hermes_tool_parser.py
OR 08-13 22:13:17 [hermes_tool_parser.py:370] Error trying to handle streaming tool call. ERROR 08-13 22:13:17 [hermes_tool_parser.py:370] Traceback (most recent call last): ERROR 08-13 22:13:17 [hermes_tool_parser.py:370] File "/home/hzw/miniconda3/envs/vllm_env/lib/python3.11/site-packages/vllm/entrypoints/openai/tool_parsers/hermes_tool_parser.py", line 241, in extract_tool_calls_streaming ERROR 08-13 22:13:17 [hermes_tool_parser.py:370] current_tool_call = partial_json_parser.loads( ERROR 08-13 22:13:17 [hermes_tool_parser.py:370] ^^^^^^^^^^^^^^^^^^^^^^^^^^ ERROR 08-13 22:13:17 [hermes_tool_parser.py:370] File "/home/hzw/miniconda3/envs/vllm_env/lib/python3.11/site-packages/partial_json_parser/core/api.py", line 15, in parse_json ERROR 08-13 22:13:17 [hermes_tool_parser.py:370] return parser(ensure_json(json_string, allow_partial, use_fast_fix)) ERROR 08-13 22:13:17 [hermes_tool_parser.py:370]
Based on the error logs, the issue occurs in the extract_tool_calls_streaming method when trying to parse partial JSON using the partial_json_parser library. The error suggests that the parser is failing when trying to handle incomplete JSON strings during streaming.
Here's a repaired version of the method with better error handling:
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import json
from collections.abc import Sequence
from typing import Union
import partial_json_parser
import regex as re
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,
ExtractedToolCallInformation,
FunctionCall, ToolCall)
from vllm.entrypoints.openai.tool_parsers.abstract_tool_parser import (
ToolParser, ToolParserManager)
from vllm.logger import init_logger
from vllm.transformers_utils.tokenizer import AnyTokenizer, MistralTokenizer
logger = init_logger(__name__)
@ToolParserManager.register_module("hermes")
class Hermes2ProToolParser(ToolParser):
def __init__(self, tokenizer: AnyTokenizer):
super().__init__(tokenizer)
if isinstance(self.model_tokenizer, MistralTokenizer):
logger.error(
"Detected Mistral tokenizer when using a Hermes model")
self.model_tokenizer = self.model_tokenizer.tokenizer
self.current_tool_name_sent: bool = False
self.prev_tool_call_arr: list[dict] = []
self.current_tool_id: int = -1
self.streamed_args_for_tool: list[str] = [
] # map what has been streamed for each tool so far to a list
self.tool_call_start_token: str = "<tool_call>"
self.tool_call_end_token: str = "</tool_call>"
self.tool_call_regex = re.compile(
r"<tool_call>(.*?)</tool_call>|<tool_call>(.*)", re.DOTALL)
self.scratch_pad_regex = re.compile(
r"<scratch_pad>(.*?)</scratch_pad>", re.DOTALL)
if not self.model_tokenizer:
raise ValueError(
"The model tokenizer must be passed to the ToolParser "
"constructor during construction.")
self.tool_call_start_token_id = self.vocab.get(
self.tool_call_start_token)
self.tool_call_end_token_id = self.vocab.get(self.tool_call_end_token)
if (self.tool_call_start_token_id is None
or self.tool_call_end_token_id is None):
raise RuntimeError(
"Hermes 2 Pro Tool parser could not locate tool call start/end "
"tokens in the tokenizer!")
def extract_tool_calls(
self,
model_output: str,
request: ChatCompletionRequest,
) -> ExtractedToolCallInformation:
# sanity check; avoid unnecessary processing
if self.tool_call_start_token not in model_output:
return ExtractedToolCallInformation(tools_called=False,
tool_calls=[],
content=model_output)
else:
try:
# there are two possible captures - between tags, or between a
# tag and end-of-string so the result of
# findall is an array of tuples where one is a function call and
# the other is None
function_call_tuples = (
self.tool_call_regex.findall(model_output))
# load the JSON, and then use it to build the Function and
# Tool Call
raw_function_calls = [
json.loads(match[0] if match[0] else match[1])
for match in function_call_tuples
]
tool_calls = [
ToolCall(
type="function",
function=FunctionCall(
name=function_call["name"],
# function call args are JSON but as a string
arguments=json.dumps(function_call["arguments"],
ensure_ascii=False)))
for function_call in raw_function_calls
]
content = model_output[:model_output.
find(self.tool_call_start_token)]
return ExtractedToolCallInformation(
tools_called=True,
tool_calls=tool_calls,
content=content if content else None)
except Exception:
logger.exception(
"Error in extracting tool call from response.")
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]:
logger.debug("delta_text: %s", delta_text)
logger.debug("delta_token_ids: %s", delta_token_ids)
# check to see if we should be streaming a tool call
if self.tool_call_start_token_id not in current_token_ids:
logger.debug("No tool call tokens found!")
return DeltaMessage(content=delta_text)
try:
# figure out where we are in the parsing by counting tool call start & end tags
prev_tool_start_count = previous_token_ids.count(self.tool_call_start_token_id)
prev_tool_end_count = previous_token_ids.count(self.tool_call_end_token_id)
cur_tool_start_count = current_token_ids.count(self.tool_call_start_token_id)
cur_tool_end_count = current_token_ids.count(self.tool_call_end_token_id)
tool_call_portion = None
text_portion = None
# case: if we're generating text, OR rounding out a tool call
if (cur_tool_start_count == cur_tool_end_count
and prev_tool_end_count == cur_tool_end_count
and self.tool_call_end_token not in delta_text):
logger.debug("Generating text content! skipping tool parsing.")
return DeltaMessage(content=delta_text)
if self.tool_call_end_token in delta_text:
logger.debug("tool_call_end_token in delta_text")
try:
full_text = current_text + delta_text
tool_call_portion = full_text.split(self.tool_call_start_token)[-1].split(
self.tool_call_end_token)[0].rstrip()
delta_text = delta_text.split(self.tool_call_end_token)[0].rstrip()
text_portion = delta_text.split(self.tool_call_end_token)[-1].lstrip()
except IndexError:
logger.debug("Incomplete tool call tags, skipping")
return DeltaMessage(content=delta_text)
# flags for partial JSON parsing
flags = Allow.ALL if self.current_tool_name_sent else Allow.ALL & ~Allow.STR
# case -- we're starting a new tool call
if (cur_tool_start_count > cur_tool_end_count
and cur_tool_start_count > prev_tool_start_count):
if len(delta_token_ids) > 1:
try:
tool_call_portion = current_text.split(self.tool_call_start_token)[-1]
except IndexError:
tool_call_portion = None
else:
tool_call_portion = None
text_portion = None
# set cursors and state appropriately
self.current_tool_id += 1
self.current_tool_name_sent = False
self.streamed_args_for_tool.append("")
logger.debug("Starting on a new tool %s", self.current_tool_id)
# case -- we're updating an existing tool call
elif (cur_tool_start_count > cur_tool_end_count
and cur_tool_start_count == prev_tool_start_count):
try:
# get the portion of the text that's the tool call
tool_call_portion = current_text.split(self.tool_call_start_token)[-1]
except IndexError:
tool_call_portion = None
text_portion = None
# case -- the current tool call is being closed.
elif (cur_tool_start_count == cur_tool_end_count
and cur_tool_end_count >= prev_tool_end_count):
if (not self.prev_tool_call_arr or len(self.prev_tool_call_arr) == 0):
logger.debug("attempting to close tool call, but no tool call")
return None
diff = self.prev_tool_call_arr[self.current_tool_id].get("arguments")
if diff:
try:
diff = diff.encode('utf-8').decode('unicode_escape') if isinstance(diff, str) else diff
if ('"}' not in delta_text):
return None
end_loc = delta_text.rindex('"}')
diff = delta_text[:end_loc] + '"}'
logger.debug("Finishing tool and found diff that had not been streamed yet: %s", diff)
self.streamed_args_for_tool[self.current_tool_id] += diff
return DeltaMessage(tool_calls=[
DeltaToolCall(index=self.current_tool_id,
function=DeltaFunctionCall(
arguments=diff).model_dump(
exclude_none=True))
])
except (ValueError, AttributeError) as e:
logger.debug(f"Error processing tool call arguments: {e}")
return None
# case -- otherwise we're just generating text
else:
text = delta_text.replace(self.tool_call_start_token, "")
text = text.replace(self.tool_call_end_token, "")
delta = DeltaMessage(tool_calls=[], content=text)
return delta
try:
if tool_call_portion:
# More robust JSON parsing with error handling
try:
current_tool_call = partial_json_parser.loads(
tool_call_portion, flags) if tool_call_portion else None
logger.debug("Parsed tool call %s", current_tool_call)
except (partial_json_parser.core.exceptions.MalformedJSON,
json.decoder.JSONDecodeError) as e:
logger.debug(f'Partial JSON parsing failed: {e}')
return None
else:
current_tool_call = None
except Exception as e:
logger.debug(f"Error in tool call processing: {e}")
return None
# case - we haven't sent the tool name yet
if not self.current_tool_name_sent:
if current_tool_call is None:
return None
function_name = 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=random_tool_call_id(),
function=DeltaFunctionCall(
name=function_name).model_dump(
exclude_none=True))
])
else:
return None
# if the tool call portion is None, send the delta as text
if tool_call_portion is None:
return DeltaMessage(content=delta_text) if text_portion is not None else None
logger.debug("Trying to parse current tool call with ID %s", self.current_tool_id)
# if we're starting a new tool call, push an empty object as a placeholder
if len(self.prev_tool_call_arr) <= self.current_tool_id:
self.prev_tool_call_arr.append({})
# main logic for tool parsing
prev_arguments = self.prev_tool_call_arr[self.current_tool_id].get("arguments")
cur_arguments = current_tool_call.get("arguments") if current_tool_call else None
logger.debug("diffing old arguments: %s", prev_arguments)
logger.debug("against new ones: %s", cur_arguments)
# Handle different cases for argument processing
try:
if not cur_arguments and not prev_arguments:
logger.debug("Skipping text %s - no arguments", delta_text)
delta = None
elif not cur_arguments and prev_arguments:
logger.error("Arguments reset mid-call. Skipping streaming.")
delta = None
elif cur_arguments and not prev_arguments:
try:
cur_arguments_json = json.dumps(cur_arguments, ensure_ascii=False)
if delta_text not in cur_arguments_json[:-2]:
return None
args_delta_start_loc = cur_arguments_json[:-2].rindex(delta_text) + len(delta_text)
arguments_delta = cur_arguments_json[:args_delta_start_loc]
logger.debug("First tokens in arguments received: %s", arguments_delta)
delta = DeltaMessage(tool_calls=[
DeltaToolCall(index=self.current_tool_id,
function=DeltaFunctionCall(
arguments=arguments_delta).model_dump(
exclude_none=True))
])
self.streamed_args_for_tool[self.current_tool_id] += arguments_delta
except (ValueError, TypeError) as e:
logger.debug(f"Error processing initial arguments: {e}")
return None
elif cur_arguments and prev_arguments:
try:
if isinstance(delta_text, str) and len(delta_text.rstrip()) >= 1 and delta_text.rstrip()[-1] == '}':
delta_text = delta_text.rstrip()[:-1]
logger.debug("got diff %s", delta_text)
delta = DeltaMessage(tool_calls=[
DeltaToolCall(index=self.current_tool_id,
function=DeltaFunctionCall(
arguments=delta_text).model_dump(
exclude_none=True))
])
self.streamed_args_for_tool[self.current_tool_id] += delta_text
except Exception as e:
logger.debug(f"Error processing argument delta: {e}")
return None
except Exception as e:
logger.debug(f"Error in argument processing: {e}")
return None
# handle saving the state for the current tool
try:
if self.current_tool_id == len(self.prev_tool_call_arr) - 1:
self.prev_tool_call_arr[self.current_tool_id] = current_tool_call
else:
self.prev_tool_call_arr.append(current_tool_call)
except Exception as e:
logger.debug(f"Error saving tool call state: {e}")
return delta
except Exception as e:
logger.exception(f"Error in streaming tool call processing: {e}")
return None
