dot-context 0.1.0a1

Dot — a local-first AI context memory daemon. Stop re-explaining your codebase to every AI tool.

● dot

A local-first AI context memory daemon. Stop re-explaining your codebase to every AI tool.

Every AI tool starts from zero: you explain your architecture to Claude Code, then again to Copilot, then again in a new chat. Dot ends that. It runs silently in the background, builds a deep understanding of your codebase and the decisions behind it, and serves the right context to any AI tool through a local REST API.

Local. Private. Model-agnostic. Open source. No code leaves your machine — embeddings are generated locally with sentence-transformers, storage is SQLite + ChromaDB on disk.

How it works

 filesystem ──▶ watcher ──▶ AST parser ──▶ semantic chunker ──▶ local embeddings
 git history ──▶ decision miner ──────────────────────────────▶ ┌─────────────┐
 AI chats ────▶ decision capture ─────────────────────────────▶ │ SQLite +    │
                                                                │ ChromaDB    │
        any AI tool ◀── /context (ranked, budgeted, formatted) ◀┴─────────────┘

• dot-indexer chunks code by function/class (not fixed token windows), extracts docstrings, imports, and TODO/decided to… comments, and embeds everything locally.
• dot-memory mines architectural decisions from commit messages and conversations, with a forgetting curve — stale memories decay, frequently used ones are reinforced.
• dot-context assembles context ranked by semantic similarity, file proximity, recency, and edit frequency, fills a token budget greedily, and formats it for the consumer (Claude XML, concise Copilot, markdown, or raw JSON).

Quick start

Not on PyPI yet — install from source. Full walkthrough with test experiments: docs/getting-started.md. Deep technical internals: docs/internals.md (prerequisites taught from zero in docs/foundations.md).

git clone https://github.com/aryanp-spektra/dot-context-engine.git
cd dot-context-engine && pip install -e ".[ml]"   # or ".[dev]" for the light build

cd your-project
dot init                          # index the project, wire up git + Claude Code hooks
dot daemon start                  # keep watching in the background

dot ask "how does auth middleware work?"
dot inject "refactoring the billing module" --fmt claude | pbcopy
dot status
dot dashboard                     # web UI at http://localhost:7337/ui

CLI

command	what it does
dot init	initialize Dot in a project (+ git hook, CLAUDE.md, Claude Code hook)
dot status	what Dot knows about the current project
dot ask "…"	query your codebase in natural language
dot inject [query]	print assembled context — pipe it anywhere
dot memory list/add/export/delete	browse and manage captured decisions
dot sync	force re-index
dot forget "pattern"	remove memories matching a pattern
dot dashboard	open the web UI
dot daemon run/start/stop/install-service	control the daemon (launchd/systemd)

REST API (localhost:7337)

GET  /status                 daemon health + project stats
GET  /context?query=&file=&fmt=claude|copilot|markdown|raw
POST /memory                 capture a decision        GET /memory   browse
POST /memory/conversation    extract decisions from an AI transcript
DELETE /memory/{id}          forget
GET  /graph                  dependency graph JSON
POST /ask                    natural-language codebase query
POST /sync                   force re-index

Integrations

• Claude Code — dot init adds a CLAUDE.md section and a SessionStart hook that injects context at the start of every session.
• VS Code / Copilot — the extension shows "what Dot knows about this file" in a sidebar, registers Dot as a Language Model tool for Copilot Chat (#dotContext), and offers one-click decision capture.
• Anything else — curl localhost:7337/context?query=...&fmt=raw.

Development

make install     # editable install with dev extras
make test        # pytest
make lint        # ruff
make dashboard   # build the web UI into dashboard/dist (served at /ui)
make extension   # compile the VS Code extension

The ML stack (chromadb, sentence-transformers) and tree-sitter are optional extras — without them Dot degrades to a deterministic hashing embedder, SQLite brute-force vector search, and heuristic parsing, so the full pipeline still works (and tests run) on any machine.

See docs/getting-started.md for the full walkthrough and test experiments, docs/internals.md for the complete technical deep dive (architecture, algorithms, math, and trade-offs), and docs/integrations.md for tool wiring.

License

MIT