335 lines
15 KiB
Python
335 lines
15 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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import json
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import re
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from collections.abc import Sequence
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from random import choices
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from string import ascii_letters, digits
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from typing import Union
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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 Field
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from vllm.entrypoints.openai.protocol import (ChatCompletionRequest,
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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.entrypoints.openai.tool_parsers.utils import (
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extract_intermediate_diff)
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from vllm.logger import init_logger
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from vllm.transformers_utils.tokenizer import AnyTokenizer, MistralTokenizer
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logger = init_logger(__name__)
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ALPHANUMERIC = ascii_letters + digits
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class MistralToolCall(ToolCall):
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id: str = Field(
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default_factory=lambda: MistralToolCall.generate_random_id())
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@staticmethod
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def generate_random_id():
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# Mistral Tool Call Ids must be alphanumeric with a length of 9.
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# https://github.com/mistralai/mistral-common/blob/21ee9f6cee3441e9bb1e6ed2d10173f90bd9b94b/src/mistral_common/protocol/instruct/validator.py#L299
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return "".join(choices(ALPHANUMERIC, k=9))
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@ToolParserManager.register_module("mistral")
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class MistralToolParser(ToolParser):
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"""
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Tool call parser for Mistral 7B Instruct v0.3, intended for use with the
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examples/tool_chat_template_mistral.jinja template.
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Used when --enable-auto-tool-choice --tool-call-parser mistral are all set
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"""
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def __init__(self, tokenizer: AnyTokenizer):
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super().__init__(tokenizer)
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if not isinstance(self.model_tokenizer, MistralTokenizer):
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logger.info("Non-Mistral tokenizer detected when using a Mistral "
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"model...")
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# initialize properties used for state when parsing tool calls in
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# streaming mode
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self.prev_tool_call_arr: list[dict] = []
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self.current_tool_id: int = -1
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self.current_tool_name_sent: bool = False
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self.streamed_args_for_tool: list[str] = [
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] # map what has been streamed for each tool so far to a list
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self.bot_token = "[TOOL_CALLS]"
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self.bot_token_id = self.vocab.get(self.bot_token)
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self.tool_call_regex = re.compile(r"\[{.*}\]", re.DOTALL)
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if self.bot_token_id is None:
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raise RuntimeError(
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"Mistral Tool Parser could not locate the tool call token in "
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"the tokenizer!")
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def adjust_request(
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self, request: ChatCompletionRequest) -> ChatCompletionRequest:
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if request.tools and request.tool_choice != 'none':
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# do not skip special tokens because mistral uses the special
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# tokens to indicate the start and end of the tool calls
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# information.
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request.skip_special_tokens = False
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return request
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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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"""
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Extract the tool calls from a complete model response. Requires
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find-and-replacing single quotes with double quotes for JSON parsing,
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make sure your tool call arguments don't ever include quotes!
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"""
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# case -- if a tool call token is not present, return a text response
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if self.bot_token not in model_output:
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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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# first remove the BOT token
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tool_content = model_output.replace(self.bot_token, "").strip()
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try:
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# we first try to directly load the json as parsing very nested
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# jsons is difficult
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try:
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function_call_arr = json.loads(tool_content)
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except json.JSONDecodeError:
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# use a regex to find the part corresponding to the tool call.
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# NOTE: This use case should not happen if the model is trained
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# correctly. It's a easy possible fix so it's included, but
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# can be brittle for very complex / highly nested tool calls
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raw_tool_call = self.tool_call_regex.findall(tool_content)[0]
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function_call_arr = json.loads(raw_tool_call)
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# Tool Call
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tool_calls: list[MistralToolCall] = [
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MistralToolCall(
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type="function",
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function=FunctionCall(
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name=raw_function_call["name"],
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# function call args are JSON but as a string
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arguments=json.dumps(raw_function_call["arguments"],
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ensure_ascii=False)))
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for raw_function_call in function_call_arr
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]
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# get any content before the tool call
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content = model_output.split(self.bot_token)[0]
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return ExtractedToolCallInformation(
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tools_called=True,
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tool_calls=tool_calls,
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content=content if len(content) > 0 else None)
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except Exception:
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logger.exception("Error in extracting tool call from response.")
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# return information to just treat the tool call as regular JSON
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return ExtractedToolCallInformation(tools_called=False,
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tool_calls=[],
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content=tool_content)
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def extract_tool_calls_streaming(
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self,
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previous_text: str,
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current_text: str,
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delta_text: str,
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previous_token_ids: Sequence[int],
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current_token_ids: Sequence[int],
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delta_token_ids: Sequence[int],
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request: ChatCompletionRequest,
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) -> Union[DeltaMessage, None]:
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# if the tool call token is not in the tokens generated so far, append
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# output to contents since it's not a tool
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if self.bot_token not in current_text:
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return DeltaMessage(content=delta_text)
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# if the tool call token ID IS in the tokens generated so far, that
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# means we're parsing as tool calls now
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# handle if we detected the BOT token which means the start of tool
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# calling
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if (self.bot_token_id in delta_token_ids
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and len(delta_token_ids) == 1):
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# if it's the only token, return None, so we don't send a chat
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# completion any don't send a control token
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return None
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# bit mask flags for partial JSON parsing. If the name hasn't been
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# sent yet, don't allow sending
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# an incomplete string since OpenAI only ever (as far as I have
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# seen) allows sending the entire tool/ function name at once.
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flags = Allow.ALL if self.current_tool_name_sent \
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else Allow.ALL & ~Allow.STR
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try:
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# replace BOT token with empty string, and convert single quotes
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# to double to allow parsing as JSON since mistral uses single
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# quotes instead of double for tool calls
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parsable_arr = current_text.split(self.bot_token)[-1]
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# tool calls are generated in an array, so do partial JSON
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# parsing on the entire array
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try:
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tool_call_arr: list[dict] = partial_json_parser.loads(
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parsable_arr, flags)
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except partial_json_parser.core.exceptions.MalformedJSON:
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logger.debug('not enough tokens to parse into JSON yet')
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return None
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# select as the current tool call the one we're on the state at
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current_tool_call: dict = tool_call_arr[self.current_tool_id] \
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if len(tool_call_arr) > 0 else {}
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# case -- if no tokens have been streamed for the tool, e.g.
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# only the array brackets, stream nothing
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if len(tool_call_arr) == 0:
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return None
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# case: we are starting a new tool in the array
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# -> array has > 0 length AND length has moved past cursor
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elif (len(tool_call_arr) > 0
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and len(tool_call_arr) > self.current_tool_id + 1):
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# if we're moving on to a new call, first make sure we
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# haven't missed anything in the previous one that was
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# auto-generated due to JSON completions, but wasn't
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# streamed to the client yet.
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if self.current_tool_id >= 0:
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diff: Union[str, None] = current_tool_call.get("arguments")
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if diff:
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diff = json.dumps(diff, ensure_ascii=False).replace(
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self.streamed_args_for_tool[self.current_tool_id],
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"")
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delta = DeltaMessage(tool_calls=[
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DeltaToolCall(index=self.current_tool_id,
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function=DeltaFunctionCall(
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arguments=diff).model_dump(
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exclude_none=True))
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])
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self.streamed_args_for_tool[
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self.current_tool_id] += diff
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else:
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delta = None
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else:
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delta = None
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# re-set stuff pertaining to progress in the current tool
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self.current_tool_id = len(tool_call_arr) - 1
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self.current_tool_name_sent = False
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self.streamed_args_for_tool.append("")
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logger.debug("starting on new tool %d", self.current_tool_id)
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return delta
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# case: update an existing tool - this is handled below
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# if the current tool name hasn't been sent, send if available
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# - otherwise send nothing
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if not self.current_tool_name_sent:
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function_name = current_tool_call.get("name")
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if function_name:
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delta = DeltaMessage(tool_calls=[
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DeltaToolCall(index=self.current_tool_id,
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type="function",
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id=MistralToolCall.generate_random_id(),
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function=DeltaFunctionCall(
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name=function_name).model_dump(
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exclude_none=True))
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])
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self.current_tool_name_sent = True
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else:
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delta = None
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# now we know we're on the same tool call and we're streaming
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# arguments
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else:
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prev_arguments = self.prev_tool_call_arr[
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self.current_tool_id].get("arguments")
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cur_arguments = current_tool_call.get("arguments")
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new_text = delta_text.replace("\'", "\"")
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if ('"}' in new_text):
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new_text = new_text[:new_text.rindex('"}')]
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if not cur_arguments and not prev_arguments:
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delta = None
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elif not cur_arguments and prev_arguments:
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logger.error(
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"INVARIANT - impossible to have arguments reset "
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"mid-arguments")
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delta = None
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elif cur_arguments and not prev_arguments:
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cur_arguments_json = json.dumps(cur_arguments,
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ensure_ascii=False)[:-2]
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logger.debug("finding %s in %s", new_text,
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cur_arguments_json)
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if (new_text not in cur_arguments_json):
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return None
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arguments_delta = cur_arguments_json[:cur_arguments_json.
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rindex(new_text) +
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len(new_text)]
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logger.debug("First tokens in arguments received: %s",
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arguments_delta)
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delta = DeltaMessage(tool_calls=[
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DeltaToolCall(index=self.current_tool_id,
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function=DeltaFunctionCall(
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arguments=arguments_delta).
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model_dump(exclude_none=True))
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])
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self.streamed_args_for_tool[
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self.current_tool_id] += arguments_delta
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elif cur_arguments and prev_arguments:
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cur_args_json = json.dumps(cur_arguments,
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ensure_ascii=False)
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prev_args_json = json.dumps(prev_arguments,
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ensure_ascii=False)
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logger.debug("Searching for diff between \n%s\n%s",
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cur_args_json, prev_args_json)
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argument_diff = extract_intermediate_diff(
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cur_args_json, prev_args_json)
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logger.debug("got arguments diff: %s", argument_diff)
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delta = DeltaMessage(tool_calls=[
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DeltaToolCall(index=self.current_tool_id,
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function=DeltaFunctionCall(
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arguments=argument_diff).model_dump(
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exclude_none=True))
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])
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self.streamed_args_for_tool[
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self.current_tool_id] += argument_diff
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else:
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# try parsing it with regular JSON - if it works we're
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# at the end, and we need to send the difference between
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# tokens streamed so far and the valid JSON
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delta = None
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# check to see if the name is defined and has been sent. if so,
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# stream the name - otherwise keep waiting
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# finish by setting old and returning None as base case
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self.prev_tool_call_arr = tool_call_arr
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return delta
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except Exception:
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logger.exception("Error trying to handle streaming tool call.")
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logger.debug(
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"Skipping chunk as a result of tool streaming extraction "
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"error")
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return None
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