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Merge branch 'main' into issue-3443-require_approval

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Matthew Farrellee 2025-09-30 12:10:13 -04:00
commit d2fdc70a8d
72 changed files with 1380 additions and 1406 deletions
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2
llama_stack/providers/inline/agents/meta_reference/agent_instance.py
View file

@ -924,7 +924,7 @@ async def get_raw_document_text(document: Document) -> str:
DeprecationWarning,
stacklevel=2,
)
elif not (document.mime_type.startswith("text/") or document.mime_type == "application/yaml"):
elif not (document.mime_type.startswith("text/") or document.mime_type in ("application/yaml", "application/json")):
raise ValueError(f"Unexpected document mime type: {document.mime_type}")
if isinstance(document.content, URL):

48
llama_stack/providers/inline/agents/meta_reference/responses/streaming.py
View file

@ -52,6 +52,36 @@ from .utils import convert_chat_choice_to_response_message, is_function_tool_cal
logger = get_logger(name=__name__, category="agents::meta_reference")
def convert_tooldef_to_chat_tool(tool_def):
"""Convert a ToolDef to OpenAI ChatCompletionToolParam format.
Args:
tool_def: ToolDef from the tools API
Returns:
ChatCompletionToolParam suitable for OpenAI chat completion
"""
from llama_stack.models.llama.datatypes import ToolDefinition, ToolParamDefinition
from llama_stack.providers.utils.inference.openai_compat import convert_tooldef_to_openai_tool
internal_tool_def = ToolDefinition(
tool_name=tool_def.name,
description=tool_def.description,
parameters={
param.name: ToolParamDefinition(
param_type=param.parameter_type,
description=param.description,
required=param.required,
default=param.default,
items=param.items,
)
for param in tool_def.parameters
},
)
return convert_tooldef_to_openai_tool(internal_tool_def)
class StreamingResponseOrchestrator:
def __init__(
self,
@ -580,23 +610,7 @@ class StreamingResponseOrchestrator:
continue
if not always_allowed or t.name in always_allowed:
# Add to chat tools for inference
from llama_stack.models.llama.datatypes import ToolDefinition, ToolParamDefinition
from llama_stack.providers.utils.inference.openai_compat import convert_tooldef_to_openai_tool
tool_def = ToolDefinition(
tool_name=t.name,
description=t.description,
parameters={
param.name: ToolParamDefinition(
param_type=param.parameter_type,
description=param.description,
required=param.required,
default=param.default,
)
for param in t.parameters
},
)
openai_tool = convert_tooldef_to_openai_tool(tool_def)
openai_tool = convert_tooldef_to_chat_tool(t)
if self.ctx.chat_tools is None:
self.ctx.chat_tools = []
self.ctx.chat_tools.append(openai_tool)

31
llama_stack/providers/inline/eval/meta_reference/eval.py
View file

@ -12,7 +12,7 @@ from llama_stack.apis.agents import Agents, StepType
from llama_stack.apis.benchmarks import Benchmark
from llama_stack.apis.datasetio import DatasetIO
from llama_stack.apis.datasets import Datasets
from llama_stack.apis.inference import Inference, SystemMessage, UserMessage
from llama_stack.apis.inference import Inference, OpenAISystemMessageParam, OpenAIUserMessageParam, UserMessage
from llama_stack.apis.scoring import Scoring
from llama_stack.providers.datatypes import BenchmarksProtocolPrivate
from llama_stack.providers.inline.agents.meta_reference.agent_instance import (
@ -159,31 +159,40 @@ class MetaReferenceEvalImpl(
) -> list[dict[str, Any]]:
candidate = benchmark_config.eval_candidate
assert candidate.sampling_params.max_tokens is not None, "SamplingParams.max_tokens must be provided"
sampling_params = {"max_tokens": candidate.sampling_params.max_tokens}
generations = []
for x in tqdm(input_rows):
if ColumnName.completion_input.value in x:
if candidate.sampling_params.stop:
sampling_params["stop"] = candidate.sampling_params.stop
input_content = json.loads(x[ColumnName.completion_input.value])
response = await self.inference_api.completion(
response = await self.inference_api.openai_completion(
model=candidate.model,
content=input_content,
sampling_params=candidate.sampling_params,
prompt=input_content,
**sampling_params,
)
generations.append({ColumnName.generated_answer.value: response.completion_message.content})
generations.append({ColumnName.generated_answer.value: response.choices[0].text})
elif ColumnName.chat_completion_input.value in x:
chat_completion_input_json = json.loads(x[ColumnName.chat_completion_input.value])
input_messages = [UserMessage(**x) for x in chat_completion_input_json if x["role"] == "user"]
input_messages = [
OpenAIUserMessageParam(**x) for x in chat_completion_input_json if x["role"] == "user"
]
messages = []
if candidate.system_message:
messages.append(candidate.system_message)
messages += [SystemMessage(**x) for x in chat_completion_input_json if x["role"] == "system"]
messages += [OpenAISystemMessageParam(**x) for x in chat_completion_input_json if x["role"] == "system"]
messages += input_messages
response = await self.inference_api.chat_completion(
model_id=candidate.model,
response = await self.inference_api.openai_chat_completion(
model=candidate.model,
messages=messages,
sampling_params=candidate.sampling_params,
**sampling_params,
)
generations.append({ColumnName.generated_answer.value: response.completion_message.content})
generations.append({ColumnName.generated_answer.value: response.choices[0].message.content})
else:
raise ValueError("Invalid input row")

6
llama_stack/providers/inline/files/localfs/files.py
View file

@ -14,6 +14,7 @@ from fastapi import File, Form, Response, UploadFile
from llama_stack.apis.common.errors import ResourceNotFoundError
from llama_stack.apis.common.responses import Order
from llama_stack.apis.files import (
ExpiresAfter,
Files,
ListOpenAIFileResponse,
OpenAIFileDeleteResponse,
@ -86,14 +87,13 @@ class LocalfsFilesImpl(Files):
self,
file: Annotated[UploadFile, File()],
purpose: Annotated[OpenAIFilePurpose, Form()],
expires_after_anchor: Annotated[str | None, Form(alias="expires_after[anchor]")] = None,
expires_after_seconds: Annotated[int | None, Form(alias="expires_after[seconds]")] = None,
expires_after: Annotated[ExpiresAfter | None, Form()] = None,
) -> OpenAIFileObject:
"""Upload a file that can be used across various endpoints."""
if not self.sql_store:
raise RuntimeError("Files provider not initialized")
if expires_after_anchor is not None or expires_after_seconds is not None:
if expires_after is not None:
raise NotImplementedError("File expiration is not supported by this provider")
file_id = self._generate_file_id()