fix: converts litellm generate config err

Merge #1509 Fixs: #1302 Previous PR: #1450 COPYBARA_INTEGRATE_REVIEW=#1509 from simonwei97:fix/litellm-gen-config-converting-err 3120887 PiperOrigin-RevId: 775903671

Author: simonwei97

src/google/adk/models/lite_llm.py

@@ -23,6 +23,7 @@
 from typing import Dict
 from typing import Generator
 from typing import Iterable
+from typing import List
 from typing import Literal
 from typing import Optional
 from typing import Tuple
@@ -485,16 +486,22 @@ def _message_to_generate_content_response(
 
 def _get_completion_inputs(
  llm_request: LlmRequest,
-) -> tuple[Iterable[Message], Iterable[dict]]:
- """Converts an LlmRequest to litellm inputs.
+) -> Tuple[
+ List[Message],
+ Optional[List[Dict]],
+ Optional[types.SchemaUnion],
+ Optional[Dict],
+]:
+ """Converts an LlmRequest to litellm inputs and extracts generation params.
 
  Args:
  llm_request: The LlmRequest to convert.
 
  Returns:
- The litellm inputs (message list, tool dictionary and response format).
+ The litellm inputs (message list, tool dictionary, response format and generation params).
  """
- messages = []
+ # 1. Construct messages
+ messages: List[Message] = []
  for content in llm_request.contents or []:
  message_param_or_list = _content_to_message_param(content)
  if isinstance(message_param_or_list, list):
@@ -511,7 +518,8 @@ def _get_completion_inputs(
  ),
  )
 
- tools = None
+ # 2. Convert tool declarations
+ tools: Optional[List[Dict]] = None
  if (
  llm_request.config
  and llm_request.config.tools
@@ -522,12 +530,39 @@ def _get_completion_inputs(
  for tool in llm_request.config.tools[0].function_declarations
  ]
 
- response_format = None
-
- if llm_request.config.response_schema:
+ # 3. Handle response format
+ response_format: Optional[types.SchemaUnion] = None
+ if llm_request.config and llm_request.config.response_schema:
  response_format = llm_request.config.response_schema
 
- return messages, tools, response_format
+ # 4. Extract generation parameters
+ generation_params: Optional[Dict] = None
+ if llm_request.config:
+ config_dict = llm_request.config.model_dump(exclude_none=True)
+ # Generate LiteLlm parameters here,
+ # Following https://docs.litellm.ai/docs/completion/input.
+ generation_params = {}
+ param_mapping = {
+ "max_output_tokens": "max_completion_tokens",
+ "stop_sequences": "stop",
+ }
+ for key in (
+ "temperature",
+ "max_output_tokens",
+ "top_p",
+ "top_k",
+ "stop_sequences",
+ "presence_penalty",
+ "frequency_penalty",
+ ):
+ if key in config_dict:
+ mapped_key = param_mapping.get(key, key)
+ generation_params[mapped_key] = config_dict[key]
+
+ if not generation_params:
+ generation_params = None
+
+ return messages, tools, response_format, generation_params
 
 
 def _build_function_declaration_log(
@@ -664,7 +699,9 @@ async def generate_content_async(
  self._maybe_append_user_content(llm_request)
  logger.debug(_build_request_log(llm_request))
 
- messages, tools, response_format = _get_completion_inputs(llm_request)
+ messages, tools, response_format, generation_params = (
+ _get_completion_inputs(llm_request)
+ )
 
  if "functions" in self._additional_args:
  # LiteLLM does not support both tools and functions together.
@@ -678,6 +715,9 @@ async def generate_content_async(
  }
  completion_args.update(self._additional_args)
 
+ if generation_params:
+ completion_args.update(generation_params)
+
  if stream:
  text = ""
  # Track function calls by index

tests/unittests/models/test_litellm.py

@@ -1447,3 +1447,35 @@ async def test_generate_content_async_non_compliant_multiple_function_calls(
  assert final_response.content.parts[1].function_call.name == "function_2"
  assert final_response.content.parts[1].function_call.id == "1"
  assert final_response.content.parts[1].function_call.args == {"arg": "value2"}
+
+
+@pytest.mark.asyncio
+def test_get_completion_inputs_generation_params():
+ # Test that generation_params are extracted and mapped correctly
+ req = LlmRequest(
+ contents=[
+ types.Content(role="user", parts=[types.Part.from_text(text="hi")]),
+ ],
+ config=types.GenerateContentConfig(
+ temperature=0.33,
+ max_output_tokens=123,
+ top_p=0.88,
+ top_k=7,
+ stop_sequences=["foo", "bar"],
+ presence_penalty=0.1,
+ frequency_penalty=0.2,
+ ),
+ )
+ from google.adk.models.lite_llm import _get_completion_inputs
+
+ _, _, _, generation_params = _get_completion_inputs(req)
+ assert generation_params["temperature"] == 0.33
+ assert generation_params["max_completion_tokens"] == 123
+ assert generation_params["top_p"] == 0.88
+ assert generation_params["top_k"] == 7
+ assert generation_params["stop"] == ["foo", "bar"]
+ assert generation_params["presence_penalty"] == 0.1
+ assert generation_params["frequency_penalty"] == 0.2
+ # Should not include max_output_tokens
+ assert "max_output_tokens" not in generation_params
+ assert "stop_sequences" not in generation_params