Aphrodite Tool Parser Plugin System

Tool parsers in Aphrodite help convert model outputs into OpenAI-compatible function/tool calls. This guide goes through creating and using custom tool parsers.

Creating a Tool Parser Plugin

Basic Structure

Create a new Python file (e.g. my_tool_parser.py) with the following structure:

from typing import Dict, Sequence, Union

from aphrodite.endpoints.openai.protocol import (
    ChatCompletionRequest,
    DeltaMessage,
    ExtractedToolCallInformation,
    ToolCall,
    FunctionCall
)
from aphrodite.endpoints.openai.tool_parsers.abstract_tool_parser import (
    ToolParser,
    ToolParserManager
)

@ToolParserManager.register_module(["my_parser"])  # register the unique tool name
class MyToolParser(ToolParser):
    def __init__(self, tokenizer):
        super().__init__(tokenizer)
        # add any custom initialization here

    def adjust_request(self, request: ChatCompletionRequest) -> ChatCompletionRequest:
        """Optional: Modify the request before processing"""
        return request

    def extract_tool_calls(
        self,
        model_output: str,
        request: ChatCompletionRequest,
    ) -> ExtractedToolCallInformation:
        """Handle non-streaming tool call extraction"""
        # implementation here

    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],
    ) -> Union[DeltaMessage, None]:
        """Handle streaming tool call extraction"""
        # implementation here

Required Methods

extracted_tool_calls

This method handles complete (non-streaming) responses.

• Input: Complete model output string.
• Output: ExtractedToolCallInformation obj containing:
  • tools_called: bool indicating if tool calls were detected
  • tool_calls: List of parsed tool calls
  • content: Any non-tool-call content Example:

def extract_tool_calls(
    self,
    model_output: str,
    request: ChatCompletionRequest,
) -> ExtractedToolCallInformation:
    # Example: Parse format "FUNCTION_NAME(arguments)"
    if "(" not in model_output:
        return ExtractedToolCallInformation(
            tools_called=False,
            tool_calls=[],
            content=model_output
        )

    try:
        name, args = model_output.split("(", 1)
        args = args.rsplit(")", 1)[0]

        tool_calls = [
            ToolCall(
                function=FunctionCall(
                    name=name.strip(),
                    arguments=args.strip()
                )
            )
        ]

        return ExtractedToolCallInformation(
            tools_called=True,
            tool_calls=tool_calls,
            content=None
        )
    except Exception:
        return ExtractedToolCallInformation(
            tools_called=False,
            tool_calls=[],
            content=model_output
        )

extract_tool_calls_streaming

This method handles streaming responses token by token:

• called for each new token
• must track state between calls
• return None if not ready to send delta
• return DeltaMessage when ready to send updates

Optional Methods

adjust_request

Modify the request before processing if needed.

def adjust_request(
    self,
    request: ChatCompletionRequest
) -> ChatCompletionRequest:
    # Example: Disable special token skipping
    request.skip_special_tokens = False
    return request

Using the Tool Parser

Starting the Server

Launch a local Aphrodite instance:

aphrodite run <your_model> \
    --tool-parser-plugin /path/to/my_tool_parser.py \
    --tool-call-parser my_parser \
    --enable-auto-tool-choice \
    --chat-template /path/to/chat_template.jinja

Making API calls

Use the OpenAI API format:

import openai

response = openai.ChatCompletion.create(
    model="your_model",
    messages=[
        {"role": "user", "content": "What's the weather in Paris?"}
    ],
    tools=[{
        "type": "function",
        "function": {
            "name": "get_weather",
            "description": "Get the weather in a location",
            "parameters": {
                "type": "object",
                "properties": {
                    "location": {
                        "type": "string",
                        "description": "The city and state, e.g. San Francisco, CA"
                    }
                },
                "required": ["location"]
            }
        }
    }],
    tool_choice="auto"
)

Example Implementations

For a more complete example set, see tool_parsers.