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The fix is usually to make the AI URL valid from the container that sends the request. Identify that requesting process, determine where the AI service runs, and test the configured hostname and port from the same Docker network context. A browser or host shell reaching the service does not prove that a document-management container can reach it.
Most failures fall into four groups: the client uses a loopback address that points to itself, the AI server listens only on a loopback interface, the Docker host is addressed incorrectly for the platform, or document processing is failing in a broker or worker rather than at the AI endpoint. The procedure below separates those cases without assuming a particular application. Paperless-ngx and Open WebUI with Ollama are used as documented examples.
What the error actually means
An AI connection error is reported by the component that made the outbound request. In a Dockerized document-management stack, that may be a web application, background worker, plugin, or separate integration container. The address must be reachable from that container, not merely from your laptop, browser, or the Docker host.
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- Read the application log at the time of the failure and note the container name, hostname, destination, port, and error text.
- Find the effective AI endpoint in the container’s environment, Compose file, application settings, or secret configuration. Remove credentials before sharing it.
- Confirm whether the failed operation is synchronous (the web request waits for an answer) or asynchronous (a queue sends work to a worker later). The process that performs the request may not be the container you initially opened in a browser.
For Open WebUI, backend or administrative connections need an address reachable by the Open WebUI backend. Its documented patterns include a Docker service name, host.docker.internal, or an internal IP, depending on the deployment. For any other application, apply the same rule to the process named in its logs.
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Why localhost causes so many failures
Inside a container, 127.0.0.1 and localhost refer to that container’s own network namespace. They do not generally mean another Compose service and do not automatically mean the physical or virtual Docker host. If the AI server is in a different container or on the host, a loopback URL sends the request to the wrong machine.
There is one legitimate exception: a client and AI process deliberately share the same network namespace, such as a container using host networking or two processes in one container. Treat that as a topology fact to verify, not as a default assumption.
Use this diagnostic sequence
1. Record the endpoint and the exact error
Capture the complete URL minus any token, the port, and the timestamp. Distinguish these messages:
- Name resolution failure: the container cannot translate the hostname to an address.
- Connection refused: an address was reached, but no process accepted the connection on that port, or a firewall actively rejected it.
- Timeout: routing, firewall rules, an incorrect address, or a service that is not responding may be involved.
- HTTP authentication or application error: networking succeeded; investigate credentials, API paths, model availability, or provider policy instead.
Do not replace a network diagnosis with a generic “AI is down” conclusion until the destination and error are known.
2. Identify the container and inspect its network
List the project services and verify which one runs the failing code:
docker compose ps
docker compose logs --tail=200 <requesting-service>Inspect its networks and addresses:
docker inspect <container-name>In the inspection output, check the attached network names, container IP addresses, aliases, and environment variables. The AI service must be attached to a network that the requester can use, or the requester must have a route to the host or remote machine.
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3. Test DNS and the TCP port from inside the requester
Enter the running container using its available shell:
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getent hosts <ai-hostname>nc -vz <ai-hostname> <port>curl -v http://<ai-hostname>:<port>/Use one command at a time; the first checks name resolution, the second checks TCP reachability, and the third shows the HTTP exchange. A minimal production image may not include these utilities. In that case, run a temporary diagnostic container attached to the same Compose network, or use the application’s own health endpoint if its documentation defines one. Testing from the host shell is insufficient because it uses a different network context.
4. Verify the AI service is running and listening
On the AI service’s machine, confirm that the process is running and that its listening address includes the interface clients need. A listener bound only to 127.0.0.1 accepts local requests but rejects connections arriving through a container bridge or host gateway.
For an Ollama process on the host with Open WebUI in Docker, Open WebUI identifies a localhost-only Ollama listener as a likely cause and documents setting OLLAMA_HOST=0.0.0.0, followed by an Ollama restart. Apply that setting only when it matches your topology and security policy; it is not a universal requirement for every AI server.
5. Retest after every single change
Restart the process whose configuration changed, then rerun the in-container DNS, TCP, and HTTP checks. A Compose environment change normally requires recreating the affected service:
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docker compose up -d --force-recreate <requesting-service>Use the least disruptive restart that actually reloads the setting. If the listener was changed on the host, restart that AI service according to its installation method before testing again.
Match the URL to the deployment topology
The correct hostname is determined by where the AI service runs and which platform supplies Docker. The table describes the address pattern to investigate; the actual port is the one on which your AI server listens.
| AI service location | Address from the requesting container | Checks and common failure |
|---|---|---|
| Another service in the same Compose project and network | The Compose service name, such as http://ollama:<container-port> |
Both services must share a network. Use the container listening port, not an unrelated host-published port. A misspelled service name produces a DNS failure. |
| Docker host on Docker Desktop | Often host.docker.internal with the AI listener’s port |
Docker Desktop resolves this hostname for containers. Confirm the host process listens beyond loopback and that the selected port is exposed to the container path. |
| Docker host on Linux Docker Engine | A host-gateway address, an explicitly configured host name, or host networking where appropriate | Docker’s integration guidance documents host networking or a host-gateway address as alternatives. Configure and verify the route rather than assuming Docker Desktop behavior. |
| Another machine on the LAN or internet | An address routable from the container, normally a DNS name or internal IP | Check container routing, DNS, firewall rules, listening interface, VPN paths, and port exposure. An address reachable from the host may still be blocked from the bridge network. |
Same Compose network
Declare a shared network and address the AI service by its service name. Compose’s internal DNS maps that name to the service’s container address and keeps traffic inside the Docker network. Do not use the host-published port merely because it appears in the Compose file; service-to-service traffic normally uses the port on which the AI process listens inside its container.
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AI service on the Docker host
Docker’s Model Runner integration example configures OLLAMA_BASE_URL as http://host.docker.internal:12434 and adds host.docker.internal:host-gateway in Compose. That port is an example for that integration; use it only when the service actually listens there. Docker Desktop resolves host.docker.internal automatically. Linux Docker Engine deployments may instead use host networking or a host-gateway address, as documented by Docker.
A host-gateway name solves routing, not listening. The AI process must still accept connections on the host interface selected by the route, and host firewalls must permit the traffic. Test the exact hostname and port from the requester after configuring the mapping.
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AI service on another machine
Use the machine’s address as seen from the container, not an address valid only on the host. Verify that the remote service binds to an interface reachable from the Docker subnet, that intermediate firewalls allow the port, and that DNS inside the container returns the intended address. If the remote endpoint is behind a reverse proxy, test the proxy hostname and path exactly as configured by the application.
Fix the listener before changing the client
A correct client URL cannot reach a server that listens only on loopback or on a different port. On the AI host, inspect the service’s bind address and port, then compare them with the container’s URL. A port published by Docker is also not proof that the application is listening correctly; publication forwards traffic to a container port, while the process still has to bind that port.
Ollama with Open WebUI as a documented example
Open WebUI’s troubleshooting guidance describes a localhost-only Ollama listener as a deployment and networking condition rather than an Open WebUI bug. For that arrangement, configure Ollama with OLLAMA_HOST=0.0.0.0 and restart Ollama so the setting takes effect. Then set Open WebUI’s backend URL to an address reachable from the Open WebUI container: the Ollama Compose service name when both share a network, or the platform-appropriate host gateway when Ollama runs on the host.
Binding to all interfaces increases the set of reachable clients. Restrict exposure with Docker networks, host firewall rules, a private interface, or an authenticated reverse proxy as appropriate. Do not expose an unauthenticated inference endpoint to an untrusted network merely to make a connectivity test pass.
Open WebUI connection checks
Open WebUI can be deployed with Ollama in the same Compose project, with Ollama installed on the host, or with a remote Ollama server. The backend setting must reflect that choice.
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When both services are in Compose
- Put Open WebUI and Ollama on the same user-defined network.
- Use Ollama’s Compose service name and its internal listening port in the backend URL.
- Resolve that name from the Open WebUI container and check the port before troubleshooting model names or credentials.
When Ollama runs on the host
- Use
host.docker.internalon Docker Desktop when the host listener and port match. - On Linux Docker Engine, configure the host-gateway mapping or use the documented host-networking alternative.
- Ensure Ollama is not restricted to
127.0.0.1; Open WebUI specifically documentsOLLAMA_HOST=0.0.0.0for this case.
When the URL works but the UI still reports failure
Inspect Open WebUI’s backend logs and compare the effective URL inside the running container with the value in your Compose file. An old container may retain an old environment value until it is recreated. If the TCP and HTTP checks succeed, investigate the configured API path, model availability, authentication, or response format rather than Docker routing.
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Paperless-ngx: distinguish AI connectivity from task processing
Paperless-ngx commonly performs document work asynchronously. A document that remains unprocessed, or a UI message that appears to implicate AI, may actually indicate that the broker or task processor is stopped, unhealthy, or unable to consume queued work.
Check the broker and worker
- List the Paperless-ngx services and confirm the broker and task-processing service are running.
- Inspect application, worker, and broker logs around the document’s submission time.
- Open the administration interface and inspect failed tasks, including the recorded error rather than only the document status.
- Only after the worker is processing jobs should you treat an outbound AI error as the primary cause.
Paperless-ngx troubleshooting guidance recommends these checks because queue failures can look like an integration failure from the user’s perspective. A worker that cannot resolve a hostname is a network problem; a worker that never starts is a process or broker problem and needs a different repair.
Use the failed-task detail as the branch point
- Failed task contains DNS, timeout, or connection-refused text: return to the requester-container endpoint tests.
- Failed task reports broker, queue, or worker errors: repair the asynchronous processing path first.
- Failed task reports provider authentication, quota, or model errors: connectivity succeeded; correct the provider configuration or account.
Read the symptom as a decision tree
| Observed symptom | Most useful next check | Likely corrective action |
|---|---|---|
| Hostname cannot be resolved inside the container | Inspect Compose network membership, service spelling, and container DNS output | Attach both services to a shared network or use a resolvable host-gateway/remote hostname. |
| Connection refused immediately | Check the AI process’s listening port and bind address | Start the service, correct the port, or change a loopback-only listener when the topology requires external container access. |
| Connection times out | Trace routing and firewall rules from the container network | Correct the route, permit the Docker subnet, or use the address reachable from that network. |
| HTTP 401 or 403 | Verify credentials and authorization at the AI endpoint | Keep the working URL and correct authentication or provider policy. |
| HTTP 404 or incompatible response | Compare the configured API path and expected API type | Use the endpoint path required by the client and server combination. |
| Paperless-ngx documents remain queued | Check broker, worker, logs, and failed-task details | Repair task processing before changing an AI URL. |
| Works from host but not from container | Run DNS, TCP, and HTTP tests inside the requester | Replace host-only or loopback addressing with a container-reachable route. |
Compose patterns you can adapt
The following fragments illustrate the address choices; substitute your real service names, ports, and secrets.
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AI service in the same network
services:
document-app:
environment:
AI_BASE_URL: http://ai-service:11434
networks: [ai-net]
ai-service:
networks: [ai-net]
networks:
ai-net:The service name ai-service is resolved by Docker’s internal DNS. The number 11434 is only an example of an internal Ollama port; verify the listener in your installation.
Host service through a gateway name
services:
document-app:
extra_hosts:
- "host.docker.internal:host-gateway"
environment:
AI_BASE_URL: http://host.docker.internal:<host-listener-port>Docker’s Model Runner example uses OLLAMA_BASE_URL=http://host.docker.internal:12434 with the host-gateway mapping. Keep that value only when the Model Runner or Ollama listener is actually available on port 12434. A different installation needs its own port.
Security, privacy, and operating cost
Making a listener reachable from containers changes the trust boundary. Limit the Docker network, host firewall, and remote firewall to the clients that need access. Prefer authenticated transport or a protected private network when the endpoint crosses machine or network boundaries. Remove temporary diagnostic exposure after testing.
Paperless-ngx documentation warns that using a hosted OpenAI-compatible API sends document data to the configured provider and may incur usage charges. Treat the endpoint decision as a data-governance and budget decision as well as a networking change. Check retention, training, regional processing, access controls, and per-request costs with the provider before sending sensitive documents.
Final verification checklist
- The logs identify the process that originates the AI request.
- The configured hostname and port are recorded with secrets removed.
- The requester and AI service share a Docker network, use a verified host gateway, or have a tested route to the remote machine.
- DNS resolution succeeds from the requester container.
- TCP connection succeeds from that same container.
- The AI process listens on the expected interface and port.
- The client URL uses the correct service name, host gateway, remote address, and API path for the topology.
- Changed environment variables have been loaded by recreating or restarting the affected service.
- For Paperless-ngx, the broker, worker, application logs, and failed-task details have been checked separately.
- Firewall exposure, document-data handling, provider retention, and possible usage charges are understood.
Once these checks pass, a remaining error is usually at the application or provider layer—credentials, API compatibility, model selection, quota, or task logic—rather than basic Docker reachability.
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