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feat: add (openai, anthropic, gemini) providers via litellm (#1267)

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# What does this PR do?

This PR introduces more non-llama model support to llama stack.
Providers introduced: openai, anthropic and gemini. All of these
providers use essentially the same piece of code -- the implementation
works via the `litellm` library.

We will expose only specific models for providers we enable making sure
they all work well and pass tests. This setup (instead of automatically
enabling _all_ providers and models allowed by LiteLLM) ensures we can
also perform any needed prompt tuning on a per-model basis as needed
(just like we do it for llama models.)

## Test Plan

```bash
#!/bin/bash

args=("$@")
for model in openai/gpt-4o anthropic/claude-3-5-sonnet-latest gemini/gemini-1.5-flash; do
    LLAMA_STACK_CONFIG=dev pytest -s -v tests/client-sdk/inference/test_text_inference.py \
        --embedding-model=all-MiniLM-L6-v2 \
        --vision-inference-model="" \
        --inference-model=$model "${args[@]}"
done
```
This commit is contained in:
Ashwin Bharambe 2025-02-25 22:07:33 -08:00 • committed by GitHub
parent b0310af177
commit 63e6acd0c3
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25 changed files with 1048 additions and 33 deletions
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23
llama_stack/templates/ci-tests/ci_tests.py
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@ -57,17 +57,6 @@ def get_distribution_template() -> DistributionTemplate:
config=SentenceTransformersInferenceConfig.sample_run_config(),
)
core_model_to_hf_repo = {m.descriptor(): m.huggingface_repo for m in all_registered_models()}
default_models = [
ModelInput(
model_id=core_model_to_hf_repo[m.llama_model] if m.llama_model else m.provider_model_id,
provider_model_id=m.provider_model_id,
provider_id="fireworks",
metadata=m.metadata,
model_type=m.model_type,
)
for m in MODEL_ENTRIES
]
default_tool_groups = [
ToolGroupInput(
toolgroup_id="builtin::websearch",
@ -82,6 +71,16 @@ def get_distribution_template() -> DistributionTemplate:
provider_id="code-interpreter",
),
]
core_model_to_hf_repo = {m.descriptor(): m.huggingface_repo for m in all_registered_models()}
default_models = [
ModelInput(
model_id=core_model_to_hf_repo[m.llama_model] if m.llama_model else m.provider_model_id,
provider_id="fireworks",
model_type=m.model_type,
metadata=m.metadata,
)
for m in MODEL_ENTRIES
]
embedding_model = ModelInput(
model_id="all-MiniLM-L6-v2",
provider_id="sentence-transformers",
@ -98,7 +97,7 @@ def get_distribution_template() -> DistributionTemplate:
container_image=None,
template_path=None,
providers=providers,
default_models=default_models,
default_models=default_models + [embedding_model],
run_configs={
"run.yaml": RunConfigSettings(
provider_overrides={