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https://github.com/meta-llama/llama-stack.git
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0e96279bee
Kill the `builtin::rag` tool group completely since it is no longer targeted. We use the Responses implementation for knowledge_search which uses the `openai_vector_stores` pathway. --------- Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>
120 lines
4.2 KiB
Python
120 lines
4.2 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 llama_stack.apis.models import ModelType
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from llama_stack.core.datatypes import (
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BuildProvider,
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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.distributions.template import (
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DistributionTemplate,
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RunConfigSettings,
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)
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from llama_stack.providers.inline.inference.sentence_transformers import SentenceTransformersInferenceConfig
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from llama_stack.providers.remote.inference.vllm import VLLMInferenceAdapterConfig
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from llama_stack.providers.remote.vector_io.chroma.config import ChromaVectorIOConfig
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from llama_stack.providers.utils.kvstore.config import PostgresKVStoreConfig
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from llama_stack.providers.utils.sqlstore.sqlstore import PostgresSqlStoreConfig
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def get_distribution_template() -> DistributionTemplate:
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inference_providers = [
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Provider(
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provider_id="vllm-inference",
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provider_type="remote::vllm",
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config=VLLMInferenceAdapterConfig.sample_run_config(
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url="${env.VLLM_URL:=http://localhost:8000/v1}",
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),
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),
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]
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providers = {
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"inference": [
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BuildProvider(provider_type="remote::vllm"),
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BuildProvider(provider_type="inline::sentence-transformers"),
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],
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"vector_io": [BuildProvider(provider_type="remote::chromadb")],
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"safety": [BuildProvider(provider_type="inline::llama-guard")],
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"agents": [BuildProvider(provider_type="inline::meta-reference")],
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"tool_runtime": [
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BuildProvider(provider_type="remote::brave-search"),
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BuildProvider(provider_type="remote::tavily-search"),
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BuildProvider(provider_type="remote::model-context-protocol"),
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],
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}
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name = "postgres-demo"
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vector_io_providers = [
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Provider(
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provider_id="${env.ENABLE_CHROMADB:+chromadb}",
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provider_type="remote::chromadb",
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config=ChromaVectorIOConfig.sample_run_config(
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f"~/.llama/distributions/{name}",
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url="${env.CHROMADB_URL:=}",
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),
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),
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]
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default_tool_groups = [
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ToolGroupInput(
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toolgroup_id="builtin::websearch",
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provider_id="tavily-search",
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),
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]
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default_models = [
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ModelInput(
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model_id="${env.INFERENCE_MODEL}",
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provider_id="vllm-inference",
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)
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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="nomic-embed-text-v1.5",
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provider_id=embedding_provider.provider_id,
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model_type=ModelType.embedding,
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metadata={
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"embedding_dimension": 768,
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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="Quick start template for running Llama Stack with several popular providers",
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container_image=None,
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template_path=None,
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providers=providers,
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available_models_by_provider={},
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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_providers + [embedding_provider],
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"vector_io": vector_io_providers,
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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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default_shields=[ShieldInput(shield_id="meta-llama/Llama-Guard-3-8B")],
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storage_backends={
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"kv_default": PostgresKVStoreConfig.sample_run_config(
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table_name="llamastack_kvstore",
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),
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"sql_default": PostgresSqlStoreConfig.sample_run_config(),
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},
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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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