moorcheh-edge up

Synopsis

moorcheh-edge up [options]

Options

Flag	Default	Description
`--server-port`	`8080`	Host port for `http://localhost:<port>`
`--server-image`	`moorcheh/moorcheh-edge:latest`	Docker image to run
`-y`, `--yes`	off	Non-interactive: no prompts, use defaults + flags
`--skip-embedding`	off	Skip BGE entirely (vector-only; no warm embed daemon)
`--with-llm`	off	Install Ollama and pull the answer model (~400 MB)
`--skip-ollama`	off	Explicit search-only (same as default without `--with-llm`)
`--warm-llm`	off	With `--with-llm`: preload LLM in RAM (default keep-alive 24h)

Defaults

Component	Default `up`
Docker server	Yes
BGE embed daemon (warm)	Yes
Ollama + LLM model	No (use `--with-llm`)
LLM kept in RAM	No (use `--with-llm --warm-llm`)

In an interactive terminal, up asks up to three questions (embedding, AI answer, keep LLM in RAM). Use -y or flags to skip prompts.

Examples

moorcheh-edge up
moorcheh-edge up -y
moorcheh-edge up --with-llm
moorcheh-edge up --with-llm --warm-llm
moorcheh-edge up --skip-embedding
moorcheh-edge up --server-port 8083
moorcheh-edge up --server-image moorcheh/moorcheh-edge:0.1.0

On first run with embedding enabled, up downloads BGE-small-en-v1.5 (~67 MB) and starts a warm embedding daemon (stopped by down). With --with-llm on Linux, up also installs Ollama into ~/.moorcheh-edge/ollama/ and pulls qwen2.5:0.5b-instruct for answer.

Output

On success:

Data directory: ~/.moorcheh-edge/data
Moorcheh Edge API: http://localhost:8080

With --with-llm:

[moorcheh-edge] LLM endpoint (in container): http://host.docker.internal:11434/v1

If the container is already running, up attaches and prints the same lines without starting a second container. Run moorcheh-edge down then up after upgrades so LLM settings are applied to the container.

Requirements

Docker running
Pull access for moorcheh/moorcheh-edge:latest (or a local cached image)

Getting Started

Runtime

Search

AI Generation

Data Operations

Delete

Voice

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Examples

Output

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