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324
vllm/tool_parsers/llama_tool_parser.py
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324
vllm/tool_parsers/llama_tool_parser.py
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# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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import json
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from collections.abc import Sequence
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import partial_json_parser
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import regex as re
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from partial_json_parser.core.options import Allow
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from transformers import PreTrainedTokenizerBase
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import vllm.envs as envs
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from vllm.entrypoints.chat_utils import make_tool_call_id
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from vllm.entrypoints.openai.protocol import (
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ChatCompletionRequest,
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DeltaFunctionCall,
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DeltaMessage,
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DeltaToolCall,
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ExtractedToolCallInformation,
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FunctionCall,
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ToolCall,
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)
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from vllm.logger import init_logger
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from vllm.tool_parsers.abstract_tool_parser import (
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ToolParser,
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)
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from vllm.tool_parsers.utils import (
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find_common_prefix,
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is_complete_json,
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partial_json_loads,
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)
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logger = init_logger(__name__)
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class Llama3JsonToolParser(ToolParser):
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"""
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Tool call parser for Llama 3.x and 4 models intended for use with the
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examples/tool_chat_template_llama.jinja template.
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Used when --enable-auto-tool-choice --tool-call-parser llama3_json or
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llama4_json are set.
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"""
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def __init__(self, tokenizer: PreTrainedTokenizerBase):
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super().__init__(tokenizer)
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# initialize properties used for state when parsing tool calls in
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# streaming mode
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self.prev_tool_call_arr: list[dict] = []
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self.current_tool_id: int = -1
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self.current_tool_name_sent: bool = False
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self.streamed_args_for_tool: list[
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str
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] = [] # map what has been streamed for each tool so far to a list
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self.bot_token = "<|python_tag|>"
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self.bot_token_id = tokenizer.encode(self.bot_token, add_special_tokens=False)[
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0
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]
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# Simple regex to find opening braces - we'll use JSON decoder for parsing
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# This handles arbitrary nesting depth correctly
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self.tool_call_start_regex = re.compile(r"\{")
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self.json_decoder = json.JSONDecoder()
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def extract_tool_calls(
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self, model_output: str, request: ChatCompletionRequest
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) -> ExtractedToolCallInformation:
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"""
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Extract the tool calls from a complete model response.
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Only extracts JSON content and ignores any surrounding plain text.
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Supports both single JSON and multiple JSONs separated by semicolons.
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"""
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# Quick check before running regex
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if not (self.bot_token in model_output or "{" in model_output):
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return ExtractedToolCallInformation(
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tools_called=False, tool_calls=[], content=model_output
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)
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# Keep track of the end index of the last parsed JSON object
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# so we don't parse inner brackets
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end_index = -1
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tool_calls: list[ToolCall] = []
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try:
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for match in self.tool_call_start_regex.finditer(
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model_output, timeout=envs.VLLM_TOOL_PARSE_REGEX_TIMEOUT_SECONDS
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):
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start_index = match.start()
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# Skip if this brace is inside a previously parsed JSON object
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if start_index <= end_index:
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continue
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try:
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obj, json_end_index = self.json_decoder.raw_decode(
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model_output[start_index:]
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)
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end_index = start_index + json_end_index
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# raise KeyError if missing
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name = obj["name"]
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arguments_or_params = (
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obj["arguments"] if "arguments" in obj else obj["parameters"]
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)
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tool_calls.append(
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ToolCall(
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type="function",
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function=FunctionCall(
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name=name,
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# function call args are JSON but as a string
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arguments=json.dumps(
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arguments_or_params, ensure_ascii=False
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),
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),
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)
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)
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except KeyError as e:
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# Missing required key
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missing_key = str(e).strip("'\"")
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logger.exception(
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"Couldn't extract tool call from JSON response. "
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"Required key '%s' not present. "
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"Returning output in content with empty tool calls.",
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missing_key,
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)
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return ExtractedToolCallInformation(
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tools_called=False, tool_calls=[], content=model_output
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)
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except Exception:
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# Any other error during parsing
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logger.exception(
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"Error in extracting tool call from response. "
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"Returning output in content with empty tool calls"
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)
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return ExtractedToolCallInformation(
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tools_called=False, tool_calls=[], content=model_output
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)
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except TimeoutError:
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logger.warning("Regex timeout occurred when matching tool call pattern.")
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logger.debug(
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"Regex timeout occurred when matching user input: %s", model_output
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)
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return ExtractedToolCallInformation(
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tools_called=False, tool_calls=[], content=model_output
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)
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# If we have valid tool calls, return them normally
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if tool_calls:
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return ExtractedToolCallInformation(
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tools_called=True, tool_calls=tool_calls, content=None
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)
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# No valid tool calls found
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return ExtractedToolCallInformation(
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tools_called=False, tool_calls=[], content=model_output
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)
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def extract_tool_calls_streaming(
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self,
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previous_text: str,
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current_text: str,
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delta_text: str,
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previous_token_ids: Sequence[int],
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current_token_ids: Sequence[int],
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delta_token_ids: Sequence[int],
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request: ChatCompletionRequest,
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) -> DeltaMessage | None:
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if not (
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current_text.startswith(self.bot_token) or current_text.startswith("{")
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):
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return DeltaMessage(content=delta_text)
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# bit mask flags for partial JSON parsing. If the name hasn't been
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# sent yet, don't allow sending
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# an incomplete string since OpenAI only ever (as far as I have
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# seen) allows sending the entire tool/ function name at once.
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flags = Allow.ALL if self.current_tool_name_sent else Allow.ALL & ~Allow.STR
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try:
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tool_call_arr = []
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is_complete = []
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try:
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# depending on the prompt format the Llama model may or may not
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# prefix the output with the <|python_tag|> token
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start_idx = (
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len(self.bot_token)
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if current_text.startswith(self.bot_token)
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else 0
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)
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while start_idx < len(current_text):
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(obj, end_idx) = partial_json_loads(current_text[start_idx:], flags)
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is_complete.append(
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is_complete_json(current_text[start_idx : start_idx + end_idx])
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)
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start_idx += end_idx + len("; ")
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# depending on the prompt Llama can use
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# either arguments or parameters
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if "parameters" in obj:
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assert "arguments" not in obj, (
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"model generated both parameters and arguments"
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)
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obj["arguments"] = obj["parameters"]
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tool_call_arr.append(obj)
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except partial_json_parser.core.exceptions.MalformedJSON:
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logger.debug("not enough tokens to parse into JSON yet")
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return None
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# select as the current tool call the one we're on the state at
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current_tool_call: dict = (
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tool_call_arr[self.current_tool_id] if len(tool_call_arr) > 0 else {}
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)
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# case -- if no tokens have been streamed for the tool, e.g.
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# only the array brackets, stream nothing
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if len(tool_call_arr) == 0:
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return None
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# case: we are starting a new tool in the array
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# -> array has > 0 length AND length has moved past cursor
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elif (
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len(tool_call_arr) > 0 and len(tool_call_arr) > self.current_tool_id + 1
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):
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# if we're moving on to a new call, first make sure we
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# haven't missed anything in the previous one that was
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# auto-generated due to JSON completions, but wasn't
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# streamed to the client yet.
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if self.current_tool_id >= 0:
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cur_arguments = current_tool_call.get("arguments")
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if cur_arguments:
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cur_args_json = json.dumps(cur_arguments, ensure_ascii=False)
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sent = len(self.streamed_args_for_tool[self.current_tool_id])
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argument_diff = cur_args_json[sent:]
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logger.debug("got arguments diff: %s", argument_diff)
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delta = DeltaMessage(
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tool_calls=[
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DeltaToolCall(
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index=self.current_tool_id,
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function=DeltaFunctionCall(
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arguments=argument_diff
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).model_dump(exclude_none=True),
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)
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]
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)
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self.streamed_args_for_tool[self.current_tool_id] += (
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argument_diff
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)
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else:
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delta = None
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else:
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delta = None
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# re-set stuff pertaining to progress in the current tool
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self.current_tool_id = len(tool_call_arr) - 1
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self.current_tool_name_sent = False
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self.streamed_args_for_tool.append("")
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logger.debug("starting on new tool %d", self.current_tool_id)
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return delta
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# if the current tool name hasn't been sent, send if available
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# - otherwise send nothing
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elif not self.current_tool_name_sent:
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function_name = current_tool_call.get("name")
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if function_name:
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delta = DeltaMessage(
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tool_calls=[
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DeltaToolCall(
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index=self.current_tool_id,
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type="function",
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id=make_tool_call_id(),
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function=DeltaFunctionCall(
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name=function_name
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).model_dump(exclude_none=True),
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)
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]
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)
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self.current_tool_name_sent = True
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else:
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delta = None
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# now we know we're on the same tool call and we're streaming
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# arguments
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else:
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cur_arguments = current_tool_call.get("arguments")
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delta = None
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if cur_arguments:
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sent = len(self.streamed_args_for_tool[self.current_tool_id])
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cur_args_json = json.dumps(cur_arguments, ensure_ascii=False)
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prev_arguments = self.prev_tool_call_arr[self.current_tool_id].get(
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"arguments"
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)
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argument_diff = None
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if is_complete[self.current_tool_id]:
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argument_diff = cur_args_json[sent:]
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elif prev_arguments:
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prev_args_json = json.dumps(prev_arguments, ensure_ascii=False)
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if cur_args_json != prev_args_json:
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prefix = find_common_prefix(prev_args_json, cur_args_json)
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argument_diff = prefix[sent:]
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if argument_diff is not None:
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delta = DeltaMessage(
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tool_calls=[
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DeltaToolCall(
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index=self.current_tool_id,
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function=DeltaFunctionCall(
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arguments=argument_diff
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).model_dump(exclude_none=True),
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)
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]
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)
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self.streamed_args_for_tool[self.current_tool_id] += (
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argument_diff
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)
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self.prev_tool_call_arr = tool_call_arr
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return delta
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except Exception:
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logger.exception("Error trying to handle streaming tool call.")
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logger.debug(
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"Skipping chunk as a result of tool streaming extraction error"
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)
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return None
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