llama-stack-mirror/docs/source/distributions/configuration.md

Configuring a "Stack"

The Llama Stack runtime configuration is specified as a YAML file. Here is a simplified version of an example configuration file for the Ollama distribution:

```yaml
version: 2
conda_env: ollama
apis:
- agents
- inference
- vector_io
- safety
- telemetry
providers:
  inference:
  - provider_id: ollama
    provider_type: remote::ollama
    config:
      url: ${env.OLLAMA_URL:http://localhost:11434}
  vector_io:
  - provider_id: faiss
    provider_type: inline::faiss
    config:
      kvstore:
        type: sqlite
        namespace: null
        db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/ollama}/faiss_store.db
  safety:
  - provider_id: llama-guard
    provider_type: inline::llama-guard
    config: {}
  agents:
  - provider_id: meta-reference
    provider_type: inline::meta-reference
    config:
      persistence_store:
        type: sqlite
        namespace: null
        db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/ollama}/agents_store.db
  telemetry:
  - provider_id: meta-reference
    provider_type: inline::meta-reference
    config: {}
metadata_store:
  namespace: null
  type: sqlite
  db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/ollama}/registry.db
models:
- metadata: {}
  model_id: ${env.INFERENCE_MODEL}
  provider_id: ollama
  provider_model_id: null
shields: []

Let's break this down into the different sections. The first section specifies the set of APIs that the stack server will serve:

apis:
- agents
- inference
- memory
- safety
- telemetry

Providers

Next up is the most critical part: the set of providers that the stack will use to serve the above APIs. Consider the inference API:

providers:
  inference:
  # provider_id is a string you can choose freely
  - provider_id: ollama
    # provider_type is a string that specifies the type of provider.
    # in this case, the provider for inference is ollama and it is run remotely (outside of the distribution)
    provider_type: remote::ollama
    # config is a dictionary that contains the configuration for the provider.
    # in this case, the configuration is the url of the ollama server
    config:
      url: ${env.OLLAMA_URL:http://localhost:11434}

A few things to note:

• A provider instance is identified with an (id, type, configuration) triplet.
• The id is a string you can choose freely.
• You can instantiate any number of provider instances of the same type.
• The configuration dictionary is provider-specific.
• Notice that configuration can reference environment variables (with default values), which are expanded at runtime. When you run a stack server (via docker or via llama stack run), you can specify --env OLLAMA_URL=http://my-server:11434 to override the default value.

Resources

Finally, let's look at the models section:

models:
- metadata: {}
  model_id: ${env.INFERENCE_MODEL}
  provider_id: ollama
  provider_model_id: null

A Model is an instance of a "Resource" (see Concepts) and is associated with a specific inference provider (in this case, the provider with identifier ollama). This is an instance of a "pre-registered" model. While we always encourage the clients to always register models before using them, some Stack servers may come up a list of "already known and available" models.

What's with the provider_model_id field? This is an identifier for the model inside the provider's model catalog. Contrast it with model_id which is the identifier for the same model for Llama Stack's purposes. For example, you may want to name "llama3.2:vision-11b" as "image_captioning_model" when you use it in your Stack interactions. When omitted, the server will set provider_model_id to be the same as model_id.

Extending to handle Safety

Configuring Safety can be a little involved so it is instructive to go through an example.

The Safety API works with the associated Resource called a Shield. Providers can support various kinds of Shields. Good examples include the Llama Guard system-safety models, or Bedrock Guardrails.

To configure a Bedrock Shield, you would need to add:

• A Safety API provider instance with type remote::bedrock
• A Shield resource served by this provider.

...
providers:
  safety:
  - provider_id: bedrock
    provider_type: remote::bedrock
    config:
      aws_access_key_id: ${env.AWS_ACCESS_KEY_ID}
      aws_secret_access_key: ${env.AWS_SECRET_ACCESS_KEY}
...
shields:
- provider_id: bedrock
  params:
    guardrailVersion: ${env.GUARDRAIL_VERSION}
  provider_shield_id: ${env.GUARDRAIL_ID}
...

The situation is more involved if the Shield needs Inference of an associated model. This is the case with Llama Guard. In that case, you would need to add:

• A Safety API provider instance with type inline::llama-guard
• An Inference API provider instance for serving the model.
• A Model resource associated with this provider.
• A Shield resource served by the Safety provider.

The yaml configuration for this setup, assuming you were using vLLM as your inference server, would look like:

...
providers:
  safety:
  - provider_id: llama-guard
    provider_type: inline::llama-guard
    config: {}
  inference:
  # this vLLM server serves the "normal" inference model (e.g., llama3.2:3b)
  - provider_id: vllm-0
    provider_type: remote::vllm
    config:
      url: ${env.VLLM_URL:http://localhost:8000}
  # this vLLM server serves the llama-guard model (e.g., llama-guard:3b)
  - provider_id: vllm-1
    provider_type: remote::vllm
    config:
      url: ${env.SAFETY_VLLM_URL:http://localhost:8001}
...
models:
- metadata: {}
  model_id: ${env.INFERENCE_MODEL}
  provider_id: vllm-0
  provider_model_id: null
- metadata: {}
  model_id: ${env.SAFETY_MODEL}
  provider_id: vllm-1
  provider_model_id: null
shields:
- provider_id: llama-guard
  shield_id: ${env.SAFETY_MODEL}   # Llama Guard shields are identified by the corresponding LlamaGuard model
  provider_shield_id: null
...