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Add groq inference adapter.
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llama_stack/templates/groq/groq.py
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76
llama_stack/templates/groq/groq.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 pathlib import Path
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from llama_models.sku_list import all_registered_models
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from llama_stack.distribution.datatypes import ModelInput, Provider, ShieldInput
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from llama_stack.providers.remote.inference.groq import GroqImplConfig
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from llama_stack.providers.remote.inference.groq.groq import MODEL_ALIASES
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from llama_stack.templates.template import (
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DistributionTemplate,
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RunConfigSettings,
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)
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def get_distribution_template() -> DistributionTemplate:
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providers = {
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"inference": ["remote::groq"],
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"memory": ["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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}
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inference_provider = Provider(
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provider_id="groq",
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provider_type="remote::groq",
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config=GroqImplConfig.sample_run_config(),
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)
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core_model_to_hf_repo = {
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m.descriptor(): m.huggingface_repo for m in all_registered_models()
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}
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default_models = [
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ModelInput(
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model_id=core_model_to_hf_repo[m.llama_model],
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provider_model_id=m.provider_model_id,
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)
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for m in MODEL_ALIASES
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]
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return DistributionTemplate(
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name="groq",
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distro_type="self_hosted",
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description="Use Groq for running LLM inference",
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docker_image=None,
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template_path=Path(__file__).parent / "doc_template.md",
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providers=providers,
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default_models=default_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_provider],
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},
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default_models=default_models,
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default_shields=[
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ShieldInput(shield_id="meta-llama/Llama-Guard-3-8B")
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],
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),
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},
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run_config_env_vars={
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"LLAMASTACK_PORT": (
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"5001",
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"Port for the Llama Stack distribution server",
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),
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"GROQ_API_KEY": (
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"",
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"Groq API Key for authentication",
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),
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},
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)
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