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from typing import List
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import numpy as np
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import pytest
from haystack import Document
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from haystack . generator . transformers import Seq2SeqGenerator
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from haystack . pipeline import TranslationWrapperPipeline , GenerativeQAPipeline
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# Avoid Warning .huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
import os
os . environ [ " TOKENIZERS_PARALLELISM " ] = " false "
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DOCS_WITH_EMBEDDINGS = [
Document (
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content = """ The capital of Germany is the city state of Berlin. """ ,
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embedding = np . array ( [ 2.22920075e-01 , 1.07770450e-02 , 3.35382462e-01 , - 7.27265477e-02 ,
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2.95721531e-01 , 7.53905892e-01 , - 3.36153835e-01 , 1.94666490e-01 ,
2.92297024e-02 , 6.56022906e-01 , 2.67616689e-01 , - 3.81376356e-01 ,
- 2.98582464e-01 , - 1.89207539e-01 , 6.07246757e-01 , 1.67709842e-01 ,
2.75577039e-01 , - 9.33986664e-01 , 4.31648612e-01 , - 1.00929722e-01 ,
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- 2.78178096e-01 , 6.61195964e-02 , 4.15457308e-01 , 3.25128995e-02 ,
2.66546309e-01 , 1.30013347e-01 , 3.52349013e-01 , - 6.64731681e-01 ,
- 6.83372736e-01 , - 3.16153020e-01 , 3.67267191e-01 , - 4.05127078e-01 ,
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3.29131901e-01 , 9.00070250e-01 , 4.34159547e-01 , - 5.65743327e-01 ,
- 7.94787586e-01 , - 9.83037204e-02 , - 1.01550505e-01 , 1.17718965e-01 ,
2.48768821e-01 , 2.64568210e-01 , - 1.21708572e-01 , 3.54779810e-01 ,
7.25113750e-01 , 4.65293467e-01 , - 4.09185141e-02 , - 8.67474079e-03 ,
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1.61721796e-01 , 2.01941788e-01 , 2.18551666e-01 , 8.89380276e-02 ,
9.31400955e-02 , - 1.68867663e-01 , 1.93741471e-01 , 9.80174169e-03 ,
3.96567971e-01 , 3.25188875e-01 , - 3.59817073e-02 , - 3.05083066e-01 ,
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1.59089297e-01 , 3.25954586e-01 , 1.59884766e-01 , 6.17780030e-01 ,
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3.99156392e-01 , - 4.60546672e-01 , - 3.68922651e-01 , 4.64805663e-01 ,
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3.56542349e-01 , - 2.10935414e-01 , - 6.86178625e-01 , - 4.68558609e-01 ,
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dtype = np . float32 )
) ,
Document (
2021-10-13 14:23:23 +02:00
content = """ Berlin is the capital and largest city of Germany by both area and population. """ ,
2020-10-30 18:06:02 +01:00
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- 5.02151966e-01 , 2.66258836e-01 , - 7.80072629e-01 , 6.64352655e-01 ,
9.51846957e-01 , 4.84095603e-01 , - 4.69458699e-01 , - 6.44100308e-01 ,
- 2.09408700e-01 , 1.88016608e-01 , 5.53907156e-01 , 2.46309891e-01 ,
3.69830072e-01 , 3.00819635e-01 , - 5.66207170e-01 , 3.10216904e-01 ,
- 7.62370825e-01 , 6.62293613e-01 , 2.95080066e-01 , 2.16252878e-01 ,
1.02028084e+00 , - 5.11510437e-03 , 5.37559271e-01 , 3.52584869e-01 ,
2.35834837e-01 , - 4.11791533e-01 , 7.64139056e-01 , 1.23637088e-01 ] ,
dtype = np . float32 )
)
]
@pytest.mark.slow
@pytest.mark.generator
def test_rag_token_generator ( rag_generator ) :
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query = " What is capital of the Germany? "
generated_docs = rag_generator . predict ( query = query , documents = DOCS_WITH_EMBEDDINGS , top_k = 1 )
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answers = generated_docs [ " answers " ]
assert len ( answers ) == 1
assert " berlin " in answers [ 0 ] [ " answer " ]
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@pytest.mark.slow
@pytest.mark.generator
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@pytest.mark.parametrize ( " document_store " , [ " memory " ] , indirect = True )
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@pytest.mark.parametrize ( " retriever " , [ " embedding " ] , indirect = True )
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def test_generator_pipeline ( document_store , retriever , rag_generator ) :
document_store . write_documents ( DOCS_WITH_EMBEDDINGS )
query = " What is capital of the Germany? "
pipeline = GenerativeQAPipeline ( retriever = retriever , generator = rag_generator )
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output = pipeline . run ( query = query , params = { " Generator " : { " top_k " : 2 } , " Retriever " : { " top_k " : 1 } } )
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answers = output [ " answers " ]
assert len ( answers ) == 2
assert " berlin " in answers [ 0 ] [ " answer " ]
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@pytest.mark.slow
@pytest.mark.generator
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@pytest.mark.parametrize ( " document_store " , [ " memory " ] , indirect = True )
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@pytest.mark.parametrize ( " retriever " , [ " retribert " ] , indirect = True )
@pytest.mark.vector_dim ( 128 )
def test_lfqa_pipeline ( document_store , retriever , eli5_generator ) :
# reuse existing DOCS but regenerate embeddings with retribert
docs : List [ Document ] = [ ]
for idx , d in enumerate ( DOCS_WITH_EMBEDDINGS ) :
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docs . append ( Document ( d . content , str ( idx ) ) )
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document_store . write_documents ( docs )
document_store . update_embeddings ( retriever )
query = " Tell me about Berlin? "
pipeline = GenerativeQAPipeline ( retriever = retriever , generator = eli5_generator )
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output = pipeline . run ( query = query , params = { " top_k " : 1 } )
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answers = output [ " answers " ]
assert len ( answers ) == 1
assert " Germany " in answers [ 0 ]
@pytest.mark.slow
@pytest.mark.generator
@pytest.mark.parametrize ( " document_store " , [ " memory " ] , indirect = True )
@pytest.mark.parametrize ( " retriever " , [ " retribert " ] , indirect = True )
@pytest.mark.vector_dim ( 128 )
def test_lfqa_pipeline_unknown_converter ( document_store , retriever ) :
# reuse existing DOCS but regenerate embeddings with retribert
docs : List [ Document ] = [ ]
for idx , d in enumerate ( DOCS_WITH_EMBEDDINGS ) :
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docs . append ( Document ( d . content , str ( idx ) ) )
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document_store . write_documents ( docs )
document_store . update_embeddings ( retriever )
seq2seq = Seq2SeqGenerator ( model_name_or_path = " patrickvonplaten/t5-tiny-random " )
query = " Tell me about Berlin? "
pipeline = GenerativeQAPipeline ( retriever = retriever , generator = seq2seq )
# raises exception as we don't have converter for "patrickvonplaten/t5-tiny-random" in Seq2SeqGenerator
with pytest . raises ( Exception ) :
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output = pipeline . run ( query = query , params = { " top_k " : 1 } )
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@pytest.mark.slow
@pytest.mark.generator
@pytest.mark.parametrize ( " document_store " , [ " memory " ] , indirect = True )
@pytest.mark.parametrize ( " retriever " , [ " retribert " ] , indirect = True )
@pytest.mark.vector_dim ( 128 )
def test_lfqa_pipeline_invalid_converter ( document_store , retriever ) :
# reuse existing DOCS but regenerate embeddings with retribert
docs : List [ Document ] = [ ]
for idx , d in enumerate ( DOCS_WITH_EMBEDDINGS ) :
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docs . append ( Document ( d . content , str ( idx ) ) )
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document_store . write_documents ( docs )
document_store . update_embeddings ( retriever )
class _InvalidConverter :
def __call__ ( self , some_invalid_para : str , another_invalid_param : str ) - > None :
pass
seq2seq = Seq2SeqGenerator ( model_name_or_path = " yjernite/bart_eli5 " , input_converter = _InvalidConverter ( ) )
query = " This query will fail due to InvalidConverter used "
pipeline = GenerativeQAPipeline ( retriever = retriever , generator = seq2seq )
# raises exception as we are using invalid method signature in _InvalidConverter
with pytest . raises ( Exception ) :
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output = pipeline . run ( query = query , params = { " top_k " : 1 } )
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# Keeping few (retriever,document_store) combination to reduce test time
@pytest.mark.slow
@pytest.mark.generator
@pytest.mark.parametrize (
" retriever,document_store " ,
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[ ( " embedding " , " memory " ) ] ,
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indirect = True ,
)
def test_generator_pipeline_with_translator (
document_store ,
retriever ,
rag_generator ,
en_to_de_translator ,
de_to_en_translator
) :
document_store . write_documents ( DOCS_WITH_EMBEDDINGS )
query = " Was ist die Hauptstadt der Bundesrepublik Deutschland? "
base_pipeline = GenerativeQAPipeline ( retriever = retriever , generator = rag_generator )
pipeline = TranslationWrapperPipeline (
input_translator = de_to_en_translator ,
output_translator = en_to_de_translator ,
pipeline = base_pipeline
)
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output = pipeline . run ( query = query , params = { " Generator " : { " top_k " : 2 } , " Retriever " : { " top_k " : 1 } } )
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answers = output [ " answers " ]
assert len ( answers ) == 2
assert " berlin " in answers [ 0 ] [ " answer " ]
