llama-stack-mirror/llama_toolchain/agentic_system/tools/custom.py
Ashwin Bharambe be19b22391 Bring agentic system api to toolchain
Add adapter dependencies and resolve adapters using a topological sort
2024-08-04 17:33:29 -07:00

103 lines
3.1 KiB
Python

# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
import json
from abc import abstractmethod
from typing import Dict, List
from llama_models.llama3_1.api.datatypes import * # noqa: F403
from llama_toolchain.agentic_system.api import * # noqa: F403
from .builtin import interpret_content_as_attachment
class CustomTool:
"""
Developers can define their custom tools that models can use
by extending this class.
Developers need to provide
- name
- description
- params_definition
- implement tool's behavior in `run_impl` method
NOTE: The return of the `run` method needs to be json serializable
"""
@abstractmethod
def get_name(self) -> str:
raise NotImplementedError
@abstractmethod
def get_description(self) -> str:
raise NotImplementedError
@abstractmethod
def get_params_definition(self) -> Dict[str, ToolParamDefinition]:
raise NotImplementedError
def get_instruction_string(self) -> str:
return f"Use the function '{self.get_name()}' to: {self.get_description()}"
def parameters_for_system_prompt(self) -> str:
return json.dumps(
{
"name": self.get_name(),
"description": self.get_description(),
"parameters": {
name: definition.__dict__
for name, definition in self.get_params_definition().items()
},
}
)
def get_tool_definition(self) -> AgenticSystemToolDefinition:
return AgenticSystemToolDefinition(
tool_name=self.get_name(),
description=self.get_description(),
parameters=self.get_params_definition(),
)
@abstractmethod
async def run(self, messages: List[Message]) -> List[Message]:
raise NotImplementedError
class SingleMessageCustomTool(CustomTool):
"""
Helper class to handle custom tools that take a single message
Extending this class and implementing the `run_impl` method will
allow for the tool be called by the model and the necessary plumbing.
"""
async def run(self, messages: List[CompletionMessage]) -> List[ToolResponseMessage]:
assert len(messages) == 1, "Expected single message"
message = messages[0]
tool_call = message.tool_calls[0]
try:
response = await self.run_impl(**tool_call.arguments)
response_str = json.dumps(response, ensure_ascii=False)
except Exception as e:
response_str = f"Error when running tool: {e}"
message = ToolResponseMessage(
call_id=tool_call.call_id,
tool_name=tool_call.tool_name,
content=response_str,
)
if attachment := interpret_content_as_attachment(response_str):
message.content = attachment
return [message]
@abstractmethod
async def run_impl(self, *args, **kwargs):
raise NotImplementedError()