mirror of
https://github.com/meta-llama/llama-stack.git
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b69bfab019
Some templates were still using the old environment variable
substition syntax instead of the new one and were not getting
substituted properly.
Also, some places didn't handle the new None vs old empty string ("")
values that come from the conditional environment variable
substitution.
This gets the starter and remote-vllm distributions starting again,
and I tested various permutations of the starter as chroma and
pgvector needed some adjustments to their config classes to handle the
new possible `None` values. And, I had to tweak our `Provider` class
to also handle `None` values, for cases where we disable providers in
the starter config via environment variables.
This may not have caught everything that was missed, but I did grep
around quite a bit to try and find anything lingering.
Signed-off-by: Ben Browning <bbrownin@redhat.com>
305 lines
11 KiB
Python
305 lines
11 KiB
Python
# 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 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.inference.sentence_transformers import (
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SentenceTransformersInferenceConfig,
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)
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from llama_stack.providers.inline.vector_io.faiss.config import FaissVectorIOConfig
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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.ollama.config import OllamaImplConfig
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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.inference.together.config import TogetherImplConfig
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from llama_stack.providers.remote.inference.together.models import (
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MODEL_ENTRIES as TOGETHER_MODEL_ENTRIES,
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)
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from llama_stack.providers.remote.inference.vllm import VLLMInferenceAdapterConfig
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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.providers.utils.sqlstore.sqlstore import PostgresSqlStoreConfig
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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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"together",
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TOGETHER_MODEL_ENTRIES,
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TogetherImplConfig.sample_run_config(api_key="${env.TOGETHER_API_KEY:+}"),
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),
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(
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"ollama",
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[
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ProviderModelEntry(
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provider_model_id="${env.OLLAMA_INFERENCE_MODEL:=__disabled__}",
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model_type=ModelType.llm,
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),
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ProviderModelEntry(
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provider_model_id="${env.OLLAMA_EMBEDDING_MODEL:=__disabled__}",
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model_type=ModelType.embedding,
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metadata={
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"embedding_dimension": "${env.OLLAMA_EMBEDDING_DIMENSION:=384}",
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},
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),
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],
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OllamaImplConfig.sample_run_config(
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url="${env.OLLAMA_URL:=http://localhost:11434}", raise_on_connect_error=False
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),
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),
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(
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"anthropic",
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ANTHROPIC_MODEL_ENTRIES,
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AnthropicConfig.sample_run_config(api_key="${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="${env.SAMBANOVA_API_KEY:+}"),
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),
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(
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"vllm",
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[
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ProviderModelEntry(
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provider_model_id="${env.VLLM_INFERENCE_MODEL:=__disabled__}",
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model_type=ModelType.llm,
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),
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],
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VLLMInferenceAdapterConfig.sample_run_config(
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url="${env.VLLM_URL:=http://localhost:8000/v1}",
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),
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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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"files": ["inline::localfs"],
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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="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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Provider(
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provider_id="${env.ENABLE_SQLITE_VEC:+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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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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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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postgres_store = PostgresSqlStoreConfig.sample_run_config()
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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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additional_pip_packages=postgres_store.pip_packages,
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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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"files": [files_provider],
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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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"GROQ_API_KEY": (
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"",
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"Groq API Key",
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),
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"ANTHROPIC_API_KEY": (
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"",
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"Anthropic API Key",
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),
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"GEMINI_API_KEY": (
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"",
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"Gemini API Key",
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),
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"SAMBANOVA_API_KEY": (
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"",
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"SambaNova API Key",
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),
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"VLLM_URL": (
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"http://localhost:8000/v1",
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"vLLM URL",
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),
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"VLLM_INFERENCE_MODEL": (
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"",
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"Optional vLLM Inference Model to register on startup",
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),
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"OLLAMA_URL": (
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"http://localhost:11434",
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"Ollama URL",
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),
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"OLLAMA_INFERENCE_MODEL": (
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"",
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"Optional Ollama Inference Model to register on startup",
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),
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"OLLAMA_EMBEDDING_MODEL": (
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"",
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"Optional Ollama Embedding Model to register on startup",
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),
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"OLLAMA_EMBEDDING_DIMENSION": (
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"384",
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"Ollama Embedding Dimension",
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),
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},
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)
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