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@ -1,11 +1,12 @@
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from __future__ import annotations
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from dataclasses import dataclass
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from typing import NamedTuple, Union
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from typing import Any, Sequence, Union
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from langchain.load.serializable import Serializable
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from langchain.schema.messages import BaseMessage
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@dataclass
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class AgentAction:
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class AgentAction(Serializable):
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"""A full description of an action for an ActionAgent to execute."""
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tool: str
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@ -13,13 +14,57 @@ class AgentAction:
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tool_input: Union[str, dict]
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"""The input to pass in to the Tool."""
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log: str
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"""Additional information to log about the action."""
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"""Additional information to log about the action.
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This log can be used in a few ways. First, it can be used to audit
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what exactly the LLM predicted to lead to this (tool, tool_input).
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Second, it can be used in future iterations to show the LLMs prior
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thoughts. This is useful when (tool, tool_input) does not contain
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full information about the LLM prediction (for example, any `thought`
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before the tool/tool_input)."""
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def __init__(
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self, tool: str, tool_input: Union[str, dict], log: str, **kwargs: Any
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):
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super().__init__(tool=tool, tool_input=tool_input, log=log, **kwargs)
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@property
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def lc_serializable(self) -> bool:
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"""
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Return whether or not the class is serializable.
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"""
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return True
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class AgentActionMessageLog(AgentAction):
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message_log: Sequence[BaseMessage]
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"""Similar to log, this can be used to pass along extra
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information about what exact messages were predicted by the LLM
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before parsing out the (tool, tool_input). This is again useful
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if (tool, tool_input) cannot be used to fully recreate the LLM
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prediction, and you need that LLM prediction (for future agent iteration).
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Compared to `log`, this is useful when the underlying LLM is a
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ChatModel (and therefore returns messages rather than a string)."""
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class AgentFinish(NamedTuple):
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class AgentFinish(Serializable):
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"""The final return value of an ActionAgent."""
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return_values: dict
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"""Dictionary of return values."""
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log: str
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"""Additional information to log about the return value"""
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"""Additional information to log about the return value.
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This is used to pass along the full LLM prediction, not just the parsed out
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return value. For example, if the full LLM prediction was
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`Final Answer: 2` you may want to just return `2` as a return value, but pass
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along the full string as a `log` (for debugging or observability purposes).
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"""
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def __init__(self, return_values: dict, log: str, **kwargs: Any):
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super().__init__(return_values=return_values, log=log, **kwargs)
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@property
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def lc_serializable(self) -> bool:
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"""
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Return whether or not the class is serializable.
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"""
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return True
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