mirror of
https://github.com/hwchase17/langchain
synced 2024-11-08 07:10:35 +00:00
368 lines
13 KiB
Python
368 lines
13 KiB
Python
"""FlyteKit callback handler."""
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from __future__ import annotations
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import logging
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from copy import deepcopy
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from typing import TYPE_CHECKING, Any, Dict, List, Tuple
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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_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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if TYPE_CHECKING:
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import flytekit
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from flytekitplugins.deck import renderer
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logger = logging.getLogger(__name__)
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def import_flytekit() -> Tuple[flytekit, renderer]:
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"""Import flytekit and flytekitplugins-deck-standard."""
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try:
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import flytekit # noqa: F401
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from flytekitplugins.deck import renderer # noqa: F401
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except ImportError:
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raise ImportError(
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"To use the flyte callback manager you need"
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"to have the `flytekit` and `flytekitplugins-deck-standard`"
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"packages installed. Please install them with `pip install flytekit`"
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"and `pip install flytekitplugins-deck-standard`."
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)
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return flytekit, renderer
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def analyze_text(
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text: str,
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nlp: Any = None,
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textstat: 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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if textstat is not None:
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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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"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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spacy = import_spacy()
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doc = nlp(text)
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dep_out = spacy.displacy.render(doc, style="dep", jupyter=False, page=True)
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ent_out = spacy.displacy.render(doc, style="ent", jupyter=False, page=True)
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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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class FlyteCallbackHandler(BaseMetadataCallbackHandler, BaseCallbackHandler):
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"""This callback handler that is used within a Flyte task."""
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def __init__(self) -> None:
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"""Initialize callback handler."""
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flytekit, renderer = import_flytekit()
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self.pandas = import_pandas()
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self.textstat = None
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try:
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self.textstat = import_textstat()
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except ImportError:
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logger.warning(
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"Textstat library is not installed. \
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It may result in the inability to log \
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certain metrics that can be captured with Textstat."
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)
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spacy = None
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try:
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spacy = import_spacy()
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except ImportError:
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logger.warning(
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"Spacy library is not installed. \
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It may result in the inability to log \
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certain metrics that can be captured with Spacy."
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)
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super().__init__()
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self.nlp = None
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if spacy:
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try:
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self.nlp = spacy.load("en_core_web_sm")
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except OSError:
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logger.warning(
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"FlyteCallbackHandler uses spacy's en_core_web_sm model"
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" for certain metrics. To download,"
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" run the following command in your terminal:"
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" `python -m spacy download en_core_web_sm`"
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)
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self.table_renderer = renderer.TableRenderer
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self.markdown_renderer = renderer.MarkdownRenderer
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self.deck = flytekit.Deck(
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"LangChain Metrics",
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self.markdown_renderer().to_html("## LangChain Metrics"),
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)
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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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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.get_custom_callback_meta())
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prompt_responses = []
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for prompt in prompts:
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prompt_responses.append(prompt)
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resp.update({"prompts": prompt_responses})
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self.deck.append(self.markdown_renderer().to_html("### LLM Start"))
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self.deck.append(
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self.table_renderer().to_html(self.pandas.DataFrame([resp])) + "\n"
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)
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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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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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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.get_custom_callback_meta())
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self.deck.append(self.markdown_renderer().to_html("### LLM End"))
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self.deck.append(self.table_renderer().to_html(self.pandas.DataFrame([resp])))
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for generations in response.generations:
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for generation in generations:
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generation_resp = deepcopy(resp)
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generation_resp.update(flatten_dict(generation.dict()))
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if self.nlp or self.textstat:
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generation_resp.update(
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analyze_text(
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generation.text, nlp=self.nlp, textstat=self.textstat
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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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)
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self.deck.append(
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self.markdown_renderer().to_html("#### Text Complexity Metrics")
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)
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self.deck.append(
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self.table_renderer().to_html(
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self.pandas.DataFrame([complexity_metrics])
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)
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+ "\n"
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)
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dependency_tree = generation_resp["dependency_tree"]
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self.deck.append(
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self.markdown_renderer().to_html("#### Dependency Tree")
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)
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self.deck.append(dependency_tree)
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entities = generation_resp["entities"]
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self.deck.append(self.markdown_renderer().to_html("#### Entities"))
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self.deck.append(entities)
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else:
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self.deck.append(
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self.markdown_renderer().to_html("#### Generated Response")
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)
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self.deck.append(self.markdown_renderer().to_html(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.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: 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.get_custom_callback_meta())
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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.deck.append(self.markdown_renderer().to_html("### Chain Start"))
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self.deck.append(
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self.table_renderer().to_html(self.pandas.DataFrame([input_resp])) + "\n"
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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: 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.get_custom_callback_meta())
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self.deck.append(self.markdown_renderer().to_html("### Chain End"))
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self.deck.append(
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self.table_renderer().to_html(self.pandas.DataFrame([resp])) + "\n"
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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: 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.get_custom_callback_meta())
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self.deck.append(self.markdown_renderer().to_html("### Tool Start"))
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self.deck.append(
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self.table_renderer().to_html(self.pandas.DataFrame([resp])) + "\n"
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)
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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: Dict[str, Any] = {}
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resp.update({"action": "on_tool_end", "output": output})
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resp.update(self.get_custom_callback_meta())
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self.deck.append(self.markdown_renderer().to_html("### Tool End"))
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self.deck.append(
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self.table_renderer().to_html(self.pandas.DataFrame([resp])) + "\n"
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)
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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: Dict[str, Any] = {}
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resp.update({"action": "on_text", "text": text})
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resp.update(self.get_custom_callback_meta())
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self.deck.append(self.markdown_renderer().to_html("### On Text"))
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self.deck.append(
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self.table_renderer().to_html(self.pandas.DataFrame([resp])) + "\n"
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)
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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: Dict[str, Any] = {}
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resp.update(
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{
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"action": "on_agent_finish",
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"output": finish.return_values["output"],
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"log": finish.log,
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}
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)
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resp.update(self.get_custom_callback_meta())
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self.deck.append(self.markdown_renderer().to_html("### Agent Finish"))
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self.deck.append(
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self.table_renderer().to_html(self.pandas.DataFrame([resp])) + "\n"
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)
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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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resp: Dict[str, Any] = {}
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resp.update(
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{
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"action": "on_agent_action",
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"tool": action.tool,
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"tool_input": action.tool_input,
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"log": action.log,
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}
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)
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resp.update(self.get_custom_callback_meta())
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self.deck.append(self.markdown_renderer().to_html("### Agent Action"))
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self.deck.append(
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self.table_renderer().to_html(self.pandas.DataFrame([resp])) + "\n"
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)
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