forked from phoenix-oss/llama-stack-mirror
feat: add (openai, anthropic, gemini) providers via litellm (#1267)
# 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
GitHub
parent
b0310af177
commit
63e6acd0c3
25 changed files with 1048 additions and 33 deletions
174
llama_stack/templates/dev/dev.py
Normal file
174
llama_stack/templates/dev/dev.py
Normal file
|
|
@ -0,0 +1,174 @@
|
|||
# Copyright (c) Meta Platforms, Inc. and affiliates.
|
||||
# All rights reserved.
|
||||
#
|
||||
# This source code is licensed under the terms described in the LICENSE file in
|
||||
# the root directory of this source tree.
|
||||
|
||||
from typing import List, Tuple
|
||||
|
||||
from llama_stack.apis.models.models import ModelType
|
||||
from llama_stack.distribution.datatypes import (
|
||||
ModelInput,
|
||||
Provider,
|
||||
ShieldInput,
|
||||
ToolGroupInput,
|
||||
)
|
||||
from llama_stack.models.llama.sku_list import all_registered_models
|
||||
from llama_stack.providers.inline.inference.sentence_transformers import (
|
||||
SentenceTransformersInferenceConfig,
|
||||
)
|
||||
from llama_stack.providers.inline.vector_io.sqlite_vec.config import SQLiteVectorIOConfig
|
||||
from llama_stack.providers.remote.inference.anthropic.config import AnthropicConfig
|
||||
from llama_stack.providers.remote.inference.anthropic.models import MODEL_ENTRIES as ANTHROPIC_MODEL_ENTRIES
|
||||
from llama_stack.providers.remote.inference.fireworks.config import FireworksImplConfig
|
||||
from llama_stack.providers.remote.inference.fireworks.models import MODEL_ENTRIES as FIREWORKS_MODEL_ENTRIES
|
||||
from llama_stack.providers.remote.inference.gemini.config import GeminiConfig
|
||||
from llama_stack.providers.remote.inference.gemini.models import MODEL_ENTRIES as GEMINI_MODEL_ENTRIES
|
||||
from llama_stack.providers.remote.inference.openai.config import OpenAIConfig
|
||||
from llama_stack.providers.remote.inference.openai.models import MODEL_ENTRIES as OPENAI_MODEL_ENTRIES
|
||||
from llama_stack.templates.template import DistributionTemplate, RunConfigSettings
|
||||
|
||||
|
||||
def get_inference_providers() -> Tuple[List[Provider], List[ModelInput]]:
|
||||
# in this template, we allow each API key to be optional
|
||||
providers = [
|
||||
(
|
||||
"openai",
|
||||
OPENAI_MODEL_ENTRIES,
|
||||
OpenAIConfig.sample_run_config(api_key="${env.OPENAI_API_KEY:}"),
|
||||
),
|
||||
(
|
||||
"fireworks",
|
||||
FIREWORKS_MODEL_ENTRIES,
|
||||
FireworksImplConfig.sample_run_config(api_key="${env.FIREWORKS_API_KEY:}"),
|
||||
),
|
||||
(
|
||||
"anthropic",
|
||||
ANTHROPIC_MODEL_ENTRIES,
|
||||
AnthropicConfig.sample_run_config(api_key="${env.ANTHROPIC_API_KEY:}"),
|
||||
),
|
||||
(
|
||||
"gemini",
|
||||
GEMINI_MODEL_ENTRIES,
|
||||
GeminiConfig.sample_run_config(api_key="${env.GEMINI_API_KEY:}"),
|
||||
),
|
||||
]
|
||||
inference_providers = []
|
||||
default_models = []
|
||||
core_model_to_hf_repo = {m.descriptor(): m.huggingface_repo for m in all_registered_models()}
|
||||
for provider_id, model_entries, config in providers:
|
||||
inference_providers.append(
|
||||
Provider(
|
||||
provider_id=provider_id,
|
||||
provider_type=f"remote::{provider_id}",
|
||||
config=config,
|
||||
)
|
||||
)
|
||||
default_models.extend(
|
||||
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=provider_id,
|
||||
model_type=m.model_type,
|
||||
metadata=m.metadata,
|
||||
)
|
||||
for m in model_entries
|
||||
)
|
||||
return inference_providers, default_models
|
||||
|
||||
|
||||
def get_distribution_template() -> DistributionTemplate:
|
||||
providers = {
|
||||
"inference": [
|
||||
"remote::openai",
|
||||
"remote::fireworks",
|
||||
"remote::anthropic",
|
||||
"remote::gemini",
|
||||
"inline::sentence-transformers",
|
||||
],
|
||||
"vector_io": ["inline::sqlite-vec", "remote::chromadb", "remote::pgvector"],
|
||||
"safety": ["inline::llama-guard"],
|
||||
"agents": ["inline::meta-reference"],
|
||||
"telemetry": ["inline::meta-reference"],
|
||||
"eval": ["inline::meta-reference"],
|
||||
"datasetio": ["remote::huggingface", "inline::localfs"],
|
||||
"scoring": ["inline::basic", "inline::llm-as-judge", "inline::braintrust"],
|
||||
"tool_runtime": [
|
||||
"remote::brave-search",
|
||||
"remote::tavily-search",
|
||||
"inline::code-interpreter",
|
||||
"inline::rag-runtime",
|
||||
"remote::model-context-protocol",
|
||||
],
|
||||
}
|
||||
name = "dev"
|
||||
|
||||
vector_io_provider = Provider(
|
||||
provider_id="sqlite-vec",
|
||||
provider_type="inline::sqlite-vec",
|
||||
config=SQLiteVectorIOConfig.sample_run_config(f"distributions/{name}"),
|
||||
)
|
||||
embedding_provider = Provider(
|
||||
provider_id="sentence-transformers",
|
||||
provider_type="inline::sentence-transformers",
|
||||
config=SentenceTransformersInferenceConfig.sample_run_config(),
|
||||
)
|
||||
|
||||
default_tool_groups = [
|
||||
ToolGroupInput(
|
||||
toolgroup_id="builtin::websearch",
|
||||
provider_id="tavily-search",
|
||||
),
|
||||
ToolGroupInput(
|
||||
toolgroup_id="builtin::rag",
|
||||
provider_id="rag-runtime",
|
||||
),
|
||||
ToolGroupInput(
|
||||
toolgroup_id="builtin::code_interpreter",
|
||||
provider_id="code-interpreter",
|
||||
),
|
||||
]
|
||||
embedding_model = ModelInput(
|
||||
model_id="all-MiniLM-L6-v2",
|
||||
provider_id=embedding_provider.provider_id,
|
||||
model_type=ModelType.embedding,
|
||||
metadata={
|
||||
"embedding_dimension": 384,
|
||||
},
|
||||
)
|
||||
inference_providers, default_models = get_inference_providers()
|
||||
|
||||
return DistributionTemplate(
|
||||
name=name,
|
||||
distro_type="self_hosted",
|
||||
description="Distribution for running e2e tests in CI",
|
||||
container_image=None,
|
||||
template_path=None,
|
||||
providers=providers,
|
||||
default_models=[],
|
||||
run_configs={
|
||||
"run.yaml": RunConfigSettings(
|
||||
provider_overrides={
|
||||
"inference": inference_providers + [embedding_provider],
|
||||
"vector_io": [vector_io_provider],
|
||||
},
|
||||
default_models=default_models + [embedding_model],
|
||||
default_tool_groups=default_tool_groups,
|
||||
default_shields=[ShieldInput(shield_id="meta-llama/Llama-Guard-3-8B")],
|
||||
),
|
||||
},
|
||||
run_config_env_vars={
|
||||
"LLAMA_STACK_PORT": (
|
||||
"5001",
|
||||
"Port for the Llama Stack distribution server",
|
||||
),
|
||||
"FIREWORKS_API_KEY": (
|
||||
"",
|
||||
"Fireworks API Key",
|
||||
),
|
||||
"OPENAI_API_KEY": (
|
||||
"",
|
||||
"OpenAI API Key",
|
||||
),
|
||||
},
|
||||
)
|
||||
Loading…
Add table
Add a link
Reference in a new issue