    database.py            — Database connection
    embeddings.py          — OpenAI embedding client
    chunker.py             — Text chunking
    contextualizer.py      — Contextual chunking via LLM
    extractors.py          — PDF/DOCX/TXT extraction
    scraper.py             — URL content extraction
  services/
    ingestion.py           — Unified ingestion pipeline
    rag_query.py           — Hybrid search + formatting
  routes/
    knowledge.py           — FastAPI route handlers
  models/
    schemas.py             — Pydantic models

Adapt to the user's existing project structure. If they already have a src/lib folder, follow their conventions. If they use Prisma, generate a Prisma schema alongside the raw SQL. Match their patterns.

Step 6: Deliver

Present the generated files with a brief setup guide.

n8n Mode setup:

1. Run schema.sql in Supabase SQL Editor / psql
2. Import workflow JSONs into n8n
3. Update credential IDs in all nodes
4. Test with sample upload

Code Mode setup:

1. Run schema.sql
2. Install dependencies (list them)
3. Set environment variables (DB URL, OpenAI key)
4. Integrate routes/services into existing app
5. Test with sample upload

Customization Parameters (both modes)

Architecture (both modes share this)

┌─────────────────────────────────────────────────────┐
│                 INGESTION LAYER                      │
│                                                      │
│  n8n Mode: Webhook workflows (JSON)                  │
│  Code Mode: REST API endpoints (Express/FastAPI)     │
│                                                      │
│  ┌─────────┐  ┌──────────┐  ┌──────────┐           │
│  │  Files   │  │   URLs   │  │   Text   │           │
│  └────┬─────┘  └────┬─────┘  └────┬─────┘           │
│       └──────────────┼─────────────┘                 │
│                      ↓                               │
│     ┌──────────────────────────────────┐             │
│     │   Shared Processing Pipeline     │             │
│     │   Extract → Chunk → Contextualize│             │
│     │   → Embed → Store                │             │
│     └──────────────────────────────────┘             │
└──────────────────┬──────────────────────────────────┘
                   ↓
┌─────────────────────────────────────────────────────┐
│          STORAGE LAYER (PostgreSQL + pgvector)       │
│          (identical for both modes)                  │
│                                                      │
│  knowledge_sources  → source metadata + status       │
│  document_chunks    → text + embedding + tsvector    │
│  hybrid_search()    → vector + BM25 combined         │
└──────────────────┬──────────────────────────────────┘
                   ↓
┌─────────────────────────────────────────────────────┐
│                  QUERY LAYER                         │
│                                                      │
│  n8n Mode: Tool Workflow for AI Agent node           │
│  Code Mode: Service function / API endpoint          │
│                                                      │
│  Query → Embed → Hybrid Search → Rerank → Context    │
└─────────────────────────────────────────────────────┘

Best Practices

1. Always use hybrid search in production — Vector-only search misses exact keywords (product codes, acronyms, IDs). Combining vector + BM25 consistently outperforms either alone.
2. Enable contextual chunking for any serious use case — The one-time ingestion cost pays for itself with 49-67% fewer retrieval failures (Anthropic research).
3. Keep chunks between 500-1000 tokens — Too large dilutes embeddings, too small loses context. 800 tokens with 200 overlap is a solid default.
4. Use the same embedding model for ingestion AND querying — Mixing models produces incompatible vector spaces. This is the #1 silent failure mode.
5. Retrieve broadly, return precisely — Fetch 10-20 candidates, then rerank or filter down to the top 5 for the LLM. This dramatically improves answer quality.
6. Match BM25 language config to your content — PostgreSQL text search needs the correct language configuration ('portuguese', 'english', etc.) for proper stemming.
7. Process ingestion asynchronously for large files — Return immediately with a status endpoint. Use background processing or a job queue for files > 5MB.
8. Add evaluation early — Track retrieval precision with test queries before going to production. A simple "expected answer in top-5 results" test catches most issues.

Common Issues & Solutions