[dev]add glm4.7 tool-parser (#151)
Signed-off-by: zhangzhenyi <zhangzhenyi@baidu.com> Co-authored-by: Li Wei <liwei.109@outlook.com>
This commit is contained in:
astrophel0
@@ -0,0 +1,912 @@
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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 ast
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from functools import partial
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from importlib.resources import contents
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import json
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from collections.abc import Sequence
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from typing import Any, Optional, Union
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import regex as re
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from enum import Enum
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from vllm.utils import random_uuid
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from vllm.entrypoints.openai.protocol import (ChatCompletionRequest,
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ChatCompletionToolsParam,
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DeltaFunctionCall, DeltaMessage,
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DeltaToolCall,
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ExtractedToolCallInformation,
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FunctionCall, ToolCall)
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from vllm.entrypoints.openai.tool_parsers.abstract_tool_parser import (
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ToolParser, ToolParserManager)
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from vllm.logger import init_logger
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from vllm.transformers_utils.tokenizer import AnyTokenizer
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logger = init_logger(__name__)
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class StreamState(str, Enum):
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"""State machine states for XML to JSON streaming conversion."""
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INIT = "INIT"
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BETWEEN = "BETWEEN"
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IN_KEY = "IN_KEY"
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WAITING_VALUE = "WAITING_VALUE"
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IN_VALUE = "IN_VALUE"
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def random_tool_call_id() -> str:
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return f"chatcmpl-tool-{random_uuid()}"
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def get_argument_type(
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func_name: str, arg_key: str, defined_tools: list[ChatCompletionToolsParam]
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) -> Optional[str]:
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"""Get the expected type of a function argument from tool definitions.
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Supports complex JSON Schema definitions including:
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- Direct type field (including type arrays)
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- anyOf/oneOf: parameter can be any of multiple types
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- enum: parameter must be one of enum values
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- allOf: parameter must satisfy all type definitions
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- properties: inferred as object type
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- items: inferred as array type
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Args:
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func_name: Name of the function/tool
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arg_key: Name of the argument
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defined_tools: List of available tools
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Returns:
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The type string (e.g., 'string', 'number', 'object') or None if not found
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"""
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name2tool = {tool.function.name: tool for tool in defined_tools}
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# Check if function exists
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tool = name2tool.get(func_name)
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if not tool:
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return None
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# Get parameters safely using getattr
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params = getattr(tool.function, "parameters", None)
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if not isinstance(params, dict):
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return None
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# Navigate to the type using dict.get() for safe access
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properties = params.get("properties")
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if not isinstance(properties, dict):
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return None
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arg_spec = properties.get(arg_key)
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if isinstance(arg_spec, dict):
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# Use the new type inference function for complex JSON Schema support
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return infer_type_from_json_schema(arg_spec)
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return None
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def _convert_to_number(value: str) -> Any:
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"""Convert string to appropriate number type (int or float).
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Args:
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value: String value to convert
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Returns:
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Converted number or original string if conversion fails
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"""
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try:
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if "." in value or "e" in value.lower():
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return float(value)
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else:
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return int(value)
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except (ValueError, AttributeError):
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return value
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def parse_arguments(
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json_value: str, arg_type: Optional[str] = None
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) -> tuple[Any, bool]:
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"""Parse argument value with multiple fallback strategies.
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Args:
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json_value: Raw string value to parse
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arg_type: Expected type hint ('string', 'number', 'object', etc.)
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Returns:
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Tuple of (parsed_value, is_valid_json)
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"""
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# Strategy 1: Direct JSON parsing
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try:
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parsed_value = json.loads(json_value)
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# Type coercion for number type
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if arg_type == "number" and isinstance(parsed_value, str):
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parsed_value = _convert_to_number(parsed_value)
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return parsed_value, True
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except (json.JSONDecodeError, ValueError):
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pass
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# Strategy 2: Unescape and parse
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try:
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wrapped = json.loads('{"tmp": "' + json_value + '"}')
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parsed_value = json.loads(wrapped["tmp"])
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if arg_type == "number" and isinstance(parsed_value, str):
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parsed_value = _convert_to_number(parsed_value)
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return parsed_value, True
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except (json.JSONDecodeError, ValueError, KeyError):
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pass
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# Strategy 3: ast.literal_eval
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try:
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parsed_value = ast.literal_eval(json_value)
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return parsed_value, True
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except (ValueError, SyntaxError):
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pass
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# Strategy 4: Treat as string
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try:
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quoted_value = json.dumps(str(json_value))
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return json.loads(quoted_value), True
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except (json.JSONDecodeError, ValueError):
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return json_value, False
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def infer_type_from_json_schema(schema: dict[str, Any]) -> Optional[str]:
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"""
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Infer the primary type of a parameter from JSON Schema.
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Supports complex JSON Schema structures including:
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- Direct type field (including type arrays)
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- anyOf/oneOf: parameter can be any of multiple types
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- enum: parameter must be one of enum values
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- allOf: parameter must satisfy all type definitions
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- properties: inferred as object type
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- items: inferred as array type
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Args:
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schema: JSON Schema definition
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Returns:
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Inferred type ('string', 'number', 'object', 'array', etc.) or None
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"""
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if not isinstance(schema, dict):
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return None
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# Priority 1: Direct type field (including type arrays)
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if "type" in schema:
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type_value = schema["type"]
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if isinstance(type_value, str):
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return type_value
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elif isinstance(type_value, list) and type_value:
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# Handle type arrays: return first non-null type
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non_null_types = [t for t in type_value if t != "null"]
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if non_null_types:
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return non_null_types[0]
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return "string" # If only null, default to string
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# Priority 2: Handle anyOf/oneOf
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if "anyOf" in schema or "oneOf" in schema:
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schemas = schema.get("anyOf") or schema.get("oneOf")
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types = []
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if isinstance(schemas, list):
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for sub_schema in schemas:
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inferred_type = infer_type_from_json_schema(sub_schema)
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if inferred_type:
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types.append(inferred_type)
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if types:
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# If all types are the same, return unified type
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if len(set(types)) == 1:
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return types[0]
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# When types differ, prioritize string (safest)
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if "string" in types:
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return "string"
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# Otherwise return first type
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return types[0]
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# Priority 3: Handle enum (infer type from enum values)
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if "enum" in schema and isinstance(schema["enum"], list):
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if not schema["enum"]:
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return "string"
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# Infer type from enum values
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enum_types = set()
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for value in schema["enum"]:
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if value is None:
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enum_types.add("null")
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elif isinstance(value, bool):
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enum_types.add("boolean")
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elif isinstance(value, int):
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enum_types.add("integer")
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elif isinstance(value, float):
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enum_types.add("number")
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elif isinstance(value, str):
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enum_types.add("string")
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elif isinstance(value, list):
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enum_types.add("array")
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elif isinstance(value, dict):
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enum_types.add("object")
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# If type is uniform, return that type
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if len(enum_types) == 1:
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return enum_types.pop()
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# Mixed types, prioritize string
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return "string"
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# Priority 4: Handle allOf (must satisfy all types)
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if "allOf" in schema and isinstance(schema["allOf"], list):
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schemas = schema["allOf"]
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for sub_schema in schemas:
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inferred_type = infer_type_from_json_schema(sub_schema)
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if inferred_type and inferred_type != "string":
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return inferred_type
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return "string"
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# Priority 5: Infer object type
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if "properties" in schema:
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return "object"
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# Priority 6: Infer array type
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if "items" in schema:
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return "array"
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return None
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@ToolParserManager.register_module("glm47")
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class Glm47MoeModelToolParser(ToolParser):
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def __init__(self, tokenizer: AnyTokenizer):
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super().__init__(tokenizer)
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self.current_tool_name_sent = False
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self.prev_tool_call_arr: list[dict] = []
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self.current_tool_id = -1
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self.streamed_args_for_tool: list[str] = []
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self.tool_call_start_token = "<tool_call>"
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self.tool_call_end_token = "</tool_call>"
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self._tool_indices = 0
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self._last_arguments: str = ""
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self._streamed_raw_length = 0
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self.tool_calls_start_token = self.tool_call_start_token
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self.func_call_regex = re.compile(r"<tool_call>.*?</tool_call>",
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re.DOTALL)
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self.func_detail_regex = re.compile(
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r"<tool_call>([^\n<]*)\n?(.*)</tool_call>", re.DOTALL)
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self.func_arg_regex = re.compile(
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r"<arg_key>(.*?)</arg_key>\s*<arg_value>(.*?)</arg_value>",
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re.DOTALL)
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if not self.model_tokenizer:
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raise ValueError(
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"The model tokenizer must be passed to the ToolParser "
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"constructor during construction.")
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self.tool_call_start_token_id = self.vocab.get(
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self.tool_call_start_token)
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self.tool_call_end_token_id = self.vocab.get(self.tool_call_end_token)
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self._buffer = ""
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self._reset_streaming_state()
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def _reset_streaming_state(self) -> None:
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"""Reset the streaming state machine for a new tool call."""
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self._stream_state = StreamState.INIT
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self._current_key = ""
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self._current_value = ""
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self._xml_tag_buffer = ""
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self._is_first_param = True
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self._value_started = False
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self._cached_value_type: Optional[str] = (
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None # Cache the value type for consistency
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)
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self._tool_call_completed = False # Reset tool call completion status
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self._sent_empty_object = False # Reset empty object sent status
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def extract_tool_calls(
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self,
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model_output: str,
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request: ChatCompletionRequest,
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) -> ExtractedToolCallInformation:
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def _is_string_type(
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tool_name: str, arg_name: str,
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tools: Optional[list[ChatCompletionToolsParam]]) -> bool:
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if tools is None:
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return False
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for tool in tools:
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if tool.function.name == tool_name:
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if tool.function.parameters is None:
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return False
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arg_type = tool.function.parameters.get(
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"properties", {}).get(arg_name, {}).get("type", None)
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return arg_type == "string"
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logger.warning("No tool named '%s'.", tool_name)
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return False
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def _deserialize(value: str) -> Any:
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try:
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return json.loads(value)
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except Exception:
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pass
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try:
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return ast.literal_eval(value)
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except Exception:
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pass
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return value
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matched_tool_calls = self.func_call_regex.findall(model_output)
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logger.debug("model_output: %s", model_output)
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try:
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tool_calls = []
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for match in matched_tool_calls:
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tc_detail = self.func_detail_regex.search(match)
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tc_name = tc_detail.group(1)
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tc_args = tc_detail.group(2)
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pairs = self.func_arg_regex.findall(tc_args)
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arg_dct = {}
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for key, value in pairs:
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arg_key = key.strip()
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arg_val = value.strip()
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if not _is_string_type(tc_name, arg_key, request.tools):
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arg_val = _deserialize(arg_val)
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logger.debug("arg_key = %s, arg_val = %s", arg_key,
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arg_val)
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arg_dct[arg_key] = arg_val
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tool_calls.append(
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ToolCall(type="function",
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function=FunctionCall(
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name=tc_name, arguments=json.dumps(arg_dct))))
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except Exception:
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logger.exception("Failed to extract tool call spec")
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return ExtractedToolCallInformation(tools_called=False,
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tool_calls=[],
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content=model_output)
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else:
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if len(tool_calls) > 0:
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content = model_output[:model_output.
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find(self.tool_calls_start_token)]
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return ExtractedToolCallInformation(tools_called=True,
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tool_calls=tool_calls,
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content=content)
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return ExtractedToolCallInformation(tools_called=False,
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tool_calls=[],
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content=model_output)
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def _extract_match_groups(self, match: re.Match) -> tuple[str, str, str]:
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"""Extract function name, arguments and end marker from regex match.
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Args:
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match: Regex match object
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Returns:
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(func_name, func_args_raw, is_tool_end)
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"""
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func_name = match.group(1).strip()
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func_args_raw = match.group(2).strip() if match.group(2) else ""
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is_tool_end = match.group(3) or ""
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return func_name, func_args_raw, is_tool_end
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def _send_tool_name_if_needed(
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self, func_name: str, has_arg_key: bool, is_tool_end: str
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) -> Optional[DeltaToolCall]:
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"""Send tool name if needed.
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Args:
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func_name: Function name
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has_arg_key: Whether current text contains <arg_key
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is_tool_end: Whether end marker is encountered
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Returns:
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Tool call item or None
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"""
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if self.current_tool_name_sent:
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return None
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# Function name completeness check
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is_func_name_complete = has_arg_key or is_tool_end == self.tool_call_end_token
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if not is_func_name_complete:
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return None
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if not func_name:
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logger.warning("Empty function name detected, skipping tool call")
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return None
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# Send tool name
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self.current_tool_name_sent = True
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self._streamed_raw_length = 0
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self._reset_streaming_state()
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# Record tool info
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self.prev_tool_call_arr[self.current_tool_id] = {
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"name": func_name,
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"arguments": {},
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}
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return DeltaToolCall(
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id=random_tool_call_id(),
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index=self.current_tool_id,
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type="function",
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function=DeltaFunctionCall(name=func_name, arguments=""),
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)
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def _parse_argument_pairs(
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self, pairs: list[tuple[str, str]], func_name: str, tools: list[ChatCompletionToolsParam]
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) -> dict[str, Any]:
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"""Parse argument key-value pairs with type coercion.
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Args:
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pairs: List of (key, value) tuples from regex matching
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func_name: Name of the function
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tools: List of available tools
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Returns:
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Dictionary of parsed arguments
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"""
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arguments = {}
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for arg_key, arg_value in pairs:
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arg_key = arg_key.strip()
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arg_value = arg_value.strip()
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arg_type = get_argument_type(func_name, arg_key, tools)
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parsed_value, is_good_json = parse_arguments(arg_value, arg_type)
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if arg_type == "string":
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# Only convert to string if explicitly defined as string type
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if isinstance(parsed_value, str):
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arguments[arg_key] = parsed_value
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elif isinstance(parsed_value, (dict, list)):
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# If parsed as dict/list but schema says string, convert to JSON string
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arguments[arg_key] = json.dumps(parsed_value, ensure_ascii=False)
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else:
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arguments[arg_key] = str(parsed_value)
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elif arg_type is None:
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# If type is not defined, keep the parsed value as-is
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arguments[arg_key] = parsed_value if is_good_json else arg_value
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else:
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# For other types (number, object, array, etc.), use parsed value
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arguments[arg_key] = parsed_value if is_good_json else arg_value
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return arguments
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def _finalize_tool_call(
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self,
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func_name: str,
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||||
func_args_raw: str,
|
||||
tools: list[ChatCompletionToolsParam],
|
||||
match_end_pos: int,
|
||||
current_text: str,
|
||||
) -> list[DeltaToolCall]:
|
||||
"""Complete tool call processing.
|
||||
|
||||
Args:
|
||||
func_name: Function name
|
||||
func_args_raw: Raw argument string
|
||||
tools: List of available tools
|
||||
match_end_pos: Match end position
|
||||
current_text: Current text
|
||||
|
||||
Returns:
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||||
List of tool call items to add
|
||||
"""
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||||
calls = []
|
||||
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||||
# Handle no-arg function or need to close braces
|
||||
if self._is_first_param and not self._sent_empty_object:
|
||||
# No-arg function
|
||||
calls.append(
|
||||
DeltaToolCall(
|
||||
index=self.current_tool_id,
|
||||
function=DeltaFunctionCall(name=None, arguments="{}"),
|
||||
)
|
||||
)
|
||||
self._last_arguments += "{}"
|
||||
self.streamed_args_for_tool[self.current_tool_id] += "{}"
|
||||
self._sent_empty_object = True
|
||||
elif not self._last_arguments.endswith("}") and not self._sent_empty_object:
|
||||
# Need to close brace
|
||||
calls.append(
|
||||
DeltaToolCall(
|
||||
index=self.current_tool_id,
|
||||
function=DeltaFunctionCall(name=None, arguments="}"),
|
||||
)
|
||||
)
|
||||
self._last_arguments += "}"
|
||||
self.streamed_args_for_tool[self.current_tool_id] += "}"
|
||||
self._sent_empty_object = True
|
||||
|
||||
# Parse final arguments
|
||||
if func_args_raw:
|
||||
try:
|
||||
pairs = self.func_arg_regex.findall(func_args_raw)
|
||||
if pairs:
|
||||
arguments = self._parse_argument_pairs(pairs, func_name, tools)
|
||||
self.prev_tool_call_arr[self.current_tool_id][
|
||||
"arguments"
|
||||
] = arguments
|
||||
except Exception as e:
|
||||
logger.debug(f"Failed to parse arguments: {e}", exc_info=True)
|
||||
|
||||
# Clean buffer
|
||||
self._buffer = current_text[match_end_pos:]
|
||||
|
||||
# Reset state for next tool call
|
||||
self._tool_call_completed = True
|
||||
self.current_tool_id += 1
|
||||
self._last_arguments = ""
|
||||
self.current_tool_name_sent = False
|
||||
self._streamed_raw_length = 0
|
||||
self._reset_streaming_state()
|
||||
|
||||
return calls
|
||||
|
||||
def _format_value_complete(self, value: str, value_type: str) -> str:
|
||||
"""Format complete value based on type.
|
||||
|
||||
Args:
|
||||
value: Raw value string
|
||||
value_type: Expected type ('string', 'number', 'object')
|
||||
|
||||
Returns:
|
||||
Properly formatted JSON value string
|
||||
"""
|
||||
if value_type == "string":
|
||||
# Ensure proper JSON string formatting with quotes
|
||||
return json.dumps(value, ensure_ascii=False)
|
||||
elif value_type == "number":
|
||||
try:
|
||||
num = _convert_to_number(value.strip() if value else "")
|
||||
return str(num)
|
||||
except (ValueError, AttributeError):
|
||||
# Fallback to string if not a valid number
|
||||
logger.warning(
|
||||
f"Failed to parse '{value}' as number, treating as string"
|
||||
)
|
||||
return json.dumps(str(value) if value else "", ensure_ascii=False)
|
||||
else:
|
||||
# For object/array types, return as-is (should already be valid JSON)
|
||||
return value
|
||||
|
||||
|
||||
def _process_xml_to_json_streaming(
|
||||
self, raw_increment: str, func_name: str, tools: list[ChatCompletionToolsParam]
|
||||
) -> str:
|
||||
"""Convert XML increment to JSON streaming output using state machine.
|
||||
|
||||
This method processes XML fragments character by character and converts them
|
||||
to JSON format incrementally. It maintains state across calls to handle
|
||||
partial XML tags and values.
|
||||
|
||||
Args:
|
||||
raw_increment: New XML content to process
|
||||
func_name: Name of the function being called
|
||||
tools: List of available tools for type inference
|
||||
|
||||
Returns:
|
||||
JSON string increment to append to the output
|
||||
"""
|
||||
json_output = ""
|
||||
|
||||
for char in raw_increment:
|
||||
self._xml_tag_buffer += char
|
||||
|
||||
if self._stream_state in [StreamState.INIT, StreamState.BETWEEN]:
|
||||
if self._xml_tag_buffer.endswith("<arg_key>"):
|
||||
self._stream_state = StreamState.IN_KEY
|
||||
self._current_key = ""
|
||||
self._xml_tag_buffer = ""
|
||||
json_output += "{" if self._is_first_param else ", "
|
||||
self._is_first_param = False
|
||||
|
||||
elif self._stream_state == StreamState.IN_KEY:
|
||||
if self._xml_tag_buffer.endswith("</arg_key>"):
|
||||
self._current_key = self._xml_tag_buffer[:-10].strip()
|
||||
self._xml_tag_buffer = ""
|
||||
self._stream_state = StreamState.WAITING_VALUE
|
||||
json_output += (
|
||||
json.dumps(self._current_key, ensure_ascii=False) + ": "
|
||||
)
|
||||
|
||||
elif self._stream_state == StreamState.WAITING_VALUE:
|
||||
if self._xml_tag_buffer.endswith("<arg_value>"):
|
||||
self._stream_state = StreamState.IN_VALUE
|
||||
self._current_value = ""
|
||||
self._xml_tag_buffer = ""
|
||||
self._value_started = False
|
||||
# Determine and cache the value type at the start
|
||||
self._cached_value_type = self._get_value_type(
|
||||
func_name, self._current_key, tools
|
||||
)
|
||||
|
||||
elif self._stream_state == StreamState.IN_VALUE:
|
||||
if self._xml_tag_buffer.endswith("</arg_value>"):
|
||||
final_value = self._xml_tag_buffer[:-12]
|
||||
self._current_value += final_value
|
||||
|
||||
# Use cached value type for consistency
|
||||
value_type = self._cached_value_type or "string"
|
||||
|
||||
if self._value_started:
|
||||
# Output any remaining content
|
||||
if final_value:
|
||||
if value_type == "string":
|
||||
json_output += json.dumps(
|
||||
final_value, ensure_ascii=False
|
||||
)[1:-1]
|
||||
else:
|
||||
json_output += final_value
|
||||
# Always output closing quote for string type when value was started
|
||||
if value_type == "string":
|
||||
json_output += '"'
|
||||
else:
|
||||
# Value was never started (empty or complete in one chunk)
|
||||
json_output += self._format_value_complete(
|
||||
self._current_value, value_type
|
||||
)
|
||||
|
||||
self._xml_tag_buffer = ""
|
||||
self._stream_state = StreamState.BETWEEN
|
||||
self._current_value = ""
|
||||
self._value_started = False
|
||||
self._cached_value_type = None # Reset cached type
|
||||
else:
|
||||
closing_tag = "</arg_value>"
|
||||
is_potential_closing = len(self._xml_tag_buffer) <= len(
|
||||
closing_tag
|
||||
) and closing_tag.startswith(self._xml_tag_buffer)
|
||||
|
||||
if not is_potential_closing:
|
||||
content = self._xml_tag_buffer
|
||||
# Use cached value type for consistency
|
||||
value_type = self._cached_value_type or "string"
|
||||
|
||||
if value_type == "string":
|
||||
if not self._value_started:
|
||||
json_output += '"'
|
||||
self._value_started = True
|
||||
if content:
|
||||
json_output += json.dumps(content, ensure_ascii=False)[
|
||||
1:-1
|
||||
]
|
||||
self._current_value += content
|
||||
self._xml_tag_buffer = ""
|
||||
elif value_type == "number":
|
||||
if content:
|
||||
if not self._value_started:
|
||||
self._value_started = True
|
||||
json_output += content
|
||||
self._current_value += content
|
||||
self._xml_tag_buffer = ""
|
||||
else:
|
||||
# For object/array types, output as-is
|
||||
if content:
|
||||
if not self._value_started:
|
||||
self._value_started = True
|
||||
json_output += content
|
||||
self._current_value += content
|
||||
self._xml_tag_buffer = ""
|
||||
|
||||
return json_output
|
||||
|
||||
def _get_value_type(self, func_name: str, key: str, tools: list[ChatCompletionToolsParam]) -> str:
|
||||
"""Get parameter type from tool definition, with fallback to auto-detection.
|
||||
|
||||
Args:
|
||||
func_name: Name of the function
|
||||
key: Parameter name
|
||||
tools: List of available tools
|
||||
|
||||
Returns:
|
||||
Type string: 'string', 'number', 'object', 'array', or 'boolean'
|
||||
"""
|
||||
arg_type = get_argument_type(func_name, key, tools)
|
||||
if arg_type:
|
||||
return arg_type
|
||||
|
||||
# Improved auto-detection type from value (best effort)
|
||||
value_content = self._current_value.strip() if self._current_value else ""
|
||||
|
||||
if not value_content:
|
||||
return "string"
|
||||
|
||||
# Try to parse as valid JSON first
|
||||
try:
|
||||
parsed = json.loads(value_content)
|
||||
if isinstance(parsed, dict):
|
||||
return "object"
|
||||
elif isinstance(parsed, list):
|
||||
return "array"
|
||||
elif isinstance(parsed, bool):
|
||||
return "boolean"
|
||||
elif isinstance(parsed, (int, float)):
|
||||
return "number"
|
||||
# For string values, check if they look like numbers
|
||||
elif isinstance(parsed, str):
|
||||
if parsed.isdigit() or (
|
||||
parsed.startswith("-") and parsed[1:].isdigit()
|
||||
):
|
||||
return "number"
|
||||
return "string"
|
||||
except json.JSONDecodeError:
|
||||
# Not valid JSON, try heuristic detection
|
||||
first_char = value_content[0] if value_content else ""
|
||||
|
||||
if first_char.isdigit() or first_char in ["-", "."]:
|
||||
return "number"
|
||||
elif first_char in ["{", "["]:
|
||||
return "object"
|
||||
elif first_char in ['"', "'"]:
|
||||
return "string"
|
||||
|
||||
# Default to string (safest fallback)
|
||||
return "string"
|
||||
|
||||
|
||||
def _process_arguments_streaming(
|
||||
self, func_name: str, func_args_raw: str, tools: list[ChatCompletionToolsParam]
|
||||
) -> Optional[DeltaToolCall]:
|
||||
"""Process streaming arguments.
|
||||
|
||||
Args:
|
||||
func_name: Function name
|
||||
func_args_raw: Raw argument string
|
||||
tools: List of available tools
|
||||
|
||||
Returns:
|
||||
Tool call item with parameter updates or None
|
||||
"""
|
||||
current_raw_length = len(func_args_raw)
|
||||
|
||||
if current_raw_length <= self._streamed_raw_length:
|
||||
return None
|
||||
|
||||
# Get new raw XML content
|
||||
raw_increment = func_args_raw[self._streamed_raw_length :]
|
||||
|
||||
# Convert XML to JSON using state machine
|
||||
json_increment = self._process_xml_to_json_streaming(
|
||||
raw_increment, func_name, tools
|
||||
)
|
||||
|
||||
# CRITICAL: Update streamed length BEFORE early return
|
||||
# Even if json_increment is empty, the input has been consumed by the state machine
|
||||
self._streamed_raw_length = current_raw_length
|
||||
|
||||
if not json_increment:
|
||||
return None
|
||||
|
||||
# Update state
|
||||
self._last_arguments += json_increment
|
||||
self.streamed_args_for_tool[self.current_tool_id] += json_increment
|
||||
|
||||
return DeltaToolCall(
|
||||
index=self.current_tool_id,
|
||||
function=DeltaFunctionCall(name=None, arguments=json_increment),
|
||||
)
|
||||
|
||||
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]:
|
||||
self._buffer += delta_text
|
||||
current_text = self._buffer
|
||||
# Check if we have a tool call
|
||||
has_tool_call = self.tool_call_start_token in current_text
|
||||
|
||||
if not has_tool_call:
|
||||
# Check if buffer could be the start of a tool call
|
||||
# Keep buffer if it could be a partial match of bot_token
|
||||
is_potential_start = any(
|
||||
self.tool_call_start_token.startswith(current_text[-i:])
|
||||
for i in range(1, min(len(current_text), len(self.tool_call_start_token)) + 1)
|
||||
)
|
||||
|
||||
if not is_potential_start:
|
||||
# Not a potential tool call, return as normal text
|
||||
# Must return the entire buffer (current_text), not just new_text,
|
||||
# because buffer may contain previously accumulated characters like '<'
|
||||
# that turned out not to be part of a tool call
|
||||
output_text = current_text
|
||||
self._buffer = ""
|
||||
if self.tool_call_end_token in output_text:
|
||||
output_text = output_text.replace(self.tool_call_end_token, "")
|
||||
return DeltaMessage(content=output_text)
|
||||
else:
|
||||
# Could be start of tool call, keep buffering
|
||||
return None
|
||||
|
||||
# Extract any text before the first bot_token and return it as normal_text
|
||||
output_text = ""
|
||||
first_bot_token_idx = current_text.find(self.tool_call_start_token)
|
||||
if first_bot_token_idx > 0:
|
||||
output_text= current_text[:first_bot_token_idx]
|
||||
current_text = current_text[first_bot_token_idx:]
|
||||
# Update buffer to only include from the bot token onwards
|
||||
self._buffer = current_text
|
||||
if not hasattr(self, "_tool_indices"):
|
||||
self._tool_indices += 1
|
||||
|
||||
calls: list[DeltaToolCall] = []
|
||||
try:
|
||||
# Try to match a partial or complete tool call
|
||||
# Use a single flexible regex pattern that handles all cases
|
||||
partial_match = re.search(
|
||||
r"<tool_call>(.*?)(?:(<arg_key.*?))?(?:(</tool_call>)|$)",
|
||||
current_text,
|
||||
re.DOTALL,
|
||||
)
|
||||
if not partial_match:
|
||||
return None
|
||||
# return DeltaMessage(content=output_text, tool_calls=[])
|
||||
|
||||
# Extract match groups using helper method
|
||||
func_name, func_args_raw, is_tool_end = self._extract_match_groups(
|
||||
match=partial_match
|
||||
)
|
||||
|
||||
# Initialize tool call state if needed (keeping existing logic)
|
||||
if self.current_tool_id == -1:
|
||||
self.current_tool_id = 0
|
||||
self.prev_tool_call_arr = []
|
||||
self.streamed_args_for_tool = [""]
|
||||
self._streamed_raw_length = 0
|
||||
self.current_tool_name_sent = False # Reset for new tool call
|
||||
self._reset_streaming_state()
|
||||
# Check if this is a continuation of an existing tool call or a new one
|
||||
elif not self.current_tool_name_sent:
|
||||
# Only increment tool_id if we're truly starting a NEW tool call
|
||||
# Don't increment if this is just the first time we're processing
|
||||
# a tool call that was received in the buffer
|
||||
# The key insight: only increment when we've COMPLETED a previous tool call
|
||||
# and now see another bot_token in new_text
|
||||
pass # Remove the problematic auto-increment logic
|
||||
|
||||
# Ensure tracking arrays are large enough (keeping existing logic)
|
||||
while len(self.prev_tool_call_arr) <= self.current_tool_id:
|
||||
self.prev_tool_call_arr.append({})
|
||||
while len(self.streamed_args_for_tool) <= self.current_tool_id:
|
||||
self.streamed_args_for_tool.append("")
|
||||
|
||||
# Determine if function name is complete by checking for <arg_key> in the full text
|
||||
# This is important for streaming scenarios where args come in later chunks
|
||||
has_arg_key = "<arg_key" in current_text
|
||||
# Send tool name if needed
|
||||
tool_name_item = self._send_tool_name_if_needed(
|
||||
func_name, has_arg_key, is_tool_end
|
||||
)
|
||||
|
||||
if tool_name_item:
|
||||
calls.append(tool_name_item)
|
||||
# Process streaming arguments if tool name has been sent
|
||||
if self.current_tool_name_sent and request.tools:
|
||||
arg_item = self._process_arguments_streaming(
|
||||
func_name, func_args_raw, request.tools
|
||||
)
|
||||
if arg_item:
|
||||
calls.append(arg_item)
|
||||
# Finalize tool call if end token is encountered
|
||||
if is_tool_end == self.tool_call_end_token and not self._tool_call_completed:
|
||||
finalize_calls = self._finalize_tool_call(
|
||||
func_name,
|
||||
func_args_raw,
|
||||
request.tools,
|
||||
partial_match.end(),
|
||||
current_text,
|
||||
)
|
||||
calls.extend(finalize_calls)
|
||||
return DeltaMessage(content=output_text, tool_calls=calls)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error in parse_streaming_increment: {e}", exc_info=True)
|
||||
return DeltaMessage(content=output_text)
|
||||
|
||||
# Only return if we have meaningful content or tool calls to avoid empty chunks
|
||||
if output_text.strip() or calls:
|
||||
return DeltaMessage(content=output_text, tool_calls=calls)
|
||||
else:
|
||||
return None
|
||||
Reference in New Issue
Block a user