fully convert the input message types to the openai message types

previously the types were passed around as TypedDicts and converted to dicts
This commit is contained in:
Matthew Farrellee 2025-10-01 21:32:04 -04:00
parent 0327ef3daf
commit 0399f89c1f
32 changed files with 12183 additions and 4 deletions

View file

@ -50,6 +50,12 @@ from llama_stack.apis.inference import (
CompletionMessage,
Inference,
Message,
OpenAIAssistantMessageParam,
OpenAIDeveloperMessageParam,
OpenAIMessageParam,
OpenAISystemMessageParam,
OpenAIToolMessageParam,
OpenAIUserMessageParam,
SamplingParams,
StopReason,
SystemMessage,
@ -515,10 +521,42 @@ class ChatAgent(ShieldRunnerMixin):
async with tracing.span("inference") as span:
if self.agent_config.name:
span.set_attribute("agent_name", self.agent_config.name)
def _serialize_nested(value):
"""Recursively serialize nested Pydantic models to dicts."""
from pydantic import BaseModel
if isinstance(value, BaseModel):
return value.model_dump(mode="json")
elif isinstance(value, dict):
return {k: _serialize_nested(v) for k, v in value.items()}
elif isinstance(value, list):
return [_serialize_nested(item) for item in value]
else:
return value
def _add_type(openai_msg: dict) -> OpenAIMessageParam:
# Serialize any nested Pydantic models to plain dicts
openai_msg = _serialize_nested(openai_msg)
role = openai_msg.get("role")
if role == "user":
return OpenAIUserMessageParam(**openai_msg)
elif role == "system":
return OpenAISystemMessageParam(**openai_msg)
elif role == "assistant":
return OpenAIAssistantMessageParam(**openai_msg)
elif role == "tool":
return OpenAIToolMessageParam(**openai_msg)
elif role == "developer":
return OpenAIDeveloperMessageParam(**openai_msg)
else:
raise ValueError(f"Unknown message role: {role}")
# Convert messages to OpenAI format
openai_messages: list[dict] = []
for message in input_messages:
openai_messages.append(await convert_message_to_openai_dict_new(message))
openai_messages: list[OpenAIMessageParam] = [
_add_type(await convert_message_to_openai_dict_new(message)) for message in input_messages
]
# Convert tool definitions to OpenAI format
openai_tools = [convert_tooldef_to_openai_tool(x) for x in (self.tool_defs or [])]
@ -528,7 +566,13 @@ class ChatAgent(ShieldRunnerMixin):
tool_choice = None
if openai_tools and self.agent_config.tool_config and self.agent_config.tool_config.tool_choice:
tc = self.agent_config.tool_config.tool_choice
tool_choice = tc.value if hasattr(tc, "value") else str(tc)
tool_choice_str = tc.value if hasattr(tc, "value") else str(tc)
# Convert tool_choice to OpenAI format
if tool_choice_str in ("auto", "none", "required"):
tool_choice = tool_choice_str
else:
# It's a specific tool name, wrap it in the proper format
tool_choice = {"type": "function", "function": {"name": tool_choice_str}}
# Convert sampling params to OpenAI format (temperature, top_p, max_tokens)
temperature = getattr(getattr(sampling_params, "strategy", None), "temperature", None)

View file

@ -0,0 +1,609 @@
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"messages": [
{
"role": "system",
"content": "You are a helpful assistant"
},
{
"role": "user",
"content": "What is 2 + 2?"
},
{
"role": "assistant",
"content": "2 + 2 = 4"
},
{
"role": "user",
"content": "Tell me a short joke"
}
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}

View file

@ -0,0 +1,415 @@
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"headers": {},
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"messages": [
{
"role": "system",
"content": "You are a helpful assistant Always respond with tool calls no matter what. "
},
{
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},
{
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