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version: 2
apis:
- agents
- batches
- datasetio
- eval
- files
- inference
- safety
- scoring
- tool_runtime
- vector_io
benchmarks: []
datasets: []
image_name: starter
external_providers_dir: ${env.EXTERNAL_PROVIDERS_DIR}
providers:
inference:
- provider_id: openai # This ID is a reference to 'providers.inference'
provider_type: remote::openai
config:
api_key: ${env.OPENAI_API_KEY}
allowed_models: ["${env.E2E_OPENAI_MODEL:=gpt-4o-mini}"]
- provider_id: sentence-transformers
provider_type: inline::sentence-transformers
files:
- config:
metadata_store:
table_name: files_metadata
backend: sql_default
storage_dir: ~/.llama/storage/files
provider_id: meta-reference-files
provider_type: inline::localfs
safety:
- config:
excluded_categories: []
provider_id: llama-guard
provider_type: inline::llama-guard
scoring:
- provider_id: basic
provider_type: inline::basic
config: {}
- provider_id: llm-as-judge
provider_type: inline::llm-as-judge
config: {}
- provider_id: braintrust
provider_type: inline::braintrust
config:
openai_api_key: '********'
tool_runtime:
- config: {} # Enable the RAG tool
provider_id: rag-runtime
provider_type: inline::rag-runtime
vector_io:
- config:
persistence:
namespace: vector_io::faiss
backend: kv_default
provider_id: faiss
provider_type: inline::faiss
agents:
- config:
persistence:
agent_state:
namespace: agents_state
backend: kv_default
responses:
table_name: agents_responses
backend: sql_default
provider_id: meta-reference
provider_type: inline::meta-reference
batches:
- config:
kvstore:
namespace: batches_store
backend: kv_default
provider_id: reference
provider_type: inline::reference
datasetio:
- config:
kvstore:
namespace: huggingface_datasetio
backend: kv_default
provider_id: huggingface
provider_type: remote::huggingface
- config:
kvstore:
namespace: localfs_datasetio
backend: kv_default
provider_id: localfs
provider_type: inline::localfs
eval:
- config:
kvstore:
namespace: eval_store
backend: kv_default
provider_id: meta-reference
provider_type: inline::meta-reference
scoring_fns: []
server:
port: 8321
storage:
backends:
kv_default: # Define the storage backend type for RAG, in this case registry and RAG are unified i.e. information on registered resources (e.g. models, vector_stores) are saved together with the RAG chunks
type: kv_sqlite
db_path: ${env.KV_STORE_PATH:=~/.llama/storage/rag/kv_store.db}
sql_default:
type: sql_sqlite
db_path: ${env.SQL_STORE_PATH:=~/.llama/storage/sql_store.db}
stores:
metadata:
namespace: registry
backend: kv_default
inference:
table_name: inference_store
backend: sql_default
max_write_queue_size: 10000
num_writers: 4
conversations:
table_name: openai_conversations
backend: sql_default
prompts:
namespace: prompts
backend: kv_default
registered_resources:
models: []
shields:
- shield_id: llama-guard
provider_id: llama-guard
provider_shield_id: openai/gpt-4o-mini
vector_stores: []
datasets: []
scoring_fns: []
benchmarks: []
tool_groups:
- toolgroup_id: builtin::rag # Register the RAG tool
provider_id: rag-runtime
# REQUIRED: This section is necessary for file_search tool calls to work.
# Without it, llama-stack's rag-runtime silently fails all file_search operations
# with no error logged.
vector_stores:
# LCORE-1498: Disables Llama Stack RAG annotation generation
# causing unwanted citation/file markers in model output.
annotation_prompt_params:
enable_annotations: false
default_provider_id: faiss
default_embedding_model: # Define the default embedding model for RAG
provider_id: sentence-transformers
model_id: nomic-ai/nomic-embed-text-v1.5
safety:
default_shield_id: llama-guard