# Copyright (c) Meta Platforms, Inc. and affiliates. # All rights reserved. # # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. from typing import Any from llama_stack.apis.models import ModelType from llama_stack.distribution.datatypes import ( ModelInput, Provider, ProviderSpec, ToolGroupInput, ) from llama_stack.distribution.utils.dynamic import instantiate_class_type from llama_stack.providers.inline.files.localfs.config import LocalfsFilesImplConfig from llama_stack.providers.inline.inference.sentence_transformers import ( SentenceTransformersInferenceConfig, ) from llama_stack.providers.inline.post_training.huggingface import HuggingFacePostTrainingConfig from llama_stack.providers.inline.vector_io.faiss.config import FaissVectorIOConfig from llama_stack.providers.inline.vector_io.milvus.config import ( MilvusVectorIOConfig, ) from llama_stack.providers.inline.vector_io.sqlite_vec.config import ( SQLiteVectorIOConfig, ) from llama_stack.providers.registry.inference import available_providers from llama_stack.providers.remote.inference.anthropic.models import ( MODEL_ENTRIES as ANTHROPIC_MODEL_ENTRIES, ) from llama_stack.providers.remote.inference.fireworks.models import ( MODEL_ENTRIES as FIREWORKS_MODEL_ENTRIES, ) from llama_stack.providers.remote.inference.gemini.models import ( MODEL_ENTRIES as GEMINI_MODEL_ENTRIES, ) from llama_stack.providers.remote.inference.groq.models import ( MODEL_ENTRIES as GROQ_MODEL_ENTRIES, ) from llama_stack.providers.remote.inference.openai.models import ( MODEL_ENTRIES as OPENAI_MODEL_ENTRIES, ) from llama_stack.providers.remote.inference.sambanova.models import ( MODEL_ENTRIES as SAMBANOVA_MODEL_ENTRIES, ) from llama_stack.providers.remote.inference.together.models import ( MODEL_ENTRIES as TOGETHER_MODEL_ENTRIES, ) from llama_stack.providers.remote.vector_io.chroma.config import ChromaVectorIOConfig from llama_stack.providers.remote.vector_io.pgvector.config import ( PGVectorVectorIOConfig, ) from llama_stack.providers.utils.inference.model_registry import ProviderModelEntry from llama_stack.providers.utils.sqlstore.sqlstore import PostgresSqlStoreConfig from llama_stack.templates.template import ( DistributionTemplate, RunConfigSettings, get_model_registry, ) def _get_model_entries_for_provider(provider_type: str) -> list[ProviderModelEntry]: """Get model entries for a specific provider type.""" model_entries_map = { "openai": OPENAI_MODEL_ENTRIES, "fireworks": FIREWORKS_MODEL_ENTRIES, "together": TOGETHER_MODEL_ENTRIES, "anthropic": ANTHROPIC_MODEL_ENTRIES, "gemini": GEMINI_MODEL_ENTRIES, "groq": GROQ_MODEL_ENTRIES, "sambanova": SAMBANOVA_MODEL_ENTRIES, } # Special handling for providers with dynamic model entries if provider_type == "ollama": return [ ProviderModelEntry( provider_model_id="${env.OLLAMA_INFERENCE_MODEL:=__disabled__}", model_type=ModelType.llm, ), ProviderModelEntry( provider_model_id="${env.OLLAMA_EMBEDDING_MODEL:=__disabled__}", model_type=ModelType.embedding, metadata={ "embedding_dimension": "${env.OLLAMA_EMBEDDING_DIMENSION:=384}", }, ), ] elif provider_type == "vllm": return [ ProviderModelEntry( provider_model_id="${env.VLLM_INFERENCE_MODEL:=__disabled__}", model_type=ModelType.llm, ), ] return model_entries_map.get(provider_type, []) def _get_config_for_provider(provider_spec: ProviderSpec) -> dict[str, Any]: """Get configuration for a provider using its adapter's config class.""" config_class = instantiate_class_type(provider_spec.config_class) if hasattr(config_class, "sample_run_config"): config: dict[str, Any] = config_class.sample_run_config() return config return {} def get_remote_inference_providers() -> tuple[list[Provider], dict[str, list[ProviderModelEntry]]]: all_providers = available_providers() # Filter out inline providers and watsonx - the starter distro only exposes remote providers remote_providers = [ provider for provider in all_providers # TODO: re-add once the Python 3.13 issue is fixed # discussion: https://github.com/meta-llama/llama-stack/pull/2327#discussion_r2156883828 if hasattr(provider, "adapter") and provider.adapter.adapter_type != "watsonx" ] providers = [] available_models = {} for provider_spec in remote_providers: provider_type = provider_spec.adapter.adapter_type # Build the environment variable name for enabling this provider env_var = f"ENABLE_{provider_type.upper().replace('-', '_').replace('::', '_')}" model_entries = _get_model_entries_for_provider(provider_type) config = _get_config_for_provider(provider_spec) providers.append( ( f"${{env.{env_var}:=__disabled__}}", provider_type, model_entries, config, ) ) available_models[f"${{env.{env_var}:=__disabled__}}"] = model_entries inference_providers = [] for provider_id, provider_type, model_entries, config in providers: inference_providers.append( Provider( provider_id=provider_id, provider_type=f"remote::{provider_type}", config=config, ) ) available_models[provider_id] = model_entries return inference_providers, available_models def get_distribution_template() -> DistributionTemplate: remote_inference_providers, available_models = get_remote_inference_providers() providers = { "inference": ([p.provider_type for p in remote_inference_providers] + ["inline::sentence-transformers"]), "vector_io": ["inline::sqlite-vec", "inline::milvus", "remote::chromadb", "remote::pgvector"], "files": ["inline::localfs"], "safety": ["inline::llama-guard"], "agents": ["inline::meta-reference"], "telemetry": ["inline::meta-reference"], "post_training": ["inline::huggingface"], "eval": ["inline::meta-reference"], "datasetio": ["remote::huggingface", "inline::localfs"], "scoring": ["inline::basic", "inline::llm-as-judge", "inline::braintrust"], "tool_runtime": [ "remote::brave-search", "remote::tavily-search", "inline::rag-runtime", "remote::model-context-protocol", ], } name = "starter" vector_io_providers = [ Provider( provider_id="${env.ENABLE_FAISS:=faiss}", provider_type="inline::faiss", config=FaissVectorIOConfig.sample_run_config(f"~/.llama/distributions/{name}"), ), Provider( provider_id="${env.ENABLE_SQLITE_VEC:=__disabled__}", provider_type="inline::sqlite-vec", config=SQLiteVectorIOConfig.sample_run_config(f"~/.llama/distributions/{name}"), ), Provider( provider_id="${env.ENABLE_MILVUS:=__disabled__}", provider_type="inline::milvus", config=MilvusVectorIOConfig.sample_run_config(f"~/.llama/distributions/{name}"), ), Provider( provider_id="${env.ENABLE_CHROMADB:=__disabled__}", provider_type="remote::chromadb", config=ChromaVectorIOConfig.sample_run_config(url="${env.CHROMADB_URL:=}"), ), Provider( provider_id="${env.ENABLE_PGVECTOR:=__disabled__}", provider_type="remote::pgvector", config=PGVectorVectorIOConfig.sample_run_config( db="${env.PGVECTOR_DB:=}", user="${env.PGVECTOR_USER:=}", password="${env.PGVECTOR_PASSWORD:=}", ), ), ] files_provider = Provider( provider_id="meta-reference-files", provider_type="inline::localfs", config=LocalfsFilesImplConfig.sample_run_config(f"~/.llama/distributions/{name}"), ) embedding_provider = Provider( provider_id="${env.ENABLE_SENTENCE_TRANSFORMERS:=sentence-transformers}", provider_type="inline::sentence-transformers", config=SentenceTransformersInferenceConfig.sample_run_config(), ) post_training_provider = Provider( provider_id="huggingface", provider_type="inline::huggingface", config=HuggingFacePostTrainingConfig.sample_run_config(f"~/.llama/distributions/{name}"), ) default_tool_groups = [ ToolGroupInput( toolgroup_id="builtin::websearch", provider_id="tavily-search", ), ToolGroupInput( toolgroup_id="builtin::rag", provider_id="rag-runtime", ), ] embedding_model = ModelInput( model_id="all-MiniLM-L6-v2", provider_id=embedding_provider.provider_id, model_type=ModelType.embedding, metadata={ "embedding_dimension": 384, }, ) default_models = get_model_registry(available_models) return DistributionTemplate( name=name, distro_type="self_hosted", description="Quick start template for running Llama Stack with several popular providers", container_image=None, template_path=None, providers=providers, available_models_by_provider=available_models, additional_pip_packages=PostgresSqlStoreConfig.pip_packages(), run_configs={ "run.yaml": RunConfigSettings( provider_overrides={ "inference": remote_inference_providers + [embedding_provider], "vector_io": vector_io_providers, "files": [files_provider], "post_training": [post_training_provider], }, default_models=default_models + [embedding_model], default_tool_groups=default_tool_groups, # TODO: add a way to enable/disable shields on the fly # default_shields=[ # ShieldInput(provider_id="llama-guard", shield_id="${env.SAFETY_MODEL:=meta-llama/Llama-Guard-3-8B}") # ], ), }, run_config_env_vars={ "LLAMA_STACK_PORT": ( "8321", "Port for the Llama Stack distribution server", ), "FIREWORKS_API_KEY": ( "", "Fireworks API Key", ), "OPENAI_API_KEY": ( "", "OpenAI API Key", ), "GROQ_API_KEY": ( "", "Groq API Key", ), "ANTHROPIC_API_KEY": ( "", "Anthropic API Key", ), "GEMINI_API_KEY": ( "", "Gemini API Key", ), "SAMBANOVA_API_KEY": ( "", "SambaNova API Key", ), "VLLM_URL": ( "http://localhost:8000/v1", "vLLM URL", ), "VLLM_INFERENCE_MODEL": ( "", "Optional vLLM Inference Model to register on startup", ), "OLLAMA_URL": ( "http://localhost:11434", "Ollama URL", ), "OLLAMA_INFERENCE_MODEL": ( "", "Optional Ollama Inference Model to register on startup", ), "OLLAMA_EMBEDDING_MODEL": ( "", "Optional Ollama Embedding Model to register on startup", ), "OLLAMA_EMBEDDING_DIMENSION": ( "384", "Ollama Embedding Dimension", ), }, )