Source code for langchain_community.example_selectors.ngram_overlap

"""Select and order examples based on ngram overlap score (sentence_bleu score).
from typing import Dict, List

import numpy as np
from langchain_core.example_selectors import BaseExampleSelector
from langchain_core.prompts import PromptTemplate
from langchain_core.pydantic_v1 import BaseModel, root_validator

[docs]def ngram_overlap_score(source: List[str], example: List[str]) -> float: """Compute ngram overlap score of source and example as sentence_bleu score from NLTK package. Use sentence_bleu with method1 smoothing function and auto reweighting. Return float value between 0.0 and 1.0 inclusive. """ from nltk.translate.bleu_score import ( SmoothingFunction, # type: ignore sentence_bleu, ) hypotheses = source[0].split() references = [s.split() for s in example] return float( sentence_bleu( references, hypotheses, smoothing_function=SmoothingFunction().method1, auto_reweigh=True, ) )
[docs]class NGramOverlapExampleSelector(BaseExampleSelector, BaseModel): """Select and order examples based on ngram overlap score (sentence_bleu score from NLTK package). """ examples: List[dict] """A list of the examples that the prompt template expects.""" example_prompt: PromptTemplate """Prompt template used to format the examples.""" threshold: float = -1.0 """Threshold at which algorithm stops. Set to -1.0 by default. For negative threshold: select_examples sorts examples by ngram_overlap_score, but excludes none. For threshold greater than 1.0: select_examples excludes all examples, and returns an empty list. For threshold equal to 0.0: select_examples sorts examples by ngram_overlap_score, and excludes examples with no ngram overlap with input. """ @root_validator(pre=True) def check_dependencies(cls, values: Dict) -> Dict: """Check that valid dependencies exist.""" try: from nltk.translate.bleu_score import ( # noqa: F401 SmoothingFunction, sentence_bleu, ) except ImportError as e: raise ImportError( "Not all the correct dependencies for this ExampleSelect exist." "Please install nltk with `pip install nltk`." ) from e return values
[docs] def add_example(self, example: Dict[str, str]) -> None: """Add new example to list.""" self.examples.append(example)
[docs] def select_examples(self, input_variables: Dict[str, str]) -> List[dict]: """Return list of examples sorted by ngram_overlap_score with input. Descending order. Excludes any examples with ngram_overlap_score less than or equal to threshold. """ inputs = list(input_variables.values()) examples = [] k = len(self.examples) score = [0.0] * k first_prompt_template_key = self.example_prompt.input_variables[0] for i in range(k): score[i] = ngram_overlap_score( inputs, [self.examples[i][first_prompt_template_key]] ) while True: arg_max = np.argmax(score) if (score[arg_max] < self.threshold) or abs( score[arg_max] - self.threshold ) < 1e-9: break examples.append(self.examples[arg_max]) score[arg_max] = self.threshold - 1.0 return examples