记忆宫殿AI系统

Requirements

• Python 3.9+
• A vector-store backend (ChromaDB by default)
• ~300 MB disk for the embedding model. Onboarding (python -m mempalace.onboarding) offers embeddinggemma-300m (multilingual, 100+ languages, recommended) or all-MiniLM-L6-v2 (English-only, ~30 MB). See the docstring at mempalace/embedding.py for details and migration notes.

No API key is required for the core benchmark path.

Install

MemPalace ships a CLI, so install it in an isolated environment to avoid PEP 668 errors on Debian/Ubuntu/Homebrew Pythons and to keep mempalace's deps (chromadb, numpy, grpcio, …) from conflicting with anything else in your global site-packages.

We recommend uv — uv tool install puts the mempalace CLI in an isolated environment on your PATH:

uv tool install mempalace
mempalace init ~/projects/myapp

pipx works the same way if you prefer it: pipx install mempalace.

Prefer plain pip only inside an activated virtualenv where you explicitly want import mempalace available:

python -m venv .venv && source .venv/bin/activate
pip install mempalace

Docker

A container image is also available for running the MCP server or the CLI without a local Python toolchain. Everything persists under /data (palace, config, and the cached embedding model), so mount a volume there.

```bash

Build the image (CPU; bundles the `extract` + `spellcheck` extras)

docker build -t mempalace .

needs the optional driver: pip install mempalace[pgvector]

Quickstart

```bash

Run any CLI command instead (mount the host directory you want to mine)

docker run --rm -v mempalace-data:/data -v /path/to/project:/work mempalace mine /work docker run --rm -v mempalace-data:/data mempalace search "why GraphQL"

Wire it into an MCP client (e.g. Claude Code) as a stdio server:

docker compose run --rm mcp works too (see docker-compose.yml). For CUDA-accelerated embeddings, build the GPU variant with docker build -f Dockerfile.gpu -t mempalace:gpu . and run it with --gpus all. Customise the bundled extras at build time, e.g. docker build --build-arg EXTRAS="extract,spellcheck" -t mempalace ..

and the `vector` extension available on the server

MEMPALACE_PGVECTOR_DSN=postgresql://localhost:5432/mempalace \ mempalace mine ~/projects/myapp --backend pgvector ```

Qdrant can also be configured with MEMPALACE_QDRANT_API_KEY, MEMPALACE_QDRANT_NAMESPACE, and MEMPALACE_QDRANT_TIMEOUT; pgvector with MEMPALACE_PGVECTOR_NAMESPACE. Both external backends isolate tenants by namespace (advertised via the supports_namespace_isolation capability) and write a local marker (qdrant_backend.json / pgvector_backend.json) to guard against silently opening a palace against the wrong server.

When MEMPALACE_QDRANT_URL or MEMPALACE_PGVECTOR_DSN points anywhere other than your own local or trusted self-hosted service, MemPalace will send and store verbatim drawer text and metadata there. That is an explicit opt-in backend choice, never the default.