[Bugfix] Streamed tool calls now more strictly follow OpenAI's format; ensures...

[Bugfix] Streamed tool calls now more strictly follow OpenAI's format; ensures Vercel AI SDK compatibility (#8272)

tests/tool_use/utils.py

@@ -19,7 +19,7 @@ ARGS: List[str] = ["--enable-auto-tool-choice", "--max-model-len", "8096"]
 CONFIGS: Dict[str, ServerConfig] = {
     "hermes": {
         "model":
-        "NousResearch/Hermes-2-Pro-Llama-3-8B",
+        "NousResearch/Hermes-3-Llama-3.1-8B",
         "arguments": [
             "--tool-call-parser", "hermes", "--chat-template",
             str(VLLM_PATH / "examples/tool_chat_template_hermes.jinja")

vllm/entrypoints/openai/protocol.py

@@ -713,13 +713,6 @@ class DeltaToolCall(OpenAIBaseModel):
     function: Optional[DeltaFunctionCall] = None
 
 
-# the initial delta that gets sent once a new tool call is started;
-class InitialDeltaToolCall(DeltaToolCall):
-    id: str = Field(default_factory=lambda: f"chatcmpl-tool-{random_uuid()}")
-    type: Literal["function"] = "function"
-    index: int
-
-
 class ExtractedToolCallInformation(BaseModel):
     # indicate if tools were called
     tools_called: bool

vllm/entrypoints/openai/serving_chat.py

@@ -271,9 +271,13 @@ class OpenAIServingChat(OpenAIServing):
                     # NOTE num_choices defaults to 1 so this usually executes
                     # once per request
                     for i in range(num_choices):
+
                         choice_data = ChatCompletionResponseStreamChoice(
                             index=i,
-                            delta=DeltaMessage(role=role),
+                            delta=DeltaMessage(
+                                role=role,
+                                content="",
+                            ),
                             logprobs=None,
                             finish_reason=None)
                         chunk = ChatCompletionStreamResponse(

vllm/entrypoints/openai/tool_parsers/abstract_tool_parser.py

@@ -20,7 +20,6 @@ class ToolParser:
         # the index of the tool call that is currently being parsed
         self.current_tool_id: int = -1
         self.current_tool_name_sent: bool = False
-        self.current_tool_initial_sent: bool = False
         self.streamed_args_for_tool: List[str] = []
 
         self.model_tokenizer = tokenizer

vllm/entrypoints/openai/tool_parsers/hermes_tool_parser.py

@@ -8,14 +8,14 @@ from partial_json_parser.core.options import Allow
 from vllm.entrypoints.openai.protocol import (DeltaFunctionCall, DeltaMessage,
                                               DeltaToolCall,
                                               ExtractedToolCallInformation,
-                                              FunctionCall,
-                                              InitialDeltaToolCall, ToolCall)
+                                              FunctionCall, ToolCall)
 from vllm.entrypoints.openai.tool_parsers.abstract_tool_parser import (
     ToolParser)
 from vllm.entrypoints.openai.tool_parsers.utils import (
     extract_intermediate_diff)
 from vllm.logger import init_logger
 from vllm.transformers_utils.tokenizer import AnyTokenizer, MistralTokenizer
+from vllm.utils import random_uuid
 
 logger = init_logger(__name__)
 
@@ -34,7 +34,6 @@ class Hermes2ProToolParser(ToolParser):
         self.prev_tool_call_arr: List[Dict] = []
         self.current_tool_id: int = -1
         self.current_tool_name_sent = False
-        self.current_tool_initial_sent: bool = False
         self.streamed_args_for_tool: List[str] = [
         ]  # map what has been streamed for each tool so far to a list
 
@@ -168,7 +167,6 @@ class Hermes2ProToolParser(ToolParser):
                 # set cursors and state appropriately
                 self.current_tool_id += 1
                 self.current_tool_name_sent = False
-                self.current_tool_initial_sent = False
                 self.streamed_args_for_tool.append("")
                 logger.debug("Starting on a new tool %s", self.current_tool_id)
 
@@ -218,24 +216,16 @@ class Hermes2ProToolParser(ToolParser):
                 logger.debug('not enough tokens to parse into JSON yet')
                 return None
 
-            # case - we haven't sent the initial delta with the tool call ID
-            #   (it will be sent)
-            if not self.current_tool_initial_sent:
-                self.current_tool_initial_sent = True
-                return DeltaMessage(tool_calls=[
-                    InitialDeltaToolCall(
-                        index=self.current_tool_id).model_dump(
-                            exclude_none=True)
-                ])
-
             # case - we haven't sent the tool name yet. If it's available, send
             #   it. otherwise, wait until it's available.
-            elif not self.current_tool_name_sent:
+            if not self.current_tool_name_sent:
                 function_name: Union[str, None] = current_tool_call.get("name")
                 if function_name:
                     self.current_tool_name_sent = True
                     return DeltaMessage(tool_calls=[
                         DeltaToolCall(index=self.current_tool_id,
+                                      type="function",
+                                      id=f"chatcmpl-tool-{random_uuid()}",
                                       function=DeltaFunctionCall(
                                           name=function_name).model_dump(
                                               exclude_none=True))

vllm/entrypoints/openai/tool_parsers/mistral_tool_parser.py

@@ -8,14 +8,14 @@ from partial_json_parser.core.options import Allow
 from vllm.entrypoints.openai.protocol import (DeltaFunctionCall, DeltaMessage,
                                               DeltaToolCall,
                                               ExtractedToolCallInformation,
-                                              FunctionCall,
-                                              InitialDeltaToolCall, ToolCall)
+                                              FunctionCall, ToolCall)
 from vllm.entrypoints.openai.tool_parsers.abstract_tool_parser import (
     ToolParser)
 from vllm.entrypoints.openai.tool_parsers.utils import (
     extract_intermediate_diff)
 from vllm.logger import init_logger
 from vllm.transformers_utils.tokenizer import AnyTokenizer, MistralTokenizer
+from vllm.utils import random_uuid
 
 logger = init_logger(__name__)
 
@@ -25,7 +25,7 @@ class MistralToolParser(ToolParser):
     Tool call parser for Mistral 7B Instruct v0.3, intended for use with the
     examples/tool_chat_template_mistral.jinja template.
 
-    Used when --enable-auto-tool-choice --tool-call-parser gmistral are all set
+    Used when --enable-auto-tool-choice --tool-call-parser mistral are all set
     """
 
     def __init__(self, tokenizer: AnyTokenizer):
@@ -42,7 +42,6 @@ class MistralToolParser(ToolParser):
         self.prev_tool_call_arr: List[Dict] = []
         self.current_tool_id: int = -1
         self.current_tool_name_sent: bool = False
-        self.current_tool_initial_sent: bool = False
         self.streamed_args_for_tool: List[str] = [
         ]  # map what has been streamed for each tool so far to a list
         self.bot_token = "[TOOL_CALLS]"
@@ -91,7 +90,6 @@ class MistralToolParser(ToolParser):
 
         except Exception as e:
             logger.error("Error in extracting tool call from response: %s", e)
-            print("ERROR", e)
             # return information to just treat the tool call as regular JSON
             return ExtractedToolCallInformation(tools_called=False,
                                                 tool_calls=[],
@@ -109,7 +107,7 @@ class MistralToolParser(ToolParser):
 
         # if the tool call token is not in the tokens generated so far, append
         # output to contents since it's not a tool
-        if self.bot_token_id not in current_token_ids:
+        if self.bot_token not in current_text:
             return DeltaMessage(content=delta_text)
 
         # if the tool call token ID IS in the tokens generated so far, that
@@ -134,7 +132,7 @@ class MistralToolParser(ToolParser):
             # replace BOT token with empty string, and convert single quotes
             # to double to allow parsing as JSON since mistral uses single
             # quotes instead of double for tool calls
-            parsable_arr = current_text.split(self.bot_token)[1]
+            parsable_arr = current_text.split(self.bot_token)[-1]
 
             # tool calls are generated in an array, so do partial JSON
             # parsing on the entire array
@@ -186,31 +184,22 @@ class MistralToolParser(ToolParser):
                 # re-set stuff pertaining to progress in the current tool
                 self.current_tool_id = len(tool_call_arr) - 1
                 self.current_tool_name_sent = False
-                self.current_tool_initial_sent = False
                 self.streamed_args_for_tool.append("")
                 logger.debug("starting on new tool %d", self.current_tool_id)
                 return delta
 
             # case: update an existing tool - this is handled below
 
-            # if the current tool initial data incl. the id, type=function
-            # and idx not sent, send that
-            if not self.current_tool_initial_sent:
-                self.current_tool_initial_sent = True
-                delta = DeltaMessage(tool_calls=[
-                    InitialDeltaToolCall(
-                        index=self.current_tool_id).model_dump(
-                            exclude_none=True)
-                ])
-
             # if the current tool name hasn't been sent, send if available
             # - otherwise send nothing
-            elif not self.current_tool_name_sent:
+            if not self.current_tool_name_sent:
                 function_name = current_tool_call.get("name")
                 if function_name:
 
                     delta = DeltaMessage(tool_calls=[
                         DeltaToolCall(index=self.current_tool_id,
+                                      type="function",
+                                      id=f"chatcmpl-tool-{random_uuid()}",
                                       function=DeltaFunctionCall(
                                           name=function_name).model_dump(
                                               exclude_none=True))