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https://github.com/meta-llama/llama-stack.git
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ac5fd57387
# What does this PR do? * Given that our API packages use "import *" in `__init.py__` we don't need to do `from llama_stack.apis.models.models` but simply from llama_stack.apis.models. The decision to use `import *` is debatable and should probably be revisited at one point. * Remove unneeded Ruff F401 rule * Consolidate Ruff F403 rule in the pyprojectfrom llama_stack.apis.models.models Signed-off-by: Sébastien Han <seb@redhat.com>
103 lines
3.3 KiB
Python
103 lines
3.3 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 pathlib import Path
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from llama_stack.apis.models import ModelType
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from llama_stack.distribution.datatypes import ModelInput, Provider, ToolGroupInput
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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.remote.inference.groq import GroqConfig
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from llama_stack.providers.remote.inference.groq.models import MODEL_ENTRIES
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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_distribution_template() -> DistributionTemplate:
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providers = {
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"inference": ["remote::groq"],
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"vector_io": ["inline::faiss"],
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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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],
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}
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name = "groq"
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inference_provider = Provider(
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provider_id=name,
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provider_type=f"remote::{name}",
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config=GroqConfig.sample_run_config(),
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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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embedding_model = ModelInput(
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model_id="all-MiniLM-L6-v2",
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provider_id="sentence-transformers",
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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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available_models = {
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"groq": MODEL_ENTRIES,
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}
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default_models = get_model_registry(available_models)
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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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return DistributionTemplate(
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name=name,
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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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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_provider, embedding_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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),
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},
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run_config_env_vars={
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"LLAMASTACK_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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"GROQ_API_KEY": (
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"",
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"Groq API Key",
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),
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},
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)
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