phoenix-oss/llama-stack-mirror
0
0
Fork 1
mirror of https://github.com/meta-llama/llama-stack.git synced 2025-08-15 14:08:00 +00:00
Code Issues Projects Releases Packages Wiki Activity Actions

refactor(agents): migrate to OpenAI chat completions API

Browse source 
Replace chat_completion calls with openai_chat_completion
to eliminate dependency on legacy inference APIs.
This commit is contained in:
Aakanksha Duggal 2025-08-11 14:27:30 -04:00
parent f7adf58b1b
commit ff58e9ba8a
1 changed files with 58 additions and 9 deletions
Download patch file Download diff file Expand all files Collapse all files

67
llama_stack/providers/inline/agents/meta_reference/agent_instance.py
View file

@ -68,6 +68,11 @@ from llama_stack.models.llama.datatypes import (
BuiltinTool,
ToolCall,
)
from llama_stack.providers.utils.inference.openai_compat import (
convert_message_to_openai_dict_new,
convert_openai_chat_completion_stream,
convert_tooldef_to_openai_tool,
)
from llama_stack.providers.utils.kvstore import KVStore
from llama_stack.providers.utils.telemetry import tracing
@ -510,16 +515,60 @@ class ChatAgent(ShieldRunnerMixin):
async with tracing.span("inference") as span:
if self.agent_config.name:
span.set_attribute("agent_name", self.agent_config.name)
async for chunk in await self.inference_api.chat_completion(
self.agent_config.model,
input_messages,
tools=self.tool_defs,
tool_prompt_format=self.agent_config.tool_config.tool_prompt_format,
response_format=self.agent_config.response_format,
# Convert messages to OpenAI format
openai_messages = []
for message in input_messages:
openai_message = await convert_message_to_openai_dict_new(message)
openai_messages.append(openai_message)
# Convert tool definitions to OpenAI format
openai_tools = None
if self.tool_defs:
openai_tools = []
for tool_def in self.tool_defs:
openai_tool = convert_tooldef_to_openai_tool(tool_def)
openai_tools.append(openai_tool)
# Extract tool_choice from tool_config for OpenAI compatibility
# Note: tool_choice can only be provided when tools are also provided
tool_choice = None
if openai_tools and self.agent_config.tool_config and self.agent_config.tool_config.tool_choice:
tool_choice = (
self.agent_config.tool_config.tool_choice.value
if hasattr(self.agent_config.tool_config.tool_choice, "value")
else str(self.agent_config.tool_config.tool_choice)
)
# Convert sampling params to OpenAI format (temperature, top_p, max_tokens)
temperature = None
top_p = None
max_tokens = None
if sampling_params:
if hasattr(sampling_params.strategy, "temperature"):
temperature = sampling_params.strategy.temperature
if hasattr(sampling_params.strategy, "top_p"):
top_p = sampling_params.strategy.top_p
if sampling_params.max_tokens:
max_tokens = sampling_params.max_tokens
# Use OpenAI chat completion
openai_stream = await self.inference_api.openai_chat_completion(
model=self.agent_config.model,
messages=openai_messages,
tools=openai_tools if openai_tools else None,
tool_choice=tool_choice,
temperature=temperature,
top_p=top_p,
max_tokens=max_tokens,
stream=True,
sampling_params=sampling_params,
tool_config=self.agent_config.tool_config,
):
)
# Convert OpenAI stream back to Llama Stack format
response_stream = convert_openai_chat_completion_stream(
openai_stream, enable_incremental_tool_calls=True
)
async for chunk in response_stream:
event = chunk.event
if event.event_type == ChatCompletionResponseEventType.start:
continue