How to Deploy Semantica with Docker: A Step-by-Step Tutorial
Learn how to self-host Semantica with Docker: requirements, compose deployment, adding your first knowledge graph, and grounding LLM answers in auditable facts.
In this tutorial, you'll deploy Semantica — the graph-native infrastructure for context and accountable AI — on your own server with Docker, then connect your first knowledge graph. No cloud lock-in, no data leaving your infrastructure.
🚀 Want to deploy Semantica yourself?
Docker configs, system requirements, and installation guides — all on one page.
View Semantica Tool Page →Step 1: Check the Requirements
Semantica runs comfortably on a 2-CPU / 4 GB RAM machine for evaluation; plan for 4 CPU / 8 GB RAM in production. The official image is fadellvk/semantica:latest on Docker Hub, with the compose template available on the tool page.
Step 2: Deploy with Docker
- Create a directory and copy the docker-compose.yml from the Run This Ai tool page.
- Run
docker compose up -dto start the server. - Confirm health with
docker compose ps— the service should show as running.
💡 Pro tip: expose the API on an internal network only, and front it with your reverse proxy if you need HTTPS.
Step 3: Add Your First Knowledge
Using the Python SDK, you can now write entities and relations:
- Create an entity (e.g.,
Acme Corp) with metadata. - Create relations between entities (
Acme Corp → employs → Alice). - Query the graph and retrieve connected context for your LLM prompt.
Step 4: Ground Your LLM Answers
Feed the retrieved graph facts into your prompt and let the model generate from auditable context. Because Semantica keeps provenance, you can trace exactly which facts backed each answer — accountability built in.
🚀 Ready to run Semantica?
Get the full Docker config and requirements on the tool page.
View Semantica Tool Page →That's it — a self-hosted, graph-native memory layer for your AI systems in minutes. 🎉