forked from phoenix-oss/llama-stack-mirror
dd0db8038b
## Test Plan `LLAMA_STACK_CONFIG=inference=sentence-transformers,vector_io=sqlite-vec pytest -s -v test_vector_io.py --embedding-model all-miniLM-L6-V2 --inference-model='' --vision-inference-model=''` ``` test_vector_io.py::test_vector_db_retrieve[txt=:vis=:emb=all-miniLM-L6-V2] PASSED test_vector_io.py::test_vector_db_register[txt=:vis=:emb=all-miniLM-L6-V2] PASSED test_vector_io.py::test_insert_chunks[txt=:vis=:emb=all-miniLM-L6-V2-test_case0] PASSED test_vector_io.py::test_insert_chunks[txt=:vis=:emb=all-miniLM-L6-V2-test_case1] PASSED test_vector_io.py::test_insert_chunks[txt=:vis=:emb=all-miniLM-L6-V2-test_case2] PASSED test_vector_io.py::test_insert_chunks[txt=:vis=:emb=all-miniLM-L6-V2-test_case3] PASSED test_vector_io.py::test_insert_chunks[txt=:vis=:emb=all-miniLM-L6-V2-test_case4] PASSED ``` Same thing with: - LLAMA_STACK_CONFIG=inference=sentence-transformers,vector_io=faiss - LLAMA_STACK_CONFIG=fireworks (Note that ergonomics will soon be improved re: cmd-line options and env variables)
83 lines
2.8 KiB
Python
83 lines
2.8 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 DistributionTemplate, RunConfigSettings, get_model_registry
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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::code-interpreter",
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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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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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]
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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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"5001",
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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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