Table of Contents
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
- Inventory sources. List every doc source and format. PDFs, Notion, Drive, Google Docs, help center articles, Slack archives, GitHub wikis.
- 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).
- 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. - Schedule re-ingestion. Cron job daily for changed docs. Compare
updated_atfrom source to stored, re-embed if newer. Delete orphans. - Build retrieval. User query → embed → top-8 chunks via cosine. Add re-ranking step (cross-encoder) for top-3 final if quality matters.
- Chat UI. shadcn/ui chat pattern. Streaming LLM responses. Show source cards below each answer — clickable links with title + snippet.
- 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.
- 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 |
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.