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refactor: rename dev distro as starter (#2181)
We want this to be a "flagship" distribution we can advertize to a segment of users to get started quickly. This distro should package a bunch of remote providers and some cheap inline providers so they get a solid "AI Platform in a box" setup instantly.
This commit is contained in:
Ashwin Bharambe
GitHub
parent
87e284f1a0
commit
1a6d4af5e9
6 changed files with 54 additions and 54 deletions
205
llama_stack/templates/starter/starter.py
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llama_stack/templates/starter/starter.py
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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 llama_stack.apis.models.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.inference.sentence_transformers import (
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SentenceTransformersInferenceConfig,
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)
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from llama_stack.providers.inline.vector_io.sqlite_vec.config import (
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SQLiteVectorIOConfig,
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)
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from llama_stack.providers.remote.inference.anthropic.config import AnthropicConfig
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from llama_stack.providers.remote.inference.anthropic.models import (
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MODEL_ENTRIES as ANTHROPIC_MODEL_ENTRIES,
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)
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from llama_stack.providers.remote.inference.fireworks.config import FireworksImplConfig
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from llama_stack.providers.remote.inference.fireworks.models import (
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MODEL_ENTRIES as FIREWORKS_MODEL_ENTRIES,
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)
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from llama_stack.providers.remote.inference.gemini.config import GeminiConfig
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from llama_stack.providers.remote.inference.gemini.models import (
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MODEL_ENTRIES as GEMINI_MODEL_ENTRIES,
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)
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from llama_stack.providers.remote.inference.groq.config import GroqConfig
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from llama_stack.providers.remote.inference.groq.models import (
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MODEL_ENTRIES as GROQ_MODEL_ENTRIES,
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)
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from llama_stack.providers.remote.inference.openai.config import OpenAIConfig
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from llama_stack.providers.remote.inference.openai.models import (
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MODEL_ENTRIES as OPENAI_MODEL_ENTRIES,
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)
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from llama_stack.providers.remote.inference.sambanova.config import SambaNovaImplConfig
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from llama_stack.providers.remote.inference.sambanova.models import (
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MODEL_ENTRIES as SAMBANOVA_MODEL_ENTRIES,
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)
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from llama_stack.providers.remote.vector_io.chroma.config import ChromaVectorIOConfig
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from llama_stack.providers.remote.vector_io.pgvector.config import (
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PGVectorVectorIOConfig,
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)
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from llama_stack.providers.utils.inference.model_registry import ProviderModelEntry
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from llama_stack.templates.template import (
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DistributionTemplate,
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RunConfigSettings,
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get_model_registry,
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)
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def get_inference_providers() -> tuple[list[Provider], dict[str, list[ProviderModelEntry]]]:
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# in this template, we allow each API key to be optional
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providers = [
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(
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"openai",
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OPENAI_MODEL_ENTRIES,
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OpenAIConfig.sample_run_config(api_key="${env.OPENAI_API_KEY:}"),
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),
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(
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"fireworks",
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FIREWORKS_MODEL_ENTRIES,
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FireworksImplConfig.sample_run_config(api_key="${env.FIREWORKS_API_KEY:}"),
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),
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(
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"anthropic",
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ANTHROPIC_MODEL_ENTRIES,
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AnthropicConfig.sample_run_config(api_key="${env.ANTHROPIC_API_KEY:}"),
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),
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(
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"gemini",
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GEMINI_MODEL_ENTRIES,
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GeminiConfig.sample_run_config(api_key="${env.GEMINI_API_KEY:}"),
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),
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(
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"groq",
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GROQ_MODEL_ENTRIES,
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GroqConfig.sample_run_config(api_key="${env.GROQ_API_KEY:}"),
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),
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(
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"sambanova",
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SAMBANOVA_MODEL_ENTRIES,
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SambaNovaImplConfig.sample_run_config(api_key="${env.SAMBANOVA_API_KEY:}"),
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),
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]
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inference_providers = []
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available_models = {}
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for provider_id, model_entries, config in providers:
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inference_providers.append(
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Provider(
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provider_id=provider_id,
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provider_type=f"remote::{provider_id}",
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config=config,
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)
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)
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available_models[provider_id] = model_entries
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return inference_providers, available_models
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def get_distribution_template() -> DistributionTemplate:
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inference_providers, available_models = get_inference_providers()
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providers = {
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"inference": ([p.provider_type for p in inference_providers] + ["inline::sentence-transformers"]),
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"vector_io": ["inline::sqlite-vec", "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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"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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],
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}
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name = "starter"
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vector_io_providers = [
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Provider(
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provider_id="sqlite-vec",
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provider_type="inline::sqlite-vec",
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config=SQLiteVectorIOConfig.sample_run_config(f"~/.llama/distributions/{name}"),
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),
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Provider(
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provider_id="${env.ENABLE_CHROMADB+chromadb}",
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provider_type="remote::chromadb",
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config=ChromaVectorIOConfig.sample_run_config(url="${env.CHROMADB_URL:}"),
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),
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Provider(
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provider_id="${env.ENABLE_PGVECTOR+pgvector}",
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provider_type="remote::pgvector",
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config=PGVectorVectorIOConfig.sample_run_config(
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db="${env.PGVECTOR_DB:}",
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user="${env.PGVECTOR_USER:}",
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password="${env.PGVECTOR_PASSWORD:}",
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),
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),
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]
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embedding_provider = Provider(
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provider_id="sentence-transformers",
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provider_type="inline::sentence-transformers",
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config=SentenceTransformersInferenceConfig.sample_run_config(),
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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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]
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embedding_model = ModelInput(
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model_id="all-MiniLM-L6-v2",
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provider_id=embedding_provider.provider_id,
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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_models = get_model_registry(available_models)
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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="Quick start template for running Llama Stack with several popular providers",
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container_image=None,
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template_path=None,
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providers=providers,
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available_models_by_provider=available_models,
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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_providers + [embedding_provider],
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"vector_io": vector_io_providers,
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},
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default_models=default_models + [embedding_model],
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default_tool_groups=default_tool_groups,
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default_shields=[ShieldInput(shield_id="meta-llama/Llama-Guard-3-8B")],
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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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"FIREWORKS_API_KEY": (
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"",
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"Fireworks API Key",
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),
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"OPENAI_API_KEY": (
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"",
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"OpenAI API Key",
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),
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},
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)
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