llama-stack-mirror/llama_stack/templates/open-benchmark/run.yaml
Charlie Doern de6919ecdd
refactor: install external providers from module (#2637)
# What does this PR do?

Today, external providers are installed via the `external_providers_dir`
in the config. This necessitates users to understand the `ProviderSpec`
and set up their directories accordingly. This process splits up the
config for the stack across multiple files, directories, and formats.

Most (if not all) external providers today have a
[get_provider_spec](559cb18fbb/src/ramalama_stack/provider.py (L9))
method that sits unused. Utilizing this method rather than the
providers.d route allows for a much easier installation process for
external providers and limits the amount of extra configuration a
regular user has to do to get their stack off the ground.

To accomplish this and wire it throughout the build process, Introduce
the concept of a `module` for users to specify for an external provider
upon build time. In order to facilitate this, align the build and run
spec to use `Provider` class rather than the stringified provider_type
that build currently uses.

For example, say this is in your build config:

```
- provider_id: ramalama
  provider_type: remote::ramalama
  module: ramalama_stack
```

during build (in the various `build_...` scripts), additionally to
installing any pip dependencies we will also install this module and use
the `get_provider_spec` method to retrieve the ProviderSpec that is
currently specified using `providers.d`.

In production so far, providing instructions for installing external
providers for users has been difficult: they need to install the module
as a pre-req, create the providers.d directory, copy in the provider
spec, and also copy in the necessary build/run yaml files. Accessing an
external provider should be as easy as possible, and pointing to its
installable module aligns more with the rest of our build and dependency
management process.

For now, `external_providers_dir` still exists as an alternate more
declarative method of using external providers.

## Test Plan

added an integration test installing an external provider from module
and more unit test coverage for `get_provider_registry`


( the warning in yellow is expected, the module is installed inside of
the build env, not where we are running the command)
<img width="1119" height="400" alt="Screenshot 2025-07-24 at 11 30
48 AM"
src="https://github.com/user-attachments/assets/1efbaf45-b9e8-451a-bd63-264ed664706d"
/>

<img width="1154" height="618" alt="Screenshot 2025-07-24 at 11 31
14 AM"
src="https://github.com/user-attachments/assets/feb2b3ea-c5dd-418e-9662-9a3bd5dd6bdc"
/>

---------

Signed-off-by: Charlie Doern <cdoern@redhat.com>
2025-07-25 15:41:26 +02:00

251 lines
7.2 KiB
YAML

version: 2
image_name: open-benchmark
apis:
- agents
- datasetio
- eval
- inference
- safety
- scoring
- telemetry
- tool_runtime
- vector_io
providers:
inference:
- provider_id: openai
provider_type: remote::openai
config:
api_key: ${env.OPENAI_API_KEY:=}
- provider_id: anthropic
provider_type: remote::anthropic
config:
api_key: ${env.ANTHROPIC_API_KEY:=}
- provider_id: gemini
provider_type: remote::gemini
config:
api_key: ${env.GEMINI_API_KEY:=}
- provider_id: groq
provider_type: remote::groq
config:
url: https://api.groq.com
api_key: ${env.GROQ_API_KEY:=}
- provider_id: together
provider_type: remote::together
config:
url: https://api.together.xyz/v1
api_key: ${env.TOGETHER_API_KEY}
vector_io:
- provider_id: sqlite-vec
provider_type: inline::sqlite-vec
config:
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/open-benchmark}/sqlite_vec.db
kvstore:
type: sqlite
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/open-benchmark}/sqlite_vec_registry.db
- provider_id: ${env.ENABLE_CHROMADB:+chromadb}
provider_type: remote::chromadb
config:
url: ${env.CHROMADB_URL:=}
kvstore:
type: sqlite
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/open-benchmark}/chroma_remote_registry.db
- provider_id: ${env.ENABLE_PGVECTOR:+pgvector}
provider_type: remote::pgvector
config:
host: ${env.PGVECTOR_HOST:=localhost}
port: ${env.PGVECTOR_PORT:=5432}
db: ${env.PGVECTOR_DB:=}
user: ${env.PGVECTOR_USER:=}
password: ${env.PGVECTOR_PASSWORD:=}
kvstore:
type: sqlite
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/open-benchmark}/pgvector_registry.db
safety:
- provider_id: llama-guard
provider_type: inline::llama-guard
config:
excluded_categories: []
agents:
- provider_id: meta-reference
provider_type: inline::meta-reference
config:
persistence_store:
type: sqlite
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/open-benchmark}/agents_store.db
responses_store:
type: sqlite
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/open-benchmark}/responses_store.db
telemetry:
- provider_id: meta-reference
provider_type: inline::meta-reference
config:
service_name: "${env.OTEL_SERVICE_NAME:=\u200B}"
sinks: ${env.TELEMETRY_SINKS:=console,sqlite}
sqlite_db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/open-benchmark}/trace_store.db
otel_exporter_otlp_endpoint: ${env.OTEL_EXPORTER_OTLP_ENDPOINT:=}
eval:
- provider_id: meta-reference
provider_type: inline::meta-reference
config:
kvstore:
type: sqlite
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/open-benchmark}/meta_reference_eval.db
datasetio:
- provider_id: huggingface
provider_type: remote::huggingface
config:
kvstore:
type: sqlite
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/open-benchmark}/huggingface_datasetio.db
- provider_id: localfs
provider_type: inline::localfs
config:
kvstore:
type: sqlite
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/open-benchmark}/localfs_datasetio.db
scoring:
- provider_id: basic
provider_type: inline::basic
- provider_id: llm-as-judge
provider_type: inline::llm-as-judge
- provider_id: braintrust
provider_type: inline::braintrust
config:
openai_api_key: ${env.OPENAI_API_KEY:=}
tool_runtime:
- provider_id: brave-search
provider_type: remote::brave-search
config:
api_key: ${env.BRAVE_SEARCH_API_KEY:=}
max_results: 3
- provider_id: tavily-search
provider_type: remote::tavily-search
config:
api_key: ${env.TAVILY_SEARCH_API_KEY:=}
max_results: 3
- provider_id: rag-runtime
provider_type: inline::rag-runtime
- provider_id: model-context-protocol
provider_type: remote::model-context-protocol
metadata_store:
type: sqlite
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/open-benchmark}/registry.db
inference_store:
type: sqlite
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/open-benchmark}/inference_store.db
models:
- metadata: {}
model_id: openai/gpt-4o
provider_id: openai
provider_model_id: openai/gpt-4o
model_type: llm
- metadata: {}
model_id: anthropic/claude-3-5-sonnet-latest
provider_id: anthropic
provider_model_id: anthropic/claude-3-5-sonnet-latest
model_type: llm
- metadata: {}
model_id: gemini/gemini-1.5-flash
provider_id: gemini
provider_model_id: gemini/gemini-1.5-flash
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: meta-llama/Llama-3.1-405B-Instruct
provider_id: together
provider_model_id: meta-llama/Meta-Llama-3.1-405B-Instruct-Turbo
model_type: llm
shields:
- shield_id: meta-llama/Llama-Guard-3-8B
vector_dbs: []
datasets:
- purpose: eval/messages-answer
source:
type: uri
uri: huggingface://datasets/llamastack/simpleqa?split=train
metadata: {}
dataset_id: simpleqa
- purpose: eval/messages-answer
source:
type: uri
uri: huggingface://datasets/llamastack/mmlu_cot?split=test&name=all
metadata: {}
dataset_id: mmlu_cot
- purpose: eval/messages-answer
source:
type: uri
uri: huggingface://datasets/llamastack/gpqa_0shot_cot?split=test&name=gpqa_main
metadata: {}
dataset_id: gpqa_cot
- purpose: eval/messages-answer
source:
type: uri
uri: huggingface://datasets/llamastack/math_500?split=test
metadata: {}
dataset_id: math_500
- purpose: eval/messages-answer
source:
type: uri
uri: huggingface://datasets/llamastack/bfcl_v3?split=train
metadata: {}
dataset_id: bfcl
- purpose: eval/messages-answer
source:
type: uri
uri: huggingface://datasets/llamastack/IfEval?split=train
metadata: {}
dataset_id: ifeval
- purpose: eval/messages-answer
source:
type: uri
uri: huggingface://datasets/llamastack/docvqa?split=val
metadata: {}
dataset_id: docvqa
scoring_fns: []
benchmarks:
- dataset_id: simpleqa
scoring_functions:
- llm-as-judge::405b-simpleqa
metadata: {}
benchmark_id: meta-reference-simpleqa
- dataset_id: mmlu_cot
scoring_functions:
- basic::regex_parser_multiple_choice_answer
metadata: {}
benchmark_id: meta-reference-mmlu-cot
- dataset_id: gpqa_cot
scoring_functions:
- basic::regex_parser_multiple_choice_answer
metadata: {}
benchmark_id: meta-reference-gpqa-cot
- dataset_id: math_500
scoring_functions:
- basic::regex_parser_math_response
metadata: {}
benchmark_id: meta-reference-math-500
- dataset_id: bfcl
scoring_functions:
- basic::bfcl
metadata: {}
benchmark_id: meta-reference-bfcl
- dataset_id: ifeval
scoring_functions:
- basic::ifeval
metadata: {}
benchmark_id: meta-reference-ifeval
- dataset_id: docvqa
scoring_functions:
- basic::docvqa
metadata: {}
benchmark_id: meta-reference-docvqa
tool_groups:
- toolgroup_id: builtin::websearch
provider_id: tavily-search
- toolgroup_id: builtin::rag
provider_id: rag-runtime
server:
port: 8321