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feat: consolidate most distros into "starter" (#2516)
# What does this PR do? * Removes a bunch of distros * Removed distros were added into the "starter" distribution * Doc for "starter" has been added * Partially reverts https://github.com/meta-llama/llama-stack/pull/2482 since inference providers are disabled by default and can be turned on manually via env variable. * Disables safety in starter distro Closes: https://github.com/meta-llama/llama-stack/issues/2502. ~Needs: https://github.com/meta-llama/llama-stack/pull/2482 for Ollama to work properly in the CI.~ TODO: - [ ] We can only update `install.sh` when we get a new release. - [x] Update providers documentation - [ ] Update notebooks to reference starter instead of ollama Signed-off-by: Sébastien Han <seb@redhat.com>
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Sébastien Han
GitHub
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132 changed files with 1009 additions and 10845 deletions
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# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the terms described in the LICENSE file in
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# the root directory of this source tree.
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from pathlib import Path
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from llama_stack.apis.models import ModelType
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from llama_stack.distribution.datatypes import (
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ModelInput,
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Provider,
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ShieldInput,
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ToolGroupInput,
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)
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from llama_stack.providers.inline.files.localfs.config import LocalfsFilesImplConfig
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from llama_stack.providers.inline.post_training.huggingface import HuggingFacePostTrainingConfig
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from llama_stack.providers.inline.vector_io.faiss.config import FaissVectorIOConfig
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from llama_stack.providers.remote.inference.ollama import OllamaImplConfig
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from llama_stack.templates.template import DistributionTemplate, RunConfigSettings
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def get_distribution_template() -> DistributionTemplate:
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providers = {
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"inference": ["remote::ollama"],
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"vector_io": ["inline::faiss", "remote::chromadb", "remote::pgvector"],
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"safety": ["inline::llama-guard"],
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"agents": ["inline::meta-reference"],
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"telemetry": ["inline::meta-reference"],
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"eval": ["inline::meta-reference"],
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"datasetio": ["remote::huggingface", "inline::localfs"],
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"scoring": ["inline::basic", "inline::llm-as-judge", "inline::braintrust"],
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"files": ["inline::localfs"],
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"post_training": ["inline::huggingface"],
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"tool_runtime": [
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"remote::brave-search",
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"remote::tavily-search",
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"inline::rag-runtime",
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"remote::model-context-protocol",
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"remote::wolfram-alpha",
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],
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}
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name = "ollama"
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inference_provider = Provider(
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provider_id="ollama",
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provider_type="remote::ollama",
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config=OllamaImplConfig.sample_run_config(),
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)
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vector_io_provider_faiss = Provider(
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provider_id="faiss",
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provider_type="inline::faiss",
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config=FaissVectorIOConfig.sample_run_config(f"~/.llama/distributions/{name}"),
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)
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files_provider = Provider(
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provider_id="meta-reference-files",
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provider_type="inline::localfs",
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config=LocalfsFilesImplConfig.sample_run_config(f"~/.llama/distributions/{name}"),
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)
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posttraining_provider = Provider(
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provider_id="huggingface",
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provider_type="inline::huggingface",
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config=HuggingFacePostTrainingConfig.sample_run_config(f"~/.llama/distributions/{name}"),
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)
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inference_model = ModelInput(
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model_id="${env.INFERENCE_MODEL}",
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provider_id="ollama",
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)
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safety_model = ModelInput(
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model_id="${env.SAFETY_MODEL}",
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provider_id="ollama",
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)
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embedding_model = ModelInput(
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model_id="all-MiniLM-L6-v2",
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provider_id="ollama",
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provider_model_id="all-minilm:latest",
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model_type=ModelType.embedding,
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metadata={
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"embedding_dimension": 384,
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},
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)
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default_tool_groups = [
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ToolGroupInput(
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toolgroup_id="builtin::websearch",
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provider_id="tavily-search",
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),
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ToolGroupInput(
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toolgroup_id="builtin::rag",
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provider_id="rag-runtime",
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),
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ToolGroupInput(
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toolgroup_id="builtin::wolfram_alpha",
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provider_id="wolfram-alpha",
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),
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]
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return DistributionTemplate(
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name=name,
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distro_type="self_hosted",
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description="Use (an external) Ollama server for running LLM inference",
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container_image=None,
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template_path=Path(__file__).parent / "doc_template.md",
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providers=providers,
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run_configs={
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"run.yaml": RunConfigSettings(
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provider_overrides={
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"inference": [inference_provider],
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"vector_io": [vector_io_provider_faiss],
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"files": [files_provider],
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"post_training": [posttraining_provider],
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},
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default_models=[inference_model, embedding_model],
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default_tool_groups=default_tool_groups,
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),
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"run-with-safety.yaml": RunConfigSettings(
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provider_overrides={
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"inference": [inference_provider],
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"vector_io": [vector_io_provider_faiss],
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"files": [files_provider],
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"post_training": [posttraining_provider],
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"safety": [
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Provider(
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provider_id="llama-guard",
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provider_type="inline::llama-guard",
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config={},
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),
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Provider(
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provider_id="code-scanner",
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provider_type="inline::code-scanner",
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config={},
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),
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],
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},
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default_models=[
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inference_model,
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safety_model,
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embedding_model,
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],
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default_shields=[
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ShieldInput(
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shield_id="${env.SAFETY_MODEL}",
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provider_id="llama-guard",
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),
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ShieldInput(
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shield_id="CodeScanner",
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provider_id="code-scanner",
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),
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],
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default_tool_groups=default_tool_groups,
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),
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},
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run_config_env_vars={
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"LLAMA_STACK_PORT": (
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"8321",
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"Port for the Llama Stack distribution server",
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),
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"OLLAMA_URL": (
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"http://127.0.0.1:11434",
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"URL of the Ollama server",
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),
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"INFERENCE_MODEL": (
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"meta-llama/Llama-3.2-3B-Instruct",
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"Inference model loaded into the Ollama server",
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),
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"SAFETY_MODEL": (
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"meta-llama/Llama-Guard-3-1B",
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"Safety model loaded into the Ollama server",
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),
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},
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)
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