class Llama3JsonToolParser(ToolParser):
"""
Tool call parser for Llama 3.x and 4 models intended for use with the
examples/tool_chat_template_llama.jinja template.
Used when --enable-auto-tool-choice --tool-call-parser llama3_json or
llama4_json are set.
"""
def __init__(self, tokenizer: PreTrainedTokenizerBase):
super().__init__(tokenizer)
# initialize properties used for state when parsing tool calls in
# streaming mode
self.prev_tool_call_arr: list[dict] = []
self.current_tool_id: int = -1
self.current_tool_name_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 = "<|python_tag|>"
self.bot_token_id = tokenizer.encode(self.bot_token, add_special_tokens=False)[
0
]
# Simple regex to find opening braces - we'll use JSON decoder for parsing
# This handles arbitrary nesting depth correctly
self.tool_call_start_regex = re.compile(r"\{")
self.json_decoder = json.JSONDecoder()
def extract_tool_calls(
self, model_output: str, request: ChatCompletionRequest
) -> ExtractedToolCallInformation:
"""
Extract the tool calls from a complete model response.
Only extracts JSON content and ignores any surrounding plain text.
Supports both single JSON and multiple JSONs separated by semicolons.
"""
# Quick check before running regex
if not (self.bot_token in model_output or "{" in model_output):
return ExtractedToolCallInformation(
tools_called=False, tool_calls=[], content=model_output
)
# Keep track of the end index of the last parsed JSON object
# so we don't parse inner brackets
end_index = -1
tool_calls: list[ToolCall] = []
try:
for match in self.tool_call_start_regex.finditer(
model_output, timeout=envs.VLLM_TOOL_PARSE_REGEX_TIMEOUT_SECONDS
):
start_index = match.start()
# Skip if this brace is inside a previously parsed JSON object
if start_index <= end_index:
continue
try:
obj, json_end_index = self.json_decoder.raw_decode(
model_output[start_index:]
)
end_index = start_index + json_end_index
# raise KeyError if missing
name = obj["name"]
arguments_or_params = (
obj["arguments"] if "arguments" in obj else obj["parameters"]
)
tool_calls.append(
ToolCall(
type="function",
function=FunctionCall(
name=name,
# function call args are JSON but as a string
arguments=json.dumps(
arguments_or_params, ensure_ascii=False
),
),
)
)
except KeyError as e:
# Missing required key
missing_key = str(e).strip("'\"")
logger.exception(
"Couldn't extract tool call from JSON response. "
"Required key '%s' not present. "
"Returning output in content with empty tool calls.",
missing_key,
)
return ExtractedToolCallInformation(
tools_called=False, tool_calls=[], content=model_output
)
except Exception:
# Any other error during parsing
logger.exception(
"Error in extracting tool call from response. "
"Returning output in content with empty tool calls"
)
return ExtractedToolCallInformation(
tools_called=False, tool_calls=[], content=model_output
)
except TimeoutError:
logger.warning("Regex timeout occurred when matching tool call pattern.")
logger.debug(
"Regex timeout occurred when matching user input: %s", model_output
)
return ExtractedToolCallInformation(
tools_called=False, tool_calls=[], content=model_output
)
# If we have valid tool calls, return them normally
if tool_calls:
return ExtractedToolCallInformation(
tools_called=True, tool_calls=tool_calls, content=None
)
# No valid tool calls found
return ExtractedToolCallInformation(
tools_called=False, tool_calls=[], content=model_output
)
def extract_tool_calls_streaming(
self,
previous_text: str,
current_text: str,
delta_text: str,
previous_token_ids: Sequence[int],
current_token_ids: Sequence[int],
delta_token_ids: Sequence[int],
request: ChatCompletionRequest,
) -> DeltaMessage | None:
if not (
current_text.startswith(self.bot_token) or current_text.startswith("{")
):
return DeltaMessage(content=delta_text)
# bit mask flags for partial JSON parsing. If the name hasn't been
# sent yet, don't allow sending
# an incomplete string since OpenAI only ever (as far as I have
# seen) allows sending the entire tool/ function name at once.
flags = Allow.ALL if self.current_tool_name_sent else Allow.ALL & ~Allow.STR
try:
tool_call_arr = []
is_complete = []
try:
# depending on the prompt format the Llama model may or may not
# prefix the output with the <|python_tag|> token
start_idx = (
len(self.bot_token)
if current_text.startswith(self.bot_token)
else 0
)
while start_idx < len(current_text):
(obj, end_idx) = partial_json_loads(current_text[start_idx:], flags)
is_complete.append(
is_complete_json(current_text[start_idx : start_idx + end_idx])
)
start_idx += end_idx + len("; ")
# depending on the prompt Llama can use
# either arguments or parameters
if "parameters" in obj:
assert "arguments" not in obj, (
"model generated both parameters and arguments"
)
obj["arguments"] = obj["parameters"]
tool_call_arr.append(obj)
except partial_json_parser.core.exceptions.MalformedJSON:
logger.debug("not enough tokens to parse into JSON yet")
return None
# select as the current tool call the one we're on the state at
current_tool_call: dict = (
tool_call_arr[self.current_tool_id] if len(tool_call_arr) > 0 else {}
)
# case -- if no tokens have been streamed for the tool, e.g.
# only the array brackets, stream nothing
if len(tool_call_arr) == 0:
return None
# case: we are starting a new tool in the array
# -> array has > 0 length AND length has moved past cursor
elif (
len(tool_call_arr) > 0 and len(tool_call_arr) > self.current_tool_id + 1
):
# if we're moving on to a new call, first make sure we
# haven't missed anything in the previous one that was
# auto-generated due to JSON completions, but wasn't
# streamed to the client yet.
if self.current_tool_id >= 0:
cur_arguments = current_tool_call.get("arguments")
if cur_arguments:
cur_args_json = json.dumps(cur_arguments, ensure_ascii=False)
sent = len(self.streamed_args_for_tool[self.current_tool_id])
argument_diff = cur_args_json[sent:]
logger.debug("got arguments diff: %s", argument_diff)
delta = DeltaMessage(
tool_calls=[
DeltaToolCall(
index=self.current_tool_id,
function=DeltaFunctionCall(
arguments=argument_diff
).model_dump(exclude_none=True),
)
]
)
self.streamed_args_for_tool[self.current_tool_id] += (
argument_diff
)
else:
delta = None
else:
delta = None
# 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.streamed_args_for_tool.append("")
logger.debug("starting on new tool %d", self.current_tool_id)
return delta
# if the current tool name hasn't been sent, send if available
# - otherwise send nothing
elif 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=make_tool_call_id(),
function=DeltaFunctionCall(
name=function_name
).model_dump(exclude_none=True),
)
]
)
self.current_tool_name_sent = True
else:
delta = None
# now we know we're on the same tool call and we're streaming
# arguments
else:
cur_arguments = current_tool_call.get("arguments")
delta = None
if cur_arguments:
sent = len(self.streamed_args_for_tool[self.current_tool_id])
cur_args_json = json.dumps(cur_arguments, ensure_ascii=False)
prev_arguments = self.prev_tool_call_arr[self.current_tool_id].get(
"arguments"
)
argument_diff = None
if is_complete[self.current_tool_id]:
argument_diff = cur_args_json[sent:]
elif prev_arguments:
prev_args_json = json.dumps(prev_arguments, ensure_ascii=False)
if cur_args_json != prev_args_json:
prefix = find_common_prefix(prev_args_json, cur_args_json)
argument_diff = prefix[sent:]
if argument_diff is not None:
delta = DeltaMessage(
tool_calls=[
DeltaToolCall(
index=self.current_tool_id,
function=DeltaFunctionCall(
arguments=argument_diff
).model_dump(exclude_none=True),
)
]
)
self.streamed_args_for_tool[self.current_tool_id] += (
argument_diff
)
self.prev_tool_call_arr = tool_call_arr
return delta
except Exception:
logger.exception("Error trying to handle streaming tool call.")
logger.debug(
"Skipping chunk as a result of tool streaming extraction error"
)
return None