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c2fc84e6ea
Co-authored-by: openhands <openhands@all-hands.dev> Co-authored-by: Engel Nyst <enyst@users.noreply.github.com>
126 lines
4.6 KiB
Python
126 lines
4.6 KiB
Python
"""This file contains the function calling implementation for different actions.
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This is similar to the functionality of `CodeActResponseParser`.
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"""
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import json
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from litellm import (
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ChatCompletionToolParam,
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ModelResponse,
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)
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from openhands.agenthub.codeact_agent.function_calling import combine_thought
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from openhands.agenthub.codeact_agent.tools import FinishTool
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from openhands.agenthub.loc_agent.tools import (
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SearchEntityTool,
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SearchRepoTool,
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create_explore_tree_structure_tool,
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)
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from openhands.core.exceptions import (
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FunctionCallNotExistsError,
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)
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from openhands.core.logger import openhands_logger as logger
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from openhands.events.action import (
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Action,
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AgentFinishAction,
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IPythonRunCellAction,
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MessageAction,
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)
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from openhands.events.tool import ToolCallMetadata
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def response_to_actions(
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response: ModelResponse,
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mcp_tool_names: list[str] | None = None,
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) -> list[Action]:
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actions: list[Action] = []
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assert len(response.choices) == 1, 'Only one choice is supported for now'
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choice = response.choices[0]
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assistant_msg = choice.message
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if hasattr(assistant_msg, 'tool_calls') and assistant_msg.tool_calls:
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# Check if there's assistant_msg.content. If so, add it to the thought
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thought = ''
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if isinstance(assistant_msg.content, str):
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thought = assistant_msg.content
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elif isinstance(assistant_msg.content, list):
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for msg in assistant_msg.content:
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if msg['type'] == 'text':
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thought += msg['text']
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# Process each tool call to OpenHands action
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for i, tool_call in enumerate(assistant_msg.tool_calls):
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action: Action
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logger.debug(f'Tool call in function_calling.py: {tool_call}')
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try:
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arguments = json.loads(tool_call.function.arguments)
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except json.decoder.JSONDecodeError as e:
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raise RuntimeError(
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f'Failed to parse tool call arguments: {tool_call.function.arguments}'
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) from e
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# ================================================
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# LocAgent's Tools
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# ================================================
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ALL_FUNCTIONS = [
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'explore_tree_structure',
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'search_code_snippets',
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'get_entity_contents',
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]
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if tool_call.function.name in ALL_FUNCTIONS:
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# We implement this in agent_skills, which can be used via Jupyter
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func_name = tool_call.function.name
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code = f'print({func_name}(**{arguments}))'
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logger.debug(f'TOOL CALL: {func_name} with code: {code}')
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action = IPythonRunCellAction(code=code)
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# ================================================
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# AgentFinishAction
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# ================================================
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elif tool_call.function.name == FinishTool['function']['name']:
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action = AgentFinishAction(
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final_thought=arguments.get('message', ''),
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)
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else:
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raise FunctionCallNotExistsError(
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f'Tool {tool_call.function.name} is not registered. (arguments: {arguments}). Please check the tool name and retry with an existing tool.'
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)
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# We only add thought to the first action
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if i == 0:
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action = combine_thought(action, thought)
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# Add metadata for tool calling
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action.tool_call_metadata = ToolCallMetadata(
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tool_call_id=tool_call.id,
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function_name=tool_call.function.name,
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model_response=response,
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total_calls_in_response=len(assistant_msg.tool_calls),
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)
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actions.append(action)
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else:
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actions.append(
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MessageAction(
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content=str(assistant_msg.content) if assistant_msg.content else '',
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wait_for_response=True,
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)
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)
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# Add response id to actions
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# This will ensure we can match both actions without tool calls (e.g. MessageAction)
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# and actions with tool calls (e.g. CmdRunAction, IPythonRunCellAction, etc.)
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# with the token usage data
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for action in actions:
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action.response_id = response.id
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assert len(actions) >= 1
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return actions
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def get_tools() -> list[ChatCompletionToolParam]:
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tools = [FinishTool]
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tools.append(SearchRepoTool)
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tools.append(SearchEntityTool)
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tools.append(create_explore_tree_structure_tool(use_simplified_description=True))
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return tools
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