How to Create an AI Knowledge Base in 2026 (Step-by-Step Guide)

Quick Answer

Ingest docs (Notion, Google Drive, PDFs, websites), chunk + embed, store in pgvector, then serve a chat UI that retrieves top chunks and streams LLM answers with source citations. Stack: Next.js + Supabase + assisters.dev-compatible API.

• Time to ship: 3-7 days
• Cost: $0.10-1 per 1K queries
• Use cases: Customer support, internal wiki, product docs

What You'll Need

• Source docs (Markdown, PDF, HTML, Notion, Confluence)
• Supabase with pgvector
• Next.js 15 for chat UI
• Embedding & LLM APIs

Steps

1. Inventory sources. List every doc source and format. PDFs, Notion, Drive, Google Docs, help center articles, Slack archives, GitHub wikis.
2. Build ingestion pipeline. For each source, fetch → extract text → chunk (500 tokens, 50 overlap) → embed → upsert to pgvector with metadata (source URL, title, updated_at).
3. Schema. create table kb_chunks (id uuid, source text, url text, title text, chunk text, embedding vector(1536), updated_at timestamptz); plus an ivfflat or HNSW index.
4. Schedule re-ingestion. Cron job daily for changed docs. Compare updated_at from source to stored, re-embed if newer. Delete orphans.
5. Build retrieval. User query → embed → top-8 chunks via cosine. Add re-ranking step (cross-encoder) for top-3 final if quality matters.
6. Chat UI. shadcn/ui chat pattern. Streaming LLM responses. Show source cards below each answer — clickable links with title + snippet.
7. Prompt the LLM carefully. "Answer using ONLY the context. Cite every claim as [1]. If context doesn't cover the question, say 'I don't have info on that.'" Include retrieved chunks with numeric IDs.
8. Add feedback loop. Thumbs up/down per answer. Log misses for review. Retrain retrieval weights or add missing content.

Common Mistakes

• Too-small chunks: 100-token chunks lose context. Stick to 400-600.
• No metadata: Can't filter by product/version/language without it.
• Chat only, no search: Offer both — some users want traditional keyword search too.
• Stale data: Schedule daily re-ingestion. Badge answers "updated: 2d ago."
• No access control: Internal KBs need row-level security by team/role.

Top Tools

Tool	Best For	Price
Supabase pgvector	Vector store	Free tier
LlamaIndex	Ingestion framework	Free
Unstructured.io	PDF/doc parsing	Free tier
Cohere Rerank-compatible	Re-ranking	$1/1K
shadcn/ui	Chat components	Free

FAQs

Q: Can I use Notion/Confluence as source? Yes — both have APIs. Poll or webhook to sync.

Q: How do I handle images in docs? Use vision models (Claude, GPT-4V) to caption images; embed the captions.

Q: What's a good retrieval quality metric? Hit@3 (is correct chunk in top 3?). Aim for >85%.

Q: Can I keep data on-premise? Yes — self-host Supabase, use local embedding model (bge-m3), local LLM (Llama).

Q: How many docs can pgvector handle? Millions of chunks comfortably with HNSW index on a 4-core VPS.

Q: Do I need LangChain? No — 200 lines of plain TypeScript does this. LangChain adds complexity fast.

Conclusion

AI knowledge bases replace 80% of support tickets and onboarding questions. Start with your help center docs, measure hit rate weekly, and expand sources. One KB can save your team 20+ hours per week.