phoenix-oss/llama-stack-mirror
0
0
Fork 1
mirror of https://github.com/meta-llama/llama-stack.git synced 2025-08-03 01:03:59 +00:00
Code Issues Projects Releases Packages Wiki Activity Actions

Get fireworks provider to 100% on OpenAI API verification

Browse source 
This gets the fireworks provider passing 100% of our OpenAI API
verification tests when run against a Llama Stack server using the
fireworks provider. Testing against Fireworks directly, without Llama
Stack in the middle, has a lower pass rate.

The main changes are are in how we divert Llama model OpenAI chat
completion requests to the Llama Stack chat completion API (vs
OpenAI), which applies all the client-side formatting necessary to get
tool calls working properly on Fireworks.

A side-effect of this work is any provider using the
OpenAIChatCompletionToLlamaStackMixin (renamed from
OpenAIChatCompletioonUnsupportedMixin) will also get a better
conversion from OpenAI to Llama Stack, including streaming and
non-stream responses.

A small change was required to
`llama_stack/models/llama/llama3/tool_utils.py` to get tests to 100%
because code there was incorrectly assuming any JSON response with a
`name` key was a tool call response. One of our verification tests
produces JSON keys with a `name` key that is not a tool call response,
so I tightened up the logic there to require both a `name` and
`parameters` key in the JSON response before it gets considered a
potential tool call. The `parameters` key was required by the code
anyway, but it wasn't explicitly checking for its existence.

Lastly, this adds some new verification test configs so we can see the
results of using OpenAI APIs against SaaS services directly compared
to hitting Llama Stack with a remote provider pointing at that SaaS
service.

You can run these verification tests like:

```
llama stack run \
  --image-type venv \
  tests/verifications/openai-api-verification-run.yaml

python tests/verifications/generate_report.py \
  --run-tests \
  --provider together fireworks openai \
             together-llama-stack \
             fireworks-llama-stack \
             openai-llama-stack
```

Signed-off-by: Ben Browning <bbrownin@redhat.com>
This commit is contained in:
Ben Browning 2025-04-13 10:45:32 -04:00
parent da2d39a836
commit 657bb12e85
21 changed files with 546 additions and 51 deletions
Download patch file Download diff file Expand all files Collapse all files

44
docs/_static/llama-stack-spec.html vendored
View file

@ -8852,9 +8852,9 @@
"tool_calls": {
"type": "array",
"items": {
"$ref": "#/components/schemas/ToolCall"
"$ref": "#/components/schemas/OpenAIChatCompletionToolCall"
},
"description": "List of tool calls. Each tool call is a ToolCall object."
"description": "List of tool calls. Each tool call is an OpenAIChatCompletionToolCall object."
}
},
"additionalProperties": false,
@ -8920,6 +8920,46 @@
],
"title": "OpenAIChatCompletionContentPartTextParam"
},
"OpenAIChatCompletionToolCall": {
"type": "object",
"properties": {
"id": {
"type": "string"
},
"type": {
"type": "string",
"const": "function",
"default": "function"
},
"function": {
"$ref": "#/components/schemas/OpenAIChatCompletionToolCallFunction"
}
},
"additionalProperties": false,
"required": [
"id",
"type",
"function"
],
"title": "OpenAIChatCompletionToolCall"
},
"OpenAIChatCompletionToolCallFunction": {
"type": "object",
"properties": {
"name": {
"type": "string"
},
"arguments": {
"type": "string"
}
},
"additionalProperties": false,
"required": [
"name",
"arguments"
],
"title": "OpenAIChatCompletionToolCallFunction"
},
"OpenAIDeveloperMessageParam": {
"type": "object",
"properties": {

34
docs/_static/llama-stack-spec.yaml vendored
View file

@ -6074,9 +6074,10 @@ components:
tool_calls:
type: array
items:
$ref: '#/components/schemas/ToolCall'
$ref: '#/components/schemas/OpenAIChatCompletionToolCall'
description: >-
List of tool calls. Each tool call is a ToolCall object.
List of tool calls. Each tool call is an OpenAIChatCompletionToolCall
object.
additionalProperties: false
required:
- role
@ -6123,6 +6124,35 @@ components:
- type
- text
title: OpenAIChatCompletionContentPartTextParam
OpenAIChatCompletionToolCall:
type: object
properties:
id:
type: string
type:
type: string
const: function
default: function
function:
$ref: '#/components/schemas/OpenAIChatCompletionToolCallFunction'
additionalProperties: false
required:
- id
- type
- function
title: OpenAIChatCompletionToolCall
OpenAIChatCompletionToolCallFunction:
type: object
properties:
name:
type: string
arguments:
type: string
additionalProperties: false
required:
- name
- arguments
title: OpenAIChatCompletionToolCallFunction
OpenAIDeveloperMessageParam:
type: object
properties:

17
llama_stack/apis/inference/inference.py
View file

@ -501,6 +501,19 @@ class OpenAISystemMessageParam(BaseModel):
name: Optional[str] = None
@json_schema_type
class OpenAIChatCompletionToolCallFunction(BaseModel):
name: str
arguments: str
@json_schema_type
class OpenAIChatCompletionToolCall(BaseModel):
id: str
type: Literal["function"] = "function"
function: OpenAIChatCompletionToolCallFunction
@json_schema_type
class OpenAIAssistantMessageParam(BaseModel):
"""A message containing the model's (assistant) response in an OpenAI-compatible chat completion request.
@ -508,13 +521,13 @@ class OpenAIAssistantMessageParam(BaseModel):
:param role: Must be "assistant" to identify this as the model's response
:param content: The content of the model's response
:param name: (Optional) The name of the assistant message participant.
:param tool_calls: List of tool calls. Each tool call is a ToolCall object.
:param tool_calls: List of tool calls. Each tool call is an OpenAIChatCompletionToolCall object.
"""
role: Literal["assistant"] = "assistant"
content: OpenAIChatCompletionMessageContent
name: Optional[str] = None
tool_calls: Optional[List[ToolCall]] = Field(default_factory=list)
tool_calls: Optional[List[OpenAIChatCompletionToolCall]] = Field(default_factory=list)
@json_schema_type

4
llama_stack/models/llama/llama3/tool_utils.py
View file

@ -204,7 +204,9 @@ class ToolUtils:
return None
elif is_json(message_body):
response = json.loads(message_body)
if ("type" in response and response["type"] == "function") or ("name" in response):
if ("type" in response and response["type"] == "function") or (
"name" in response and "parameters" in response
):
function_name = response["name"]
args = response["parameters"]
return function_name, args

8
llama_stack/providers/inline/inference/meta_reference/inference.py
View file

@ -59,8 +59,8 @@ from llama_stack.providers.utils.inference.model_registry import (
build_hf_repo_model_entry,
)
from llama_stack.providers.utils.inference.openai_compat import (
OpenAIChatCompletionUnsupportedMixin,
OpenAICompletionUnsupportedMixin,
OpenAIChatCompletionToLlamaStackMixin,
OpenAICompletionToLlamaStackMixin,
)
from llama_stack.providers.utils.inference.prompt_adapter import (
augment_content_with_response_format_prompt,
@ -83,8 +83,8 @@ def llama_builder_fn(config: MetaReferenceInferenceConfig, model_id: str, llama_
class MetaReferenceInferenceImpl(
OpenAICompletionUnsupportedMixin,
OpenAIChatCompletionUnsupportedMixin,
OpenAICompletionToLlamaStackMixin,
OpenAIChatCompletionToLlamaStackMixin,
SentenceTransformerEmbeddingMixin,
Inference,
ModelsProtocolPrivate,

8
llama_stack/providers/inline/inference/sentence_transformers/sentence_transformers.py
View file

@ -25,8 +25,8 @@ from llama_stack.providers.utils.inference.embedding_mixin import (
SentenceTransformerEmbeddingMixin,
)
from llama_stack.providers.utils.inference.openai_compat import (
OpenAIChatCompletionUnsupportedMixin,
OpenAICompletionUnsupportedMixin,
OpenAIChatCompletionToLlamaStackMixin,
OpenAICompletionToLlamaStackMixin,
)
from .config import SentenceTransformersInferenceConfig
@ -35,8 +35,8 @@ log = logging.getLogger(__name__)
class SentenceTransformersInferenceImpl(
OpenAIChatCompletionUnsupportedMixin,
OpenAICompletionUnsupportedMixin,
OpenAIChatCompletionToLlamaStackMixin,
OpenAICompletionToLlamaStackMixin,
SentenceTransformerEmbeddingMixin,
Inference,
ModelsProtocolPrivate,

8
llama_stack/providers/inline/inference/vllm/vllm.py
View file

@ -66,10 +66,10 @@ from llama_stack.providers.utils.inference.model_registry import (
ModelsProtocolPrivate,
)
from llama_stack.providers.utils.inference.openai_compat import (
OpenAIChatCompletionUnsupportedMixin,
OpenAIChatCompletionToLlamaStackMixin,
OpenAICompatCompletionChoice,
OpenAICompatCompletionResponse,
OpenAICompletionUnsupportedMixin,
OpenAICompletionToLlamaStackMixin,
get_stop_reason,
process_chat_completion_stream_response,
)
@ -176,8 +176,8 @@ def _convert_sampling_params(
class VLLMInferenceImpl(
Inference,
OpenAIChatCompletionUnsupportedMixin,
OpenAICompletionUnsupportedMixin,
OpenAIChatCompletionToLlamaStackMixin,
OpenAICompletionToLlamaStackMixin,
ModelsProtocolPrivate,
):
"""

8
llama_stack/providers/remote/inference/bedrock/bedrock.py
View file

@ -36,10 +36,10 @@ from llama_stack.providers.utils.inference.model_registry import (
ModelRegistryHelper,
)
from llama_stack.providers.utils.inference.openai_compat import (
OpenAIChatCompletionUnsupportedMixin,
OpenAIChatCompletionToLlamaStackMixin,
OpenAICompatCompletionChoice,
OpenAICompatCompletionResponse,
OpenAICompletionUnsupportedMixin,
OpenAICompletionToLlamaStackMixin,
get_sampling_strategy_options,
process_chat_completion_response,
process_chat_completion_stream_response,
@ -56,8 +56,8 @@ from .models import MODEL_ENTRIES
class BedrockInferenceAdapter(
ModelRegistryHelper,
Inference,
OpenAIChatCompletionUnsupportedMixin,
OpenAICompletionUnsupportedMixin,
OpenAIChatCompletionToLlamaStackMixin,
OpenAICompletionToLlamaStackMixin,
):
def __init__(self, config: BedrockConfig) -> None:
ModelRegistryHelper.__init__(self, MODEL_ENTRIES)

8
llama_stack/providers/remote/inference/cerebras/cerebras.py
View file

@ -34,8 +34,8 @@ from llama_stack.providers.utils.inference.model_registry import (
ModelRegistryHelper,
)
from llama_stack.providers.utils.inference.openai_compat import (
OpenAIChatCompletionUnsupportedMixin,
OpenAICompletionUnsupportedMixin,
OpenAIChatCompletionToLlamaStackMixin,
OpenAICompletionToLlamaStackMixin,
get_sampling_options,
process_chat_completion_response,
process_chat_completion_stream_response,
@ -54,8 +54,8 @@ from .models import MODEL_ENTRIES
class CerebrasInferenceAdapter(
ModelRegistryHelper,
Inference,
OpenAIChatCompletionUnsupportedMixin,
OpenAICompletionUnsupportedMixin,
OpenAIChatCompletionToLlamaStackMixin,
OpenAICompletionToLlamaStackMixin,
):
def __init__(self, config: CerebrasImplConfig) -> None:
ModelRegistryHelper.__init__(

8
llama_stack/providers/remote/inference/databricks/databricks.py
View file

@ -34,8 +34,8 @@ from llama_stack.providers.utils.inference.model_registry import (
build_hf_repo_model_entry,
)
from llama_stack.providers.utils.inference.openai_compat import (
OpenAIChatCompletionUnsupportedMixin,
OpenAICompletionUnsupportedMixin,
OpenAIChatCompletionToLlamaStackMixin,
OpenAICompletionToLlamaStackMixin,
get_sampling_options,
process_chat_completion_response,
process_chat_completion_stream_response,
@ -61,8 +61,8 @@ model_entries = [
class DatabricksInferenceAdapter(
ModelRegistryHelper,
Inference,
OpenAIChatCompletionUnsupportedMixin,
OpenAICompletionUnsupportedMixin,
OpenAIChatCompletionToLlamaStackMixin,
OpenAICompletionToLlamaStackMixin,
):
def __init__(self, config: DatabricksImplConfig) -> None:
ModelRegistryHelper.__init__(self, model_entries=model_entries)

18
llama_stack/providers/remote/inference/fireworks/fireworks.py
View file

@ -45,6 +45,7 @@ from llama_stack.providers.utils.inference.model_registry import (
ModelRegistryHelper,
)
from llama_stack.providers.utils.inference.openai_compat import (
OpenAIChatCompletionToLlamaStackMixin,
convert_message_to_openai_dict,
get_sampling_options,
prepare_openai_completion_params,
@ -307,6 +308,11 @@ class FireworksInferenceAdapter(ModelRegistryHelper, Inference, NeedsRequestProv
prompt_logprobs: Optional[int] = None,
) -> OpenAICompletion:
model_obj = await self.model_store.get_model(model)
# Fireworks always prepends with BOS
if isinstance(prompt, str) and prompt.startswith("<|begin_of_text|>"):
prompt = prompt[len("<|begin_of_text|>") :]
params = await prepare_openai_completion_params(
model=model_obj.provider_resource_id,
prompt=prompt,
@ -326,6 +332,7 @@ class FireworksInferenceAdapter(ModelRegistryHelper, Inference, NeedsRequestProv
top_p=top_p,
user=user,
)
return await self._get_openai_client().completions.create(**params)
async def openai_chat_completion(
@ -356,7 +363,6 @@ class FireworksInferenceAdapter(ModelRegistryHelper, Inference, NeedsRequestProv
) -> Union[OpenAIChatCompletion, AsyncIterator[OpenAIChatCompletionChunk]]:
model_obj = await self.model_store.get_model(model)
params = await prepare_openai_completion_params(
model=model_obj.provider_resource_id,
messages=messages,
frequency_penalty=frequency_penalty,
function_call=function_call,
@ -380,4 +386,12 @@ class FireworksInferenceAdapter(ModelRegistryHelper, Inference, NeedsRequestProv
top_p=top_p,
user=user,
)
return await self._get_openai_client().chat.completions.create(**params)
# Divert Llama Models through Llama Stack inference APIs because
# Fireworks chat completions OpenAI-compatible API does not support
# tool calls properly.
llama_model = self.get_llama_model(model_obj.provider_resource_id)
if llama_model:
return await OpenAIChatCompletionToLlamaStackMixin.openai_chat_completion(self, model=model, **params)
return await self._get_openai_client().chat.completions.create(model=model_obj.provider_resource_id, **params)

8
llama_stack/providers/remote/inference/runpod/runpod.py
View file

@ -12,8 +12,8 @@ from llama_stack.apis.inference import * # noqa: F403
# from llama_stack.providers.datatypes import ModelsProtocolPrivate
from llama_stack.providers.utils.inference.model_registry import ModelRegistryHelper
from llama_stack.providers.utils.inference.openai_compat import (
OpenAIChatCompletionUnsupportedMixin,
OpenAICompletionUnsupportedMixin,
OpenAIChatCompletionToLlamaStackMixin,
OpenAICompletionToLlamaStackMixin,
get_sampling_options,
process_chat_completion_response,
process_chat_completion_stream_response,
@ -43,8 +43,8 @@ RUNPOD_SUPPORTED_MODELS = {
class RunpodInferenceAdapter(
ModelRegistryHelper,
Inference,
OpenAIChatCompletionUnsupportedMixin,
OpenAICompletionUnsupportedMixin,
OpenAIChatCompletionToLlamaStackMixin,
OpenAICompletionToLlamaStackMixin,
):
def __init__(self, config: RunpodImplConfig) -> None:
ModelRegistryHelper.__init__(self, stack_to_provider_models_map=RUNPOD_SUPPORTED_MODELS)

8
llama_stack/providers/remote/inference/sambanova/sambanova.py
View file

@ -42,8 +42,8 @@ from llama_stack.apis.inference import (
)
from llama_stack.providers.utils.inference.model_registry import ModelRegistryHelper
from llama_stack.providers.utils.inference.openai_compat import (
OpenAIChatCompletionUnsupportedMixin,
OpenAICompletionUnsupportedMixin,
OpenAIChatCompletionToLlamaStackMixin,
OpenAICompletionToLlamaStackMixin,
process_chat_completion_stream_response,
)
from llama_stack.providers.utils.inference.prompt_adapter import (
@ -57,8 +57,8 @@ from .models import MODEL_ENTRIES
class SambaNovaInferenceAdapter(
ModelRegistryHelper,
Inference,
OpenAIChatCompletionUnsupportedMixin,
OpenAICompletionUnsupportedMixin,
OpenAIChatCompletionToLlamaStackMixin,
OpenAICompletionToLlamaStackMixin,
):
def __init__(self, config: SambaNovaImplConfig) -> None:
ModelRegistryHelper.__init__(self, model_entries=MODEL_ENTRIES)

8
llama_stack/providers/remote/inference/tgi/tgi.py
View file

@ -40,10 +40,10 @@ from llama_stack.providers.utils.inference.model_registry import (
build_hf_repo_model_entry,
)
from llama_stack.providers.utils.inference.openai_compat import (
OpenAIChatCompletionUnsupportedMixin,
OpenAIChatCompletionToLlamaStackMixin,
OpenAICompatCompletionChoice,
OpenAICompatCompletionResponse,
OpenAICompletionUnsupportedMixin,
OpenAICompletionToLlamaStackMixin,
get_sampling_options,
process_chat_completion_response,
process_chat_completion_stream_response,
@ -73,8 +73,8 @@ def build_hf_repo_model_entries():
class _HfAdapter(
Inference,
OpenAIChatCompletionUnsupportedMixin,
OpenAICompletionUnsupportedMixin,
OpenAIChatCompletionToLlamaStackMixin,
OpenAICompletionToLlamaStackMixin,
ModelsProtocolPrivate,
):
client: AsyncInferenceClient

231
llama_stack/providers/utils/inference/openai_compat.py
View file

@ -8,7 +8,7 @@ import logging
import time
import uuid
import warnings
from typing import Any, AsyncGenerator, AsyncIterator, Dict, Iterable, List, Optional, Union
from typing import Any, AsyncGenerator, AsyncIterator, Awaitable, Dict, Iterable, List, Optional, Union
from openai import AsyncStream
from openai.types.chat import (
@ -50,6 +50,18 @@ from openai.types.chat.chat_completion import (
from openai.types.chat.chat_completion import (
ChoiceLogprobs as OpenAIChoiceLogprobs, # same as chat_completion_chunk ChoiceLogprobs
)
from openai.types.chat.chat_completion_chunk import (
Choice as OpenAIChatCompletionChunkChoice,
)
from openai.types.chat.chat_completion_chunk import (
ChoiceDelta as OpenAIChoiceDelta,
)
from openai.types.chat.chat_completion_chunk import (
ChoiceDeltaToolCall as OpenAIChoiceDeltaToolCall,
)
from openai.types.chat.chat_completion_chunk import (
ChoiceDeltaToolCallFunction as OpenAIChoiceDeltaToolCallFunction,
)
from openai.types.chat.chat_completion_content_part_image_param import (
ImageURL as OpenAIImageURL,
)
@ -59,6 +71,7 @@ from openai.types.chat.chat_completion_message_tool_call_param import (
from pydantic import BaseModel
from llama_stack.apis.common.content_types import (
URL,
ImageContentItem,
InterleavedContent,
TextContentItem,
@ -86,16 +99,23 @@ from llama_stack.apis.inference import (
UserMessage,
)
from llama_stack.apis.inference.inference import (
JsonSchemaResponseFormat,
OpenAIChatCompletion,
OpenAICompletion,
OpenAICompletionChoice,
OpenAIMessageParam,
OpenAIResponseFormatParam,
ToolConfig,
)
from llama_stack.apis.inference.inference import (
OpenAIChoice as OpenAIChatCompletionChoice,
)
from llama_stack.models.llama.datatypes import (
BuiltinTool,
StopReason,
ToolCall,
ToolDefinition,
ToolParamDefinition,
)
from llama_stack.providers.utils.inference.prompt_adapter import (
convert_image_content_to_url,
@ -756,6 +776,17 @@ def convert_tooldef_to_openai_tool(tool: ToolDefinition) -> dict:
return out
def _convert_stop_reason_to_openai_finish_reason(stop_reason: StopReason) -> str:
"""
Convert a StopReason to an OpenAI chat completion finish_reason.
"""
return {
StopReason.end_of_turn: "stop",
StopReason.end_of_message: "tool_calls",
StopReason.out_of_tokens: "length",
}.get(stop_reason, "stop")
def _convert_openai_finish_reason(finish_reason: str) -> StopReason:
"""
Convert an OpenAI chat completion finish_reason to a StopReason.
@ -781,6 +812,56 @@ def _convert_openai_finish_reason(finish_reason: str) -> StopReason:
}.get(finish_reason, StopReason.end_of_turn)
def _convert_openai_request_tool_config(tool_choice: Optional[Union[str, Dict[str, Any]]] = None) -> ToolConfig:
tool_config = ToolConfig()
if tool_choice:
tool_config.tool_choice = tool_choice
return tool_config
def _convert_openai_request_tools(tools: Optional[List[Dict[str, Any]]] = None) -> List[ToolDefinition]:
lls_tools = []
if not tools:
return lls_tools
for tool in tools:
tool_fn = tool.get("function", {})
tool_name = tool_fn.get("name", None)
tool_desc = tool_fn.get("description", None)
tool_params = tool_fn.get("parameters", None)
lls_tool_params = {}
if tool_params is not None:
tool_param_properties = tool_params.get("properties", {})
for tool_param_key, tool_param_value in tool_param_properties.items():
tool_param_def = ToolParamDefinition(
param_type=tool_param_value.get("type", None),
description=tool_param_value.get("description", None),
)
lls_tool_params[tool_param_key] = tool_param_def
lls_tool = ToolDefinition(
tool_name=tool_name,
description=tool_desc,
parameters=lls_tool_params,
)
lls_tools.append(lls_tool)
return lls_tools
def _convert_openai_request_response_format(response_format: OpenAIResponseFormatParam = None):
if not response_format:
return None
# response_format can be a dict or a pydantic model
response_format = dict(response_format)
if response_format.get("type", "") == "json_schema":
return JsonSchemaResponseFormat(
type="json_schema",
json_schema=response_format.get("json_schema", {}).get("schema", ""),
)
return None
def _convert_openai_tool_calls(
tool_calls: List[OpenAIChatCompletionMessageToolCall],
) -> List[ToolCall]:
@ -876,6 +957,40 @@ def _convert_openai_sampling_params(
return sampling_params
def _convert_openai_request_messages(messages: List[OpenAIMessageParam]):
# Llama Stack messages and OpenAI messages are similar, but not identical.
lls_messages = []
for message in messages:
lls_message = dict(message)
# Llama Stack expects `call_id` but OpenAI uses `tool_call_id`
tool_call_id = lls_message.pop("tool_call_id", None)
if tool_call_id:
lls_message["call_id"] = tool_call_id
content = lls_message.get("content", None)
if isinstance(content, list):
lls_content = []
for item in content:
# items can either by pydantic models or dicts here...
item = dict(item)
if item.get("type", "") == "image_url":
lls_item = ImageContentItem(
type="image",
image=URL(uri=item.get("image_url", {}).get("url", "")),
)
elif item.get("type", "") == "text":
lls_item = TextContentItem(
type="text",
text=item.get("text", ""),
)
lls_content.append(lls_item)
lls_message["content"] = lls_content
lls_messages.append(lls_message)
return lls_messages
def convert_openai_chat_completion_choice(
choice: OpenAIChoice,
) -> ChatCompletionResponse:
@ -1102,7 +1217,7 @@ async def prepare_openai_completion_params(**params):
return completion_params
class OpenAICompletionUnsupportedMixin:
class OpenAICompletionToLlamaStackMixin:
async def openai_completion(
self,
model: str,
@ -1140,6 +1255,7 @@ class OpenAICompletionUnsupportedMixin:
choices = []
# "n" is the number of completions to generate per prompt
n = n or 1
for _i in range(0, n):
# and we may have multiple prompts, if batching was used
@ -1152,7 +1268,7 @@ class OpenAICompletionUnsupportedMixin:
index = len(choices)
text = result.content
finish_reason = _convert_openai_finish_reason(result.stop_reason)
finish_reason = _convert_stop_reason_to_openai_finish_reason(result.stop_reason)
choice = OpenAICompletionChoice(
index=index,
@ -1170,7 +1286,7 @@ class OpenAICompletionUnsupportedMixin:
)
class OpenAIChatCompletionUnsupportedMixin:
class OpenAIChatCompletionToLlamaStackMixin:
async def openai_chat_completion(
self,
model: str,
@ -1197,4 +1313,109 @@ class OpenAIChatCompletionUnsupportedMixin:
top_p: Optional[float] = None,
user: Optional[str] = None,
) -> Union[OpenAIChatCompletion, AsyncIterator[OpenAIChatCompletionChunk]]:
raise ValueError(f"{self.__class__.__name__} doesn't support openai chat completion")
messages = _convert_openai_request_messages(messages)
response_format = _convert_openai_request_response_format(response_format)
sampling_params = _convert_openai_sampling_params(
max_tokens=max_tokens,
temperature=temperature,
top_p=top_p,
)
tool_config = _convert_openai_request_tool_config(tool_choice)
tools = _convert_openai_request_tools(tools)
outstanding_responses = []
# "n" is the number of completions to generate per prompt
n = n or 1
for _i in range(0, n):
response = self.chat_completion(
model_id=model,
messages=messages,
sampling_params=sampling_params,
response_format=response_format,
stream=stream,
tool_config=tool_config,
tools=tools,
)
outstanding_responses.append(response)
if stream:
return OpenAIChatCompletionToLlamaStackMixin._process_stream_response(self, model, outstanding_responses)
return await OpenAIChatCompletionToLlamaStackMixin._process_non_stream_response(
self, model, outstanding_responses
)
async def _process_stream_response(
self, model: str, outstanding_responses: List[Awaitable[AsyncIterator[ChatCompletionResponseStreamChunk]]]
):
id = f"chatcmpl-{uuid.uuid4()}"
for outstanding_response in outstanding_responses:
response = await outstanding_response
i = 0
async for chunk in response:
event = chunk.event
if event.stop_reason == StopReason.end_of_turn:
finish_reason = "stop"
elif event.stop_reason == StopReason.end_of_message:
finish_reason = "eos"
elif event.stop_reason == StopReason.out_of_tokens:
finish_reason = "length"
else:
finish_reason = None
if isinstance(event.delta, TextDelta):
text_delta = event.delta.text
delta = OpenAIChoiceDelta(content=text_delta)
yield OpenAIChatCompletionChunk(
id=id,
choices=[OpenAIChatCompletionChunkChoice(index=i, finish_reason=finish_reason, delta=delta)],
created=int(time.time()),
model=model,
object="chat.completion.chunk",
)
elif isinstance(event.delta, ToolCallDelta):
if event.delta.parse_status == ToolCallParseStatus.succeeded:
tool_call = event.delta.tool_call
openai_tool_call = OpenAIChoiceDeltaToolCall(
index=0,
id=tool_call.call_id,
function=OpenAIChoiceDeltaToolCallFunction(
name=tool_call.tool_name, arguments=tool_call.arguments_json
),
)
delta = OpenAIChoiceDelta(tool_calls=[openai_tool_call])
yield OpenAIChatCompletionChunk(
id=id,
choices=[
OpenAIChatCompletionChunkChoice(index=i, finish_reason=finish_reason, delta=delta)
],
created=int(time.time()),
model=model,
object="chat.completion.chunk",
)
i = i + 1
async def _process_non_stream_response(
self, model: str, outstanding_responses: List[Awaitable[ChatCompletionResponse]]
) -> OpenAIChatCompletion:
choices = []
for outstanding_response in outstanding_responses:
response = await outstanding_response
completion_message = response.completion_message
message = await convert_message_to_openai_dict_new(completion_message)
finish_reason = _convert_stop_reason_to_openai_finish_reason(completion_message.stop_reason)
choice = OpenAIChatCompletionChoice(
index=len(choices),
message=message,
finish_reason=finish_reason,
)
choices.append(choice)
return OpenAIChatCompletion(
id=f"chatcmpl-{uuid.uuid4()}",
choices=choices,
created=int(time.time()),
model=model,
object="chat.completion",
)

14
tests/verifications/conf/fireworks-llama-stack.yaml Normal file
View file

@ -0,0 +1,14 @@
base_url: http://localhost:8321/v1/openai/v1
api_key_var: FIREWORKS_API_KEY
models:
- fireworks/llama-v3p3-70b-instruct
- fireworks/llama4-scout-instruct-basic
- fireworks/llama4-maverick-instruct-basic
model_display_names:
fireworks/llama-v3p3-70b-instruct: Llama-3.3-70B-Instruct
fireworks/llama4-scout-instruct-basic: Llama-4-Scout-Instruct
fireworks/llama4-maverick-instruct-basic: Llama-4-Maverick-Instruct
test_exclusions:
fireworks/llama-v3p3-70b-instruct:
- test_chat_non_streaming_image
- test_chat_streaming_image

9
tests/verifications/conf/openai-llama-stack.yaml Normal file
View file

@ -0,0 +1,9 @@
base_url: http://localhost:8321/v1/openai/v1
api_key_var: OPENAI_API_KEY
models:
- gpt-4o
- gpt-4o-mini
model_display_names:
gpt-4o: gpt-4o
gpt-4o-mini: gpt-4o-mini
test_exclusions: {}

14
tests/verifications/conf/together-llama-stack.yaml Normal file
View file

@ -0,0 +1,14 @@
base_url: http://localhost:8321/v1/openai/v1
api_key_var: TOGETHER_API_KEY
models:
- together/meta-llama/Llama-3.3-70B-Instruct-Turbo
- together/meta-llama/Llama-4-Scout-17B-16E-Instruct
- together/meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8
model_display_names:
together/meta-llama/Llama-3.3-70B-Instruct-Turbo: Llama-3.3-70B-Instruct
together/meta-llama/Llama-4-Scout-17B-16E-Instruct: Llama-4-Scout-Instruct
together/meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8: Llama-4-Maverick-Instruct
test_exclusions:
together/meta-llama/Llama-3.3-70B-Instruct-Turbo:
- test_chat_non_streaming_image
- test_chat_streaming_image

11
tests/verifications/generate_report.py
View file

@ -67,7 +67,16 @@ RESULTS_DIR.mkdir(exist_ok=True)
# Maximum number of test result files to keep per provider
MAX_RESULTS_PER_PROVIDER = 1
PROVIDER_ORDER = ["together", "fireworks", "groq", "cerebras", "openai"]
PROVIDER_ORDER = [
"together",
"fireworks",
"groq",
"cerebras",
"openai",
"together-llama-stack",
"fireworks-llama-stack",
"openai-llama-stack",
]
VERIFICATION_CONFIG = _load_all_verification_configs()

126
tests/verifications/openai-api-verification-run.yaml Normal file
View file

@ -0,0 +1,126 @@
version: '2'
image_name: openai-api-verification
apis:
- inference
- telemetry
- tool_runtime
- vector_io
providers:
inference:
- provider_id: together
provider_type: remote::together
config:
url: https://api.together.xyz/v1
api_key: ${env.TOGETHER_API_KEY:}
- provider_id: fireworks
provider_type: remote::fireworks
config:
url: https://api.fireworks.ai/inference/v1
api_key: ${env.FIREWORKS_API_KEY}
- provider_id: openai
provider_type: remote::openai
config:
url: https://api.openai.com/v1
api_key: ${env.OPENAI_API_KEY:}
- provider_id: sentence-transformers
provider_type: inline::sentence-transformers
config: {}
vector_io:
- provider_id: faiss
provider_type: inline::faiss
config:
kvstore:
type: sqlite
namespace: null
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/openai}/faiss_store.db
telemetry:
- provider_id: meta-reference
provider_type: inline::meta-reference
config:
service_name: "${env.OTEL_SERVICE_NAME:\u200B}"
sinks: ${env.TELEMETRY_SINKS:console,sqlite}
sqlite_db_path: ${env.SQLITE_DB_PATH:~/.llama/distributions/openai/trace_store.db}
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: code-interpreter
provider_type: inline::code-interpreter
config: {}
- provider_id: rag-runtime
provider_type: inline::rag-runtime
config: {}
- provider_id: model-context-protocol
provider_type: remote::model-context-protocol
config: {}
- provider_id: wolfram-alpha
provider_type: remote::wolfram-alpha
config:
api_key: ${env.WOLFRAM_ALPHA_API_KEY:}
metadata_store:
type: sqlite
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/openai}/registry.db
models:
- metadata: {}
model_id: together/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: together/meta-llama/Llama-4-Scout-17B-16E-Instruct
provider_id: together
provider_model_id: meta-llama/Llama-4-Scout-17B-16E-Instruct
model_type: llm
- metadata: {}
model_id: together/meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8
provider_id: together
provider_model_id: meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8
model_type: llm
- metadata: {}
model_id: fireworks/llama-v3p3-70b-instruct
provider_id: fireworks
provider_model_id: accounts/fireworks/models/llama-v3p3-70b-instruct
model_type: llm
- metadata: {}
model_id: fireworks/llama4-scout-instruct-basic
provider_id: fireworks
provider_model_id: accounts/fireworks/models/llama4-scout-instruct-basic
model_type: llm
- metadata: {}
model_id: fireworks/llama4-maverick-instruct-basic
provider_id: fireworks
provider_model_id: accounts/fireworks/models/llama4-maverick-instruct-basic
model_type: llm
- metadata: {}
model_id: openai/gpt-4o
provider_id: openai
provider_model_id: openai/gpt-4o
model_type: llm
- metadata: {}
model_id: openai/gpt-4o-mini
provider_id: openai
provider_model_id: openai/gpt-4o-mini
model_type: llm
shields: []
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
- toolgroup_id: builtin::wolfram_alpha
provider_id: wolfram-alpha
server:
port: 8321

3
tests/verifications/openai_api/fixtures/fixtures.py
View file

@ -99,6 +99,9 @@ def model_mapping(provider, providers_model_mapping):
@pytest.fixture
def openai_client(base_url, api_key):
# Simplify running against a local Llama Stack
if "localhost" in base_url and not api_key:
api_key = "empty"
return OpenAI(
base_url=base_url,
api_key=api_key,