# 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 llama_stack.apis.models.models import ModelType from llama_stack.distribution.datatypes import ( ModelInput, Provider, ShieldInput, ToolGroupInput, ) 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.vector_io.faiss.config import FaissVectorIOConfig from llama_stack.providers.inline.vector_io.sqlite_vec.config import ( SQLiteVectorIOConfig, ) from llama_stack.providers.remote.inference.anthropic.config import AnthropicConfig from llama_stack.providers.remote.inference.anthropic.models import ( MODEL_ENTRIES as ANTHROPIC_MODEL_ENTRIES, ) from llama_stack.providers.remote.inference.fireworks.config import FireworksImplConfig from llama_stack.providers.remote.inference.fireworks.models import ( MODEL_ENTRIES as FIREWORKS_MODEL_ENTRIES, ) from llama_stack.providers.remote.inference.gemini.config import GeminiConfig from llama_stack.providers.remote.inference.gemini.models import ( MODEL_ENTRIES as GEMINI_MODEL_ENTRIES, ) from llama_stack.providers.remote.inference.groq.config import GroqConfig from llama_stack.providers.remote.inference.groq.models import ( MODEL_ENTRIES as GROQ_MODEL_ENTRIES, ) from llama_stack.providers.remote.inference.ollama.config import OllamaImplConfig from llama_stack.providers.remote.inference.openai.config import OpenAIConfig from llama_stack.providers.remote.inference.openai.models import ( MODEL_ENTRIES as OPENAI_MODEL_ENTRIES, ) from llama_stack.providers.remote.inference.sambanova.config import SambaNovaImplConfig from llama_stack.providers.remote.inference.sambanova.models import ( MODEL_ENTRIES as SAMBANOVA_MODEL_ENTRIES, ) from llama_stack.providers.remote.inference.together.config import TogetherImplConfig from llama_stack.providers.remote.inference.together.models import ( MODEL_ENTRIES as TOGETHER_MODEL_ENTRIES, ) from llama_stack.providers.remote.inference.vllm import VLLMInferenceAdapterConfig 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_inference_providers() -> tuple[list[Provider], dict[str, list[ProviderModelEntry]]]: # in this template, we allow each API key to be optional providers = [ ( "openai", OPENAI_MODEL_ENTRIES, OpenAIConfig.sample_run_config(api_key="${env.OPENAI_API_KEY:}"), ), ( "fireworks", FIREWORKS_MODEL_ENTRIES, FireworksImplConfig.sample_run_config(api_key="${env.FIREWORKS_API_KEY:}"), ), ( "together", TOGETHER_MODEL_ENTRIES, TogetherImplConfig.sample_run_config(api_key="${env.TOGETHER_API_KEY:}"), ), ( "ollama", [ ProviderModelEntry( provider_model_id="${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}", }, ), ], OllamaImplConfig.sample_run_config( url="${env.OLLAMA_URL:http://localhost:11434}", raise_on_connect_error=False ), ), ( "anthropic", ANTHROPIC_MODEL_ENTRIES, AnthropicConfig.sample_run_config(api_key="${env.ANTHROPIC_API_KEY:}"), ), ( "gemini", GEMINI_MODEL_ENTRIES, GeminiConfig.sample_run_config(api_key="${env.GEMINI_API_KEY:}"), ), ( "groq", GROQ_MODEL_ENTRIES, GroqConfig.sample_run_config(api_key="${env.GROQ_API_KEY:}"), ), ( "sambanova", SAMBANOVA_MODEL_ENTRIES, SambaNovaImplConfig.sample_run_config(api_key="${env.SAMBANOVA_API_KEY:}"), ), ( "vllm", [ ProviderModelEntry( provider_model_id="${env.VLLM_INFERENCE_MODEL:__disabled__}", model_type=ModelType.llm, ), ], VLLMInferenceAdapterConfig.sample_run_config( url="${env.VLLM_URL:http://localhost:8000/v1}", ), ), ] inference_providers = [] available_models = {} for provider_id, model_entries, config in providers: inference_providers.append( Provider( provider_id=provider_id, provider_type=f"remote::{provider_id}", config=config, ) ) available_models[provider_id] = model_entries return inference_providers, available_models def get_distribution_template() -> DistributionTemplate: inference_providers, available_models = get_inference_providers() providers = { "inference": ([p.provider_type for p in inference_providers] + ["inline::sentence-transformers"]), "vector_io": ["inline::sqlite-vec", "remote::chromadb", "remote::pgvector"], "files": ["inline::localfs"], "safety": ["inline::llama-guard"], "agents": ["inline::meta-reference"], "telemetry": ["inline::meta-reference"], "eval": ["inline::meta-reference"], "datasetio": ["remote::huggingface", "inline::localfs"], "scoring": ["inline::basic", "inline::llm-as-judge", "inline::braintrust"], "tool_runtime": [ "remote::brave-search", "remote::tavily-search", "inline::rag-runtime", "remote::model-context-protocol", ], } name = "starter" vector_io_providers = [ Provider( provider_id="faiss", provider_type="inline::faiss", config=FaissVectorIOConfig.sample_run_config(f"~/.llama/distributions/{name}"), ), Provider( provider_id="${env.ENABLE_SQLITE_VEC+sqlite-vec}", provider_type="inline::sqlite-vec", config=SQLiteVectorIOConfig.sample_run_config(f"~/.llama/distributions/{name}"), ), Provider( provider_id="${env.ENABLE_CHROMADB+chromadb}", provider_type="remote::chromadb", config=ChromaVectorIOConfig.sample_run_config(url="${env.CHROMADB_URL:}"), ), Provider( provider_id="${env.ENABLE_PGVECTOR+pgvector}", provider_type="remote::pgvector", config=PGVectorVectorIOConfig.sample_run_config( db="${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="sentence-transformers", provider_type="inline::sentence-transformers", config=SentenceTransformersInferenceConfig.sample_run_config(), ) 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) postgres_store = PostgresSqlStoreConfig.sample_run_config() 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=postgres_store.pip_packages, run_configs={ "run.yaml": RunConfigSettings( provider_overrides={ "inference": inference_providers + [embedding_provider], "vector_io": vector_io_providers, "files": [files_provider], }, default_models=default_models + [embedding_model], default_tool_groups=default_tool_groups, default_shields=[ShieldInput(shield_id="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", ), }, )