Initialize the delta tool call fields explicitly

tests/entrypoints/openai/tool_parsers/utils.py

@@ -32,7 +32,7 @@ def append_delta(self, delta: DeltaMessage):
             assert len(delta.tool_calls) < 2, (
                 "Streaming should include only one tool call per update.")
         for call_delta in delta.tool_calls:
-            assert call_delta.type == "function", (
+            assert call_delta.type is None or call_delta.type == "function", (
                 "Streaming tool calls should only emit function calls. Got "
                 f"{call_delta.type}")
             current_tool_call = self.tool_calls[

vllm/entrypoints/chat_utils.py

@@ -40,6 +40,7 @@
 from vllm.multimodal.utils import MediaConnector
 from vllm.transformers_utils.processor import cached_get_processor
 from vllm.transformers_utils.tokenizer import AnyTokenizer, MistralTokenizer
+from vllm.utils import random_uuid
 
 logger = init_logger(__name__)
 
@@ -1258,3 +1259,6 @@ def apply_mistral_chat_template(
             "An error occurred in `mistral_common` while applying chat "
             "template")
         raise ValueError from e
+
+def random_tool_call_id() -> str:
+    return f"chatcmpl-tool-{random_uuid()}"

vllm/entrypoints/openai/protocol.py

@@ -15,7 +15,8 @@
 from typing_extensions import TypeAlias
 
 from vllm import envs
-from vllm.entrypoints.chat_utils import ChatCompletionMessageParam
+from vllm.entrypoints.chat_utils import (ChatCompletionMessageParam,
+                                         random_tool_call_id)
 from vllm.logger import init_logger
 from vllm.pooling_params import PoolingParams
 from vllm.sampling_params import (BeamSearchParams, GuidedDecodingParams,
@@ -1314,7 +1315,7 @@ class FunctionCall(OpenAIBaseModel):
 
 
 class ToolCall(OpenAIBaseModel):
-    id: str = Field(default_factory=lambda: f"chatcmpl-tool-{random_uuid()}")
+    id: str = Field(default_factory=random_tool_call_id)
     type: Literal["function"] = "function"
     function: FunctionCall
 
@@ -1326,8 +1327,8 @@ class DeltaFunctionCall(BaseModel):
 
 # a tool call delta where everything is optional
 class DeltaToolCall(OpenAIBaseModel):
-    id: str = Field(default_factory=lambda: f"chatcmpl-tool-{random_uuid()}")
-    type: Literal["function"] = "function"
+    id: Optional[str] = None
+    type: Optional[Literal["function"]] = None
     index: int
     function: Optional[DeltaFunctionCall] = None
 

vllm/entrypoints/openai/serving_chat.py

@@ -16,7 +16,8 @@
 from vllm.config import ModelConfig
 from vllm.engine.protocol import EngineClient
 from vllm.entrypoints.chat_utils import (ChatTemplateContentFormatOption,
-                                         ConversationMessage)
+                                         ConversationMessage,
+                                         random_tool_call_id)
 from vllm.entrypoints.logger import RequestLogger
 from vllm.entrypoints.openai.protocol import (
     ChatCompletionLogProb, ChatCompletionLogProbs,
@@ -363,9 +364,10 @@ def extract_tool_call_required_streaming(
 
                     function_name_returned = True
                     delta_message = DeltaMessage(tool_calls=[
-                        DeltaToolCall(function=DeltaFunctionCall(
-                            name=current_tool_call["name"],
-                            arguments=arguments),
+                        DeltaToolCall(id=random_tool_call_id(),
+                                      function=DeltaFunctionCall(
+                                          name=current_tool_call["name"],
+                                          arguments=arguments),
                                       index=len(obj) - 1,
                                       type="function")
                     ])
@@ -382,8 +384,7 @@ def extract_tool_call_required_streaming(
                                     # instead of name every time
                                     name=None,
                                     arguments=delta_text),
-                                index=len(obj) - 1,
-                                type="function")
+                                index=len(obj) - 1)
                         ])
                     else:
                         delta_message = None
@@ -422,7 +423,7 @@ async def chat_completion_stream_generator(
             and self._should_stream_with_auto_tool_parsing(request))
 
         all_previous_token_ids: Optional[list[list[int]]]
-        function_name_returned: Optional[list[bool]] = None
+        function_name_returned = [False] * num_choices
 
         # Only one of these will be used, thus previous_texts and
         # all_previous_token_ids will not be used twice in the same iteration.
@@ -435,7 +436,6 @@ async def chat_completion_stream_generator(
             reasoning_end_arr = [False] * num_choices
         elif request.tool_choice == "required":
             previous_texts = [""] * num_choices
-            function_name_returned = [False] * num_choices
             all_previous_token_ids = None
         else:
             previous_texts, all_previous_token_ids = None, None
@@ -623,16 +623,27 @@ async def chat_completion_stream_generator(
                                 delta_text = previous_text + delta_text
                                 current_text = ""
 
+                            if function_name_returned[i]:
+                                delta_tool_call = DeltaToolCall(
+                                    function=DeltaFunctionCall(
+                                        arguments=delta_text),
+                                    index=i)
+                            else:
+                                delta_tool_call = DeltaToolCall(
+                                    id=random_tool_call_id(),
+                                    type="function",
+                                    function=DeltaFunctionCall(
+                                        name=tool_choice_function_name,
+                                        arguments=delta_text),
+                                    index=i)
+                                function_name_returned[i] = True
+
                             delta_message = DeltaMessage(tool_calls=[
-                                DeltaToolCall(function=DeltaFunctionCall(
-                                    name=tool_choice_function_name,
-                                    arguments=delta_text),
-                                              index=i)
+                                delta_tool_call,
                             ])
 
                     elif request.tool_choice == "required":
                         assert previous_texts is not None
-                        assert function_name_returned is not None
                         previous_text = previous_texts[i]
                         current_text = previous_text + delta_text
                         fn_name_returned = function_name_returned[i]
@@ -835,7 +846,7 @@ async def chat_completion_stream_generator(
                             total_tokens=num_prompt_tokens + completion_tokens,
                         )
 
-                    data = chunk.model_dump_json(exclude_unset=True)
+                    data = chunk.model_dump_json(exclude_none=True)
                     yield f"data: {data}\n\n"
 
             # once the final token is handled, if stream_options.include_usage

vllm/entrypoints/openai/tool_parsers/granite_20b_fc_tool_parser.py

@@ -9,6 +9,7 @@
 import partial_json_parser
 from partial_json_parser.core.options import Allow
 
+from vllm.entrypoints.chat_utils import random_tool_call_id
 from vllm.entrypoints.openai.protocol import (ChatCompletionRequest,
                                               DeltaFunctionCall, DeltaMessage,
                                               DeltaToolCall,
@@ -22,7 +23,6 @@
                                                         partial_json_loads)
 from vllm.logger import init_logger
 from vllm.transformers_utils.tokenizer import AnyTokenizer
-from vllm.utils import random_uuid
 
 logger = init_logger(__name__)
 
@@ -200,7 +200,7 @@ def extract_tool_calls_streaming(
                     delta = DeltaMessage(tool_calls=[
                         DeltaToolCall(index=self.current_tool_id,
                                       type="function",
-                                      id=f"chatcmpl-tool-{random_uuid()}",
+                                      id=random_tool_call_id(),
                                       function=DeltaFunctionCall(
                                           name=function_name).model_dump(
                                               exclude_none=True))

vllm/entrypoints/openai/tool_parsers/granite_tool_parser.py

@@ -7,6 +7,7 @@
 import partial_json_parser
 from partial_json_parser.core.options import Allow
 
+from vllm.entrypoints.chat_utils import random_tool_call_id
 from vllm.entrypoints.openai.protocol import (ChatCompletionRequest,
                                               DeltaFunctionCall, DeltaMessage,
                                               DeltaToolCall,
@@ -20,7 +21,6 @@
                                                         partial_json_loads)
 from vllm.logger import init_logger
 from vllm.transformers_utils.tokenizer import AnyTokenizer
-from vllm.utils import random_uuid
 
 logger = init_logger(__name__)
 
@@ -182,7 +182,7 @@ def extract_tool_calls_streaming(
                     delta = DeltaMessage(tool_calls=[
                         DeltaToolCall(index=self.current_tool_id,
                                       type="function",
-                                      id=f"chatcmpl-tool-{random_uuid()}",
+                                      id=random_tool_call_id(),
                                       function=DeltaFunctionCall(
                                           name=function_name).model_dump(
                                               exclude_none=True))

vllm/entrypoints/openai/tool_parsers/hermes_tool_parser.py

@@ -8,6 +8,7 @@
 import partial_json_parser
 from partial_json_parser.core.options import Allow
 
+from vllm.entrypoints.chat_utils import random_tool_call_id
 from vllm.entrypoints.openai.protocol import (ChatCompletionRequest,
                                               DeltaFunctionCall, DeltaMessage,
                                               DeltaToolCall,
@@ -17,7 +18,6 @@
     ToolParser, ToolParserManager)
 from vllm.logger import init_logger
 from vllm.transformers_utils.tokenizer import AnyTokenizer, MistralTokenizer
-from vllm.utils import random_uuid
 
 logger = init_logger(__name__)
 
@@ -259,7 +259,7 @@ def extract_tool_calls_streaming(
                     return DeltaMessage(tool_calls=[
                         DeltaToolCall(index=self.current_tool_id,
                                       type="function",
-                                      id=f"chatcmpl-tool-{random_uuid()}",
+                                      id=random_tool_call_id(),
                                       function=DeltaFunctionCall(
                                           name=function_name).model_dump(
                                               exclude_none=True))

vllm/entrypoints/openai/tool_parsers/internlm2_tool_parser.py

@@ -7,6 +7,7 @@
 import partial_json_parser
 from partial_json_parser.core.options import Allow
 
+from vllm.entrypoints.chat_utils import random_tool_call_id
 from vllm.entrypoints.openai.protocol import (ChatCompletionRequest,
                                               DeltaFunctionCall, DeltaMessage,
                                               DeltaToolCall,
@@ -18,7 +19,6 @@
     extract_intermediate_diff)
 from vllm.logger import init_logger
 from vllm.transformers_utils.tokenizer import AnyTokenizer
-from vllm.utils import random_uuid
 
 logger = init_logger(__name__)
 
@@ -106,7 +106,7 @@ def extract_tool_calls_streaming(
                     delta = DeltaMessage(tool_calls=[
                         DeltaToolCall(index=self.current_tool_id,
                                       type="function",
-                                      id=f"chatcmpl-tool-{random_uuid()}",
+                                      id=random_tool_call_id(),
                                       function=DeltaFunctionCall(
                                           name=function_name).model_dump(
                                               exclude_none=True))

vllm/entrypoints/openai/tool_parsers/jamba_tool_parser.py

@@ -8,6 +8,7 @@
 import partial_json_parser
 from partial_json_parser.core.options import Allow
 
+from vllm.entrypoints.chat_utils import random_tool_call_id
 from vllm.entrypoints.openai.protocol import (ChatCompletionRequest,
                                               DeltaFunctionCall, DeltaMessage,
                                               DeltaToolCall,
@@ -19,7 +20,6 @@
 from vllm.logger import init_logger
 from vllm.transformers_utils.tokenizer import AnyTokenizer
 from vllm.transformers_utils.tokenizers import MistralTokenizer
-from vllm.utils import random_uuid
 
 logger = init_logger(__name__)
 
@@ -220,7 +220,7 @@ def extract_tool_calls_streaming(
                     delta = DeltaMessage(tool_calls=[
                         DeltaToolCall(index=self.current_tool_id,
                                       type="function",
-                                      id=f"chatcmpl-tool-{random_uuid()}",
+                                      id=random_tool_call_id(),
                                       function=DeltaFunctionCall(
                                           name=function_name).model_dump(
                                               exclude_none=True))

vllm/entrypoints/openai/tool_parsers/llama_tool_parser.py

@@ -10,6 +10,7 @@
 from partial_json_parser.core.options import Allow
 from transformers import PreTrainedTokenizerBase
 
+from vllm.entrypoints.chat_utils import random_tool_call_id
 from vllm.entrypoints.openai.protocol import (ChatCompletionRequest,
                                               DeltaFunctionCall, DeltaMessage,
                                               DeltaToolCall,
@@ -21,7 +22,6 @@
                                                         is_complete_json,
                                                         partial_json_loads)
 from vllm.logger import init_logger
-from vllm.utils import random_uuid
 
 logger = init_logger(__name__)
 
@@ -208,7 +208,7 @@ def extract_tool_calls_streaming(
                     delta = DeltaMessage(tool_calls=[
                         DeltaToolCall(index=self.current_tool_id,
                                       type="function",
-                                      id=f"chatcmpl-tool-{random_uuid()}",
+                                      id=random_tool_call_id(),
                                       function=DeltaFunctionCall(
                                           name=function_name).model_dump(
                                               exclude_none=True))

vllm/entrypoints/openai/tool_parsers/phi4mini_tool_parser.py

@@ -7,14 +7,14 @@
 
 from transformers import PreTrainedTokenizerBase
 
+from vllm.entrypoints.chat_utils import random_tool_call_id
 from vllm.entrypoints.openai.protocol import (ChatCompletionRequest,
                                               DeltaMessage,
                                               ExtractedToolCallInformation,
                                               FunctionCall, ToolCall)
 from vllm.entrypoints.openai.tool_parsers.abstract_tool_parser import (
     ToolParser, ToolParserManager)
 from vllm.logger import init_logger
-from vllm.utils import random_uuid
 
 logger = init_logger(__name__)
 
@@ -73,7 +73,7 @@ def extract_tool_calls(
 
             tool_calls: list[ToolCall] = [
                 ToolCall(
-                    id=f"chatcmpl-tool-{random_uuid()}",
+                    id=random_tool_call_id(),
                     type="function",
                     function=FunctionCall(
                         name=raw_function_call["name"],

vllm/entrypoints/openai/tool_parsers/pythonic_tool_parser.py

@@ -280,6 +280,7 @@ def _compute_tool_delta(previously_sent_args: str, new_call: ToolCall,
         new_call_args = new_call_args[:-len(withheld_suffix)]
     if not previously_sent_args:
         return DeltaToolCall(id=new_call.id,
+                             type="function",
                              index=index,
                              function=DeltaFunctionCall(
                                  name=new_call.function.name,
@@ -288,5 +289,5 @@ def _compute_tool_delta(previously_sent_args: str, new_call: ToolCall,
 
     arg_diff = new_call_args[len(previously_sent_args):]
     return DeltaToolCall(
-        id="", index=index, function=DeltaFunctionCall(
+        id=None, index=index, function=DeltaFunctionCall(
             arguments=arg_diff)) if arg_diff else None