522 lines
19 KiB
Python
522 lines
19 KiB
Python
import json
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import logging
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import re
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from abc import ABC, abstractmethod
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from json import JSONDecodeError, JSONDecoder
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from typing import Any, Dict, List, Optional, Tuple
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import partial_json_parser
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from partial_json_parser.core.options import Allow
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from pydantic import BaseModel, Field
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logger = logging.getLogger(__name__)
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TOOLS_TAG_LIST = [
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"<|plugin|>",
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"<function=",
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"<tool_call>",
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"<|python_tag|>",
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"[TOOL_CALLS]",
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]
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class Function(BaseModel):
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"""Function Tool Template."""
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description: Optional[str] = Field(default=None, examples=[None])
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name: Optional[str] = None
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parameters: Optional[object] = None
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class ToolCallItem(BaseModel):
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"""Simple encapsulation of the parsed ToolCall result for easier usage in streaming contexts."""
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tool_index: int
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name: Optional[str] = None
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parameters: str # JSON string
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def _find_common_prefix(s1: str, s2: str) -> str:
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prefix = ""
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min_length = min(len(s1), len(s2))
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for i in range(0, min_length):
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if s1[i] == s2[i]:
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prefix += s1[i]
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else:
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break
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return prefix
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def _partial_json_loads(input_str: str, flags: Allow) -> Tuple[Any, int]:
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try:
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return (partial_json_parser.loads(input_str, flags), len(input_str))
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except JSONDecodeError as e:
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if "Extra data" in e.msg:
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dec = JSONDecoder()
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return dec.raw_decode(input_str)
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raise
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def _is_complete_json(input_str: str) -> bool:
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try:
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json.loads(input_str)
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return True
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except JSONDecodeError:
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return False
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class StreamingParseResult:
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"""Result of streaming incremental parsing."""
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def __init__(
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self, normal_text: str = "", calls: Optional[List[ToolCallItem]] = None
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):
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self.normal_text = normal_text
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self.calls = calls or []
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class BaseFormatDetector:
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"""Base class providing two sets of interfaces: one-time and streaming incremental."""
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def __init__(self):
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# initialize properties used for state when parsing tool calls in
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self._buffer = ""
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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[str] = (
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[]
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) # map what has been streamed for each tool so far to a list
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self.bot_token = ""
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self.eot_token = ""
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def parse_base_json(self, action: Any, tools: List[Function]) -> List[ToolCallItem]:
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tool_indices = {
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tool.function.name: i for i, tool in enumerate(tools) if tool.function.name
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}
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if not isinstance(action, list):
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name = action.get("name")
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if not name or name not in tool_indices:
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logger.warning(f"Model attempted to call undefined function: {name}")
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return []
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return [
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ToolCallItem(
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tool_index=tool_indices[name],
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name=name,
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parameters=json.dumps(
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action.get("parameters") or action.get("arguments", {}),
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ensure_ascii=False,
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),
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)
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]
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results = []
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for act in action:
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name = act.get("name")
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if name and name in tool_indices:
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results.append(
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ToolCallItem(
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tool_index=tool_indices[name],
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name=name,
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parameters=json.dumps(
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act.get("parameters") or act.get("arguments", {}),
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ensure_ascii=False,
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),
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)
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)
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return results
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def detect_and_parse(self, text: str, tools: List[Function]) -> List[ToolCallItem]:
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"""
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Parses the text in one go. Returns success=True if the format matches, otherwise False.
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Note that leftover_text here represents "content that this parser will not consume further".
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"""
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action = json.loads(text)
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return self.parse_base_json(action, tools)
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def parse_streaming_increment(
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self, new_text: str, tools: List[Function]
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) -> StreamingParseResult:
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"""
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Streaming incremental parsing with tool validation.
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"""
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# Append new text to buffer
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self._buffer += new_text
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current_text = self._buffer
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if not (self.bot_token in current_text or current_text.startswith("{")):
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self._buffer = ""
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if self.eot_token in new_text:
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new_text = new_text.replace(self.eot_token, "")
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return StreamingParseResult(normal_text=new_text)
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# Build tool indices if not already built
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if not hasattr(self, "_tool_indices"):
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self._tool_indices = {
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tool.function.name: i
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for i, tool in enumerate(tools)
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if tool.function and tool.function.name
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}
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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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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(
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current_text[start_idx:], flags
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)
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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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# Validate tool name if present
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if "name" in obj and obj["name"] not in self._tool_indices:
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# Invalid tool name - reset state
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self._buffer = ""
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self.current_tool_id = -1
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self.current_tool_name_sent = False
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if self.streamed_args_for_tool:
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self.streamed_args_for_tool.pop()
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return StreamingParseResult()
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# Handle parameters/arguments consistency
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if "parameters" in obj:
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assert (
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"arguments" not in obj
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), "model generated both parameters and arguments"
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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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return StreamingParseResult()
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if len(tool_call_arr) == 0:
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return StreamingParseResult()
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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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# Handle new tool in array
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if len(tool_call_arr) > 0 and len(tool_call_arr) > self.current_tool_id + 1:
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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)
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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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res = StreamingParseResult(
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calls=[
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ToolCallItem(
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tool_index=self.current_tool_id,
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name="",
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parameters=argument_diff,
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)
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],
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)
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self.streamed_args_for_tool[
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self.current_tool_id
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] += argument_diff
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else:
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res = StreamingParseResult()
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else:
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res = StreamingParseResult()
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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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return res
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# Handle tool name
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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 and function_name in self._tool_indices:
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res = StreamingParseResult(
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calls=[
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ToolCallItem(
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tool_index=self._tool_indices[function_name],
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name=function_name,
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parameters="",
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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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res = StreamingParseResult()
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# Handle streaming arguments
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else:
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cur_arguments = current_tool_call.get("arguments")
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res = StreamingParseResult()
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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)
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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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self._buffer = ""
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self.prev_tool_call_arr[self.current_tool_id].clear()
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self.current_tool_name_sent = False
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self.streamed_args_for_tool[self.current_tool_id] = ""
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elif prev_arguments:
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prev_args_json = json.dumps(prev_arguments)
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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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res = StreamingParseResult(
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calls=[
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ToolCallItem(
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tool_index=self.current_tool_id,
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name="",
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parameters=argument_diff,
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)
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],
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)
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if not is_complete[self.current_tool_id]:
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self.streamed_args_for_tool[
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self.current_tool_id
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] += argument_diff
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self.prev_tool_call_arr = tool_call_arr
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return res
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except Exception as e:
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logger.error(f"Error in parse_streaming_increment: {e}")
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return StreamingParseResult()
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class Qwen25Detector(BaseFormatDetector):
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"""
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Detector for Qwen 2.5 models.
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Assumes function call format:
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<tool_call>{"name":"xxx", "arguments":{...}}</tool_call>
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"""
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def __init__(self):
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"""
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Initializes the detector with necessary state variables.
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"""
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super().__init__()
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self.bot_token = "<tool_call>"
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self.eot_token = "</tool_call>"
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def detect_and_parse(self, text: str, tools: List[Function]) -> List[ToolCallItem]:
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"""
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One-time parsing: Detects and parses tool calls in the provided text.
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:param text: The complete text to parse.
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:param tools: List of available tools.
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:return: ParseResult indicating success or failure, consumed text, leftover text, and parsed calls.
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"""
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if "<tool_call>" not in text:
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return []
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pattern = r"<tool_call>(.*?)</tool_call>"
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match_result_list = re.findall(pattern, text, re.DOTALL)
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calls = []
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for match_result in match_result_list:
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match_result = json.loads(match_result)
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calls.extend(self.parse_base_json(match_result, tools))
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return calls
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class MistralDetector(BaseFormatDetector):
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"""
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Detector for Mistral models.
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Assumes function call format:
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<|action_start|><|plugin|>{"name":"xxx", "arguments":{...}}<|action_end|>
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"""
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def __init__(self):
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"""
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Initializes the detector with necessary state variables.
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"""
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super().__init__()
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self.bot_token = "[TOOL_CALLS] ["
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self.tool_call_regex = re.compile(r"\[{.*}\]", re.DOTALL)
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def _clean_text(self, text: str) -> str:
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"""
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clean text to only leave ''[TOOL_CALLS] [{"name": xxx, "arguments": {xxx}}]'
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for example,
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text = '[TOOL_CALLS] [{"name": "get_current_weather", "arguments": {"location": "Boston, MA", "unit": "fahrenheit"}}]\n\nToday\'s weather in Boston is :{function call result} (in Fahrenheit)\n\nIf you prefer Celsius, please let me know.'
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return '[TOOL_CALLS] [{"name": "get_current_weather", "arguments": {"location": "Boston, MA", "unit": "fahrenheit"}}]'
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The key pattern is [TOOL_CALLS] [...]
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"""
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find_results = re.findall(r"\[TOOL_CALLS\] \[.*?\]", text, re.DOTALL)
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if len(find_results) > 0:
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return find_results[0]
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else:
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return ""
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def detect_and_parse(self, text: str, tools: List[Function]) -> List[ToolCallItem]:
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"""
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One-time parsing: Detects and parses tool calls in the provided text.
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:param text: The complete text to parse.
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:param tools: List of available tools.
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:return: ParseResult indicating success or failure, consumed text, leftover text, and parsed calls.
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"""
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text = self._clean_text(text)
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tool_content = text.replace("[TOOL_CALLS]", "").strip()
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raw_tool_calls = self.tool_call_regex.findall(tool_content)
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calls = []
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if len(raw_tool_calls) > 0:
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raw_tool_call = raw_tool_calls[0]
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function_call_arr = json.loads(raw_tool_call)
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for match_result in function_call_arr:
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calls.extend(self.parse_base_json(match_result, tools))
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return calls
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class Llama32Detector(BaseFormatDetector):
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"""
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Detector for Llama 3.2 models.
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Assumes function call format:
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<|python_tag|>{"name":"xxx", "arguments":{...}}
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"""
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def __init__(self):
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super().__init__()
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self.bot_token = "<|python_tag|>"
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def detect_and_parse(self, text: str, tools: List[Function]) -> List[ToolCallItem]:
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"""Parse function calls from text, handling multiple JSON objects."""
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if "<|python_tag|>" not in text:
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return []
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_, action_text = text.split("<|python_tag|>")
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# Split by semicolon and process each part
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json_parts = [part.strip() for part in action_text.split(";") if part.strip()]
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all_actions = []
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for part in json_parts:
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try:
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# Parse each individual JSON object
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action = json.loads(part)
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all_actions.append(action)
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except json.JSONDecodeError as e:
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logger.warning(f"Failed to parse JSON part: {part}")
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logger.warning(f"JSON parse error: {str(e)}")
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continue
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# Only process if we found valid JSON objects
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if all_actions:
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return self.parse_base_json(all_actions, tools)
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return []
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class MultiFormatParser:
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def __init__(self, detectors: List[BaseFormatDetector]):
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"""
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:param detectors: A series of available Detector instances passed in
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"""
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self.detectors = detectors
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def parse_once(self, text: str, tools: List[Function]):
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"""
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One-time parsing: Loop through detectors until there are no new matches or text is exhausted
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Return: (final_text, all_calls)
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- final_text: The remaining text after parsing that was not consumed by any Detector (can be treated as normal text)
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- all_calls: All calls parsed by the Detectors
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"""
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final_calls = []
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final_normal_text = text
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for detector in self.detectors:
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tool_call_list = detector.detect_and_parse(text, tools)
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if len(tool_call_list) > 0: # parsed successfully
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final_calls = tool_call_list
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break
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# leftover_text is the normal text not consumed by any Detector
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return final_normal_text, final_calls
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def parse_streaming_increment(self, new_text: str, tools: List[Function]):
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"""
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Streaming incremental parsing: Feed new_text to each detector's parse_streaming_increment
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and merge their produced normal_text/calls to return.
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(The logic here can be "priority-based" or "parallel parsing" based on your needs)
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"""
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final_normal_text = ""
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final_calls = []
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for detector in self.detectors:
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sp_result = detector.parse_streaming_increment(new_text, tools)
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# Merge normal_text and calls
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# If one sp_result contains result call, this should be a successful parse
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# If one sp_result only contains normal_text, this can either be a successful
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# parse or it is not using the desired parsing tool.
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if sp_result.normal_text:
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final_normal_text = sp_result.normal_text
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if sp_result.calls:
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final_calls.extend(sp_result.calls)
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final_normal_text = sp_result.normal_text
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break
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return final_normal_text, final_calls
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class FunctionCallParser:
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"""
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In streaming scenarios, each time new_text is received, it calls multi_format_parser.parse_streaming_increment
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and returns the resulting normal_text and calls to the upper layer (or SSE).
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"""
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ToolCallParserEnum: Dict[str, BaseFormatDetector] = {
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"llama3": Llama32Detector,
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"qwen25": Qwen25Detector,
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"mistral": MistralDetector,
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}
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def __init__(self, tools: List[Function], tool_call_parser: str = None):
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detectors = []
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if tool_call_parser:
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detector_class = self.ToolCallParserEnum.get(tool_call_parser)
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if detector_class:
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detectors.append(detector_class())
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else:
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raise ValueError(f"Unsupported tool_call_parser: {tool_call_parser}")
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else:
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raise ValueError("Tool Call Parser Not Given!")
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self.multi_format_parser = MultiFormatParser(detectors)
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self.tools = tools
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def parse_non_stream(self, full_text: str):
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"""
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Non-streaming call: one-time parsing
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"""
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full_normal_text, calls = self.multi_format_parser.parse_once(
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full_text, self.tools
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)
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return full_normal_text, calls
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def parse_stream_chunk(self, chunk_text: str):
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"""
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Streaming call: incremental parsing
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"""
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normal_text, calls = self.multi_format_parser.parse_streaming_increment(
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chunk_text, self.tools
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)
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return normal_text, calls
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