langchain/libs/community/langchain_community/callbacks/utils.py

import hashlib
from pathlib import Path
from typing import Any, Dict, Iterable, Tuple, Union


def import_spacy() -> Any:
    """Import the spacy python package and raise an error if it is not installed."""
    try:
        import spacy
    except ImportError:
        raise ImportError(
            "This callback manager requires the `spacy` python "
            "package installed. Please install it with `pip install spacy`"
        )
    return spacy


def import_pandas() -> Any:
    """Import the pandas python package and raise an error if it is not installed."""
    try:
        import pandas
    except ImportError:
        raise ImportError(
            "This callback manager requires the `pandas` python "
            "package installed. Please install it with `pip install pandas`"
        )
    return pandas


def import_textstat() -> Any:
    """Import the textstat python package and raise an error if it is not installed."""
    try:
        import textstat
    except ImportError:
        raise ImportError(
            "This callback manager requires the `textstat` python "
            "package installed. Please install it with `pip install textstat`"
        )
    return textstat


def _flatten_dict(
    nested_dict: Dict[str, Any], parent_key: str = "", sep: str = "_"
) -> Iterable[Tuple[str, Any]]:
    """
    Generator that yields flattened items from a nested dictionary for a flat dict.

    Parameters:
        nested_dict (dict): The nested dictionary to flatten.
        parent_key (str): The prefix to prepend to the keys of the flattened dict.
        sep (str): The separator to use between the parent key and the key of the
            flattened dictionary.

    Yields:
        (str, any): A key-value pair from the flattened dictionary.
    """
    for key, value in nested_dict.items():
        new_key = parent_key + sep + key if parent_key else key
        if isinstance(value, dict):
            yield from _flatten_dict(value, new_key, sep)
        else:
            yield new_key, value


def flatten_dict(
    nested_dict: Dict[str, Any], parent_key: str = "", sep: str = "_"
) -> Dict[str, Any]:
    """Flattens a nested dictionary into a flat dictionary.

    Parameters:
        nested_dict (dict): The nested dictionary to flatten.
        parent_key (str): The prefix to prepend to the keys of the flattened dict.
        sep (str): The separator to use between the parent key and the key of the
            flattened dictionary.

    Returns:
        (dict): A flat dictionary.

    """
    flat_dict = {k: v for k, v in _flatten_dict(nested_dict, parent_key, sep)}
    return flat_dict


def hash_string(s: str) -> str:
    """Hash a string using sha1.

    Parameters:
        s (str): The string to hash.

    Returns:
        (str): The hashed string.
    """
    return hashlib.sha1(s.encode("utf-8")).hexdigest()


def load_json(json_path: Union[str, Path]) -> str:
    """Load json file to a string.

    Parameters:
        json_path (str): The path to the json file.

    Returns:
        (str): The string representation of the json file.
    """
    with open(json_path, "r") as f:
        data = f.read()
    return data


class BaseMetadataCallbackHandler:
    """This class handles 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.
        on_llm_start_records (list): A list of records of the on_llm_start method.
        on_llm_token_records (list): A list of records of the on_llm_token method.
        on_llm_end_records (list): A list of records of the on_llm_end method.
        on_chain_start_records (list): A list of records of the on_chain_start method.
        on_chain_end_records (list): A list of records of the on_chain_end method.
        on_tool_start_records (list): A list of records of the on_tool_start method.
        on_tool_end_records (list): A list of records of the on_tool_end method.
        on_agent_finish_records (list): A list of records of the on_agent_end method.
    """

    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

        self.on_llm_start_records: list = []
        self.on_llm_token_records: list = []
        self.on_llm_end_records: list = []

        self.on_chain_start_records: list = []
        self.on_chain_end_records: list = []

        self.on_tool_start_records: list = []
        self.on_tool_end_records: list = []

        self.on_text_records: list = []
        self.on_agent_finish_records: list = []
        self.on_agent_action_records: list = []

    @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_

    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,
        }

    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

        self.on_llm_start_records = []
        self.on_llm_token_records = []
        self.on_llm_end_records = []

        self.on_chain_start_records = []
        self.on_chain_end_records = []

        self.on_tool_start_records = []
        self.on_tool_end_records = []

        self.on_text_records = []
        self.on_agent_finish_records = []
        self.on_agent_action_records = []
        return None