forked from Archives/langchain
Merge branch 'ankush/async-llm' into ankush/async-llmchain
Ankush Gola
1 year ago
commit
2611fdd03e
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"""Wrapper around TensorflowHub embedding models."""
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from typing import Any, List
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from pydantic import BaseModel, Extra
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from langchain.embeddings.base import Embeddings
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DEFAULT_MODEL_URL = "https://tfhub.dev/google/universal-sentence-encoder-multilingual/3"
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class TensorflowHubEmbeddings(BaseModel, Embeddings):
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"""Wrapper around tensorflow_hub embedding models.
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To use, you should have the ``tensorflow_text`` python package installed.
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Example:
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.. code-block:: python
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from langchain.embeddings import TensorflowHubEmbeddings
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url = "https://tfhub.dev/google/universal-sentence-encoder-multilingual/3"
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tf = TensorflowHubEmbeddings(model_url=url)
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"""
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embed: Any #: :meta private:
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model_url: str = DEFAULT_MODEL_URL
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"""Model name to use."""
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def __init__(self, **kwargs: Any):
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"""Initialize the tensorflow_hub and tensorflow_text."""
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super().__init__(**kwargs)
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try:
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import tensorflow_hub
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import tensorflow_text # noqa
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self.embed = tensorflow_hub.load(self.model_url)
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except ImportError as e:
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raise ValueError(
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"Could not import some python packages." "Please install them."
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) from e
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class Config:
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"""Configuration for this pydantic object."""
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extra = Extra.forbid
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def embed_documents(self, texts: List[str]) -> List[List[float]]:
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"""Compute doc embeddings using a TensorflowHub embedding model.
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Args:
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texts: The list of texts to embed.
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Returns:
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List of embeddings, one for each text.
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"""
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texts = list(map(lambda x: x.replace("\n", " "), texts))
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embeddings = self.embed(texts).numpy()
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return embeddings.tolist()
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def embed_query(self, text: str) -> List[float]:
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"""Compute query embeddings using a TensorflowHub embedding model.
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Args:
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text: The text to embed.
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Returns:
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Embeddings for the text.
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"""
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text = text.replace("\n", " ")
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embedding = self.embed(text).numpy()[0]
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return embedding.tolist()
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"""Select and order examples based on ngram overlap score (sentence_bleu score).
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https://www.nltk.org/_modules/nltk/translate/bleu_score.html
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https://aclanthology.org/P02-1040.pdf
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"""
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from typing import Dict, List
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import numpy as np
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from pydantic import BaseModel, root_validator
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from langchain.prompts.example_selector.base import BaseExampleSelector
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from langchain.prompts.prompt import PromptTemplate
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def ngram_overlap_score(source: List[str], example: List[str]) -> float:
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"""Compute ngram overlap score of source and example as sentence_bleu score.
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Use sentence_bleu with method1 smoothing function and auto reweighting.
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Return float value between 0.0 and 1.0 inclusive.
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https://www.nltk.org/_modules/nltk/translate/bleu_score.html
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https://aclanthology.org/P02-1040.pdf
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"""
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from nltk.translate.bleu_score import ( # type: ignore
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SmoothingFunction,
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sentence_bleu,
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)
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hypotheses = source[0].split()
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references = [s.split() for s in example]
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return float(
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sentence_bleu(
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references,
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hypotheses,
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smoothing_function=SmoothingFunction().method1,
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auto_reweigh=True,
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)
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)
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class NGramOverlapExampleSelector(BaseExampleSelector, BaseModel):
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"""Select and order examples based on ngram overlap score (sentence_bleu score).
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https://www.nltk.org/_modules/nltk/translate/bleu_score.html
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https://aclanthology.org/P02-1040.pdf
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"""
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examples: List[dict]
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"""A list of the examples that the prompt template expects."""
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example_prompt: PromptTemplate
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"""Prompt template used to format the examples."""
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threshold: float = -1.0
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"""Threshold at which algorithm stops. Set to -1.0 by default.
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For negative threshold:
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select_examples sorts examples by ngram_overlap_score, but excludes none.
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For threshold greater than 1.0:
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select_examples excludes all examples, and returns an empty list.
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For threshold equal to 0.0:
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select_examples sorts examples by ngram_overlap_score,
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and excludes examples with no ngram overlap with input.
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"""
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@root_validator(pre=True)
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def check_dependencies(cls, values: Dict) -> Dict:
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"""Check that valid dependencies exist."""
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try:
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from nltk.translate.bleu_score import ( # noqa: disable=F401
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SmoothingFunction,
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sentence_bleu,
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)
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except ImportError as e:
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raise ValueError(
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"Not all the correct dependencies for this ExampleSelect exist"
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) from e
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return values
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def add_example(self, example: Dict[str, str]) -> None:
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"""Add new example to list."""
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self.examples.append(example)
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def select_examples(self, input_variables: Dict[str, str]) -> List[dict]:
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"""Return list of examples sorted by ngram_overlap_score with input.
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Descending order.
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Excludes any examples with ngram_overlap_score less than or equal to threshold.
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"""
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inputs = list(input_variables.values())
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examples = []
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k = len(self.examples)
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score = [0.0] * k
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first_prompt_template_key = self.example_prompt.input_variables[0]
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for i in range(k):
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score[i] = ngram_overlap_score(
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inputs, [self.examples[i][first_prompt_template_key]]
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)
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while True:
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arg_max = np.argmax(score)
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if (score[arg_max] < self.threshold) or abs(
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score[arg_max] - self.threshold
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) < 1e-9:
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break
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examples.append(self.examples[arg_max])
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score[arg_max] = self.threshold - 1.0
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return examples
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Load Diff
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"""Test TensorflowHub embeddings."""
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from langchain.embeddings import TensorflowHubEmbeddings
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def test_tensorflowhub_embedding_documents() -> None:
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"""Test tensorflowhub embeddings."""
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documents = ["foo bar"]
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embedding = TensorflowHubEmbeddings()
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output = embedding.embed_documents(documents)
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assert len(output) == 1
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assert len(output[0]) == 512
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def test_tensorflowhub_embedding_query() -> None:
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"""Test tensorflowhub embeddings."""
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document = "foo bar"
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embedding = TensorflowHubEmbeddings()
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output = embedding.embed_query(document)
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assert len(output) == 512
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"""Test functionality related to ngram overlap based selector."""
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import pytest
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from langchain.prompts.example_selector.ngram_overlap import (
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NGramOverlapExampleSelector,
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ngram_overlap_score,
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)
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from langchain.prompts.prompt import PromptTemplate
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EXAMPLES = [
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{"input": "See Spot run.", "output": "foo1"},
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{"input": "My dog barks.", "output": "foo2"},
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{"input": "Spot can run.", "output": "foo3"},
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]
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@pytest.fixture
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def selector() -> NGramOverlapExampleSelector:
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"""Get ngram overlap based selector to use in tests."""
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prompts = PromptTemplate(
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input_variables=["input", "output"], template="Input: {input}\nOutput: {output}"
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)
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selector = NGramOverlapExampleSelector(
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examples=EXAMPLES,
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example_prompt=prompts,
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)
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return selector
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def test_selector_valid(selector: NGramOverlapExampleSelector) -> None:
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"""Test NGramOverlapExampleSelector can select examples."""
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sentence = "Spot can run."
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output = selector.select_examples({"input": sentence})
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assert output == [EXAMPLES[2], EXAMPLES[0], EXAMPLES[1]]
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def test_selector_add_example(selector: NGramOverlapExampleSelector) -> None:
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"""Test NGramOverlapExampleSelector can add an example."""
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new_example = {"input": "Spot plays fetch.", "output": "foo4"}
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selector.add_example(new_example)
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sentence = "Spot can run."
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output = selector.select_examples({"input": sentence})
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assert output == [EXAMPLES[2], EXAMPLES[0]] + [new_example] + [EXAMPLES[1]]
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def test_selector_threshold_zero(selector: NGramOverlapExampleSelector) -> None:
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"""Tests NGramOverlapExampleSelector threshold set to 0.0."""
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selector.threshold = 0.0
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sentence = "Spot can run."
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output = selector.select_examples({"input": sentence})
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assert output == [EXAMPLES[2], EXAMPLES[0]]
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def test_selector_threshold_more_than_one(
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selector: NGramOverlapExampleSelector,
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) -> None:
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"""Tests NGramOverlapExampleSelector threshold greater than 1.0."""
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selector.threshold = 1.0 + 1e-9
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sentence = "Spot can run."
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output = selector.select_examples({"input": sentence})
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assert output == []
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def test_ngram_overlap_score(selector: NGramOverlapExampleSelector) -> None:
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"""Tests that ngram_overlap_score returns correct values."""
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selector.threshold = 1.0 + 1e-9
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none = ngram_overlap_score(["Spot can run."], ["My dog barks."])
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some = ngram_overlap_score(["Spot can run."], ["See Spot run."])
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complete = ngram_overlap_score(["Spot can run."], ["Spot can run."])
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check = [abs(none - 0.0) < 1e-9, 0.0 < some < 1.0, abs(complete - 1.0) < 1e-9]
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assert check == [True, True, True]
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