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661 lines
24 KiB
Python
661 lines
24 KiB
Python
import os
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import random
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import string
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import tempfile
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import traceback
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from copy import deepcopy
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from pathlib import Path
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from typing import Any, Dict, List, Optional, Union
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from langchain_core.agents import AgentAction, AgentFinish
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from langchain_core.callbacks import BaseCallbackHandler
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from langchain_core.outputs import LLMResult
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from langchain_core.utils import get_from_dict_or_env
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from langchain_community.callbacks.utils import (
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BaseMetadataCallbackHandler,
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flatten_dict,
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hash_string,
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import_pandas,
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import_spacy,
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import_textstat,
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)
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def import_mlflow() -> Any:
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"""Import the mlflow python package and raise an error if it is not installed."""
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try:
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import mlflow
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except ImportError:
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raise ImportError(
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"To use the mlflow callback manager you need to have the `mlflow` python "
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"package installed. Please install it with `pip install mlflow>=2.3.0`"
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)
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return mlflow
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def analyze_text(
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text: str,
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nlp: Any = None,
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) -> dict:
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"""Analyze text using textstat and spacy.
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Parameters:
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text (str): The text to analyze.
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nlp (spacy.lang): The spacy language model to use for visualization.
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Returns:
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(dict): A dictionary containing the complexity metrics and visualization
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files serialized to HTML string.
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"""
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resp: Dict[str, Any] = {}
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textstat = import_textstat()
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spacy = import_spacy()
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text_complexity_metrics = {
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"flesch_reading_ease": textstat.flesch_reading_ease(text),
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"flesch_kincaid_grade": textstat.flesch_kincaid_grade(text),
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"smog_index": textstat.smog_index(text),
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"coleman_liau_index": textstat.coleman_liau_index(text),
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"automated_readability_index": textstat.automated_readability_index(text),
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"dale_chall_readability_score": textstat.dale_chall_readability_score(text),
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"difficult_words": textstat.difficult_words(text),
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"linsear_write_formula": textstat.linsear_write_formula(text),
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"gunning_fog": textstat.gunning_fog(text),
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# "text_standard": textstat.text_standard(text),
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"fernandez_huerta": textstat.fernandez_huerta(text),
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"szigriszt_pazos": textstat.szigriszt_pazos(text),
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"gutierrez_polini": textstat.gutierrez_polini(text),
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"crawford": textstat.crawford(text),
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"gulpease_index": textstat.gulpease_index(text),
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"osman": textstat.osman(text),
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}
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resp.update({"text_complexity_metrics": text_complexity_metrics})
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resp.update(text_complexity_metrics)
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if nlp is not None:
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doc = nlp(text)
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dep_out = spacy.displacy.render( # type: ignore
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doc, style="dep", jupyter=False, page=True
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)
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ent_out = spacy.displacy.render( # type: ignore
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doc, style="ent", jupyter=False, page=True
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)
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text_visualizations = {
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"dependency_tree": dep_out,
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"entities": ent_out,
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}
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resp.update(text_visualizations)
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return resp
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def construct_html_from_prompt_and_generation(prompt: str, generation: str) -> Any:
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"""Construct an html element from a prompt and a generation.
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Parameters:
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prompt (str): The prompt.
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generation (str): The generation.
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Returns:
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(str): The html string."""
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formatted_prompt = prompt.replace("\n", "<br>")
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formatted_generation = generation.replace("\n", "<br>")
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return f"""
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<p style="color:black;">{formatted_prompt}:</p>
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<blockquote>
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<p style="color:green;">
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{formatted_generation}
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</p>
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</blockquote>
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"""
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class MlflowLogger:
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"""Callback Handler that logs metrics and artifacts to mlflow server.
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Parameters:
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name (str): Name of the run.
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experiment (str): Name of the experiment.
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tags (dict): Tags to be attached for the run.
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tracking_uri (str): MLflow tracking server uri.
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This handler implements the helper functions to initialize,
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log metrics and artifacts to the mlflow server.
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"""
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def __init__(self, **kwargs: Any):
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self.mlflow = import_mlflow()
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if "DATABRICKS_RUNTIME_VERSION" in os.environ:
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self.mlflow.set_tracking_uri("databricks")
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self.mlf_expid = self.mlflow.tracking.fluent._get_experiment_id()
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self.mlf_exp = self.mlflow.get_experiment(self.mlf_expid)
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else:
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tracking_uri = get_from_dict_or_env(
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kwargs, "tracking_uri", "MLFLOW_TRACKING_URI", ""
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)
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self.mlflow.set_tracking_uri(tracking_uri)
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# User can set other env variables described here
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# > https://www.mlflow.org/docs/latest/tracking.html#logging-to-a-tracking-server
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experiment_name = get_from_dict_or_env(
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kwargs, "experiment_name", "MLFLOW_EXPERIMENT_NAME"
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)
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self.mlf_exp = self.mlflow.get_experiment_by_name(experiment_name)
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if self.mlf_exp is not None:
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self.mlf_expid = self.mlf_exp.experiment_id
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else:
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self.mlf_expid = self.mlflow.create_experiment(experiment_name)
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self.start_run(kwargs["run_name"], kwargs["run_tags"])
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def start_run(self, name: str, tags: Dict[str, str]) -> None:
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"""To start a new run, auto generates the random suffix for name"""
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if name.endswith("-%"):
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rname = "".join(random.choices(string.ascii_uppercase + string.digits, k=7))
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name = name.replace("%", rname)
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self.run = self.mlflow.MlflowClient().create_run(
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self.mlf_expid, run_name=name, tags=tags
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)
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def finish_run(self) -> None:
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"""To finish the run."""
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with self.mlflow.start_run(
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run_id=self.run.info.run_id, experiment_id=self.mlf_expid
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):
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self.mlflow.end_run()
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def metric(self, key: str, value: float) -> None:
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"""To log metric to mlflow server."""
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with self.mlflow.start_run(
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run_id=self.run.info.run_id, experiment_id=self.mlf_expid
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):
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self.mlflow.log_metric(key, value)
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def metrics(
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self, data: Union[Dict[str, float], Dict[str, int]], step: Optional[int] = 0
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) -> None:
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"""To log all metrics in the input dict."""
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with self.mlflow.start_run(
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run_id=self.run.info.run_id, experiment_id=self.mlf_expid
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):
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self.mlflow.log_metrics(data)
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def jsonf(self, data: Dict[str, Any], filename: str) -> None:
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"""To log the input data as json file artifact."""
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with self.mlflow.start_run(
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run_id=self.run.info.run_id, experiment_id=self.mlf_expid
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):
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self.mlflow.log_dict(data, f"{filename}.json")
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def table(self, name: str, dataframe) -> None: # type: ignore
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"""To log the input pandas dataframe as a html table"""
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self.html(dataframe.to_html(), f"table_{name}")
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def html(self, html: str, filename: str) -> None:
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"""To log the input html string as html file artifact."""
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with self.mlflow.start_run(
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run_id=self.run.info.run_id, experiment_id=self.mlf_expid
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):
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self.mlflow.log_text(html, f"{filename}.html")
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def text(self, text: str, filename: str) -> None:
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"""To log the input text as text file artifact."""
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with self.mlflow.start_run(
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run_id=self.run.info.run_id, experiment_id=self.mlf_expid
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):
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self.mlflow.log_text(text, f"{filename}.txt")
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def artifact(self, path: str) -> None:
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"""To upload the file from given path as artifact."""
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with self.mlflow.start_run(
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run_id=self.run.info.run_id, experiment_id=self.mlf_expid
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):
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self.mlflow.log_artifact(path)
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def langchain_artifact(self, chain: Any) -> None:
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with self.mlflow.start_run(
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run_id=self.run.info.run_id, experiment_id=self.mlf_expid
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):
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self.mlflow.langchain.log_model(chain, "langchain-model")
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class MlflowCallbackHandler(BaseMetadataCallbackHandler, BaseCallbackHandler):
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"""Callback Handler that logs metrics and artifacts to mlflow server.
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Parameters:
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name (str): Name of the run.
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experiment (str): Name of the experiment.
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tags (dict): Tags to be attached for the run.
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tracking_uri (str): MLflow tracking server uri.
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This handler will utilize the associated callback method called and formats
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the input of each callback function with metadata regarding the state of LLM run,
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and adds the response to the list of records for both the {method}_records and
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action. It then logs the response to mlflow server.
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"""
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def __init__(
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self,
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name: Optional[str] = "langchainrun-%",
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experiment: Optional[str] = "langchain",
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tags: Optional[Dict] = None,
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tracking_uri: Optional[str] = None,
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) -> None:
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"""Initialize callback handler."""
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import_pandas()
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import_textstat()
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import_mlflow()
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spacy = import_spacy()
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super().__init__()
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self.name = name
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self.experiment = experiment
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self.tags = tags or {}
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self.tracking_uri = tracking_uri
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self.temp_dir = tempfile.TemporaryDirectory()
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self.mlflg = MlflowLogger(
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tracking_uri=self.tracking_uri,
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experiment_name=self.experiment,
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run_name=self.name,
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run_tags=self.tags,
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)
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self.action_records: list = []
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self.nlp = spacy.load("en_core_web_sm")
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self.metrics = {
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"step": 0,
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"starts": 0,
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"ends": 0,
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"errors": 0,
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"text_ctr": 0,
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"chain_starts": 0,
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"chain_ends": 0,
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"llm_starts": 0,
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"llm_ends": 0,
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"llm_streams": 0,
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"tool_starts": 0,
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"tool_ends": 0,
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"agent_ends": 0,
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}
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self.records: Dict[str, Any] = {
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"on_llm_start_records": [],
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"on_llm_token_records": [],
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"on_llm_end_records": [],
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"on_chain_start_records": [],
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"on_chain_end_records": [],
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"on_tool_start_records": [],
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"on_tool_end_records": [],
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"on_text_records": [],
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"on_agent_finish_records": [],
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"on_agent_action_records": [],
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"action_records": [],
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}
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def _reset(self) -> None:
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for k, v in self.metrics.items():
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self.metrics[k] = 0
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for k, v in self.records.items():
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self.records[k] = []
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def on_llm_start(
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self, serialized: Dict[str, Any], prompts: List[str], **kwargs: Any
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) -> None:
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"""Run when LLM starts."""
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self.metrics["step"] += 1
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self.metrics["llm_starts"] += 1
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self.metrics["starts"] += 1
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llm_starts = self.metrics["llm_starts"]
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resp: Dict[str, Any] = {}
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resp.update({"action": "on_llm_start"})
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resp.update(flatten_dict(serialized))
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resp.update(self.metrics)
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self.mlflg.metrics(self.metrics, step=self.metrics["step"])
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for idx, prompt in enumerate(prompts):
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prompt_resp = deepcopy(resp)
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prompt_resp["prompt"] = prompt
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self.records["on_llm_start_records"].append(prompt_resp)
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self.records["action_records"].append(prompt_resp)
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self.mlflg.jsonf(prompt_resp, f"llm_start_{llm_starts}_prompt_{idx}")
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def on_llm_new_token(self, token: str, **kwargs: Any) -> None:
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"""Run when LLM generates a new token."""
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self.metrics["step"] += 1
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self.metrics["llm_streams"] += 1
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llm_streams = self.metrics["llm_streams"]
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resp: Dict[str, Any] = {}
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resp.update({"action": "on_llm_new_token", "token": token})
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resp.update(self.metrics)
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self.mlflg.metrics(self.metrics, step=self.metrics["step"])
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self.records["on_llm_token_records"].append(resp)
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self.records["action_records"].append(resp)
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self.mlflg.jsonf(resp, f"llm_new_tokens_{llm_streams}")
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def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None:
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"""Run when LLM ends running."""
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self.metrics["step"] += 1
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self.metrics["llm_ends"] += 1
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self.metrics["ends"] += 1
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llm_ends = self.metrics["llm_ends"]
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resp: Dict[str, Any] = {}
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resp.update({"action": "on_llm_end"})
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resp.update(flatten_dict(response.llm_output or {}))
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resp.update(self.metrics)
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self.mlflg.metrics(self.metrics, step=self.metrics["step"])
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for generations in response.generations:
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for idx, generation in enumerate(generations):
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generation_resp = deepcopy(resp)
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generation_resp.update(flatten_dict(generation.dict()))
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generation_resp.update(
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analyze_text(
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generation.text,
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nlp=self.nlp,
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)
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)
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complexity_metrics: Dict[str, float] = generation_resp.pop(
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"text_complexity_metrics"
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) # type: ignore # noqa: E501
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self.mlflg.metrics(
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complexity_metrics,
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step=self.metrics["step"],
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)
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self.records["on_llm_end_records"].append(generation_resp)
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self.records["action_records"].append(generation_resp)
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self.mlflg.jsonf(resp, f"llm_end_{llm_ends}_generation_{idx}")
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dependency_tree = generation_resp["dependency_tree"]
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entities = generation_resp["entities"]
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self.mlflg.html(dependency_tree, "dep-" + hash_string(generation.text))
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self.mlflg.html(entities, "ent-" + hash_string(generation.text))
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def on_llm_error(self, error: BaseException, **kwargs: Any) -> None:
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"""Run when LLM errors."""
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self.metrics["step"] += 1
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self.metrics["errors"] += 1
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def on_chain_start(
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self, serialized: Dict[str, Any], inputs: Dict[str, Any], **kwargs: Any
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) -> None:
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"""Run when chain starts running."""
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self.metrics["step"] += 1
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self.metrics["chain_starts"] += 1
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self.metrics["starts"] += 1
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chain_starts = self.metrics["chain_starts"]
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resp: Dict[str, Any] = {}
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resp.update({"action": "on_chain_start"})
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resp.update(flatten_dict(serialized))
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resp.update(self.metrics)
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self.mlflg.metrics(self.metrics, step=self.metrics["step"])
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chain_input = ",".join([f"{k}={v}" for k, v in inputs.items()])
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input_resp = deepcopy(resp)
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input_resp["inputs"] = chain_input
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self.records["on_chain_start_records"].append(input_resp)
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self.records["action_records"].append(input_resp)
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self.mlflg.jsonf(input_resp, f"chain_start_{chain_starts}")
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def on_chain_end(self, outputs: Dict[str, Any], **kwargs: Any) -> None:
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"""Run when chain ends running."""
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self.metrics["step"] += 1
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self.metrics["chain_ends"] += 1
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self.metrics["ends"] += 1
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chain_ends = self.metrics["chain_ends"]
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resp: Dict[str, Any] = {}
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chain_output = ",".join([f"{k}={v}" for k, v in outputs.items()])
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resp.update({"action": "on_chain_end", "outputs": chain_output})
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resp.update(self.metrics)
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self.mlflg.metrics(self.metrics, step=self.metrics["step"])
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self.records["on_chain_end_records"].append(resp)
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self.records["action_records"].append(resp)
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self.mlflg.jsonf(resp, f"chain_end_{chain_ends}")
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def on_chain_error(self, error: BaseException, **kwargs: Any) -> None:
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"""Run when chain errors."""
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self.metrics["step"] += 1
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self.metrics["errors"] += 1
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def on_tool_start(
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self, serialized: Dict[str, Any], input_str: str, **kwargs: Any
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) -> None:
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"""Run when tool starts running."""
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self.metrics["step"] += 1
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self.metrics["tool_starts"] += 1
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self.metrics["starts"] += 1
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tool_starts = self.metrics["tool_starts"]
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resp: Dict[str, Any] = {}
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resp.update({"action": "on_tool_start", "input_str": input_str})
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resp.update(flatten_dict(serialized))
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resp.update(self.metrics)
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self.mlflg.metrics(self.metrics, step=self.metrics["step"])
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self.records["on_tool_start_records"].append(resp)
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self.records["action_records"].append(resp)
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self.mlflg.jsonf(resp, f"tool_start_{tool_starts}")
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def on_tool_end(self, output: str, **kwargs: Any) -> None:
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"""Run when tool ends running."""
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self.metrics["step"] += 1
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self.metrics["tool_ends"] += 1
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self.metrics["ends"] += 1
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tool_ends = self.metrics["tool_ends"]
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resp: Dict[str, Any] = {}
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resp.update({"action": "on_tool_end", "output": output})
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resp.update(self.metrics)
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self.mlflg.metrics(self.metrics, step=self.metrics["step"])
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|
|
self.records["on_tool_end_records"].append(resp)
|
|
self.records["action_records"].append(resp)
|
|
self.mlflg.jsonf(resp, f"tool_end_{tool_ends}")
|
|
|
|
def on_tool_error(self, error: BaseException, **kwargs: Any) -> None:
|
|
"""Run when tool errors."""
|
|
self.metrics["step"] += 1
|
|
self.metrics["errors"] += 1
|
|
|
|
def on_text(self, text: str, **kwargs: Any) -> None:
|
|
"""
|
|
Run when agent is ending.
|
|
"""
|
|
self.metrics["step"] += 1
|
|
self.metrics["text_ctr"] += 1
|
|
|
|
text_ctr = self.metrics["text_ctr"]
|
|
|
|
resp: Dict[str, Any] = {}
|
|
resp.update({"action": "on_text", "text": text})
|
|
resp.update(self.metrics)
|
|
|
|
self.mlflg.metrics(self.metrics, step=self.metrics["step"])
|
|
|
|
self.records["on_text_records"].append(resp)
|
|
self.records["action_records"].append(resp)
|
|
self.mlflg.jsonf(resp, f"on_text_{text_ctr}")
|
|
|
|
def on_agent_finish(self, finish: AgentFinish, **kwargs: Any) -> None:
|
|
"""Run when agent ends running."""
|
|
self.metrics["step"] += 1
|
|
self.metrics["agent_ends"] += 1
|
|
self.metrics["ends"] += 1
|
|
|
|
agent_ends = self.metrics["agent_ends"]
|
|
resp: Dict[str, Any] = {}
|
|
resp.update(
|
|
{
|
|
"action": "on_agent_finish",
|
|
"output": finish.return_values["output"],
|
|
"log": finish.log,
|
|
}
|
|
)
|
|
resp.update(self.metrics)
|
|
|
|
self.mlflg.metrics(self.metrics, step=self.metrics["step"])
|
|
|
|
self.records["on_agent_finish_records"].append(resp)
|
|
self.records["action_records"].append(resp)
|
|
self.mlflg.jsonf(resp, f"agent_finish_{agent_ends}")
|
|
|
|
def on_agent_action(self, action: AgentAction, **kwargs: Any) -> Any:
|
|
"""Run on agent action."""
|
|
self.metrics["step"] += 1
|
|
self.metrics["tool_starts"] += 1
|
|
self.metrics["starts"] += 1
|
|
|
|
tool_starts = self.metrics["tool_starts"]
|
|
resp: Dict[str, Any] = {}
|
|
resp.update(
|
|
{
|
|
"action": "on_agent_action",
|
|
"tool": action.tool,
|
|
"tool_input": action.tool_input,
|
|
"log": action.log,
|
|
}
|
|
)
|
|
resp.update(self.metrics)
|
|
self.mlflg.metrics(self.metrics, step=self.metrics["step"])
|
|
self.records["on_agent_action_records"].append(resp)
|
|
self.records["action_records"].append(resp)
|
|
self.mlflg.jsonf(resp, f"agent_action_{tool_starts}")
|
|
|
|
def _create_session_analysis_df(self) -> Any:
|
|
"""Create a dataframe with all the information from the session."""
|
|
pd = import_pandas()
|
|
on_llm_start_records_df = pd.DataFrame(self.records["on_llm_start_records"])
|
|
on_llm_end_records_df = pd.DataFrame(self.records["on_llm_end_records"])
|
|
|
|
llm_input_columns = ["step", "prompt"]
|
|
if "name" in on_llm_start_records_df.columns:
|
|
llm_input_columns.append("name")
|
|
elif "id" in on_llm_start_records_df.columns:
|
|
# id is llm class's full import path. For example:
|
|
# ["langchain", "llms", "openai", "AzureOpenAI"]
|
|
on_llm_start_records_df["name"] = on_llm_start_records_df["id"].apply(
|
|
lambda id_: id_[-1]
|
|
)
|
|
llm_input_columns.append("name")
|
|
llm_input_prompts_df = (
|
|
on_llm_start_records_df[llm_input_columns]
|
|
.dropna(axis=1)
|
|
.rename({"step": "prompt_step"}, axis=1)
|
|
)
|
|
complexity_metrics_columns = []
|
|
visualizations_columns = []
|
|
|
|
complexity_metrics_columns = [
|
|
"flesch_reading_ease",
|
|
"flesch_kincaid_grade",
|
|
"smog_index",
|
|
"coleman_liau_index",
|
|
"automated_readability_index",
|
|
"dale_chall_readability_score",
|
|
"difficult_words",
|
|
"linsear_write_formula",
|
|
"gunning_fog",
|
|
# "text_standard",
|
|
"fernandez_huerta",
|
|
"szigriszt_pazos",
|
|
"gutierrez_polini",
|
|
"crawford",
|
|
"gulpease_index",
|
|
"osman",
|
|
]
|
|
|
|
visualizations_columns = ["dependency_tree", "entities"]
|
|
|
|
llm_outputs_df = (
|
|
on_llm_end_records_df[
|
|
[
|
|
"step",
|
|
"text",
|
|
"token_usage_total_tokens",
|
|
"token_usage_prompt_tokens",
|
|
"token_usage_completion_tokens",
|
|
]
|
|
+ complexity_metrics_columns
|
|
+ visualizations_columns
|
|
]
|
|
.dropna(axis=1)
|
|
.rename({"step": "output_step", "text": "output"}, axis=1)
|
|
)
|
|
session_analysis_df = pd.concat([llm_input_prompts_df, llm_outputs_df], axis=1)
|
|
session_analysis_df["chat_html"] = session_analysis_df[
|
|
["prompt", "output"]
|
|
].apply(
|
|
lambda row: construct_html_from_prompt_and_generation(
|
|
row["prompt"], row["output"]
|
|
),
|
|
axis=1,
|
|
)
|
|
return session_analysis_df
|
|
|
|
def flush_tracker(self, langchain_asset: Any = None, finish: bool = False) -> None:
|
|
pd = import_pandas()
|
|
self.mlflg.table("action_records", pd.DataFrame(self.records["action_records"]))
|
|
session_analysis_df = self._create_session_analysis_df()
|
|
chat_html = session_analysis_df.pop("chat_html")
|
|
chat_html = chat_html.replace("\n", "", regex=True)
|
|
self.mlflg.table("session_analysis", pd.DataFrame(session_analysis_df))
|
|
self.mlflg.html("".join(chat_html.tolist()), "chat_html")
|
|
|
|
if langchain_asset:
|
|
# To avoid circular import error
|
|
# mlflow only supports LLMChain asset
|
|
if "langchain.chains.llm.LLMChain" in str(type(langchain_asset)):
|
|
self.mlflg.langchain_artifact(langchain_asset)
|
|
else:
|
|
langchain_asset_path = str(Path(self.temp_dir.name, "model.json"))
|
|
try:
|
|
langchain_asset.save(langchain_asset_path)
|
|
self.mlflg.artifact(langchain_asset_path)
|
|
except ValueError:
|
|
try:
|
|
langchain_asset.save_agent(langchain_asset_path)
|
|
self.mlflg.artifact(langchain_asset_path)
|
|
except AttributeError:
|
|
print("Could not save model.")
|
|
traceback.print_exc()
|
|
pass
|
|
except NotImplementedError:
|
|
print("Could not save model.")
|
|
traceback.print_exc()
|
|
pass
|
|
except NotImplementedError:
|
|
print("Could not save model.")
|
|
traceback.print_exc()
|
|
pass
|
|
if finish:
|
|
self.mlflg.finish_run()
|
|
self._reset()
|