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Keep Dify→Ollama traffic on a private segment rather than public internet.
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Agent quality is not only about model parameter count. Reliable connectivity to Ollama, context budget, tool schemas, RAG retrieval, timeouts and concurrent agents all shape performance.
Do not rely on a “local model” being safe when granting shell, HTTP or write-capable tools. Models can be prompt-injected; tool policy and server-side validation are separate security controls.
If Dify and Ollama share Docker networking, `localhost` inside one container is not the other service. For separate hosts, avoid exposing the Ollama API naked to the public internet; use private networking or an authenticated reverse proxy.
If Dify and Ollama share Docker networking, `localhost` inside one container is not the other service. For separate hosts, avoid exposing the Ollama API naked to the public internet; use private networking or an authenticated reverse proxy.
Connect Ollama local models to Dify agent workflows with network reachability, context sizing, tool permissions, RAG, timeouts, concurrency and production security.
Keep Dify→Ollama traffic on a private segment rather than public internet.
Long context increases KV-cache memory and latency.
Tool permissions determine real-world impact more than model locality.
Vector retrieval is not the same as model training.
A URL that works from the host browser may fail inside the Dify container network namespace. Test from the component that makes the request.
docker compose psdocker exec -it docker-api-1 sh -lc "getent hosts host.docker.internal || true"curl http://OLLAMA_HOST:11434/api/tagscurl http://OLLAMA_HOST:11434/api/versionA small fast model may be enough for routing/classification, while tool planning or long-document analysis may need stronger reasoning/context capacity.
| Task | Priority |
|---|---|
| Intent/router | Low latency |
| Tool planning | Instruction following |
| RAG answer | Context + citation discipline |
| Code agent | Tool accuracy + long context |
KV-cache memory and first-token latency can rise with context length. Allocate separate token budgets for RAG chunks and conversation history.
The impact of each tool matters more than tool count. Require confirmation and server-side allow lists for destructive actions.
| Class | Example | Control |
|---|---|---|
| Read | Search docs | Scope/filter |
| Write | Create ticket | Schema + idempotency |
| Destructive | Delete/pay/deploy | Human approval + allow-list |
Chunks from embeddings/vector search are injected into request context. When documents change, refresh the index rather than retraining the model.
An agent may perform multiple model and tool round trips. “50 tok/s” alone does not describe workflow capacity.
| Metric | Measure |
|---|---|
| TTFT | First model response latency |
| Tool latency | p95 per tool round trip |
| End-to-end | User request → final answer |
| Queue wait | Concurrency saturation |
LLM requests can be long; retrying a write/payment tool can duplicate side effects. Know idempotency semantics for each call type.
Use separate resource profiles for the Dify control plane and Ollama inference on Eka Sunucu GPU/VPS infrastructure and benchmark agent concurrency.
Primary documentation and technical references used by this guide.
Continue with related infrastructure and implementation guides.
Dify + Ollama
Unless they share the same network namespace, `localhost` means the Dify container itself. Use a Docker service name, host gateway or private IP as appropriate.
Usually no. Restrict access with private networking or an authenticated reverse proxy.
Supported vector-store options can change by release; verify current self-host configuration before production.
No. Tool accuracy, latency, concurrency, context and task complexity should be evaluated together.