@ToolParserManager.register_module("llama3_json")
@ToolParserManager.register_module("llama4_json")
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]
# Updated regex to match multiple JSONs separated by semicolons
# This pattern is more robust and can handle nested JSON objects
self.tool_call_regex = re.compile(
r'{[^{}]*(?:{[^{}]*}[^{}]*)*}(?:\s*;\s*{[^{}]*(?:{[^{}]*}[^{}]*)*})*',
re.DOTALL)
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)
# Find JSON object(s) in the text using regex
match = self.tool_call_regex.search(model_output)
if not match:
return ExtractedToolCallInformation(tools_called=False,
tool_calls=[],
content=model_output)
try:
json_str = match.group(0)
# Split by semicolon and strip whitespace
json_objects = [obj.strip() for obj in json_str.split(';')]
tool_calls: list[ToolCall] = []
for json_obj in json_objects:
if not json_obj: # Skip empty strings
continue
obj = json.loads(json_obj)
tool_calls.append(
ToolCall(
type="function",
function=FunctionCall(
name=obj["name"],
# function call args are JSON but as a string
arguments=json.dumps(
obj["arguments"]
if "arguments" in obj else obj["parameters"],
ensure_ascii=False))))
return ExtractedToolCallInformation(tools_called=True,
tool_calls=tool_calls,
content=None)
except Exception:
logger.exception("Error in extracting tool call from response.")
# return information to just treat the tool call as regular JSON
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,
) -> Union[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=random_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