mirror of
https://github.com/meta-llama/llama-stack.git
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fix: Fix open benchmark template (#1496)
## What does this PR do? Delete the open_benchmark template which was generated by the auto codegen by accident
This commit is contained in:
Botao Chen
GitHub
parent
d63e798f6d
commit
89e449c2cb
5 changed files with 0 additions and 622 deletions
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@ -453,42 +453,6 @@
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"transformers",
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"uvicorn"
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],
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"open_benchmark": [
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"aiosqlite",
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"autoevals",
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"blobfile",
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"chardet",
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"chromadb-client",
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"datasets",
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"fastapi",
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"fire",
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"httpx",
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"litellm",
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"matplotlib",
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"mcp",
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"nltk",
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"numpy",
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"openai",
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"opentelemetry-exporter-otlp-proto-http",
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"opentelemetry-sdk",
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"pandas",
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"pillow",
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"psycopg2-binary",
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"pymongo",
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"pypdf",
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"redis",
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"requests",
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"scikit-learn",
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"scipy",
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"sentencepiece",
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"sqlite-vec",
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"together",
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"tqdm",
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"transformers",
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"uvicorn",
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"sentence-transformers --no-deps",
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"torch torchvision --index-url https://download.pytorch.org/whl/cpu"
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],
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"remote-vllm": [
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"aiosqlite",
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"autoevals",
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@ -1,7 +0,0 @@
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# 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 .open_benchmark import get_distribution_template # noqa: F401
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@ -1,178 +0,0 @@
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# 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 typing import List, Tuple
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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.anthropic.config import AnthropicConfig
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from llama_stack.providers.remote.inference.anthropic.models import MODEL_ENTRIES as ANTHROPIC_MODEL_ENTRIES
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from llama_stack.providers.remote.inference.gemini.config import GeminiConfig
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from llama_stack.providers.remote.inference.gemini.models import MODEL_ENTRIES as GEMINI_MODEL_ENTRIES
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from llama_stack.providers.remote.inference.groq.config import GroqConfig
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from llama_stack.providers.remote.inference.groq.models import MODEL_ENTRIES as GROQ_MODEL_ENTRIES
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from llama_stack.providers.remote.inference.openai.config import OpenAIConfig
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from llama_stack.providers.remote.inference.openai.models import MODEL_ENTRIES as OPENAI_MODEL_ENTRIES
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from llama_stack.providers.remote.inference.together.config import TogetherImplConfig
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from llama_stack.providers.remote.inference.together.models import MODEL_ENTRIES as TOGETHER_MODEL_ENTRIES
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from llama_stack.providers.remote.vector_io.chroma.config import ChromaVectorIOConfig
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from llama_stack.providers.remote.vector_io.pgvector.config import PGVectorVectorIOConfig
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from llama_stack.templates.template import DistributionTemplate, RunConfigSettings, get_model_registry
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def get_inference_providers() -> Tuple[List[Provider], List[ModelInput]]:
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# in this template, we allow each API key to be optional
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providers = [
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(
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"openai",
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OPENAI_MODEL_ENTRIES,
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OpenAIConfig.sample_run_config(api_key="${env.OPENAI_API_KEY:}"),
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),
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(
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"anthropic",
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ANTHROPIC_MODEL_ENTRIES,
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AnthropicConfig.sample_run_config(api_key="${env.ANTHROPIC_API_KEY:}"),
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),
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(
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"gemini",
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GEMINI_MODEL_ENTRIES,
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GeminiConfig.sample_run_config(api_key="${env.GEMINI_API_KEY:}"),
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),
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(
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"groq",
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GROQ_MODEL_ENTRIES,
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GroqConfig.sample_run_config(api_key="${env.GROQ_API_KEY:}"),
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),
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(
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"together",
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TOGETHER_MODEL_ENTRIES,
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TogetherImplConfig.sample_run_config(api_key="${env.TOGETHER_API_KEY:}"),
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),
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]
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inference_providers = []
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available_models = {}
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for provider_id, model_entries, config in providers:
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inference_providers.append(
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Provider(
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provider_id=provider_id,
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provider_type=f"remote::{provider_id}",
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config=config,
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)
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)
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available_models[provider_id] = model_entries
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return inference_providers, available_models
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def get_distribution_template() -> DistributionTemplate:
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inference_providers, available_models = get_inference_providers()
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providers = {
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"inference": ([p.provider_type for p in inference_providers] + ["inline::sentence-transformers"]),
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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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],
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}
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name = "open_benchmark"
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vector_io_providers = [
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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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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(url="${env.CHROMADB_URL:}"),
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),
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Provider(
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provider_id="${env.ENABLE_PGVECTOR+pgvector}",
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provider_type="remote::pgvector",
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config=PGVectorVectorIOConfig.sample_run_config(
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db="${env.PGVECTOR_DB:}",
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user="${env.PGVECTOR_USER:}",
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password="${env.PGVECTOR_PASSWORD:}",
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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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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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default_models = get_model_registry(available_models)
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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="Distribution for running open benchmarks",
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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=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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"inference": inference_providers,
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"vector_io": vector_io_providers,
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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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default_shields=[ShieldInput(shield_id="meta-llama/Llama-Guard-3-8B")],
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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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"OPENAI_API_KEY": (
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"",
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"OpenAI API Key",
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),
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"GEMINI_API_KEY": (
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"",
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"Gemini API Key",
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),
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"GROQ_API_KEY": (
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"",
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"Groq API Key",
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),
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"ANTHROPIC_API_KEY": (
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"",
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"Anthropic API Key",
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),
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"TOGETHER_API_KEY": (
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"",
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"Together API Key",
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),
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},
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)
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@ -1,37 +0,0 @@
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version: '2'
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distribution_spec:
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description: Distribution for running open benchmarks
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providers:
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inference:
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- remote::openai
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- remote::anthropic
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- remote::gemini
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- remote::groq
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- remote::together
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- inline::sentence-transformers
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vector_io:
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- inline::sqlite-vec
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- remote::chromadb
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- remote::pgvector
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safety:
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- inline::llama-guard
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agents:
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- inline::meta-reference
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telemetry:
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- inline::meta-reference
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eval:
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- inline::meta-reference
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datasetio:
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- remote::huggingface
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- inline::localfs
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scoring:
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- inline::basic
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- inline::llm-as-judge
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- 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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image_type: conda
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@ -1,364 +0,0 @@
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version: '2'
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image_name: open_benchmark
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apis:
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- agents
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- datasetio
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- eval
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- inference
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- safety
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- scoring
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- telemetry
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- tool_runtime
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- vector_io
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providers:
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inference:
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- provider_id: openai
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provider_type: remote::openai
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config:
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api_key: ${env.OPENAI_API_KEY:}
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- provider_id: anthropic
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provider_type: remote::anthropic
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config:
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api_key: ${env.ANTHROPIC_API_KEY:}
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- provider_id: gemini
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provider_type: remote::gemini
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config:
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api_key: ${env.GEMINI_API_KEY:}
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- provider_id: groq
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provider_type: remote::groq
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config:
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url: https://api.groq.com
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api_key: ${env.GROQ_API_KEY:}
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- provider_id: together
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provider_type: remote::together
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config:
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url: https://api.together.xyz/v1
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api_key: ${env.TOGETHER_API_KEY}
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vector_io:
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- provider_id: sqlite-vec
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provider_type: inline::sqlite-vec
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config:
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db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/open_benchmark}/sqlite_vec.db
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- provider_id: ${env.ENABLE_CHROMADB+chromadb}
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provider_type: remote::chromadb
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config:
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url: ${env.CHROMADB_URL:}
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- provider_id: ${env.ENABLE_PGVECTOR+pgvector}
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provider_type: remote::pgvector
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config:
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host: ${env.PGVECTOR_HOST:localhost}
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port: ${env.PGVECTOR_PORT:5432}
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db: ${env.PGVECTOR_DB:}
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user: ${env.PGVECTOR_USER:}
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password: ${env.PGVECTOR_PASSWORD:}
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safety:
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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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agents:
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- provider_id: meta-reference
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provider_type: inline::meta-reference
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config:
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persistence_store:
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type: sqlite
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namespace: null
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db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/open_benchmark}/agents_store.db
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telemetry:
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- provider_id: meta-reference
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provider_type: inline::meta-reference
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config:
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service_name: ${env.OTEL_SERVICE_NAME:llama-stack}
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sinks: ${env.TELEMETRY_SINKS:console,sqlite}
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sqlite_db_path: ${env.SQLITE_DB_PATH:~/.llama/distributions/open_benchmark/trace_store.db}
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eval:
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- provider_id: meta-reference
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provider_type: inline::meta-reference
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config: {}
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datasetio:
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- provider_id: huggingface
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provider_type: remote::huggingface
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config: {}
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- provider_id: localfs
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provider_type: inline::localfs
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config: {}
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scoring:
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- provider_id: basic
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provider_type: inline::basic
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config: {}
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- provider_id: llm-as-judge
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provider_type: inline::llm-as-judge
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config: {}
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- provider_id: braintrust
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provider_type: inline::braintrust
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config:
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openai_api_key: ${env.OPENAI_API_KEY:}
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tool_runtime:
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- provider_id: brave-search
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provider_type: remote::brave-search
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config:
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api_key: ${env.BRAVE_SEARCH_API_KEY:}
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max_results: 3
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- provider_id: tavily-search
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provider_type: remote::tavily-search
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config:
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api_key: ${env.TAVILY_SEARCH_API_KEY:}
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max_results: 3
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- provider_id: code-interpreter
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provider_type: inline::code-interpreter
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config: {}
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- provider_id: rag-runtime
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provider_type: inline::rag-runtime
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config: {}
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- provider_id: model-context-protocol
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provider_type: remote::model-context-protocol
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config: {}
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metadata_store:
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type: sqlite
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db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/open_benchmark}/registry.db
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models:
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- metadata: {}
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model_id: openai/gpt-4o
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provider_id: openai
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provider_model_id: openai/gpt-4o
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model_type: llm
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- metadata: {}
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model_id: openai/gpt-4o-mini
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provider_id: openai
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provider_model_id: openai/gpt-4o-mini
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model_type: llm
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- metadata: {}
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model_id: openai/chatgpt-4o-latest
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provider_id: openai
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provider_model_id: openai/chatgpt-4o-latest
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model_type: llm
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- metadata:
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embedding_dimension: 1536
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context_length: 8192
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model_id: openai/text-embedding-3-small
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provider_id: openai
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provider_model_id: openai/text-embedding-3-small
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model_type: embedding
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- metadata:
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embedding_dimension: 3072
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context_length: 8192
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model_id: openai/text-embedding-3-large
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provider_id: openai
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provider_model_id: openai/text-embedding-3-large
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model_type: embedding
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- metadata: {}
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model_id: anthropic/claude-3-5-sonnet-latest
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provider_id: anthropic
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provider_model_id: anthropic/claude-3-5-sonnet-latest
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model_type: llm
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- metadata: {}
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model_id: anthropic/claude-3-7-sonnet-latest
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provider_id: anthropic
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provider_model_id: anthropic/claude-3-7-sonnet-latest
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model_type: llm
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- metadata: {}
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model_id: anthropic/claude-3-5-haiku-latest
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provider_id: anthropic
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provider_model_id: anthropic/claude-3-5-haiku-latest
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model_type: llm
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- metadata:
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embedding_dimension: 1024
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context_length: 32000
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model_id: anthropic/voyage-3
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provider_id: anthropic
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provider_model_id: anthropic/voyage-3
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model_type: embedding
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- metadata:
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embedding_dimension: 512
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context_length: 32000
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model_id: anthropic/voyage-3-lite
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provider_id: anthropic
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provider_model_id: anthropic/voyage-3-lite
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model_type: embedding
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- metadata:
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embedding_dimension: 1024
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context_length: 32000
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model_id: anthropic/voyage-code-3
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provider_id: anthropic
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provider_model_id: anthropic/voyage-code-3
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model_type: embedding
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- metadata: {}
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model_id: gemini/gemini-1.5-flash
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provider_id: gemini
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provider_model_id: gemini/gemini-1.5-flash
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model_type: llm
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- metadata: {}
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model_id: gemini/gemini-1.5-pro
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provider_id: gemini
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provider_model_id: gemini/gemini-1.5-pro
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model_type: llm
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- metadata:
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embedding_dimension: 768
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context_length: 2048
|
||||
model_id: gemini/text-embedding-004
|
||||
provider_id: gemini
|
||||
provider_model_id: gemini/text-embedding-004
|
||||
model_type: embedding
|
||||
- metadata: {}
|
||||
model_id: groq/llama3-8b-8192
|
||||
provider_id: groq
|
||||
provider_model_id: groq/llama3-8b-8192
|
||||
model_type: llm
|
||||
- metadata: {}
|
||||
model_id: meta-llama/Llama-3.1-8B-Instruct
|
||||
provider_id: groq
|
||||
provider_model_id: groq/llama3-8b-8192
|
||||
model_type: llm
|
||||
- metadata: {}
|
||||
model_id: groq/llama-3.1-8b-instant
|
||||
provider_id: groq
|
||||
provider_model_id: groq/llama-3.1-8b-instant
|
||||
model_type: llm
|
||||
- metadata: {}
|
||||
model_id: groq/llama3-70b-8192
|
||||
provider_id: groq
|
||||
provider_model_id: groq/llama3-70b-8192
|
||||
model_type: llm
|
||||
- metadata: {}
|
||||
model_id: meta-llama/Llama-3-70B-Instruct
|
||||
provider_id: groq
|
||||
provider_model_id: groq/llama3-70b-8192
|
||||
model_type: llm
|
||||
- metadata: {}
|
||||
model_id: groq/llama-3.3-70b-versatile
|
||||
provider_id: groq
|
||||
provider_model_id: groq/llama-3.3-70b-versatile
|
||||
model_type: llm
|
||||
- metadata: {}
|
||||
model_id: meta-llama/Llama-3.3-70B-Instruct
|
||||
provider_id: groq
|
||||
provider_model_id: groq/llama-3.3-70b-versatile
|
||||
model_type: llm
|
||||
- metadata: {}
|
||||
model_id: groq/llama-3.2-3b-preview
|
||||
provider_id: groq
|
||||
provider_model_id: groq/llama-3.2-3b-preview
|
||||
model_type: llm
|
||||
- metadata: {}
|
||||
model_id: meta-llama/Llama-3.2-3B-Instruct
|
||||
provider_id: groq
|
||||
provider_model_id: groq/llama-3.2-3b-preview
|
||||
model_type: llm
|
||||
- metadata: {}
|
||||
model_id: meta-llama/Meta-Llama-3.1-8B-Instruct-Turbo
|
||||
provider_id: together
|
||||
provider_model_id: meta-llama/Meta-Llama-3.1-8B-Instruct-Turbo
|
||||
model_type: llm
|
||||
- metadata: {}
|
||||
model_id: meta-llama/Llama-3.1-8B-Instruct
|
||||
provider_id: together
|
||||
provider_model_id: meta-llama/Meta-Llama-3.1-8B-Instruct-Turbo
|
||||
model_type: llm
|
||||
- metadata: {}
|
||||
model_id: meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo
|
||||
provider_id: together
|
||||
provider_model_id: meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo
|
||||
model_type: llm
|
||||
- metadata: {}
|
||||
model_id: meta-llama/Llama-3.1-70B-Instruct
|
||||
provider_id: together
|
||||
provider_model_id: meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo
|
||||
model_type: llm
|
||||
- metadata: {}
|
||||
model_id: meta-llama/Meta-Llama-3.1-405B-Instruct-Turbo
|
||||
provider_id: together
|
||||
provider_model_id: meta-llama/Meta-Llama-3.1-405B-Instruct-Turbo
|
||||
model_type: llm
|
||||
- metadata: {}
|
||||
model_id: meta-llama/Llama-3.1-405B-Instruct-FP8
|
||||
provider_id: together
|
||||
provider_model_id: meta-llama/Meta-Llama-3.1-405B-Instruct-Turbo
|
||||
model_type: llm
|
||||
- metadata: {}
|
||||
model_id: meta-llama/Llama-3.2-3B-Instruct-Turbo
|
||||
provider_id: together
|
||||
provider_model_id: meta-llama/Llama-3.2-3B-Instruct-Turbo
|
||||
model_type: llm
|
||||
- metadata: {}
|
||||
model_id: meta-llama/Llama-3.2-3B-Instruct
|
||||
provider_id: together
|
||||
provider_model_id: meta-llama/Llama-3.2-3B-Instruct-Turbo
|
||||
model_type: llm
|
||||
- metadata: {}
|
||||
model_id: meta-llama/Llama-3.2-11B-Vision-Instruct-Turbo
|
||||
provider_id: together
|
||||
provider_model_id: meta-llama/Llama-3.2-11B-Vision-Instruct-Turbo
|
||||
model_type: llm
|
||||
- metadata: {}
|
||||
model_id: meta-llama/Llama-3.2-11B-Vision-Instruct
|
||||
provider_id: together
|
||||
provider_model_id: meta-llama/Llama-3.2-11B-Vision-Instruct-Turbo
|
||||
model_type: llm
|
||||
- metadata: {}
|
||||
model_id: meta-llama/Llama-3.2-90B-Vision-Instruct-Turbo
|
||||
provider_id: together
|
||||
provider_model_id: meta-llama/Llama-3.2-90B-Vision-Instruct-Turbo
|
||||
model_type: llm
|
||||
- metadata: {}
|
||||
model_id: meta-llama/Llama-3.2-90B-Vision-Instruct
|
||||
provider_id: together
|
||||
provider_model_id: meta-llama/Llama-3.2-90B-Vision-Instruct-Turbo
|
||||
model_type: llm
|
||||
- metadata: {}
|
||||
model_id: meta-llama/Llama-3.3-70B-Instruct-Turbo
|
||||
provider_id: together
|
||||
provider_model_id: meta-llama/Llama-3.3-70B-Instruct-Turbo
|
||||
model_type: llm
|
||||
- metadata: {}
|
||||
model_id: meta-llama/Llama-3.3-70B-Instruct
|
||||
provider_id: together
|
||||
provider_model_id: meta-llama/Llama-3.3-70B-Instruct-Turbo
|
||||
model_type: llm
|
||||
- metadata: {}
|
||||
model_id: meta-llama/Meta-Llama-Guard-3-8B
|
||||
provider_id: together
|
||||
provider_model_id: meta-llama/Meta-Llama-Guard-3-8B
|
||||
model_type: llm
|
||||
- metadata: {}
|
||||
model_id: meta-llama/Llama-Guard-3-8B
|
||||
provider_id: together
|
||||
provider_model_id: meta-llama/Meta-Llama-Guard-3-8B
|
||||
model_type: llm
|
||||
- metadata: {}
|
||||
model_id: meta-llama/Llama-Guard-3-11B-Vision-Turbo
|
||||
provider_id: together
|
||||
provider_model_id: meta-llama/Llama-Guard-3-11B-Vision-Turbo
|
||||
model_type: llm
|
||||
- metadata: {}
|
||||
model_id: meta-llama/Llama-Guard-3-11B-Vision
|
||||
provider_id: together
|
||||
provider_model_id: meta-llama/Llama-Guard-3-11B-Vision-Turbo
|
||||
model_type: llm
|
||||
- metadata:
|
||||
embedding_dimension: 768
|
||||
context_length: 8192
|
||||
model_id: togethercomputer/m2-bert-80M-8k-retrieval
|
||||
provider_id: together
|
||||
provider_model_id: togethercomputer/m2-bert-80M-8k-retrieval
|
||||
model_type: embedding
|
||||
- metadata:
|
||||
embedding_dimension: 768
|
||||
context_length: 32768
|
||||
model_id: togethercomputer/m2-bert-80M-32k-retrieval
|
||||
provider_id: together
|
||||
provider_model_id: togethercomputer/m2-bert-80M-32k-retrieval
|
||||
model_type: embedding
|
||||
shields:
|
||||
- shield_id: meta-llama/Llama-Guard-3-8B
|
||||
vector_dbs: []
|
||||
datasets: []
|
||||
scoring_fns: []
|
||||
benchmarks: []
|
||||
tool_groups:
|
||||
- toolgroup_id: builtin::websearch
|
||||
provider_id: tavily-search
|
||||
- toolgroup_id: builtin::rag
|
||||
provider_id: rag-runtime
|
||||
- toolgroup_id: builtin::code_interpreter
|
||||
provider_id: code-interpreter
|
||||
server:
|
||||
port: 8321
|
||||
Loading…
Add table
Add a link
Reference in a new issue