haystack/tutorials/Tutorial11_Pipelines.py
Sara Zan a59bca3661
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Python

from haystack.utils import (
clean_wiki_text,
print_answers,
print_documents,
fetch_archive_from_http,
convert_files_to_dicts,
launch_es,
)
from pprint import pprint
from haystack import Pipeline
from haystack.document_stores import ElasticsearchDocumentStore
from haystack.nodes import (
ElasticsearchRetriever,
DensePassageRetriever,
FARMReader,
RAGenerator,
BaseComponent,
JoinDocuments,
)
from haystack.pipelines import ExtractiveQAPipeline, DocumentSearchPipeline, GenerativeQAPipeline
def tutorial11_pipelines():
# Download and prepare data - 517 Wikipedia articles for Game of Thrones
doc_dir = "data/article_txt_got"
s3_url = "https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/documents/wiki_gameofthrones_txt.zip"
fetch_archive_from_http(url=s3_url, output_dir=doc_dir)
# convert files to dicts containing documents that can be indexed to our datastore
got_dicts = convert_files_to_dicts(dir_path=doc_dir, clean_func=clean_wiki_text, split_paragraphs=True)
# Initialize DocumentStore and index documents
launch_es()
document_store = ElasticsearchDocumentStore()
document_store.delete_documents()
document_store.write_documents(got_dicts)
# Initialize Sparse retriever
es_retriever = ElasticsearchRetriever(document_store=document_store)
# Initialize dense retriever
dpr_retriever = DensePassageRetriever(document_store)
document_store.update_embeddings(dpr_retriever, update_existing_embeddings=False)
reader = FARMReader(model_name_or_path="deepset/roberta-base-squad2")
print()
print("######################")
print("# Prebuilt Pipelines #")
print("######################")
print()
print("# Extractive QA Pipeline")
print("########################")
query = "Who is the father of Arya Stark?"
p_extractive_premade = ExtractiveQAPipeline(reader=reader, retriever=es_retriever)
res = p_extractive_premade.run(
query=query,
params={"Retriever": {"top_k": 10}, "Reader": {"top_k": 5}},
)
print("\nQuery: ", query)
print("Answers:")
print_answers(res, details="minimum")
print()
print("# Document Search Pipeline")
print("##########################")
query = "Who is the father of Arya Stark?"
p_retrieval = DocumentSearchPipeline(es_retriever)
res = p_retrieval.run(
query=query,
params={"Retriever": {"top_k": 10}},
)
print()
print_documents(res, max_text_len=200)
print()
print("# Generator Pipeline")
print("####################")
# We set this to True so that the document store returns document embeddings
# with each document, this is needed by the Generator
document_store.return_embedding = True
# Initialize generator
rag_generator = RAGenerator()
# Generative QA
query = "Who is the father of Arya Stark?"
p_generator = GenerativeQAPipeline(generator=rag_generator, retriever=dpr_retriever)
res = p_generator.run(
query=query,
params={"Retriever": {"top_k": 10}},
)
print()
print_answers(res, details="minimum")
# We are setting this to False so that in later pipelines,
# we get a cleaner printout
document_store.return_embedding = False
##############################
# Creating Pipeline Diagrams #
##############################
p_extractive_premade.draw("pipeline_extractive_premade.png")
p_retrieval.draw("pipeline_retrieval.png")
p_generator.draw("pipeline_generator.png")
print()
print("####################")
print("# Custom Pipelines #")
print("####################")
print()
print("# Extractive QA Pipeline")
print("########################")
# Custom built extractive QA pipeline
p_extractive = Pipeline()
p_extractive.add_node(component=es_retriever, name="Retriever", inputs=["Query"])
p_extractive.add_node(component=reader, name="Reader", inputs=["Retriever"])
# Now we can run it
query = "Who is the father of Arya Stark?"
res = p_extractive.run(
query=query,
params={"Retriever": {"top_k": 10}, "Reader": {"top_k": 5}},
)
print("\nQuery: ", query)
print("Answers:")
print_answers(res, details="minimum")
p_extractive.draw("pipeline_extractive.png")
print()
print("# Ensembled Retriever Pipeline")
print("##############################")
# Create ensembled pipeline
p_ensemble = Pipeline()
p_ensemble.add_node(component=es_retriever, name="ESRetriever", inputs=["Query"])
p_ensemble.add_node(component=dpr_retriever, name="DPRRetriever", inputs=["Query"])
p_ensemble.add_node(
component=JoinDocuments(join_mode="concatenate"), name="JoinResults", inputs=["ESRetriever", "DPRRetriever"]
)
p_ensemble.add_node(component=reader, name="Reader", inputs=["JoinResults"])
p_ensemble.draw("pipeline_ensemble.png")
# Run pipeline
query = "Who is the father of Arya Stark?"
res = p_ensemble.run(
query="Who is the father of Arya Stark?",
params={"ESRetriever": {"top_k": 5}, "DPRRetriever": {"top_k": 5}},
)
print("\nQuery: ", query)
print("Answers:")
print_answers(res, details="minimum")
print()
print("# Query Classification Pipeline")
print("###############################")
# Decision Nodes help you route your data so that only certain branches of your `Pipeline` are run.
# Though this looks very similar to the ensembled pipeline shown above,
# the key difference is that only one of the retrievers is run for each request.
# By contrast both retrievers are always run in the ensembled approach.
class CustomQueryClassifier(BaseComponent):
outgoing_edges = 2
def run(self, query):
if "?" in query:
return {}, "output_2"
else:
return {}, "output_1"
# Here we build the pipeline
p_classifier = Pipeline()
p_classifier.add_node(component=CustomQueryClassifier(), name="QueryClassifier", inputs=["Query"])
p_classifier.add_node(component=es_retriever, name="ESRetriever", inputs=["QueryClassifier.output_1"])
p_classifier.add_node(component=dpr_retriever, name="DPRRetriever", inputs=["QueryClassifier.output_2"])
p_classifier.add_node(component=reader, name="QAReader", inputs=["ESRetriever", "DPRRetriever"])
p_classifier.draw("pipeline_classifier.png")
# Run only the dense retriever on the full sentence query
query = "Who is the father of Arya Stark?"
res_1 = p_classifier.run(
query=query,
)
print()
print("\nQuery: ", query)
print(" * DPR Answers:")
print_answers(res_1, details="minimum")
# Run only the sparse retriever on a keyword based query
query = "Arya Stark father"
res_2 = p_classifier.run(
query=query,
)
print()
print("\nQuery: ", query)
print(" * ES Answers:")
print_answers(res_2, details="minimum")
print("#######################")
print("# Debugging Pipelines #")
print("#######################")
# You can print out debug information from nodes in your pipelines in a few different ways.
# 1) You can set the `debug` attribute of a given node.
es_retriever.debug = True
# 2) You can provide `debug` as a parameter when running your pipeline
result = p_classifier.run(query="Who is the father of Arya Stark?", params={"ESRetriever": {"debug": True}})
# 3) You can provide the `debug` paramter to all nodes in your pipeline
result = p_classifier.run(query="Who is the father of Arya Stark?", params={"debug": True})
pprint(result["_debug"])
if __name__ == "__main__":
tutorial11_pipelines()
# This Haystack script was made with love by deepset in Berlin, Germany
# Haystack: https://github.com/deepset-ai/haystack
# deepset: https://deepset.ai/