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import json
from typing import List, Sequence, Tuple
from langchain_core.agents import AgentAction, AgentActionMessageLog
from langchain_core.messages import AIMessage, BaseMessage, FunctionMessage
def _convert_agent_action_to_messages(
agent_action: AgentAction, observation: str
) -> List[BaseMessage]:
"""Convert an agent action to a message.
This code is used to reconstruct the original AI message from the agent action.
Args:
agent_action: Agent action to convert.
Returns:
AIMessage that corresponds to the original tool invocation.
"""
if isinstance(agent_action, AgentActionMessageLog):
return list(agent_action.message_log) + [f"<tool_response>\n{_create_function_message(agent_action, observation)}\n</tool_response>"]
else:
return [AIMessage(content=agent_action.log)]
def _create_function_message(
agent_action: AgentAction, observation: str
) -> str:
"""Convert agent action and observation into a function message.
Args:
agent_action: the tool invocation request from the agent
observation: the result of the tool invocation
Returns:
FunctionMessage that corresponds to the original tool invocation
"""
if not isinstance(observation, str):
try:
content = json.dumps(observation, ensure_ascii=False)
except Exception:
content = str(observation)
else:
content = observation
tool_response = {
"name": agent_action.tool,
"content": content,
}
return json.dumps(tool_response)
def format_to_function_messages(
intermediate_steps: Sequence[Tuple[AgentAction, str]],
) -> List[BaseMessage]:
"""Convert (AgentAction, tool output) tuples into FunctionMessages.
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:
messages.extend(_convert_agent_action_to_messages(agent_action, observation))
return messages
# Backwards compatibility
format_to_functions = format_to_function_messages
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