mirror of
https://github.com/deepset-ai/haystack.git
synced 2025-07-22 16:31:16 +00:00

* Testing black on ui/ * Applying black on docstores * Add latest docstring and tutorial changes * Create a single GH action for Black and docs to reduce commit noise to the minimum, slightly refactor the OpenAPI action too * Remove comments * Relax constraints on pydoc-markdown * Split temporary black from the docs. Pydoc-markdown was obsolete and needs a separate PR to upgrade * Fix a couple of bugs * Add a type: ignore that was missing somehow * Give path to black * Apply Black * Apply Black * Relocate a couple of type: ignore * Update documentation * Make Linux CI run after applying Black * Triggering Black * Apply Black * Remove dependency, does not work well * Remove manually double trailing commas * Update documentation Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
230 lines
7.8 KiB
Python
230 lines
7.8 KiB
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/
|