Source code for langchain_community.callbacks.aim_callback

from copy import deepcopy
from typing import Any, Dict, List, Optional

from langchain_core.agents import AgentAction, AgentFinish
from langchain_core.callbacks import BaseCallbackHandler
from langchain_core.outputs import LLMResult
from langchain_core.utils import guard_import


[docs]def import_aim() -> Any: """Import the aim python package and raise an error if it is not installed.""" return guard_import("aim")
[docs]class BaseMetadataCallbackHandler: """Callback handler for the metadata and associated function states for callbacks. Attributes: step (int): The current step. starts (int): The number of times the start method has been called. ends (int): The number of times the end method has been called. errors (int): The number of times the error method has been called. text_ctr (int): The number of times the text method has been called. ignore_llm_ (bool): Whether to ignore llm callbacks. ignore_chain_ (bool): Whether to ignore chain callbacks. ignore_agent_ (bool): Whether to ignore agent callbacks. ignore_retriever_ (bool): Whether to ignore retriever callbacks. always_verbose_ (bool): Whether to always be verbose. chain_starts (int): The number of times the chain start method has been called. chain_ends (int): The number of times the chain end method has been called. llm_starts (int): The number of times the llm start method has been called. llm_ends (int): The number of times the llm end method has been called. llm_streams (int): The number of times the text method has been called. tool_starts (int): The number of times the tool start method has been called. tool_ends (int): The number of times the tool end method has been called. agent_ends (int): The number of times the agent end method has been called. """
[docs] def __init__(self) -> None: self.step = 0 self.starts = 0 self.ends = 0 self.errors = 0 self.text_ctr = 0 self.ignore_llm_ = False self.ignore_chain_ = False self.ignore_agent_ = False self.ignore_retriever_ = False self.always_verbose_ = False self.chain_starts = 0 self.chain_ends = 0 self.llm_starts = 0 self.llm_ends = 0 self.llm_streams = 0 self.tool_starts = 0 self.tool_ends = 0 self.agent_ends = 0
@property def always_verbose(self) -> bool: """Whether to call verbose callbacks even if verbose is False.""" return self.always_verbose_ @property def ignore_llm(self) -> bool: """Whether to ignore LLM callbacks.""" return self.ignore_llm_ @property def ignore_chain(self) -> bool: """Whether to ignore chain callbacks.""" return self.ignore_chain_ @property def ignore_agent(self) -> bool: """Whether to ignore agent callbacks.""" return self.ignore_agent_ @property def ignore_retriever(self) -> bool: """Whether to ignore retriever callbacks.""" return self.ignore_retriever_
[docs] def get_custom_callback_meta(self) -> Dict[str, Any]: return { "step": self.step, "starts": self.starts, "ends": self.ends, "errors": self.errors, "text_ctr": self.text_ctr, "chain_starts": self.chain_starts, "chain_ends": self.chain_ends, "llm_starts": self.llm_starts, "llm_ends": self.llm_ends, "llm_streams": self.llm_streams, "tool_starts": self.tool_starts, "tool_ends": self.tool_ends, "agent_ends": self.agent_ends, }
[docs] def reset_callback_meta(self) -> None: """Reset the callback metadata.""" self.step = 0 self.starts = 0 self.ends = 0 self.errors = 0 self.text_ctr = 0 self.ignore_llm_ = False self.ignore_chain_ = False self.ignore_agent_ = False self.always_verbose_ = False self.chain_starts = 0 self.chain_ends = 0 self.llm_starts = 0 self.llm_ends = 0 self.llm_streams = 0 self.tool_starts = 0 self.tool_ends = 0 self.agent_ends = 0 return None
[docs]class AimCallbackHandler(BaseMetadataCallbackHandler, BaseCallbackHandler): """Callback Handler that logs to Aim. Parameters: repo (:obj:`str`, optional): Aim repository path or Repo object to which Run object is bound. If skipped, default Repo is used. experiment_name (:obj:`str`, optional): Sets Run's `experiment` property. 'default' if not specified. Can be used later to query runs/sequences. system_tracking_interval (:obj:`int`, optional): Sets the tracking interval in seconds for system usage metrics (CPU, Memory, etc.). Set to `None` to disable system metrics tracking. log_system_params (:obj:`bool`, optional): Enable/Disable logging of system params such as installed packages, git info, environment variables, etc. This handler will utilize the associated callback method called and formats the input of each callback function with metadata regarding the state of LLM run and then logs the response to Aim. """
[docs] def __init__( self, repo: Optional[str] = None, experiment_name: Optional[str] = None, system_tracking_interval: Optional[int] = 10, log_system_params: bool = True, ) -> None: """Initialize callback handler.""" super().__init__() aim = import_aim() self.repo = repo self.experiment_name = experiment_name self.system_tracking_interval = system_tracking_interval self.log_system_params = log_system_params self._run = aim.Run( repo=self.repo, experiment=self.experiment_name, system_tracking_interval=self.system_tracking_interval, log_system_params=self.log_system_params, ) self._run_hash = self._run.hash self.action_records: list = []
[docs] def setup(self, **kwargs: Any) -> None: aim = import_aim() if not self._run: if self._run_hash: self._run = aim.Run( self._run_hash, repo=self.repo, system_tracking_interval=self.system_tracking_interval, ) else: self._run = aim.Run( repo=self.repo, experiment=self.experiment_name, system_tracking_interval=self.system_tracking_interval, log_system_params=self.log_system_params, ) self._run_hash = self._run.hash if kwargs: for key, value in kwargs.items(): self._run.set(key, value, strict=False)
[docs] def on_llm_start( self, serialized: Dict[str, Any], prompts: List[str], **kwargs: Any ) -> None: """Run when LLM starts.""" aim = import_aim() self.step += 1 self.llm_starts += 1 self.starts += 1 resp = {"action": "on_llm_start"} resp.update(self.get_custom_callback_meta()) prompts_res = deepcopy(prompts) self._run.track( [aim.Text(prompt) for prompt in prompts_res], name="on_llm_start", context=resp, )
[docs] def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None: """Run when LLM ends running.""" aim = import_aim() self.step += 1 self.llm_ends += 1 self.ends += 1 resp = {"action": "on_llm_end"} resp.update(self.get_custom_callback_meta()) response_res = deepcopy(response) generated = [ aim.Text(generation.text) for generations in response_res.generations for generation in generations ] self._run.track( generated, name="on_llm_end", context=resp, )
[docs] def on_llm_new_token(self, token: str, **kwargs: Any) -> None: """Run when LLM generates a new token.""" self.step += 1 self.llm_streams += 1
[docs] def on_llm_error(self, error: BaseException, **kwargs: Any) -> None: """Run when LLM errors.""" self.step += 1 self.errors += 1
[docs] def on_chain_start( self, serialized: Dict[str, Any], inputs: Dict[str, Any], **kwargs: Any ) -> None: """Run when chain starts running.""" aim = import_aim() self.step += 1 self.chain_starts += 1 self.starts += 1 resp = {"action": "on_chain_start"} resp.update(self.get_custom_callback_meta()) inputs_res = deepcopy(inputs) self._run.track( aim.Text(inputs_res["input"]), name="on_chain_start", context=resp )
[docs] def on_chain_end(self, outputs: Dict[str, Any], **kwargs: Any) -> None: """Run when chain ends running.""" aim = import_aim() self.step += 1 self.chain_ends += 1 self.ends += 1 resp = {"action": "on_chain_end"} resp.update(self.get_custom_callback_meta()) outputs_res = deepcopy(outputs) self._run.track( aim.Text(outputs_res["output"]), name="on_chain_end", context=resp )
[docs] def on_chain_error(self, error: BaseException, **kwargs: Any) -> None: """Run when chain errors.""" self.step += 1 self.errors += 1
[docs] def on_tool_start( self, serialized: Dict[str, Any], input_str: str, **kwargs: Any ) -> None: """Run when tool starts running.""" aim = import_aim() self.step += 1 self.tool_starts += 1 self.starts += 1 resp = {"action": "on_tool_start"} resp.update(self.get_custom_callback_meta()) self._run.track(aim.Text(input_str), name="on_tool_start", context=resp)
[docs] def on_tool_end(self, output: Any, **kwargs: Any) -> None: """Run when tool ends running.""" output = str(output) aim = import_aim() self.step += 1 self.tool_ends += 1 self.ends += 1 resp = {"action": "on_tool_end"} resp.update(self.get_custom_callback_meta()) self._run.track(aim.Text(output), name="on_tool_end", context=resp)
[docs] def on_tool_error(self, error: BaseException, **kwargs: Any) -> None: """Run when tool errors.""" self.step += 1 self.errors += 1
[docs] def on_text(self, text: str, **kwargs: Any) -> None: """ Run when agent is ending. """ self.step += 1 self.text_ctr += 1
[docs] def on_agent_finish(self, finish: AgentFinish, **kwargs: Any) -> None: """Run when agent ends running.""" aim = import_aim() self.step += 1 self.agent_ends += 1 self.ends += 1 resp = {"action": "on_agent_finish"} resp.update(self.get_custom_callback_meta()) finish_res = deepcopy(finish) text = "OUTPUT:\n{}\n\nLOG:\n{}".format( finish_res.return_values["output"], finish_res.log ) self._run.track(aim.Text(text), name="on_agent_finish", context=resp)
[docs] def on_agent_action(self, action: AgentAction, **kwargs: Any) -> Any: """Run on agent action.""" aim = import_aim() self.step += 1 self.tool_starts += 1 self.starts += 1 resp = { "action": "on_agent_action", "tool": action.tool, } resp.update(self.get_custom_callback_meta()) action_res = deepcopy(action) text = "TOOL INPUT:\n{}\n\nLOG:\n{}".format( action_res.tool_input, action_res.log ) self._run.track(aim.Text(text), name="on_agent_action", context=resp)
[docs] def flush_tracker( self, repo: Optional[str] = None, experiment_name: Optional[str] = None, system_tracking_interval: Optional[int] = 10, log_system_params: bool = True, langchain_asset: Any = None, reset: bool = True, finish: bool = False, ) -> None: """Flush the tracker and reset the session. Args: repo (:obj:`str`, optional): Aim repository path or Repo object to which Run object is bound. If skipped, default Repo is used. experiment_name (:obj:`str`, optional): Sets Run's `experiment` property. 'default' if not specified. Can be used later to query runs/sequences. system_tracking_interval (:obj:`int`, optional): Sets the tracking interval in seconds for system usage metrics (CPU, Memory, etc.). Set to `None` to disable system metrics tracking. log_system_params (:obj:`bool`, optional): Enable/Disable logging of system params such as installed packages, git info, environment variables, etc. langchain_asset: The langchain asset to save. reset: Whether to reset the session. finish: Whether to finish the run. Returns: None """ if langchain_asset: try: for key, value in langchain_asset.dict().items(): self._run.set(key, value, strict=False) except Exception: pass if finish or reset: self._run.close() self.reset_callback_meta() if reset: aim = import_aim() self.repo = repo if repo else self.repo self.experiment_name = ( experiment_name if experiment_name else self.experiment_name ) self.system_tracking_interval = ( system_tracking_interval if system_tracking_interval else self.system_tracking_interval ) self.log_system_params = ( log_system_params if log_system_params else self.log_system_params ) self._run = aim.Run( repo=self.repo, experiment=self.experiment_name, system_tracking_interval=self.system_tracking_interval, log_system_params=self.log_system_params, ) self._run_hash = self._run.hash self.action_records = []