mirror of
https://github.com/hwchase17/langchain
synced 2024-11-02 09:40:22 +00:00
646 lines
23 KiB
Python
646 lines
23 KiB
Python
import tempfile
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from copy import deepcopy
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from pathlib import Path
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from typing import Any, Callable, Dict, List, Optional, Sequence
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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 Generation, LLMResult
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import langchain_community
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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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import_pandas,
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import_spacy,
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import_textstat,
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)
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LANGCHAIN_MODEL_NAME = "langchain-model"
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def import_comet_ml() -> Any:
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"""Import comet_ml and raise an error if it is not installed."""
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try:
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import comet_ml # noqa: F401
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except ImportError:
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raise ImportError(
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"To use the comet_ml callback manager you need to have the "
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"`comet_ml` python package installed. Please install it with"
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" `pip install comet_ml`"
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)
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return comet_ml
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def _get_experiment(
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workspace: Optional[str] = None, project_name: Optional[str] = None
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) -> Any:
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comet_ml = import_comet_ml()
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experiment = comet_ml.Experiment(
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workspace=workspace,
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project_name=project_name,
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)
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return experiment
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def _fetch_text_complexity_metrics(text: str) -> dict:
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textstat = import_textstat()
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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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return text_complexity_metrics
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def _summarize_metrics_for_generated_outputs(metrics: Sequence) -> dict:
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pd = import_pandas()
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metrics_df = pd.DataFrame(metrics)
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metrics_summary = metrics_df.describe()
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return metrics_summary.to_dict()
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class CometCallbackHandler(BaseMetadataCallbackHandler, BaseCallbackHandler):
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"""Callback Handler that logs to Comet.
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Parameters:
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job_type (str): The type of comet_ml task such as "inference",
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"testing" or "qc"
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project_name (str): The comet_ml project name
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tags (list): Tags to add to the task
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task_name (str): Name of the comet_ml task
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visualize (bool): Whether to visualize the run.
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complexity_metrics (bool): Whether to log complexity metrics
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stream_logs (bool): Whether to stream callback actions to Comet
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This handler will utilize the associated callback method 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 Comet.
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"""
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def __init__(
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self,
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task_type: Optional[str] = "inference",
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workspace: Optional[str] = None,
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project_name: Optional[str] = None,
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tags: Optional[Sequence] = None,
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name: Optional[str] = None,
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visualizations: Optional[List[str]] = None,
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complexity_metrics: bool = False,
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custom_metrics: Optional[Callable] = None,
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stream_logs: bool = True,
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) -> None:
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"""Initialize callback handler."""
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self.comet_ml = import_comet_ml()
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super().__init__()
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self.task_type = task_type
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self.workspace = workspace
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self.project_name = project_name
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self.tags = tags
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self.visualizations = visualizations
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self.complexity_metrics = complexity_metrics
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self.custom_metrics = custom_metrics
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self.stream_logs = stream_logs
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self.temp_dir = tempfile.TemporaryDirectory()
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self.experiment = _get_experiment(workspace, project_name)
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self.experiment.log_other("Created from", "langchain")
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if tags:
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self.experiment.add_tags(tags)
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self.name = name
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if self.name:
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self.experiment.set_name(self.name)
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warning = (
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"The comet_ml callback is currently in beta and is subject to change "
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"based on updates to `langchain`. Please report any issues to "
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"https://github.com/comet-ml/issue-tracking/issues with the tag "
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"`langchain`."
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)
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self.comet_ml.LOGGER.warning(warning)
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self.callback_columns: list = []
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self.action_records: list = []
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self.complexity_metrics = complexity_metrics
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if self.visualizations:
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spacy = import_spacy()
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self.nlp = spacy.load("en_core_web_sm")
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else:
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self.nlp = None
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def _init_resp(self) -> Dict:
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return {k: None for k in self.callback_columns}
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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.step += 1
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self.llm_starts += 1
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self.starts += 1
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metadata = self._init_resp()
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metadata.update({"action": "on_llm_start"})
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metadata.update(flatten_dict(serialized))
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metadata.update(self.get_custom_callback_meta())
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for prompt in prompts:
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prompt_resp = deepcopy(metadata)
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prompt_resp["prompts"] = prompt
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self.on_llm_start_records.append(prompt_resp)
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self.action_records.append(prompt_resp)
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if self.stream_logs:
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self._log_stream(prompt, metadata, self.step)
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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.step += 1
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self.llm_streams += 1
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resp = self._init_resp()
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resp.update({"action": "on_llm_new_token", "token": token})
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resp.update(self.get_custom_callback_meta())
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self.action_records.append(resp)
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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.step += 1
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self.llm_ends += 1
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self.ends += 1
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metadata = self._init_resp()
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metadata.update({"action": "on_llm_end"})
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metadata.update(flatten_dict(response.llm_output or {}))
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metadata.update(self.get_custom_callback_meta())
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output_complexity_metrics = []
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output_custom_metrics = []
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for prompt_idx, generations in enumerate(response.generations):
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for gen_idx, generation in enumerate(generations):
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text = generation.text
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generation_resp = deepcopy(metadata)
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generation_resp.update(flatten_dict(generation.dict()))
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complexity_metrics = self._get_complexity_metrics(text)
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if complexity_metrics:
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output_complexity_metrics.append(complexity_metrics)
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generation_resp.update(complexity_metrics)
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custom_metrics = self._get_custom_metrics(
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generation, prompt_idx, gen_idx
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)
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if custom_metrics:
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output_custom_metrics.append(custom_metrics)
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generation_resp.update(custom_metrics)
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if self.stream_logs:
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self._log_stream(text, metadata, self.step)
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self.action_records.append(generation_resp)
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self.on_llm_end_records.append(generation_resp)
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self._log_text_metrics(output_complexity_metrics, step=self.step)
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self._log_text_metrics(output_custom_metrics, step=self.step)
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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.step += 1
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self.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.step += 1
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self.chain_starts += 1
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self.starts += 1
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resp = self._init_resp()
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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.get_custom_callback_meta())
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for chain_input_key, chain_input_val in inputs.items():
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if isinstance(chain_input_val, str):
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input_resp = deepcopy(resp)
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if self.stream_logs:
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self._log_stream(chain_input_val, resp, self.step)
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input_resp.update({chain_input_key: chain_input_val})
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self.action_records.append(input_resp)
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else:
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self.comet_ml.LOGGER.warning(
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f"Unexpected data format provided! "
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f"Input Value for {chain_input_key} will not be logged"
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)
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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.step += 1
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self.chain_ends += 1
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self.ends += 1
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resp = self._init_resp()
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resp.update({"action": "on_chain_end"})
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resp.update(self.get_custom_callback_meta())
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for chain_output_key, chain_output_val in outputs.items():
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if isinstance(chain_output_val, str):
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output_resp = deepcopy(resp)
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if self.stream_logs:
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self._log_stream(chain_output_val, resp, self.step)
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output_resp.update({chain_output_key: chain_output_val})
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self.action_records.append(output_resp)
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else:
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self.comet_ml.LOGGER.warning(
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f"Unexpected data format provided! "
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f"Output Value for {chain_output_key} will not be logged"
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)
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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.step += 1
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self.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.step += 1
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self.tool_starts += 1
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self.starts += 1
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resp = self._init_resp()
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resp.update({"action": "on_tool_start"})
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resp.update(flatten_dict(serialized))
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resp.update(self.get_custom_callback_meta())
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if self.stream_logs:
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self._log_stream(input_str, resp, self.step)
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resp.update({"input_str": input_str})
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self.action_records.append(resp)
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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.step += 1
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self.tool_ends += 1
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self.ends += 1
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resp = self._init_resp()
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resp.update({"action": "on_tool_end"})
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resp.update(self.get_custom_callback_meta())
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if self.stream_logs:
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self._log_stream(output, resp, self.step)
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resp.update({"output": output})
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self.action_records.append(resp)
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def on_tool_error(self, error: BaseException, **kwargs: Any) -> None:
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"""Run when tool errors."""
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self.step += 1
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self.errors += 1
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def on_text(self, text: str, **kwargs: Any) -> None:
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"""
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Run when agent is ending.
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"""
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self.step += 1
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self.text_ctr += 1
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resp = self._init_resp()
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resp.update({"action": "on_text"})
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resp.update(self.get_custom_callback_meta())
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if self.stream_logs:
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self._log_stream(text, resp, self.step)
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resp.update({"text": text})
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self.action_records.append(resp)
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def on_agent_finish(self, finish: AgentFinish, **kwargs: Any) -> None:
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"""Run when agent ends running."""
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self.step += 1
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self.agent_ends += 1
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self.ends += 1
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resp = self._init_resp()
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output = finish.return_values["output"]
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log = finish.log
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resp.update({"action": "on_agent_finish", "log": log})
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resp.update(self.get_custom_callback_meta())
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if self.stream_logs:
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self._log_stream(output, resp, self.step)
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resp.update({"output": output})
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self.action_records.append(resp)
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def on_agent_action(self, action: AgentAction, **kwargs: Any) -> Any:
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"""Run on agent action."""
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self.step += 1
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self.tool_starts += 1
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self.starts += 1
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tool = action.tool
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tool_input = str(action.tool_input)
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log = action.log
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resp = self._init_resp()
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resp.update({"action": "on_agent_action", "log": log, "tool": tool})
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resp.update(self.get_custom_callback_meta())
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if self.stream_logs:
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self._log_stream(tool_input, resp, self.step)
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resp.update({"tool_input": tool_input})
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self.action_records.append(resp)
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def _get_complexity_metrics(self, text: str) -> dict:
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"""Compute text complexity metrics using textstat.
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Parameters:
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text (str): The text to analyze.
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Returns:
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(dict): A dictionary containing the complexity metrics.
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"""
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resp = {}
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if self.complexity_metrics:
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text_complexity_metrics = _fetch_text_complexity_metrics(text)
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resp.update(text_complexity_metrics)
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return resp
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def _get_custom_metrics(
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self, generation: Generation, prompt_idx: int, gen_idx: int
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) -> dict:
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"""Compute Custom Metrics for an LLM Generated Output
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Args:
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generation (LLMResult): Output generation from an LLM
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prompt_idx (int): List index of the input prompt
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gen_idx (int): List index of the generated output
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Returns:
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dict: A dictionary containing the custom metrics.
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"""
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resp = {}
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if self.custom_metrics:
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custom_metrics = self.custom_metrics(generation, prompt_idx, gen_idx)
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resp.update(custom_metrics)
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return resp
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def flush_tracker(
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self,
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langchain_asset: Any = None,
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task_type: Optional[str] = "inference",
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workspace: Optional[str] = None,
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project_name: Optional[str] = "comet-langchain-demo",
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tags: Optional[Sequence] = None,
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name: Optional[str] = None,
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visualizations: Optional[List[str]] = None,
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complexity_metrics: bool = False,
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custom_metrics: Optional[Callable] = None,
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finish: bool = False,
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reset: bool = False,
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) -> None:
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"""Flush the tracker and setup the session.
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Everything after this will be a new table.
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Args:
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name: Name of the performed session so far so it is identifiable
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langchain_asset: The langchain asset to save.
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finish: Whether to finish the run.
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Returns:
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None
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"""
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self._log_session(langchain_asset)
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if langchain_asset:
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try:
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self._log_model(langchain_asset)
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except Exception:
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self.comet_ml.LOGGER.error(
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"Failed to export agent or LLM to Comet",
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exc_info=True,
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extra={"show_traceback": True},
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)
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if finish:
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self.experiment.end()
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if reset:
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self._reset(
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task_type,
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workspace,
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project_name,
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tags,
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name,
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visualizations,
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complexity_metrics,
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custom_metrics,
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)
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def _log_stream(self, prompt: str, metadata: dict, step: int) -> None:
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self.experiment.log_text(prompt, metadata=metadata, step=step)
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def _log_model(self, langchain_asset: Any) -> None:
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model_parameters = self._get_llm_parameters(langchain_asset)
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self.experiment.log_parameters(model_parameters, prefix="model")
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langchain_asset_path = Path(self.temp_dir.name, "model.json")
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model_name = self.name if self.name else LANGCHAIN_MODEL_NAME
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try:
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if hasattr(langchain_asset, "save"):
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langchain_asset.save(langchain_asset_path)
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self.experiment.log_model(model_name, str(langchain_asset_path))
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except (ValueError, AttributeError, NotImplementedError) as e:
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if hasattr(langchain_asset, "save_agent"):
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langchain_asset.save_agent(langchain_asset_path)
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self.experiment.log_model(model_name, str(langchain_asset_path))
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else:
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self.comet_ml.LOGGER.error(
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f"{e}"
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" Could not save Langchain Asset "
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f"for {langchain_asset.__class__.__name__}"
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)
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def _log_session(self, langchain_asset: Optional[Any] = None) -> None:
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try:
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llm_session_df = self._create_session_analysis_dataframe(langchain_asset)
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# Log the cleaned dataframe as a table
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self.experiment.log_table("langchain-llm-session.csv", llm_session_df)
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except Exception:
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self.comet_ml.LOGGER.warning(
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"Failed to log session data to Comet",
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exc_info=True,
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extra={"show_traceback": True},
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)
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try:
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metadata = {"langchain_version": str(langchain_community.__version__)}
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# Log the langchain low-level records as a JSON file directly
|
|
self.experiment.log_asset_data(
|
|
self.action_records, "langchain-action_records.json", metadata=metadata
|
|
)
|
|
except Exception:
|
|
self.comet_ml.LOGGER.warning(
|
|
"Failed to log session data to Comet",
|
|
exc_info=True,
|
|
extra={"show_traceback": True},
|
|
)
|
|
|
|
try:
|
|
self._log_visualizations(llm_session_df)
|
|
except Exception:
|
|
self.comet_ml.LOGGER.warning(
|
|
"Failed to log visualizations to Comet",
|
|
exc_info=True,
|
|
extra={"show_traceback": True},
|
|
)
|
|
|
|
def _log_text_metrics(self, metrics: Sequence[dict], step: int) -> None:
|
|
if not metrics:
|
|
return
|
|
|
|
metrics_summary = _summarize_metrics_for_generated_outputs(metrics)
|
|
for key, value in metrics_summary.items():
|
|
self.experiment.log_metrics(value, prefix=key, step=step)
|
|
|
|
def _log_visualizations(self, session_df: Any) -> None:
|
|
if not (self.visualizations and self.nlp):
|
|
return
|
|
|
|
spacy = import_spacy()
|
|
|
|
prompts = session_df["prompts"].tolist()
|
|
outputs = session_df["text"].tolist()
|
|
|
|
for idx, (prompt, output) in enumerate(zip(prompts, outputs)):
|
|
doc = self.nlp(output)
|
|
sentence_spans = list(doc.sents)
|
|
|
|
for visualization in self.visualizations:
|
|
try:
|
|
html = spacy.displacy.render(
|
|
sentence_spans,
|
|
style=visualization,
|
|
options={"compact": True},
|
|
jupyter=False,
|
|
page=True,
|
|
)
|
|
self.experiment.log_asset_data(
|
|
html,
|
|
name=f"langchain-viz-{visualization}-{idx}.html",
|
|
metadata={"prompt": prompt},
|
|
step=idx,
|
|
)
|
|
except Exception as e:
|
|
self.comet_ml.LOGGER.warning(
|
|
e, exc_info=True, extra={"show_traceback": True}
|
|
)
|
|
|
|
return
|
|
|
|
def _reset(
|
|
self,
|
|
task_type: Optional[str] = None,
|
|
workspace: Optional[str] = None,
|
|
project_name: Optional[str] = None,
|
|
tags: Optional[Sequence] = None,
|
|
name: Optional[str] = None,
|
|
visualizations: Optional[List[str]] = None,
|
|
complexity_metrics: bool = False,
|
|
custom_metrics: Optional[Callable] = None,
|
|
) -> None:
|
|
_task_type = task_type if task_type else self.task_type
|
|
_workspace = workspace if workspace else self.workspace
|
|
_project_name = project_name if project_name else self.project_name
|
|
_tags = tags if tags else self.tags
|
|
_name = name if name else self.name
|
|
_visualizations = visualizations if visualizations else self.visualizations
|
|
_complexity_metrics = (
|
|
complexity_metrics if complexity_metrics else self.complexity_metrics
|
|
)
|
|
_custom_metrics = custom_metrics if custom_metrics else self.custom_metrics
|
|
|
|
self.__init__( # type: ignore
|
|
task_type=_task_type,
|
|
workspace=_workspace,
|
|
project_name=_project_name,
|
|
tags=_tags,
|
|
name=_name,
|
|
visualizations=_visualizations,
|
|
complexity_metrics=_complexity_metrics,
|
|
custom_metrics=_custom_metrics,
|
|
)
|
|
|
|
self.reset_callback_meta()
|
|
self.temp_dir = tempfile.TemporaryDirectory()
|
|
|
|
def _create_session_analysis_dataframe(self, langchain_asset: Any = None) -> dict:
|
|
pd = import_pandas()
|
|
|
|
llm_parameters = self._get_llm_parameters(langchain_asset)
|
|
num_generations_per_prompt = llm_parameters.get("n", 1)
|
|
|
|
llm_start_records_df = pd.DataFrame(self.on_llm_start_records)
|
|
# Repeat each input row based on the number of outputs generated per prompt
|
|
llm_start_records_df = llm_start_records_df.loc[
|
|
llm_start_records_df.index.repeat(num_generations_per_prompt)
|
|
].reset_index(drop=True)
|
|
llm_end_records_df = pd.DataFrame(self.on_llm_end_records)
|
|
|
|
llm_session_df = pd.merge(
|
|
llm_start_records_df,
|
|
llm_end_records_df,
|
|
left_index=True,
|
|
right_index=True,
|
|
suffixes=["_llm_start", "_llm_end"],
|
|
)
|
|
|
|
return llm_session_df
|
|
|
|
def _get_llm_parameters(self, langchain_asset: Any = None) -> dict:
|
|
if not langchain_asset:
|
|
return {}
|
|
try:
|
|
if hasattr(langchain_asset, "agent"):
|
|
llm_parameters = langchain_asset.agent.llm_chain.llm.dict()
|
|
elif hasattr(langchain_asset, "llm_chain"):
|
|
llm_parameters = langchain_asset.llm_chain.llm.dict()
|
|
elif hasattr(langchain_asset, "llm"):
|
|
llm_parameters = langchain_asset.llm.dict()
|
|
else:
|
|
llm_parameters = langchain_asset.dict()
|
|
except Exception:
|
|
return {}
|
|
|
|
return llm_parameters
|