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| cookbook | ||
| experiencescope | ||
| .gitignore | ||
| pyproject.toml | ||
| README.md | ||
run service
cd BeyondAgent
python beyondagent/core/service/model_service.py
test service
python beyondagent/test/test_service.py
qingxu
# 1. edit query, port, vm etc
nano docker-compose.yml
# 2. run
docker compose down && docker compose build && docker compose up
# 3. then watch vm at http://localhost:16901 (default password is headless)
vector store
If a vector database is involved, you will need an Elasticsearch environment. You can refer to the following steps:
- If you don’t have Docker installed, download and install Docker Desktop for your operating system.
- To set up Elasticsearch and Kibana locally, run the start-local script in the command line:
curl -fsSL https://elastic.co/start-local | sh
Or manually download and load the image. Here, we take elasticsearch-wolfi:9.0.0 as an example:
docker pull docker.elastic.co/elasticsearch/elasticsearch-wolfi:9.0.0
docker run -p 9200:9200 \
-e "discovery.type=single-node" \
-e "xpack.security.enabled=false" \
-e "xpack.license.self_generated.type=trial" \
docker.elastic.co/elasticsearch/elasticsearch-wolfi:9.0.0