llama-stack-mirror/llama_stack/templates/passthrough/passthrough.py
Sébastien Han ac5fd57387
chore: remove nested imports (#2515)
# 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>
2025-06-26 08:01:05 +05:30

193 lines
6.7 KiB
Python

# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
from pathlib import Path
from llama_stack.apis.models import ModelType
from llama_stack.distribution.datatypes import (
ModelInput,
Provider,
ShieldInput,
ToolGroupInput,
)
from llama_stack.providers.inline.inference.sentence_transformers import (
SentenceTransformersInferenceConfig,
)
from llama_stack.providers.inline.vector_io.faiss.config import FaissVectorIOConfig
from llama_stack.providers.remote.inference.passthrough.config import (
PassthroughImplConfig,
)
from llama_stack.providers.utils.inference.model_registry import ProviderModelEntry
from llama_stack.templates.template import DistributionTemplate, RunConfigSettings
def get_distribution_template() -> DistributionTemplate:
providers = {
"inference": ["remote::passthrough", "inline::sentence-transformers"],
"vector_io": ["inline::faiss", "remote::chromadb", "remote::pgvector"],
"safety": ["inline::llama-guard"],
"agents": ["inline::meta-reference"],
"telemetry": ["inline::meta-reference"],
"eval": ["inline::meta-reference"],
"datasetio": ["remote::huggingface", "inline::localfs"],
"scoring": ["inline::basic", "inline::llm-as-judge", "inline::braintrust"],
"tool_runtime": [
"remote::brave-search",
"remote::tavily-search",
"remote::wolfram-alpha",
"inline::rag-runtime",
"remote::model-context-protocol",
],
}
name = "passthrough"
inference_provider = Provider(
provider_id="passthrough",
provider_type="remote::passthrough",
config=PassthroughImplConfig.sample_run_config(),
)
embedding_provider = Provider(
provider_id="sentence-transformers",
provider_type="inline::sentence-transformers",
config=SentenceTransformersInferenceConfig.sample_run_config(),
)
vector_io_provider = Provider(
provider_id="faiss",
provider_type="inline::faiss",
config=FaissVectorIOConfig.sample_run_config(f"~/.llama/distributions/{name}"),
)
default_models = [
ModelInput(
metadata={},
model_id="meta-llama/Llama-3.1-8B-Instruct",
provider_id="passthrough",
provider_model_id="llama3.1-8b-instruct",
model_type=ModelType.llm,
),
ModelInput(
metadata={},
model_id="meta-llama/Llama-3.2-11B-Vision-Instruct",
provider_id="passthrough",
provider_model_id="llama3.2-11b-vision-instruct",
model_type=ModelType.llm,
),
]
embedding_model = ModelInput(
model_id="all-MiniLM-L6-v2",
provider_id="sentence-transformers",
model_type=ModelType.embedding,
metadata={
"embedding_dimension": 384,
},
)
default_tool_groups = [
ToolGroupInput(
toolgroup_id="builtin::websearch",
provider_id="tavily-search",
),
ToolGroupInput(
toolgroup_id="builtin::wolfram_alpha",
provider_id="wolfram-alpha",
),
ToolGroupInput(
toolgroup_id="builtin::rag",
provider_id="rag-runtime",
),
]
return DistributionTemplate(
name=name,
distro_type="self_hosted",
description="Use Passthrough hosted llama-stack endpoint for LLM inference",
container_image=None,
template_path=Path(__file__).parent / "doc_template.md",
providers=providers,
available_models_by_provider={
"passthrough": [
ProviderModelEntry(
provider_model_id="llama3.1-8b-instruct",
model_type=ModelType.llm,
),
ProviderModelEntry(
provider_model_id="llama3.2-11b-vision-instruct",
model_type=ModelType.llm,
),
],
},
run_configs={
"run.yaml": RunConfigSettings(
provider_overrides={
"inference": [inference_provider, embedding_provider],
"vector_io": [vector_io_provider],
},
default_models=default_models + [embedding_model],
default_shields=[ShieldInput(shield_id="meta-llama/Llama-Guard-3-8B")],
default_tool_groups=default_tool_groups,
),
"run-with-safety.yaml": RunConfigSettings(
provider_overrides={
"inference": [
inference_provider,
embedding_provider,
],
"vector_io": [vector_io_provider],
"safety": [
Provider(
provider_id="llama-guard",
provider_type="inline::llama-guard",
config={},
),
Provider(
provider_id="llama-guard-vision",
provider_type="inline::llama-guard",
config={},
),
Provider(
provider_id="code-scanner",
provider_type="inline::code-scanner",
config={},
),
],
},
default_models=[
*default_models,
embedding_model,
],
default_shields=[
ShieldInput(
shield_id="meta-llama/Llama-Guard-3-8B",
provider_id="llama-guard",
),
ShieldInput(
shield_id="meta-llama/Llama-Guard-3-11B-Vision",
provider_id="llama-guard-vision",
),
ShieldInput(
shield_id="CodeScanner",
provider_id="code-scanner",
),
],
default_tool_groups=default_tool_groups,
),
},
run_config_env_vars={
"LLAMA_STACK_PORT": (
"8321",
"Port for the Llama Stack distribution server",
),
"PASSTHROUGH_API_KEY": (
"",
"Passthrough API Key",
),
"PASSTHROUGH_URL": (
"",
"Passthrough URL",
),
},
)