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Docker + Ollama Setup

Run Resume Matcher in Docker with Ollama on your machine: set the base URL to host.docker.internal:11434 so the container can reach your local models.

Default Docker + Ollama Flow

The checked-in Docker setup still serves Resume Matcher on a single public origin at http://localhost:3000. To use Ollama with Docker, run Ollama on the host machine and point Resume Matcher at it.

Host Ollama Setup

  1. Install Ollama from ollama.com.
  2. Pull a model on the host, for example:
ollama pull gemma3:4b
  1. Start Resume Matcher from the app repo root with Ollama wired in:
LLM_API_BASE=http://host.docker.internal:11434 docker compose up -d
  1. Open http://localhost:3000/settings and set:

    • Provider: Ollama
    • Model: gemma3:4b (the default) or another model you already pulled
    • Base URL: http://host.docker.internal:11434

    Switching the provider to Ollama fills the Base URL with http://localhost:11434. Inside the container, that address points at the container itself, not your machine, so change it before you save. A Base URL saved in Settings takes priority over LLM_API_BASE.

URLs

Surface URL
App http://localhost:3000
Settings http://localhost:3000/settings
Health http://localhost:3000/api/v1/health
API docs http://localhost:3000/docs

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What This Page Covers

This page documents the supported host-Ollama pattern for Docker.

  • Resume Matcher stays on http://localhost:3000
  • Ollama runs on the host machine
  • LLM_API_BASE points the containerized app at the host Ollama server

If you decide to run Ollama in a separate container, treat that as a custom compose extension. It is not the checked-in default Docker flow.

Troubleshooting

Ollama is not responding

Check the host Ollama service:

curl http://localhost:11434/api/tags

Resume Matcher cannot reach Ollama

Open Settings and check that the Base URL is http://host.docker.internal:11434, not http://localhost:11434. Then make sure you started Docker with:

LLM_API_BASE=http://host.docker.internal:11434 docker compose up -d

Responses are slow

Use a smaller local model or free up memory on the host.

Next Steps