bigger-boat

Meta repository for the klikkikuri service

Components

• meri 🌊: The main service

• karikko 🪨: Gradio based UI for testing the backend.

• suola 🧂: WebAssembly module for url normalization.

• laituri ⚓: Scheduler for the scraping jobs

  Laituri reads the extractors frequency and schedules the next scanning job accordingly.

• lautta 🚢: Extractor worker instance.

Running Meri

Copy the example configuration files:

cp .env.example .env
cp config.example.yaml instance/config.yaml

Build the production image:

docker build --target production -t klikkikuri-meri:latest .

Run the container:

docker run --rm \
  -v ./instance:/app/instance:rw \
  -e KLIKKIKURI_CONFIG_FILE=/app/instance/config.yaml \
  --env-file .env \
  klikkikuri-meri:latest meri run --sample

Alternatively, you can use docker-compose to run the service:

docker compose up --build meri

Configuration

Settings can be configured using environment variables, using a .env file in the root of the project, or by using config.yaml file. To generate default config in the current directory, run:

python -m meri.settings generate

Similarly, you can show the current configuration by running:

python -m meri.settings show

LLM Configuration

LLM:s can be configured in the config.yaml file in llm -section. If no specific LLM is configured, autodetection from environment variables is attempted (see below).

Environment Variables

• DEBUG: If set to true, debug mode is enabled.
• KLIKKIKURI_CONFIG_FILE: Path to the configuration file. Default is user $XDG_CONFIG_DIR/meri/config.yaml

If LLM's are not explicitly configured in the config.yaml file, the following environment variables are used to autodetect the LLM:

• OPENAI_API_KEY: OpenAI API key (e.g. sk-...)
• GEMINI_API_KEY: Google Gemini API key.
• OLLAMA_HOST: ollama host. (e.g. http://localhost:11434)
• OLLAMA_MODEL: ollama model name (e.g. deepseek-r1:8b). If not set, the first model listed by ollama is used.