import json
from typing import List, Sequence, Tuple
from langchain_core.agents import AgentAction
from langchain_core.messages import (
AIMessage,
BaseMessage,
ToolMessage,
)
from langchain.agents.output_parsers.tools import ToolAgentAction
def _create_tool_message(
agent_action: ToolAgentAction, observation: str
) -> ToolMessage:
"""Convert agent action and observation into a tool message.
Args:
agent_action: the tool invocation request from the agent.
observation: the result of the tool invocation.
Returns:
ToolMessage that corresponds to the original tool invocation.
Raises:
ValueError: if the observation cannot be converted to a string.
"""
if not isinstance(observation, str):
try:
content = json.dumps(observation, ensure_ascii=False)
except Exception:
content = str(observation)
else:
content = observation
return ToolMessage(
tool_call_id=agent_action.tool_call_id,
content=content,
additional_kwargs={"name": agent_action.tool},
)
def format_to_tool_messages(
intermediate_steps: Sequence[Tuple[AgentAction, str]],
) -> List[BaseMessage]:
"""Convert (AgentAction, tool output) tuples into ToolMessages.
Args:
intermediate_steps: Steps the LLM has taken to date, along with observations.
Returns:
list of messages to send to the LLM for the next prediction.
"""
messages = []
for agent_action, observation in intermediate_steps:
if isinstance(agent_action, ToolAgentAction):
new_messages = list(agent_action.message_log) + [
_create_tool_message(agent_action, observation)
]
messages.extend([new for new in new_messages if new not in messages])
else:
messages.append(AIMessage(content=agent_action.log))
return messages