Source code for langchain_community.callbacks.mlflow_callback

import logging
import os
import random
import string
import tempfile
import traceback
from copy import deepcopy
from pathlib import Path
from typing import Any, Dict, List, Optional, Sequence, Union

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

from langchain_community.callbacks.utils import (

logger = logging.getLogger(__name__)

[docs]def import_mlflow() -> Any: """Import the mlflow python package and raise an error if it is not installed.""" return guard_import("mlflow")
[docs]def mlflow_callback_metrics() -> List[str]: """Get the metrics to log to MLFlow.""" return [ "step", "starts", "ends", "errors", "text_ctr", "chain_starts", "chain_ends", "llm_starts", "llm_ends", "llm_streams", "tool_starts", "tool_ends", "agent_ends", "retriever_starts", "retriever_ends", ]
[docs]def get_text_complexity_metrics() -> List[str]: """Get the text complexity metrics from textstat.""" return [ "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", ]
[docs]def analyze_text( text: str, nlp: Any = None, textstat: Any = None, ) -> dict: """Analyze text using textstat and spacy. Parameters: text (str): The text to analyze. nlp (spacy.lang): The spacy language model to use for visualization. textstat: The textstat library to use for complexity metrics calculation. Returns: (dict): A dictionary containing the complexity metrics and visualization files serialized to HTML string. """ resp: Dict[str, Any] = {} if textstat is not None: text_complexity_metrics = { key: getattr(textstat, key)(text) for key in get_text_complexity_metrics() } resp.update({"text_complexity_metrics": text_complexity_metrics}) resp.update(text_complexity_metrics) if nlp is not None: spacy = import_spacy() doc = nlp(text) dep_out = spacy.displacy.render(doc, style="dep", jupyter=False, page=True) ent_out = spacy.displacy.render(doc, style="ent", jupyter=False, page=True) text_visualizations = { "dependency_tree": dep_out, "entities": ent_out, } resp.update(text_visualizations) return resp
[docs]def construct_html_from_prompt_and_generation(prompt: str, generation: str) -> Any: """Construct an html element from a prompt and a generation. Parameters: prompt (str): The prompt. generation (str): The generation. Returns: (str): The html string.""" formatted_prompt = prompt.replace("\n", "<br>") formatted_generation = generation.replace("\n", "<br>") return f""" <p style="color:black;">{formatted_prompt}:</p> <blockquote> <p style="color:green;"> {formatted_generation} </p> </blockquote> """
[docs]class MlflowLogger: """Callback Handler that logs metrics and artifacts to mlflow server. Parameters: name (str): Name of the run. experiment (str): Name of the experiment. tags (dict): Tags to be attached for the run. tracking_uri (str): MLflow tracking server uri. This handler implements the helper functions to initialize, log metrics and artifacts to the mlflow server. """
[docs] def __init__(self, **kwargs: Any): self.mlflow = import_mlflow() if "DATABRICKS_RUNTIME_VERSION" in os.environ: self.mlflow.set_tracking_uri("databricks") self.mlf_expid = self.mlflow.tracking.fluent._get_experiment_id() self.mlf_exp = self.mlflow.get_experiment(self.mlf_expid) else: tracking_uri = get_from_dict_or_env( kwargs, "tracking_uri", "MLFLOW_TRACKING_URI", "" ) self.mlflow.set_tracking_uri(tracking_uri) if run_id := kwargs.get("run_id"): self.mlf_expid = self.mlflow.get_run(run_id).info.experiment_id else: # User can set other env variables described here # > experiment_name = get_from_dict_or_env( kwargs, "experiment_name", "MLFLOW_EXPERIMENT_NAME" ) self.mlf_exp = self.mlflow.get_experiment_by_name(experiment_name) if self.mlf_exp is not None: self.mlf_expid = self.mlf_exp.experiment_id else: self.mlf_expid = self.mlflow.create_experiment(experiment_name) self.start_run( kwargs["run_name"], kwargs["run_tags"], kwargs.get("run_id", None) ) self.dir = kwargs.get("artifacts_dir", "")
[docs] def start_run( self, name: str, tags: Dict[str, str], run_id: Optional[str] = None ) -> None: """ If run_id is provided, it will reuse the run with the given run_id. Otherwise, it starts a new run, auto generates the random suffix for name. """ if run_id is None: if name.endswith("-%"): rname = "".join( random.choices(string.ascii_uppercase + string.digits, k=7) ) name = name[:-1] + rname run = self.mlflow.MlflowClient().create_run( self.mlf_expid, run_name=name, tags=tags ) run_id = self.run_id = run_id
[docs] def finish_run(self) -> None: """To finish the run.""" self.mlflow.end_run()
[docs] def metric(self, key: str, value: float) -> None: """To log metric to mlflow server.""" self.mlflow.log_metric(key, value, run_id=self.run_id)
[docs] def metrics( self, data: Union[Dict[str, float], Dict[str, int]], step: Optional[int] = 0 ) -> None: """To log all metrics in the input dict.""" self.mlflow.log_metrics(data, run_id=self.run_id)
[docs] def jsonf(self, data: Dict[str, Any], filename: str) -> None: """To log the input data as json file artifact.""" self.mlflow.log_dict( data, os.path.join(self.dir, f"{filename}.json"), run_id=self.run_id )
[docs] def table(self, name: str, dataframe: Any) -> None: """To log the input pandas dataframe as a html table""" self.html(dataframe.to_html(), f"table_{name}")
[docs] def html(self, html: str, filename: str) -> None: """To log the input html string as html file artifact.""" self.mlflow.log_text( html, os.path.join(self.dir, f"{filename}.html"), run_id=self.run_id )
[docs] def text(self, text: str, filename: str) -> None: """To log the input text as text file artifact.""" self.mlflow.log_text( text, os.path.join(self.dir, f"{filename}.txt"), run_id=self.run_id )
[docs] def artifact(self, path: str) -> None: """To upload the file from given path as artifact.""" self.mlflow.log_artifact(path, run_id=self.run_id)
[docs] def langchain_artifact(self, chain: Any) -> None: self.mlflow.langchain.log_model(chain, "langchain-model", run_id=self.run_id)
[docs]class MlflowCallbackHandler(BaseMetadataCallbackHandler, BaseCallbackHandler): """Callback Handler that logs metrics and artifacts to mlflow server. Parameters: name (str): Name of the run. experiment (str): Name of the experiment. tags (dict): Tags to be attached for the run. tracking_uri (str): MLflow tracking server uri. 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 adds the response to the list of records for both the {method}_records and action. It then logs the response to mlflow server. """
[docs] def __init__( self, name: Optional[str] = "langchainrun-%", experiment: Optional[str] = "langchain", tags: Optional[Dict] = None, tracking_uri: Optional[str] = None, run_id: Optional[str] = None, artifacts_dir: str = "", ) -> None: """Initialize callback handler.""" import_pandas() import_mlflow() super().__init__() = name self.experiment = experiment self.tags = tags or {} self.tracking_uri = tracking_uri self.run_id = run_id self.artifacts_dir = artifacts_dir self.temp_dir = tempfile.TemporaryDirectory() self.mlflg = MlflowLogger( tracking_uri=self.tracking_uri, experiment_name=self.experiment,, run_tags=self.tags, run_id=self.run_id, artifacts_dir=self.artifacts_dir, ) self.action_records: list = [] self.nlp = None try: spacy = import_spacy() except ImportError as e: logger.warning(e.msg) else: try: self.nlp = spacy.load("en_core_web_sm") except OSError: logger.warning( "Run `python -m spacy download en_core_web_sm` " "to download en_core_web_sm model for text visualization." ) try: self.textstat = import_textstat() except ImportError as e: logger.warning(e.msg) self.textstat = None self.metrics = {key: 0 for key in mlflow_callback_metrics()} self.records: Dict[str, Any] = { "on_llm_start_records": [], "on_llm_token_records": [], "on_llm_end_records": [], "on_chain_start_records": [], "on_chain_end_records": [], "on_tool_start_records": [], "on_tool_end_records": [], "on_text_records": [], "on_agent_finish_records": [], "on_agent_action_records": [], "on_retriever_start_records": [], "on_retriever_end_records": [], "action_records": [], }
def _reset(self) -> None: for k, v in self.metrics.items(): self.metrics[k] = 0 for k, v in self.records.items(): self.records[k] = []
[docs] def on_llm_start( self, serialized: Dict[str, Any], prompts: List[str], **kwargs: Any ) -> None: """Run when LLM starts.""" self.metrics["step"] += 1 self.metrics["llm_starts"] += 1 self.metrics["starts"] += 1 llm_starts = self.metrics["llm_starts"] resp: Dict[str, Any] = {} resp.update({"action": "on_llm_start"}) resp.update(flatten_dict(serialized)) resp.update(self.metrics) self.mlflg.metrics(self.metrics, step=self.metrics["step"]) for idx, prompt in enumerate(prompts): prompt_resp = deepcopy(resp) prompt_resp["prompt"] = prompt self.records["on_llm_start_records"].append(prompt_resp) self.records["action_records"].append(prompt_resp) self.mlflg.jsonf(prompt_resp, f"llm_start_{llm_starts}_prompt_{idx}")
[docs] def on_llm_new_token(self, token: str, **kwargs: Any) -> None: """Run when LLM generates a new token.""" self.metrics["step"] += 1 self.metrics["llm_streams"] += 1 llm_streams = self.metrics["llm_streams"] resp: Dict[str, Any] = {} resp.update({"action": "on_llm_new_token", "token": token}) resp.update(self.metrics) self.mlflg.metrics(self.metrics, step=self.metrics["step"]) self.records["on_llm_token_records"].append(resp) self.records["action_records"].append(resp) self.mlflg.jsonf(resp, f"llm_new_tokens_{llm_streams}")
[docs] def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None: """Run when LLM ends running.""" self.metrics["step"] += 1 self.metrics["llm_ends"] += 1 self.metrics["ends"] += 1 llm_ends = self.metrics["llm_ends"] resp: Dict[str, Any] = {} resp.update({"action": "on_llm_end"}) resp.update(flatten_dict(response.llm_output or {})) resp.update(self.metrics) self.mlflg.metrics(self.metrics, step=self.metrics["step"]) for generations in response.generations: for idx, generation in enumerate(generations): generation_resp = deepcopy(resp) generation_resp.update(flatten_dict(generation.dict())) generation_resp.update( analyze_text( generation.text, nlp=self.nlp, textstat=self.textstat, ) ) if "text_complexity_metrics" in generation_resp: complexity_metrics: Dict[str, float] = generation_resp.pop( "text_complexity_metrics" ) self.mlflg.metrics( complexity_metrics, step=self.metrics["step"], ) self.records["on_llm_end_records"].append(generation_resp) self.records["action_records"].append(generation_resp) self.mlflg.jsonf(resp, f"llm_end_{llm_ends}_generation_{idx}") if "dependency_tree" in generation_resp: dependency_tree = generation_resp["dependency_tree"] self.mlflg.html( dependency_tree, "dep-" + hash_string(generation.text) ) if "entities" in generation_resp: entities = generation_resp["entities"] self.mlflg.html(entities, "ent-" + hash_string(generation.text))
[docs] def on_llm_error(self, error: BaseException, **kwargs: Any) -> None: """Run when LLM errors.""" self.metrics["step"] += 1 self.metrics["errors"] += 1
[docs] def on_chain_start( self, serialized: Dict[str, Any], inputs: Dict[str, Any], **kwargs: Any ) -> None: """Run when chain starts running.""" self.metrics["step"] += 1 self.metrics["chain_starts"] += 1 self.metrics["starts"] += 1 chain_starts = self.metrics["chain_starts"] resp: Dict[str, Any] = {} resp.update({"action": "on_chain_start"}) resp.update(flatten_dict(serialized)) resp.update(self.metrics) self.mlflg.metrics(self.metrics, step=self.metrics["step"]) if isinstance(inputs, dict): chain_input = ",".join([f"{k}={v}" for k, v in inputs.items()]) elif isinstance(inputs, list): chain_input = ",".join([str(input) for input in inputs]) else: chain_input = str(inputs) input_resp = deepcopy(resp) input_resp["inputs"] = chain_input self.records["on_chain_start_records"].append(input_resp) self.records["action_records"].append(input_resp) self.mlflg.jsonf(input_resp, f"chain_start_{chain_starts}")
[docs] def on_chain_end( self, outputs: Union[Dict[str, Any], str, List[str]], **kwargs: Any ) -> None: """Run when chain ends running.""" self.metrics["step"] += 1 self.metrics["chain_ends"] += 1 self.metrics["ends"] += 1 chain_ends = self.metrics["chain_ends"] resp: Dict[str, Any] = {} if isinstance(outputs, dict): chain_output = ",".join([f"{k}={v}" for k, v in outputs.items()]) elif isinstance(outputs, list): chain_output = ",".join(map(str, outputs)) else: chain_output = str(outputs) resp.update({"action": "on_chain_end", "outputs": chain_output}) resp.update(self.metrics) self.mlflg.metrics(self.metrics, step=self.metrics["step"]) self.records["on_chain_end_records"].append(resp) self.records["action_records"].append(resp) self.mlflg.jsonf(resp, f"chain_end_{chain_ends}")
[docs] def on_chain_error(self, error: BaseException, **kwargs: Any) -> None: """Run when chain errors.""" self.metrics["step"] += 1 self.metrics["errors"] += 1
[docs] def on_tool_start( self, serialized: Dict[str, Any], input_str: str, **kwargs: Any ) -> None: """Run when tool starts running.""" 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_tool_start", "input_str": input_str}) resp.update(flatten_dict(serialized)) resp.update(self.metrics) self.mlflg.metrics(self.metrics, step=self.metrics["step"]) self.records["on_tool_start_records"].append(resp) self.records["action_records"].append(resp) self.mlflg.jsonf(resp, f"tool_start_{tool_starts}")
[docs] def on_tool_end(self, output: Any, **kwargs: Any) -> None: """Run when tool ends running.""" output = str(output) self.metrics["step"] += 1 self.metrics["tool_ends"] += 1 self.metrics["ends"] += 1 tool_ends = self.metrics["tool_ends"] resp: Dict[str, Any] = {} resp.update({"action": "on_tool_end", "output": output}) resp.update(self.metrics) self.mlflg.metrics(self.metrics, step=self.metrics["step"]) self.records["on_tool_end_records"].append(resp) self.records["action_records"].append(resp) self.mlflg.jsonf(resp, f"tool_end_{tool_ends}")
[docs] def on_tool_error(self, error: BaseException, **kwargs: Any) -> None: """Run when tool errors.""" self.metrics["step"] += 1 self.metrics["errors"] += 1
[docs] def on_text(self, text: str, **kwargs: Any) -> None: """ Run when text is received. """ 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}")
[docs] 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}")
[docs] 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}")
[docs] def on_retriever_start( self, serialized: Dict[str, Any], query: str, **kwargs: Any, ) -> Any: """Run when Retriever starts running.""" self.metrics["step"] += 1 self.metrics["retriever_starts"] += 1 self.metrics["starts"] += 1 retriever_starts = self.metrics["retriever_starts"] resp: Dict[str, Any] = {} resp.update({"action": "on_retriever_start", "query": query}) resp.update(flatten_dict(serialized)) resp.update(self.metrics) self.mlflg.metrics(self.metrics, step=self.metrics["step"]) self.records["on_retriever_start_records"].append(resp) self.records["action_records"].append(resp) self.mlflg.jsonf(resp, f"retriever_start_{retriever_starts}")
[docs] def on_retriever_end( self, documents: Sequence[Document], **kwargs: Any, ) -> Any: """Run when Retriever ends running.""" self.metrics["step"] += 1 self.metrics["retriever_ends"] += 1 self.metrics["ends"] += 1 retriever_ends = self.metrics["retriever_ends"] resp: Dict[str, Any] = {} retriever_documents = [ { "page_content": doc.page_content, "metadata": { k: ( str(v) if not isinstance(v, list) else ",".join(str(x) for x in v) ) for k, v in doc.metadata.items() }, } for doc in documents ] resp.update({"action": "on_retriever_end", "documents": retriever_documents}) resp.update(self.metrics) self.mlflg.metrics(self.metrics, step=self.metrics["step"]) self.records["on_retriever_end_records"].append(resp) self.records["action_records"].append(resp) self.mlflg.jsonf(resp, f"retriever_end_{retriever_ends}")
[docs] def on_retriever_error(self, error: BaseException, **kwargs: Any) -> Any: """Run when Retriever errors.""" self.metrics["step"] += 1 self.metrics["errors"] += 1
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 = ( get_text_complexity_metrics() if self.textstat is not None else [] ) visualizations_columns = ( ["dependency_tree", "entities"] if self.nlp is not None else [] ) token_usage_columns = [ "token_usage_total_tokens", "token_usage_prompt_tokens", "token_usage_completion_tokens", ] token_usage_columns = [ x for x in token_usage_columns if x in on_llm_end_records_df.columns ] llm_outputs_df = ( on_llm_end_records_df[ [ "step", "text", ] + token_usage_columns + 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 _contain_llm_records(self) -> bool: return bool(self.records["on_llm_start_records"])
[docs] 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"])) if self._contain_llm_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(, "model.json")) try: 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.") # noqa: T201 traceback.print_exc() pass except NotImplementedError: print("Could not save model.") # noqa: T201 traceback.print_exc() pass except NotImplementedError: print("Could not save model.") # noqa: T201 traceback.print_exc() pass if finish: self.mlflg.finish_run() self._reset()