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https://github.com/meta-llama/llama-stack.git
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272d3359ee
# What does this PR do? The builtin implementation of code interpreter is not robust and has a really weak sandboxing shell (the `bubblewrap` container). Given the availability of better MCP code interpreter servers coming up, we should use them instead of baking an implementation into the Stack and expanding the vulnerability surface to the rest of the Stack. This PR only does the removal. We will add examples with how to integrate with MCPs in subsequent ones. ## Test Plan Existing tests.
82 lines
2.7 KiB
Python
82 lines
2.7 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.distribution.datatypes import Provider, ToolGroupInput
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from llama_stack.providers.inline.vector_io.faiss.config import FaissVectorIOConfig
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from llama_stack.providers.remote.inference.bedrock.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::bedrock"],
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"vector_io": ["inline::faiss", "remote::chromadb", "remote::pgvector"],
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"safety": ["remote::bedrock"],
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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 = "bedrock"
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vector_io_provider = 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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available_models = {
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"bedrock": 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 AWS Bedrock for running LLM inference and safety",
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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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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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"vector_io": [vector_io_provider],
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},
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default_models=default_models,
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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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"8321",
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"Port for the Llama Stack distribution server",
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),
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},
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)
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