Engram

A local-first notes app with hybrid search and RAG chat. Everything runs on your machine using Ollama for embeddings and LLM inference.

Stack

• Next.js 16 (App Router, TypeScript, Tailwind CSS)
• tRPC for type-safe API layer
• Drizzle ORM + better-sqlite3 for persistence
• sqlite-vec for vector similarity search
• SQLite FTS5 for lexical full-text search
• Vercel AI SDK + Ollama for embeddings and chat
• Husky + lint-staged + commitlint for Conventional Commits enforcement

Prerequisites

• Node.js 20+
• Ollama installed and running

Pull the required models:

ollama pull llama3.1:8b
ollama pull nomic-embed-text

Setup

pnpm install
cp .env.example .env.local   # optional — defaults work without it
pnpm exec drizzle-kit generate      # generate migration SQL
sqlite3 ./data/engram.db < drizzle/<generated-file>.sql   # apply it
pnpm run dev

Note: pnpm exec drizzle-kit push is broken — see Schema Changes below. Use drizzle-kit generate + sqlite3 instead. The virtual tables (note_embeddings, note_fts) are created automatically on server start.

Open http://localhost:3000.

Docker

Production (full stack)

Runs the Next.js app and Ollama in containers. Models are pulled automatically on first run.

mkdir -p data
docker compose up --build

Open http://localhost:3000.

Data persists between runs — the SQLite database is bind-mounted to ./data/ and Ollama models are stored in a named volume. Use docker compose down -v only if you want to wipe model storage.

Dev (Ollama only)

Runs only Ollama in Docker while the app runs natively. Gives full hot reload speed with Metal GPU acceleration on macOS.

docker compose -f docker-compose.dev.yml up
pnpm run dev

Note: On macOS, Ollama inside Docker runs on CPU only (no Metal GPU). Inference with llama3.1:8b will be slow. For daily development, prefer native Ollama or switch to a smaller model via OLLAMA_CHAT_MODEL=llama3.2:3b in .env.local.

Environment Variables

See .env.example for all available options. Defaults work out of the box if Ollama is running locally.

Variable	Default	Description
OLLAMA_BASE_URL	http://localhost:11434/v1	Ollama API endpoint
OLLAMA_CHAT_MODEL	llama3.1:8b	Model used for RAG chat
OLLAMA_EMBEDDING_MODEL	nomic-embed-text	Model used for embeddings
EMBEDDING_DIMENSION	768	Vector dimension (must match embedding model)
DATABASE_PATH	./data/engram.db	SQLite database file path

Schema Changes

drizzle-kit push is broken — it introspects the database without loading sqlite-vec, hits the note_embeddings virtual table and crashes. Use drizzle-kit generate to produce a SQL migration file and apply it manually:

pnpm exec drizzle-kit generate
sqlite3 ./data/engram.db < drizzle/<generated-file>.sql

Resetting the Database

rm -f ./data/engram.db ./data/engram.db-shm ./data/engram.db-wal
pnpm exec drizzle-kit generate
sqlite3 ./data/engram.db < drizzle/<generated-file>.sql
pnpm run dev   # virtual tables are recreated automatically on startup

Running Tests

pnpm test

Running CI Locally

Install act and Docker, then:

act pull_request

Project Structure

src/
  app/              # Next.js pages and API routes
    api/chat/       # Streaming RAG chat endpoint
    api/trpc/       # tRPC handler
    chat/           # Chat page
    notes/new/      # Create note page
    search/         # Hybrid search page
  components/       # React components
  lib/
    ai/             # Ollama provider, embedding generation
    db/             # Drizzle schema, sqlite-vec helpers, FTS5 helpers
  trpc/             # tRPC router, React provider, server helpers
data/               # SQLite database (gitignored)
scripts/
  init-ollama.sh    # Pulls required Ollama models on first Docker run