First commit

vllm/entrypoints/openai/tool_parsers/__init__.py

@@ -0,0 +1,10 @@
+from .abstract_tool_parser import ToolParser, ToolParserManager
+from .hermes_tool_parser import Hermes2ProToolParser
+from .internlm2_tool_parser import Internlm2ToolParser
+from .llama_tool_parser import Llama3JsonToolParser
+from .mistral_tool_parser import MistralToolParser
+
+__all__ = [
+    "ToolParser", "ToolParserManager", "Hermes2ProToolParser",
+    "MistralToolParser", "Internlm2ToolParser", "Llama3JsonToolParser"
+]

vllm/entrypoints/openai/tool_parsers/abstract_tool_parser.py

@@ -0,0 +1,161 @@
+import importlib
+import importlib.util
+import os
+from functools import cached_property
+from typing import Callable, Dict, List, Optional, Sequence, Type, Union
+
+from vllm.entrypoints.openai.protocol import (ChatCompletionRequest,
+                                              DeltaMessage,
+                                              ExtractedToolCallInformation)
+from vllm.logger import init_logger
+from vllm.transformers_utils.tokenizer import AnyTokenizer
+from vllm.utils import is_list_of
+
+logger = init_logger(__name__)
+
+
+class ToolParser:
+    """
+    Abstract ToolParser class that should not be used directly. Provided
+    properties and methods should be used in
+    derived classes.
+    """
+
+    def __init__(self, tokenizer: AnyTokenizer):
+        self.prev_tool_call_arr: List[Dict] = []
+        # the index of the tool call that is currently being parsed
+        self.current_tool_id: int = -1
+        self.current_tool_name_sent: bool = False
+        self.streamed_args_for_tool: List[str] = []
+
+        self.model_tokenizer = tokenizer
+
+    @cached_property
+    def vocab(self) -> Dict[str, int]:
+        # NOTE: Only PreTrainedTokenizerFast is guaranteed to have .vocab
+        # whereas all tokenizers have .get_vocab()
+        return self.model_tokenizer.get_vocab()
+
+    def adjust_request(
+            self, request: ChatCompletionRequest) -> ChatCompletionRequest:
+        """
+        Static method that used to adjust the request parameters.
+        """
+        return request
+
+    def extract_tool_calls(
+            self, model_output: str,
+            request: ChatCompletionRequest) -> ExtractedToolCallInformation:
+        """
+        Static method that should be implemented for extracting tool calls from
+        a complete model-generated string.
+        Used for non-streaming responses where we have the entire model response
+        available before sending to the client.
+        Static because it's stateless.
+        """
+        raise NotImplementedError(
+            "AbstractToolParser.extract_tool_calls has not been implemented!")
+
+    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]:
+        """
+        Instance method that should be implemented for extracting tool calls
+        from an incomplete response; for use when handling tool calls and
+        streaming. Has to be an instance method because  it requires state -
+        the current tokens/diffs, but also the information about what has
+        previously been parsed and extracted (see constructor)
+        """
+        raise NotImplementedError(
+            "AbstractToolParser.extract_tool_calls_streaming has not been "
+            "implemented!")
+
+
+class ToolParserManager:
+    tool_parsers: Dict[str, Type] = {}
+
+    @classmethod
+    def get_tool_parser(cls, name) -> Type:
+        """
+        Get tool parser by name which is registered by `register_module`.
+
+        Raise a KeyError exception if the name is not registered.
+        """
+        if name in cls.tool_parsers:
+            return cls.tool_parsers[name]
+
+        raise KeyError(f"tool helper: '{name}' not found in tool_parsers")
+
+    @classmethod
+    def _register_module(cls,
+                         module: Type,
+                         module_name: Optional[Union[str, List[str]]] = None,
+                         force: bool = True) -> None:
+        if not issubclass(module, ToolParser):
+            raise TypeError(
+                f'module must be subclass of ToolParser, but got {type(module)}'
+            )
+        if module_name is None:
+            module_name = module.__name__
+        if isinstance(module_name, str):
+            module_name = [module_name]
+        for name in module_name:
+            if not force and name in cls.tool_parsers:
+                existed_module = cls.tool_parsers[name]
+                raise KeyError(f'{name} is already registered '
+                               f'at {existed_module.__module__}')
+            cls.tool_parsers[name] = module
+
+    @classmethod
+    def register_module(
+            cls,
+            name: Optional[Union[str, List[str]]] = None,
+            force: bool = True,
+            module: Union[Type, None] = None) -> Union[type, Callable]:
+        """
+        Register module with the given name or name list. it can be used as a
+        decoder(with module as None) or normal function(with module as not 
+        None).
+        """
+        if not isinstance(force, bool):
+            raise TypeError(f'force must be a boolean, but got {type(force)}')
+
+        # raise the error ahead of time
+        if not (name is None or isinstance(name, str)
+                or is_list_of(name, str)):
+            raise TypeError(
+                'name must be None, an instance of str, or a sequence of str, '
+                f'but got {type(name)}')
+
+        # use it as a normal method: x.register_module(module=SomeClass)
+        if module is not None:
+            cls._register_module(module=module, module_name=name, force=force)
+            return module
+
+        # use it as a decorator: @x.register_module()
+        def _register(module):
+            cls._register_module(module=module, module_name=name, force=force)
+            return module
+
+        return _register
+
+    @classmethod
+    def import_tool_parser(cls, plugin_path: str) -> None:
+        """
+        Import a user defined tool parser by the path of the tool parser define
+        file.
+        """
+        module_name = os.path.splitext(os.path.basename(plugin_path))[0]
+        spec = importlib.util.spec_from_file_location(module_name, plugin_path)
+        if spec is None or spec.loader is None:
+            logger.error("load %s from %s failed.", module_name, plugin_path)
+            return
+        module = importlib.util.module_from_spec(spec)
+        spec.loader.exec_module(module)

vllm/entrypoints/openai/tool_parsers/hermes_tool_parser.py

@@ -0,0 +1,338 @@
+import json
+import re
+from typing import Dict, List, Sequence, Union
+
+import partial_json_parser
+from partial_json_parser.core.options import Allow
+
+from vllm.entrypoints.openai.protocol import (ChatCompletionRequest,
+                                              DeltaFunctionCall, DeltaMessage,
+                                              DeltaToolCall,
+                                              ExtractedToolCallInformation,
+                                              FunctionCall, ToolCall)
+from vllm.entrypoints.openai.tool_parsers.abstract_tool_parser import (
+    ToolParser, ToolParserManager)
+from vllm.entrypoints.openai.tool_parsers.utils import (
+    extract_intermediate_diff)
+from vllm.logger import init_logger
+from vllm.transformers_utils.tokenizer import AnyTokenizer, MistralTokenizer
+from vllm.utils import random_uuid
+
+logger = init_logger(__name__)
+
+
+@ToolParserManager.register_module("hermes")
+class Hermes2ProToolParser(ToolParser):
+
+    def __init__(self, tokenizer: AnyTokenizer):
+        super().__init__(tokenizer)
+
+        if isinstance(self.model_tokenizer, MistralTokenizer):
+            logger.error(
+                "Detected Mistral tokenizer when using a Hermes model")
+            self.model_tokenizer = self.model_tokenizer.tokenizer
+
+        self.current_tool_name_sent: bool = False
+        self.prev_tool_call_arr: List[Dict] = []
+        self.current_tool_id: int = -1
+        self.streamed_args_for_tool: List[str] = [
+        ]  # map what has been streamed for each tool so far to a list
+
+        self.tool_call_start_token: str = "<tool_call>"
+        self.tool_call_end_token: str = "</tool_call>"
+
+        self.tool_call_regex = re.compile(
+            r"<tool_call>(.*?)</tool_call>|<tool_call>(.*)", re.DOTALL)
+        self.scratch_pad_regex = re.compile(
+            r"<scratch_pad>(.*?)</scratch_pad>", re.DOTALL)
+
+        if not self.model_tokenizer:
+            raise ValueError(
+                "The model tokenizer must be passed to the ToolParser "
+                "constructor during construction.")
+        self.tool_call_start_token_id = self.vocab.get(
+            self.tool_call_start_token)
+        self.tool_call_end_token_id = self.vocab.get(self.tool_call_end_token)
+        if not self.tool_call_start_token_id or not self.tool_call_end_token_id:
+            raise RuntimeError(
+                "Hermes 2 Pro Tool parser could not locate tool call start/end "
+                "tokens in the tokenizer!")
+
+    def extract_tool_calls(
+        self,
+        model_output: str,
+        request: ChatCompletionRequest,
+    ) -> ExtractedToolCallInformation:
+
+        # sanity check; avoid unnecessary processing
+        if self.tool_call_start_token not in model_output:
+            return ExtractedToolCallInformation(tools_called=False,
+                                                tool_calls=[],
+                                                content=model_output)
+
+        else:
+
+            try:
+                # there are two possible captures - between tags, or between a
+                # tag and end-of-string so the result of
+                # findall is an array of tuples where one is a function call and
+                # the other is None
+                function_call_tuples = (
+                    self.tool_call_regex.findall(model_output))
+
+                # load the JSON, and then use it to build the Function and
+                # Tool Call
+                raw_function_calls = [
+                    json.loads(match[0] if match[0] else match[1])
+                    for match in function_call_tuples
+                ]
+                tool_calls = [
+                    ToolCall(
+                        type="function",
+                        function=FunctionCall(
+                            name=function_call["name"],
+                            # function call args are JSON but as a string
+                            arguments=json.dumps(function_call["arguments"])))
+                    for function_call in raw_function_calls
+                ]
+
+                content = model_output[:model_output.
+                                       find(self.tool_call_start_token)]
+                return ExtractedToolCallInformation(
+                    tools_called=True,
+                    tool_calls=tool_calls,
+                    content=content if content else None)
+
+            except Exception as e:
+                logger.error("Error in extracting tool call from response %s",
+                             e)
+                return ExtractedToolCallInformation(tools_called=False,
+                                                    tool_calls=[],
+                                                    content=model_output)
+
+    def extract_tool_calls_streaming(
+        self,
+        previous_text: str,
+        current_text: str,
+        delta_text: str,
+        previous_token_ids: Sequence[int],
+        current_token_ids: Sequence[int],
+        delta_token_ids: Sequence[int],
+        request: ChatCompletionRequest,
+    ) -> Union[DeltaMessage, None]:
+
+        logger.debug("delta_text: %s", delta_text)
+        logger.debug("delta_token_ids: %s", delta_token_ids)
+        # check to see if we should be streaming a tool call - is there a
+        if self.tool_call_start_token_id not in current_token_ids:
+            logger.debug("No tool call tokens found!")
+            return DeltaMessage(content=delta_text)
+
+        try:
+
+            # figure out where we are in the parsing by counting tool call
+            # start & end tags
+            prev_tool_start_count = previous_token_ids.count(
+                self.tool_call_start_token_id)
+            prev_tool_end_count = previous_token_ids.count(
+                self.tool_call_end_token_id)
+            cur_tool_start_count = current_token_ids.count(
+                self.tool_call_start_token_id)
+            cur_tool_end_count = current_token_ids.count(
+                self.tool_call_end_token_id)
+
+            # case: if we're generating text, OR rounding out a tool call
+            if (cur_tool_start_count == cur_tool_end_count
+                    and prev_tool_end_count == cur_tool_end_count):
+                logger.debug("Generating text content! skipping tool parsing.")
+                if delta_text != self.tool_call_end_token:
+                    return DeltaMessage(content=delta_text)
+
+            # case: if tool open & close tag counts don't match, we're doing
+            # imaginary "else" block here
+            # something with tools with this diff.
+            # flags for partial JSON parting. exported constants from
+            # "Allow" are handled via BIT MASK
+            flags = Allow.ALL if self.current_tool_name_sent \
+                else Allow.ALL & ~Allow.STR
+
+            # case -- we're starting a new tool call
+            if (cur_tool_start_count > cur_tool_end_count
+                    and cur_tool_start_count > prev_tool_start_count):
+                if len(delta_token_ids) > 1:
+                    tool_call_portion = current_text.split(
+                        self.tool_call_start_token)[-1]
+                else:
+                    tool_call_portion = None
+                    delta = None
+
+                text_portion = None
+
+                # set cursors and state appropriately
+                self.current_tool_id += 1
+                self.current_tool_name_sent = False
+                self.streamed_args_for_tool.append("")
+                logger.debug("Starting on a new tool %s", self.current_tool_id)
+
+            # case -- we're updating an existing tool call
+            elif (cur_tool_start_count > cur_tool_end_count
+                  and cur_tool_start_count == prev_tool_start_count):
+
+                # get the portion of the text that's the tool call
+                tool_call_portion = current_text.split(
+                    self.tool_call_start_token)[-1]
+                text_portion = None
+
+            # case -- the current tool call is being closed.
+            elif (cur_tool_start_count == cur_tool_end_count
+                  and cur_tool_end_count > prev_tool_end_count):
+                diff = self.prev_tool_call_arr[self.current_tool_id].get(
+                    "arguments")
+                if diff:
+                    diff = json.dumps(diff).replace(
+                        self.streamed_args_for_tool[self.current_tool_id], "")
+                    logger.debug(
+                        "Finishing tool and found diff that had not "
+                        "been streamed yet: %s", diff)
+                    self.streamed_args_for_tool[self.current_tool_id] \
+                        += diff
+                    return DeltaMessage(tool_calls=[
+                        DeltaToolCall(index=self.current_tool_id,
+                                      function=DeltaFunctionCall(
+                                          arguments=diff).model_dump(
+                                              exclude_none=True))
+                    ])
+
+            # case -- otherwise we're just generating text
+            else:
+                text = delta_text.replace(self.tool_call_start_token, "")
+                text = text.replace(self.tool_call_end_token, "")
+                delta = DeltaMessage(tool_calls=[], content=text)
+                return delta
+
+            try:
+
+                current_tool_call = partial_json_parser.loads(
+                    tool_call_portion or "{}",
+                    flags) if tool_call_portion else None
+                logger.debug("Parsed tool call %s", current_tool_call)
+            except partial_json_parser.core.exceptions.MalformedJSON:
+                logger.debug('not enough tokens to parse into JSON yet')
+                return None
+
+            # case - we haven't sent the tool name yet. If it's available, send
+            #   it. otherwise, wait until it's available.
+            if not self.current_tool_name_sent:
+                function_name: Union[str, None] = current_tool_call.get("name")
+                if function_name:
+                    self.current_tool_name_sent = True
+                    return DeltaMessage(tool_calls=[
+                        DeltaToolCall(index=self.current_tool_id,
+                                      type="function",
+                                      id=f"chatcmpl-tool-{random_uuid()}",
+                                      function=DeltaFunctionCall(
+                                          name=function_name).model_dump(
+                                              exclude_none=True))
+                    ])
+                else:
+                    return None
+            # case -- otherwise, send the tool call delta
+
+            # if the tool call portion is None, send the delta as text
+            if tool_call_portion is None:
+                # if there's text but not tool calls, send that -
+                # otherwise None to skip chunk
+                delta = DeltaMessage(content=delta_text) \
+                    if text_portion is not None else None
+                return delta
+
+            # now, the nitty-gritty of tool calls
+            # now we have the portion to parse as tool call.
+
+            logger.debug("Trying to parse current tool call with ID %s",
+                         self.current_tool_id)
+
+            # if we're starting a new tool call, push an empty object in as
+            #   a placeholder for the arguments
+            if len(self.prev_tool_call_arr) <= self.current_tool_id:
+                self.prev_tool_call_arr.append({})
+
+            # main logic for tool parsing here - compare prev. partially-parsed
+            #   JSON to the current partially-parsed JSON
+            prev_arguments = (
+                self.prev_tool_call_arr[self.current_tool_id].get("arguments"))
+            cur_arguments = current_tool_call.get("arguments")
+
+            logger.debug("diffing old arguments: %s", prev_arguments)
+            logger.debug("against new ones: %s", cur_arguments)
+
+            # case -- no arguments have been created yet. skip sending a delta.
+            if not cur_arguments and not prev_arguments:
+                logger.debug("Skipping text %s - no arguments", delta_text)
+                delta = None
+
+            # case -- prev arguments are defined, but non are now.
+            #   probably impossible, but not a fatal error - just keep going
+            elif not cur_arguments and prev_arguments:
+                logger.error("should be impossible to have arguments reset "
+                             "mid-call. skipping streaming anything.")
+                delta = None
+
+            # case -- we now have the first info about arguments available from
+            #   autocompleting the JSON
+            elif cur_arguments and not prev_arguments:
+
+                cur_arguments_json = json.dumps(cur_arguments)
+                logger.debug("finding %s in %s", delta_text,
+                             cur_arguments_json)
+
+                # get the location where previous args differ from current
+                args_delta_start_loc = cur_arguments_json.index(delta_text) \
+                                       + len(delta_text)
+
+                # use that to find the actual delta
+                arguments_delta = cur_arguments_json[:args_delta_start_loc]
+                logger.debug("First tokens in arguments received: %s",
+                             arguments_delta)
+
+                delta = DeltaMessage(tool_calls=[
+                    DeltaToolCall(index=self.current_tool_id,
+                                  function=DeltaFunctionCall(
+                                      arguments=arguments_delta).model_dump(
+                                          exclude_none=True))
+                ])
+                self.streamed_args_for_tool[self.current_tool_id] \
+                    += arguments_delta
+
+            # last case -- we have an update to existing arguments.
+            elif cur_arguments and prev_arguments:
+
+                cur_args_json = json.dumps(cur_arguments)
+                prev_args_json = json.dumps(prev_arguments)
+                logger.debug("Searching for diff between\n%s", cur_args_json)
+                logger.debug("and\n%s", prev_args_json)
+                argument_diff = extract_intermediate_diff(
+                    cur_args_json, prev_args_json)
+                logger.debug("got argument diff %s", argument_diff)
+                delta = DeltaMessage(tool_calls=[
+                    DeltaToolCall(index=self.current_tool_id,
+                                  function=DeltaFunctionCall(
+                                      arguments=argument_diff).model_dump(
+                                          exclude_none=True))
+                ])
+                self.streamed_args_for_tool[self.current_tool_id] \
+                    += argument_diff
+
+            # handle saving the state for the current tool into
+            # the "prev" list for use in diffing for the next iteration
+            if self.current_tool_id == len(self.prev_tool_call_arr) - 1:
+                self.prev_tool_call_arr[self.current_tool_id] = \
+                    current_tool_call
+            else:
+                self.prev_tool_call_arr.append(current_tool_call)
+
+            return delta
+
+        except Exception as e:
+            logger.error("Error trying to handle streaming tool call: %s", e)
+            return None  # do not stream a delta. skip this token ID.

vllm/entrypoints/openai/tool_parsers/internlm2_tool_parser.py

@@ -0,0 +1,208 @@
+import json
+from typing import Dict, Sequence, Union
+
+import partial_json_parser
+from partial_json_parser.core.options import Allow
+
+from vllm.entrypoints.openai.protocol import (ChatCompletionRequest,
+                                              DeltaFunctionCall, DeltaMessage,
+                                              DeltaToolCall,
+                                              ExtractedToolCallInformation,
+                                              FunctionCall, ToolCall)
+from vllm.entrypoints.openai.tool_parsers.abstract_tool_parser import (
+    ToolParser, ToolParserManager)
+from vllm.entrypoints.openai.tool_parsers.utils import (
+    extract_intermediate_diff)
+from vllm.logger import init_logger
+from vllm.transformers_utils.tokenizer import AnyTokenizer
+from vllm.utils import random_uuid
+
+logger = init_logger(__name__)
+
+
+@ToolParserManager.register_module(["internlm"])
+class Internlm2ToolParser(ToolParser):
+
+    def __init__(self, tokenizer: AnyTokenizer):
+        super().__init__(tokenizer)
+        self.position = 0
+
+    def adjust_request(
+            self, request: ChatCompletionRequest) -> ChatCompletionRequest:
+        if request.tools and request.tool_choice != 'none':
+            # do not skip special tokens because internlm use the special
+            # tokens to indicated the start and end of the tool calls
+            # information.
+            request.skip_special_tokens = False
+        return request
+
+    def get_argments(self, obj):
+        if "parameters" in obj:
+            return obj.get("parameters")
+        elif "arguments" in obj:
+            return obj.get("arguments")
+        return None
+
+    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]:
+        if '<|action_start|>' not in current_text:
+            self.position = len(current_text)
+            return DeltaMessage(content=delta_text)
+        # if the tool call is sended, return a empty delta message
+        # to make sure the finish_reason will be send correctly.
+        if self.current_tool_id > 0:
+            return DeltaMessage(content='')
+
+        last_pos = self.position
+        if '<|action_start|><|plugin|>' not in current_text[last_pos:]:
+            return None
+
+        new_delta = current_text[last_pos:]
+        text, action = new_delta.split('<|action_start|><|plugin|>')
+
+        if len(text) > 0:
+            self.position = self.position + len(text)
+            return DeltaMessage(content=text)
+
+        action = action.strip()
+        action = action.split('<|action_end|>'.strip())[0]
+
+        # bit mask flags for partial JSON parsing. If the name hasn't been
+        # sent yet, don't allow sending
+        # an incomplete string since OpenAI only ever (as far as I have
+        # seen) allows sending the entire tool/ function name at once.
+        flags = Allow.ALL if self.current_tool_name_sent \
+            else Allow.ALL & ~Allow.STR
+
+        try:
+            parsable_arr = action
+
+            # tool calls are generated in an object in inernlm2
+            # it's not support parallel tool calls
+            try:
+                tool_call_arr: Dict = partial_json_parser.loads(
+                    parsable_arr, flags)
+            except partial_json_parser.core.exceptions.MalformedJSON:
+                logger.debug('not enough tokens to parse into JSON yet')
+                return None
+
+            # if the current tool name hasn't been sent, send if available
+            # - otherwise send nothing
+            if not self.current_tool_name_sent:
+                function_name = tool_call_arr.get("name")
+                if function_name:
+                    self.current_tool_id = self.current_tool_id + 1
+                    delta = DeltaMessage(tool_calls=[
+                        DeltaToolCall(index=self.current_tool_id,
+                                      type="function",
+                                      id=f"chatcmpl-tool-{random_uuid()}",
+                                      function=DeltaFunctionCall(
+                                          name=function_name).model_dump(
+                                              exclude_none=True))
+                    ])
+                    self.current_tool_name_sent = True
+                    self.streamed_args_for_tool.append("")
+                else:
+                    delta = None
+            # now we know we're on the same tool call and we're streaming
+            # arguments
+            else:
+                prev_arguments = self.get_argments(
+                    self.prev_tool_call_arr[self.current_tool_id])
+                cur_arguments = self.get_argments(tool_call_arr)
+
+                # not arguments generated
+                if not cur_arguments and not prev_arguments:
+                    delta = None
+                # will never happen
+                elif not cur_arguments and prev_arguments:
+                    logger.error(
+                        "INVARIANT - impossible to have arguments reset "
+                        "mid-arguments")
+                    delta = None
+                # first time to get parameters
+                elif cur_arguments and not prev_arguments:
+                    cur_arguments_json = json.dumps(cur_arguments)
+
+                    arguments_delta = cur_arguments_json[:cur_arguments_json.
+                                                         index(delta_text) +
+                                                         len(delta_text)]
+                    delta = DeltaMessage(tool_calls=[
+                        DeltaToolCall(index=self.current_tool_id,
+                                      function=DeltaFunctionCall(
+                                          arguments=arguments_delta).
+                                      model_dump(exclude_none=True))
+                    ])
+                    self.streamed_args_for_tool[
+                        self.current_tool_id] += arguments_delta
+                # both prev and cur parameters, send the increase parameters
+                elif cur_arguments and prev_arguments:
+                    cur_args_json = json.dumps(cur_arguments)
+                    prev_args_json = json.dumps(prev_arguments)
+
+                    argument_diff = extract_intermediate_diff(
+                        cur_args_json, prev_args_json)
+
+                    delta = DeltaMessage(tool_calls=[
+                        DeltaToolCall(index=self.current_tool_id,
+                                      function=DeltaFunctionCall(
+                                          arguments=argument_diff).model_dump(
+                                              exclude_none=True))
+                    ])
+                    self.streamed_args_for_tool[
+                        self.current_tool_id] += argument_diff
+
+            # check to see if the name is defined and has been sent. if so,
+            # stream the name - otherwise keep waiting
+            # finish by setting old and returning None as base case
+            tool_call_arr["arguments"] = self.get_argments(tool_call_arr)
+            self.prev_tool_call_arr = [tool_call_arr]
+            return delta
+        except Exception as e:
+            logger.error("Error trying to handle streaming tool call: %s", e)
+            logger.debug(
+                "Skipping chunk as a result of tool streaming extraction "
+                "error")
+            return None
+
+    def extract_tool_calls(
+        self,
+        model_output: str,
+        request: ChatCompletionRequest,
+    ) -> ExtractedToolCallInformation:
+        text = model_output
+        tools = request.tools
+        if '<|action_start|><|plugin|>' in text:
+            text, action = text.split('<|action_start|><|plugin|>')
+            action = action.split('<|action_end|>'.strip())[0]
+            action = action[action.find('{'):]
+            action_dict = json.loads(action)
+            name, parameters = action_dict['name'], json.dumps(
+                action_dict.get('parameters', action_dict.get('arguments',
+                                                              {})))
+
+            if not tools or name not in [t.function.name for t in tools]:
+                ExtractedToolCallInformation(tools_called=False,
+                                             tool_calls=[],
+                                             content=text)
+
+            tool_calls = [
+                ToolCall(
+                    function=FunctionCall(name=name, arguments=parameters))
+            ]
+            return ExtractedToolCallInformation(
+                tools_called=True,
+                tool_calls=tool_calls,
+                content=text if len(text) > 0 else None)
+
+        return ExtractedToolCallInformation(tools_called=False,
+                                            tool_calls=[],
+                                            content=text)

vllm/entrypoints/openai/tool_parsers/llama_tool_parser.py

@@ -0,0 +1,277 @@
+import json
+import re
+from json import JSONDecodeError, JSONDecoder
+from typing import Dict, List, Sequence, Union
+
+import partial_json_parser
+from partial_json_parser.core.options import Allow
+from transformers import PreTrainedTokenizerBase
+
+from vllm.entrypoints.openai.protocol import (ChatCompletionRequest,
+                                              DeltaFunctionCall, DeltaMessage,
+                                              DeltaToolCall,
+                                              ExtractedToolCallInformation,
+                                              FunctionCall, ToolCall)
+from vllm.entrypoints.openai.tool_parsers.abstract_tool_parser import (
+    ToolParser, ToolParserManager)
+from vllm.entrypoints.openai.tool_parsers.utils import find_common_prefix
+from vllm.logger import init_logger
+from vllm.utils import random_uuid
+
+logger = init_logger(__name__)
+
+
+# partial_json_parser doesn't support extra data and
+# JSONDecorder.raw_decode doesn't support partial JSON
+def partial_json_loads(input_str, flags):
+    try:
+        return (partial_json_parser.loads(input_str, flags), len(input_str))
+    except JSONDecodeError as e:
+        if "Extra data" in e.msg:
+            dec = JSONDecoder()
+            return dec.raw_decode(input_str)
+        else:
+            raise
+
+
+def is_complete_json(input_str):
+    try:
+        json.loads(input_str)
+        return True
+    except JSONDecodeError:
+        return False
+
+
+@ToolParserManager.register_module("llama3_json")
+class Llama3JsonToolParser(ToolParser):
+    """
+    Tool call parser for Llama 3.1 models intended for use with the
+    examples/tool_chat_template_llama.jinja template.
+
+    Used when --enable-auto-tool-choice --tool-call-parser mistral are all set
+    """
+
+    def __init__(self, tokenizer: PreTrainedTokenizerBase):
+        super().__init__(tokenizer)
+
+        # initialize properties used for state when parsing tool calls in
+        # streaming mode
+        self.prev_tool_call_arr: List[Dict] = []
+        self.current_tool_id: int = -1
+        self.current_tool_name_sent: bool = False
+        self.streamed_args_for_tool: List[str] = [
+        ]  # map what has been streamed for each tool so far to a list
+        self.bot_token = "<|python_tag|>"
+        self.bot_token_id = tokenizer.encode(self.bot_token,
+                                             add_special_tokens=False)[0]
+        self.tool_call_regex = re.compile(r"\[{.*?}\]", re.DOTALL)
+
+    def extract_tool_calls(
+            self, model_output: str,
+            request: ChatCompletionRequest) -> ExtractedToolCallInformation:
+        """
+        Extract the tool calls from a complete model response.
+        """
+        # case -- if a tool call token is not present, return a text response
+        if not (model_output.startswith(self.bot_token)
+                or model_output.startswith('{')):
+            return ExtractedToolCallInformation(tools_called=False,
+                                                tool_calls=[],
+                                                content=model_output)
+
+        try:
+            # load the JSON, and then use it to build the Function and
+            # Tool Call
+            dec = JSONDecoder()
+            function_call_arr = []
+
+            # depending on the prompt format the Llama model may or may not
+            # prefix the output with the <|python_tag|> token
+            start_idx = len(self.bot_token) if model_output.startswith(
+                self.bot_token) else 0
+            while start_idx < len(model_output):
+                (obj, end_idx) = dec.raw_decode(model_output[start_idx:])
+                start_idx += end_idx + len('; ')
+                function_call_arr.append(obj)
+
+            tool_calls: List[ToolCall] = [
+                ToolCall(
+                    type="function",
+                    function=FunctionCall(
+                        name=raw_function_call["name"],
+                        # function call args are JSON but as a string
+                        arguments=json.dumps(raw_function_call["arguments"] \
+                                if "arguments" in raw_function_call \
+                                else raw_function_call["parameters"])))
+                for raw_function_call in function_call_arr
+            ]
+
+            # get any content before  the tool call
+            ret = ExtractedToolCallInformation(tools_called=True,
+                                               tool_calls=tool_calls,
+                                               content=None)
+            return ret
+
+        except Exception as e:
+            logger.error("Error in extracting tool call from response: %s", e)
+            print("ERROR", e)
+            # return information to just treat the tool call as regular JSON
+            return ExtractedToolCallInformation(tools_called=False,
+                                                tool_calls=[],
+                                                content=model_output)
+
+    def extract_tool_calls_streaming(
+        self,
+        previous_text: str,
+        current_text: str,
+        delta_text: str,
+        previous_token_ids: Sequence[int],
+        current_token_ids: Sequence[int],
+        delta_token_ids: Sequence[int],
+        request: ChatCompletionRequest,
+    ) -> Union[DeltaMessage, None]:
+
+        if not (current_text.startswith(self.bot_token)
+                or current_text.startswith('{')):
+            return DeltaMessage(content=delta_text)
+
+        # bit mask flags for partial JSON parsing. If the name hasn't been
+        # sent yet, don't allow sending
+        # an incomplete string since OpenAI only ever (as far as I have
+        # seen) allows sending the entire tool/ function name at once.
+        flags = Allow.ALL if self.current_tool_name_sent \
+            else Allow.ALL & ~Allow.STR
+        try:
+            tool_call_arr = []
+            is_complete = []
+            try:
+                # depending on the prompt format the Llama model may or may not
+                # prefix the output with the <|python_tag|> token
+                start_idx = len(self.bot_token) if current_text.startswith(
+                    self.bot_token) else 0
+                while start_idx < len(current_text):
+                    (obj,
+                     end_idx) = partial_json_loads(current_text[start_idx:],
+                                                   flags)
+                    is_complete.append(
+                        is_complete_json(current_text[start_idx:start_idx +
+                                                      end_idx]))
+                    start_idx += end_idx + len('; ')
+                    # depending on the prompt Llama can use
+                    # either arguments or parameters
+                    if "parameters" in obj:
+                        assert "arguments" not in obj, \
+                            "model generated both parameters and arguments"
+                        obj["arguments"] = obj["parameters"]
+                    tool_call_arr.append(obj)
+            except partial_json_parser.core.exceptions.MalformedJSON:
+                logger.debug('not enough tokens to parse into JSON yet')
+                return None
+
+            # select as the current tool call the one we're on the state at
+            current_tool_call: Dict = tool_call_arr[self.current_tool_id] \
+                if len(tool_call_arr) > 0 else {}
+
+            # case -- if no tokens have been streamed for the tool, e.g.
+            #   only the array brackets, stream nothing
+            if len(tool_call_arr) == 0:
+                return None
+
+            # case: we are starting a new tool in the array
+            #   -> array has > 0 length AND length has moved past cursor
+            elif (len(tool_call_arr) > 0
+                  and len(tool_call_arr) > self.current_tool_id + 1):
+
+                # if we're moving on to a new call, first make sure we
+                # haven't missed anything in the previous one that was
+                # auto-generated due to JSON completions, but wasn't
+                # streamed to the client yet.
+                if self.current_tool_id >= 0:
+                    cur_arguments = current_tool_call.get("arguments")
+                    if cur_arguments:
+                        cur_args_json = json.dumps(cur_arguments)
+                        sent = len(
+                            self.streamed_args_for_tool[self.current_tool_id])
+                        argument_diff = cur_args_json[sent:]
+
+                        logger.debug("got arguments diff: %s", argument_diff)
+                        delta = DeltaMessage(tool_calls=[
+                            DeltaToolCall(index=self.current_tool_id,
+                                          function=DeltaFunctionCall(
+                                              arguments=argument_diff).
+                                          model_dump(exclude_none=True))
+                        ])
+                        self.streamed_args_for_tool[
+                            self.current_tool_id] += argument_diff
+                    else:
+                        delta = None
+                else:
+                    delta = None
+                # re-set stuff pertaining to progress in the current tool
+                self.current_tool_id = len(tool_call_arr) - 1
+                self.current_tool_name_sent = False
+                self.streamed_args_for_tool.append("")
+                logger.debug("starting on new tool %d", self.current_tool_id)
+                return delta
+
+            # if the current tool name hasn't been sent, send if available
+            # - otherwise send nothing
+            elif not self.current_tool_name_sent:
+                function_name = current_tool_call.get("name")
+                if function_name:
+
+                    delta = DeltaMessage(tool_calls=[
+                        DeltaToolCall(index=self.current_tool_id,
+                                      type="function",
+                                      id=f"chatcmpl-tool-{random_uuid()}",
+                                      function=DeltaFunctionCall(
+                                          name=function_name).model_dump(
+                                              exclude_none=True))
+                    ])
+                    self.current_tool_name_sent = True
+                else:
+                    delta = None
+
+            # now we know we're on the same tool call and we're streaming
+            # arguments
+            else:
+                cur_arguments = current_tool_call.get("arguments")
+                delta = None
+
+                if cur_arguments:
+                    sent = len(
+                        self.streamed_args_for_tool[self.current_tool_id])
+                    cur_args_json = json.dumps(cur_arguments)
+                    prev_arguments = self.prev_tool_call_arr[
+                        self.current_tool_id].get("arguments")
+
+                    argument_diff = None
+                    if is_complete[self.current_tool_id]:
+                        argument_diff = cur_args_json[sent:]
+                    elif prev_arguments:
+                        prev_args_json = json.dumps(prev_arguments)
+                        if cur_args_json != prev_args_json:
+
+                            prefix = find_common_prefix(
+                                prev_args_json, cur_args_json)
+                            argument_diff = prefix[sent:]
+
+                    if argument_diff is not None:
+                        delta = DeltaMessage(tool_calls=[
+                            DeltaToolCall(index=self.current_tool_id,
+                                          function=DeltaFunctionCall(
+                                              arguments=argument_diff).
+                                          model_dump(exclude_none=True))
+                        ])
+                        self.streamed_args_for_tool[
+                            self.current_tool_id] += argument_diff
+
+            self.prev_tool_call_arr = tool_call_arr
+            return delta
+
+        except Exception as e:
+            logger.error("Error trying to handle streaming tool call: %s", e)
+            logger.debug(
+                "Skipping chunk as a result of tool streaming extraction "
+                "error")
+            return None

vllm/entrypoints/openai/tool_parsers/mistral_tool_parser.py

@@ -0,0 +1,306 @@
+import json
+import re
+from random import choices
+from string import ascii_letters, digits
+from typing import Dict, List, Sequence, Union
+
+import partial_json_parser
+from partial_json_parser.core.options import Allow
+from pydantic import Field
+
+from vllm.entrypoints.openai.protocol import (ChatCompletionRequest,
+                                              DeltaFunctionCall, DeltaMessage,
+                                              DeltaToolCall,
+                                              ExtractedToolCallInformation,
+                                              FunctionCall, ToolCall)
+from vllm.entrypoints.openai.tool_parsers.abstract_tool_parser import (
+    ToolParser, ToolParserManager)
+from vllm.entrypoints.openai.tool_parsers.utils import (
+    extract_intermediate_diff)
+from vllm.logger import init_logger
+from vllm.transformers_utils.tokenizer import AnyTokenizer, MistralTokenizer
+from vllm.utils import random_uuid
+
+logger = init_logger(__name__)
+
+ALPHANUMERIC = ascii_letters + digits
+
+
+class MistralToolCall(ToolCall):
+    id: str = Field(
+        default_factory=lambda: MistralToolCall.generate_random_id())
+
+    @staticmethod
+    def generate_random_id():
+        # Mistral Tool Call Ids must be alphanumeric with a maximum length of 9.
+        # https://github.com/mistralai/mistral-common/blob/21ee9f6cee3441e9bb1e6ed2d10173f90bd9b94b/src/mistral_common/protocol/instruct/validator.py#L299
+        return "".join(choices(ALPHANUMERIC, k=9))
+
+
+@ToolParserManager.register_module("mistral")
+class MistralToolParser(ToolParser):
+    """
+    Tool call parser for Mistral 7B Instruct v0.3, intended for use with the
+    examples/tool_chat_template_mistral.jinja template.
+
+    Used when --enable-auto-tool-choice --tool-call-parser mistral are all set
+    """
+
+    def __init__(self, tokenizer: AnyTokenizer):
+        super().__init__(tokenizer)
+
+        if not isinstance(self.model_tokenizer, MistralTokenizer):
+            logger.info("Non-Mistral tokenizer detected when using a Mistral "
+                        "model...")
+
+        # initialize properties used for state when parsing tool calls in
+        # streaming mode
+        self.prev_tool_call_arr: List[Dict] = []
+        self.current_tool_id: int = -1
+        self.current_tool_name_sent: bool = False
+        self.streamed_args_for_tool: List[str] = [
+        ]  # map what has been streamed for each tool so far to a list
+        self.bot_token = "[TOOL_CALLS]"
+        self.bot_token_id = self.vocab.get(self.bot_token)
+        self.tool_call_regex = re.compile(r"\[{.*?}\]", re.DOTALL)
+        if not self.bot_token_id:
+            raise RuntimeError(
+                "Mistral Tool Parser could not locate the tool call token in "
+                "the tokenizer!")
+
+    def extract_tool_calls(
+        self,
+        model_output: str,
+        request: ChatCompletionRequest,
+    ) -> ExtractedToolCallInformation:
+        """
+        Extract the tool calls from a complete model response. Requires
+        find-and-replacing single quotes with double quotes for JSON parsing,
+        make sure your tool call arguments don't ever include quotes!
+        """
+
+        # case -- if a tool call token is not present, return a text response
+        if self.bot_token not in model_output:
+            return ExtractedToolCallInformation(tools_called=False,
+                                                tool_calls=[],
+                                                content=model_output)
+        try:
+
+            # use a regex to find the tool call. remove the BOT token
+            #   and make sure to replace single quotes with double quotes
+            raw_tool_call = self.tool_call_regex.findall(
+                model_output.replace(self.bot_token, ""))[0]
+
+            # load the JSON, and then use it to build the Function and
+            # Tool Call
+            function_call_arr = json.loads(raw_tool_call)
+            tool_calls: List[MistralToolCall] = [
+                MistralToolCall(
+                    type="function",
+                    function=FunctionCall(
+                        name=raw_function_call["name"],
+                        # function call args are JSON but as a string
+                        arguments=json.dumps(raw_function_call["arguments"])))
+                for raw_function_call in function_call_arr
+            ]
+
+            # get any content before  the tool call
+            content = model_output.split(self.bot_token)[0]
+            return ExtractedToolCallInformation(
+                tools_called=True,
+                tool_calls=tool_calls,
+                content=content if len(content) > 0 else None)
+
+        except Exception as e:
+            logger.error("Error in extracting tool call from response: %s", e)
+            # return information to just treat the tool call as regular JSON
+            return ExtractedToolCallInformation(tools_called=False,
+                                                tool_calls=[],
+                                                content=model_output)
+
+    def extract_tool_calls_streaming(
+        self,
+        previous_text: str,
+        current_text: str,
+        delta_text: str,
+        previous_token_ids: Sequence[int],
+        current_token_ids: Sequence[int],
+        delta_token_ids: Sequence[int],
+        request: ChatCompletionRequest,
+    ) -> Union[DeltaMessage, None]:
+
+        # if the tool call token is not in the tokens generated so far, append
+        # output to contents since it's not a tool
+        if self.bot_token not in current_text:
+            return DeltaMessage(content=delta_text)
+
+        # if the tool call token ID IS in the tokens generated so far, that
+        # means we're parsing as tool calls now
+
+        # handle if we detected the BOT token which means the start of tool
+        # calling
+        if (self.bot_token_id in delta_token_ids
+                and len(delta_token_ids) == 1):
+            # if it's the only token, return None, so we don't send a chat
+            # completion any don't send a control token
+            return None
+
+        # bit mask flags for partial JSON parsing. If the name hasn't been
+        # sent yet, don't allow sending
+        # an incomplete string since OpenAI only ever (as far as I have
+        # seen) allows sending the entire tool/ function name at once.
+        flags = Allow.ALL if self.current_tool_name_sent \
+            else Allow.ALL & ~Allow.STR
+        try:
+
+            # replace BOT token with empty string, and convert single quotes
+            # to double to allow parsing as JSON since mistral uses single
+            # quotes instead of double for tool calls
+            parsable_arr = current_text.split(self.bot_token)[-1]
+
+            # tool calls are generated in an array, so do partial JSON
+            # parsing on the entire array
+            try:
+                tool_call_arr: List[Dict] = partial_json_parser.loads(
+                    parsable_arr, flags)
+            except partial_json_parser.core.exceptions.MalformedJSON:
+                logger.debug('not enough tokens to parse into JSON yet')
+                return None
+
+            # select as the current tool call the one we're on the state at
+
+            current_tool_call: Dict = tool_call_arr[self.current_tool_id] \
+                if len(tool_call_arr) > 0 else {}
+
+            # case -- if no tokens have been streamed for the tool, e.g.
+            #   only the array brackets, stream nothing
+            if len(tool_call_arr) == 0:
+                return None
+
+            # case: we are starting a new tool in the array
+            #   -> array has > 0 length AND length has moved past cursor
+            elif (len(tool_call_arr) > 0
+                  and len(tool_call_arr) > self.current_tool_id + 1):
+
+                # if we're moving on to a new call, first make sure we
+                # haven't missed anything in the previous one that was
+                # auto-generated due to JSON completions, but wasn't
+                # streamed to the client yet.
+                if self.current_tool_id >= 0:
+                    diff: Union[str, None] = current_tool_call.get("arguments")
+
+                    if diff:
+                        diff = json.dumps(diff).replace(
+                            self.streamed_args_for_tool[self.current_tool_id],
+                            "")
+                        delta = DeltaMessage(tool_calls=[
+                            DeltaToolCall(index=self.current_tool_id,
+                                          function=DeltaFunctionCall(
+                                              arguments=diff).model_dump(
+                                                  exclude_none=True))
+                        ])
+                        self.streamed_args_for_tool[
+                            self.current_tool_id] += diff
+                    else:
+                        delta = None
+                else:
+                    delta = None
+                # re-set stuff pertaining to progress in the current tool
+                self.current_tool_id = len(tool_call_arr) - 1
+                self.current_tool_name_sent = False
+                self.streamed_args_for_tool.append("")
+                logger.debug("starting on new tool %d", self.current_tool_id)
+                return delta
+
+            # case: update an existing tool - this is handled below
+
+            # if the current tool name hasn't been sent, send if available
+            # - otherwise send nothing
+            if not self.current_tool_name_sent:
+                function_name = current_tool_call.get("name")
+                if function_name:
+
+                    delta = DeltaMessage(tool_calls=[
+                        DeltaToolCall(index=self.current_tool_id,
+                                      type="function",
+                                      id=f"chatcmpl-tool-{random_uuid()}",
+                                      function=DeltaFunctionCall(
+                                          name=function_name).model_dump(
+                                              exclude_none=True))
+                    ])
+                    self.current_tool_name_sent = True
+                else:
+                    delta = None
+
+            # now we know we're on the same tool call and we're streaming
+            # arguments
+            else:
+
+                prev_arguments = self.prev_tool_call_arr[
+                    self.current_tool_id].get("arguments")
+                cur_arguments = current_tool_call.get("arguments")
+
+                new_text = delta_text.replace("\'", "\"")
+
+                if not cur_arguments and not prev_arguments:
+
+                    delta = None
+                elif not cur_arguments and prev_arguments:
+                    logger.error(
+                        "INVARIANT - impossible to have arguments reset "
+                        "mid-arguments")
+                    delta = None
+                elif cur_arguments and not prev_arguments:
+                    cur_arguments_json = json.dumps(cur_arguments)
+                    logger.debug("finding %s in %s", new_text,
+                                 cur_arguments_json)
+
+                    arguments_delta = cur_arguments_json[:cur_arguments_json.
+                                                         index(new_text) +
+                                                         len(new_text)]
+                    logger.debug("First tokens in arguments received: %s",
+                                 arguments_delta)
+                    delta = DeltaMessage(tool_calls=[
+                        DeltaToolCall(index=self.current_tool_id,
+                                      function=DeltaFunctionCall(
+                                          arguments=arguments_delta).
+                                      model_dump(exclude_none=True))
+                    ])
+                    self.streamed_args_for_tool[
+                        self.current_tool_id] += arguments_delta
+
+                elif cur_arguments and prev_arguments:
+                    cur_args_json = json.dumps(cur_arguments)
+                    prev_args_json = json.dumps(prev_arguments)
+                    logger.debug("Searching for diff between \n%s\n%s",
+                                 cur_args_json, prev_args_json)
+
+                    argument_diff = extract_intermediate_diff(
+                        cur_args_json, prev_args_json)
+                    logger.debug("got arguments diff: %s", argument_diff)
+                    delta = DeltaMessage(tool_calls=[
+                        DeltaToolCall(index=self.current_tool_id,
+                                      function=DeltaFunctionCall(
+                                          arguments=argument_diff).model_dump(
+                                              exclude_none=True))
+                    ])
+                    self.streamed_args_for_tool[
+                        self.current_tool_id] += argument_diff
+                else:
+                    # try parsing it with regular JSON - if it works we're
+                    # at the end, and we need to send the difference between
+                    # tokens streamed so far and the valid JSON
+                    delta = None
+
+            # check to see if the name is defined and has been sent. if so,
+            # stream the name - otherwise keep waiting
+            # finish by setting old and returning None as base case
+            self.prev_tool_call_arr = tool_call_arr
+            return delta
+
+        except Exception as e:
+            logger.error("Error trying to handle streaming tool call: %s", e)
+            logger.debug(
+                "Skipping chunk as a result of tool streaming extraction "
+                "error")
+            return None

vllm/entrypoints/openai/tool_parsers/utils.py

@@ -0,0 +1,87 @@
+def find_common_prefix(s1: str, s2: str) -> str:
+    """
+    Finds a common prefix that is shared between two strings, if there is one.
+    Order of arguments is NOT important.
+
+    This function is provided as a UTILITY for extracting information from JSON
+    generated by partial_json_parser, to help in ensuring that the right tokens
+    are returned in streaming, so that close-quotes, close-brackets and
+    close-braces are not returned prematurely.
+
+    e.g. find_common_prefix('{"fruit": "ap"}', '{"fruit": "apple"}') ->
+    '{"fruit": "ap'
+    """
+    prefix = ''
+    min_length = min(len(s1), len(s2))
+    for i in range(0, min_length):
+        if s1[i] == s2[i]:
+            prefix += s1[i]
+        else:
+            break
+    return prefix
+
+
+def find_common_suffix(s1: str, s2: str) -> str:
+    """
+    Finds a common suffix shared between two strings, if there is one. Order of
+    arguments is NOT important.
+    Stops when the suffix ends OR it hits an alphanumeric character
+
+    e.g. find_common_suffix('{"fruit": "ap"}', '{"fruit": "apple"}') -> '"}'
+    """
+    suffix = ''
+    min_length = min(len(s1), len(s2))
+    for i in range(1, min_length + 1):
+        if s1[-i] == s2[-i] and not s1[-i].isalnum():
+            suffix = s1[-i] + suffix
+        else:
+            break
+    return suffix
+
+
+def extract_intermediate_diff(curr: str, old: str) -> str:
+    """
+    Given two strings, extract the difference in the middle between two strings
+    that are known to have a common prefix and/or suffix.
+
+    This function is provided as a UTILITY for extracting information from JSON
+    generated by partial_json_parser, to help in ensuring that the right tokens
+    are returned in streaming, so that close-quotes, close-brackets and
+    close-braces are not returned prematurely. The order of arguments IS
+    important - the new version of the partially-parsed JSON must be the first
+    argument, and the secnod argument must be from the previous generation.
+
+    What it returns, is tokens that should be streamed to the client.
+
+    e.g. extract_intermediate_diff('{"fruit": "apple"}', '{"fruit": "ap"}')
+        -> 'ple'
+
+    """
+    suffix = find_common_suffix(curr, old)
+
+    old = old[::-1].replace(suffix[::-1], '', 1)[::-1]
+    prefix = find_common_prefix(curr, old)
+    diff = curr
+    if len(suffix):
+        diff = diff[::-1].replace(suffix[::-1], '', 1)[::-1]
+
+    if len(prefix):
+        # replace the prefix only once in case it's mirrored
+        diff = diff.replace(prefix, '', 1)
+
+    return diff
+
+
+def find_all_indices(string, substring):
+    """
+    Find all (starting) indices of a substring in a given string. Useful for
+    tool call extraction
+    """
+    indices = []
+    index = -1
+    while True:
+        index = string.find(substring, index + 1)
+        if index == -1:
+            break
+        indices.append(index)
+    return indices