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https://github.com/meta-llama/llama-stack.git
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Merge branch 'main' into remove-deprecated-chat-completion
This commit is contained in:
Matthew Farrellee
commit
ee6a502289
209 changed files with 109297 additions and 8828 deletions
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@ -22,7 +22,7 @@ from ..sqlstore.api import ColumnDefinition, ColumnType
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from ..sqlstore.authorized_sqlstore import AuthorizedSqlStore
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from ..sqlstore.sqlstore import SqlStoreConfig, SqlStoreType, sqlstore_impl
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logger = get_logger(name=__name__, category="inference_store")
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logger = get_logger(name=__name__, category="inference")
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class InferenceStore:
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@ -125,7 +125,6 @@ from llama_stack.models.llama.datatypes import (
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StopReason,
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ToolCall,
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ToolDefinition,
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ToolParamDefinition,
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)
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from llama_stack.providers.utils.inference.prompt_adapter import (
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convert_image_content_to_url,
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@ -537,18 +536,13 @@ async def convert_message_to_openai_dict(message: Message, download: bool = Fals
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if isinstance(tool_name, BuiltinTool):
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tool_name = tool_name.value
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# arguments_json can be None, so attempt it first and fall back to arguments
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if hasattr(tc, "arguments_json") and tc.arguments_json:
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arguments = tc.arguments_json
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else:
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arguments = json.dumps(tc.arguments)
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result["tool_calls"].append(
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{
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"id": tc.call_id,
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"type": "function",
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"function": {
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"name": tool_name,
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"arguments": arguments,
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"arguments": tc.arguments,
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},
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}
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)
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@ -641,7 +635,7 @@ async def convert_message_to_openai_dict_new(
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id=tool.call_id,
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function=OpenAIFunction(
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name=(tool.tool_name if not isinstance(tool.tool_name, BuiltinTool) else tool.tool_name.value),
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arguments=json.dumps(tool.arguments),
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arguments=tool.arguments, # Already a JSON string, don't double-encode
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),
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type="function",
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)
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@ -684,8 +678,7 @@ def convert_tool_call(
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valid_tool_call = ToolCall(
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call_id=tool_call.id,
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tool_name=tool_call.function.name,
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arguments=json.loads(tool_call.function.arguments),
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arguments_json=tool_call.function.arguments,
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arguments=tool_call.function.arguments,
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)
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except Exception:
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return UnparseableToolCall(
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@ -745,14 +738,8 @@ def convert_tooldef_to_openai_tool(tool: ToolDefinition) -> dict:
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ToolDefinition:
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tool_name: str | BuiltinTool
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description: Optional[str]
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parameters: Optional[Dict[str, ToolParamDefinition]]
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ToolParamDefinition:
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param_type: str
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description: Optional[str]
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required: Optional[bool]
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default: Optional[Any]
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input_schema: Optional[Dict[str, Any]] # JSON Schema
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output_schema: Optional[Dict[str, Any]] # JSON Schema (not used by OpenAI)
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OpenAI spec -
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@ -761,20 +748,11 @@ def convert_tooldef_to_openai_tool(tool: ToolDefinition) -> dict:
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"function": {
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"name": tool_name,
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"description": description,
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"parameters": {
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"type": "object",
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"properties": {
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param_name: {
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"type": param_type,
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"description": description,
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"default": default,
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},
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...
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},
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"required": [param_name, ...],
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},
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"parameters": {<JSON Schema>},
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},
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}
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NOTE: OpenAI does not support output_schema, so it is dropped here.
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"""
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out = {
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"type": "function",
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@ -783,37 +761,19 @@ def convert_tooldef_to_openai_tool(tool: ToolDefinition) -> dict:
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function = out["function"]
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if isinstance(tool.tool_name, BuiltinTool):
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function.update(name=tool.tool_name.value) # TODO(mf): is this sufficient?
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function["name"] = tool.tool_name.value
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else:
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function.update(name=tool.tool_name)
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function["name"] = tool.tool_name
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if tool.description:
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function.update(description=tool.description)
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function["description"] = tool.description
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if tool.parameters:
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parameters = {
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"type": "object",
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"properties": {},
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}
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properties = parameters["properties"]
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required = []
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for param_name, param in tool.parameters.items():
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properties[param_name] = to_openai_param_type(param.param_type)
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if param.description:
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properties[param_name].update(description=param.description)
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if param.default:
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properties[param_name].update(default=param.default)
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if param.items:
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properties[param_name].update(items=param.items)
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if param.title:
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properties[param_name].update(title=param.title)
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if param.required:
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required.append(param_name)
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if tool.input_schema:
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# Pass through the entire JSON Schema as-is
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function["parameters"] = tool.input_schema
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if required:
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parameters.update(required=required)
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function.update(parameters=parameters)
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# NOTE: OpenAI does not support output_schema, so we drop it here
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# It's stored in LlamaStack for validation and other provider usage
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return out
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@ -874,22 +834,12 @@ def _convert_openai_request_tools(tools: list[dict[str, Any]] | None = None) ->
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tool_fn = tool.get("function", {})
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tool_name = tool_fn.get("name", None)
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tool_desc = tool_fn.get("description", None)
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tool_params = tool_fn.get("parameters", None)
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lls_tool_params = {}
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if tool_params is not None:
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tool_param_properties = tool_params.get("properties", {})
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for tool_param_key, tool_param_value in tool_param_properties.items():
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tool_param_def = ToolParamDefinition(
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param_type=str(tool_param_value.get("type", None)),
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description=tool_param_value.get("description", None),
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)
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lls_tool_params[tool_param_key] = tool_param_def
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lls_tool = ToolDefinition(
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tool_name=tool_name,
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description=tool_desc,
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parameters=lls_tool_params,
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input_schema=tool_params, # Pass through entire JSON Schema
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)
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lls_tools.append(lls_tool)
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return lls_tools
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@ -939,8 +889,7 @@ def _convert_openai_tool_calls(
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ToolCall(
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call_id=call.id,
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tool_name=call.function.name,
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arguments=json.loads(call.function.arguments),
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arguments_json=call.function.arguments,
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arguments=call.function.arguments,
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)
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for call in tool_calls
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]
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@ -1222,12 +1171,10 @@ async def convert_openai_chat_completion_stream(
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)
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try:
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arguments = json.loads(buffer["arguments"])
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tool_call = ToolCall(
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call_id=buffer["call_id"],
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tool_name=buffer["name"],
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arguments=arguments,
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arguments_json=buffer["arguments"],
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arguments=buffer["arguments"],
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)
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yield ChatCompletionResponseStreamChunk(
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event=ChatCompletionResponseEvent(
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@ -1390,7 +1337,7 @@ class OpenAIChatCompletionToLlamaStackMixin:
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openai_tool_call = OpenAIChoiceDeltaToolCall(
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index=0,
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function=OpenAIChoiceDeltaToolCallFunction(
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arguments=tool_call.arguments_json,
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arguments=tool_call.arguments,
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),
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)
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delta = OpenAIChoiceDelta(tool_calls=[openai_tool_call])
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@ -286,34 +286,34 @@ class OpenAIMixin(ModelRegistryHelper, NeedsRequestProviderData, ABC):
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messages = [await _localize_image_url(m) for m in messages]
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resp = await self.client.chat.completions.create(
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**await prepare_openai_completion_params(
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model=await self._get_provider_model_id(model),
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messages=messages,
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frequency_penalty=frequency_penalty,
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function_call=function_call,
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functions=functions,
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logit_bias=logit_bias,
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logprobs=logprobs,
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max_completion_tokens=max_completion_tokens,
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max_tokens=max_tokens,
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n=n,
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parallel_tool_calls=parallel_tool_calls,
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presence_penalty=presence_penalty,
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response_format=response_format,
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seed=seed,
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stop=stop,
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stream=stream,
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stream_options=stream_options,
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temperature=temperature,
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tool_choice=tool_choice,
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tools=tools,
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top_logprobs=top_logprobs,
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top_p=top_p,
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user=user,
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)
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params = await prepare_openai_completion_params(
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model=await self._get_provider_model_id(model),
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messages=messages,
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frequency_penalty=frequency_penalty,
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function_call=function_call,
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functions=functions,
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logit_bias=logit_bias,
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logprobs=logprobs,
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max_completion_tokens=max_completion_tokens,
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max_tokens=max_tokens,
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n=n,
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parallel_tool_calls=parallel_tool_calls,
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presence_penalty=presence_penalty,
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response_format=response_format,
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seed=seed,
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stop=stop,
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stream=stream,
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stream_options=stream_options,
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temperature=temperature,
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tool_choice=tool_choice,
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tools=tools,
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top_logprobs=top_logprobs,
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top_p=top_p,
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user=user,
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)
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resp = await self.client.chat.completions.create(**params)
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return await self._maybe_overwrite_id(resp, stream) # type: ignore[no-any-return]
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async def openai_embeddings(
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