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All of the tests from `llama_stack/providers/tests/` are now moved to `tests/integration`. I converted the `tools`, `scoring` and `datasetio` tests to use API. However, `eval` and `post_training` proved to be a bit challenging to leaving those. I think `post_training` should be relatively straightforward also. As part of this, I noticed that `wolfram_alpha` tool wasn't added to some of our commonly used distros so I added it. I am going to remove a lot of code duplication from distros next so while this looks like a one-off right now, it will go away and be there uniformly for all distros.
157 lines
5.4 KiB
Python
157 lines
5.4 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.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.vector_io.sqlite_vec.config import SQLiteVectorIOConfig
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from llama_stack.providers.remote.inference.ollama import OllamaImplConfig
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from llama_stack.templates.template import DistributionTemplate, RunConfigSettings
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def get_distribution_template() -> DistributionTemplate:
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providers = {
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"inference": ["remote::ollama"],
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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::code-interpreter",
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"inline::rag-runtime",
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"remote::model-context-protocol",
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"remote::wolfram-alpha",
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],
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}
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name = "ollama"
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inference_provider = Provider(
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provider_id="ollama",
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provider_type="remote::ollama",
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config=OllamaImplConfig.sample_run_config(),
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)
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vector_io_provider_sqlite = 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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inference_model = ModelInput(
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model_id="${env.INFERENCE_MODEL}",
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provider_id="ollama",
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)
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safety_model = ModelInput(
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model_id="${env.SAFETY_MODEL}",
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provider_id="ollama",
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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="ollama",
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provider_model_id="all-minilm:latest",
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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_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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ToolGroupInput(
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toolgroup_id="builtin::code_interpreter",
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provider_id="code-interpreter",
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),
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ToolGroupInput(
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toolgroup_id="builtin::wolfram_alpha",
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provider_id="wolfram-alpha",
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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 (an external) Ollama server for running LLM inference",
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container_image=None,
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template_path=Path(__file__).parent / "doc_template.md",
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providers=providers,
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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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"vector_io": [vector_io_provider_sqlite],
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},
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default_models=[inference_model, embedding_model],
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default_tool_groups=default_tool_groups,
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),
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"run-with-safety.yaml": RunConfigSettings(
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provider_overrides={
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"inference": [inference_provider],
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"vector_io": [vector_io_provider_sqlite],
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"safety": [
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Provider(
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provider_id="llama-guard",
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provider_type="inline::llama-guard",
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config={},
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),
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Provider(
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provider_id="code-scanner",
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provider_type="inline::code-scanner",
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config={},
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),
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],
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},
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default_models=[
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inference_model,
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safety_model,
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embedding_model,
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],
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default_shields=[
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ShieldInput(
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shield_id="${env.SAFETY_MODEL}",
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provider_id="llama-guard",
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),
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ShieldInput(
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shield_id="CodeScanner",
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provider_id="code-scanner",
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),
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],
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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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"LLAMA_STACK_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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"OLLAMA_URL": (
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"http://127.0.0.1:11434",
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"URL of the Ollama server",
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),
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"INFERENCE_MODEL": (
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"meta-llama/Llama-3.2-3B-Instruct",
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"Inference model loaded into the Ollama server",
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),
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"SAFETY_MODEL": (
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"meta-llama/Llama-Guard-3-1B",
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"Safety model loaded into the Ollama server",
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),
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},
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)
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