mirror of
https://github.com/deepset-ai/haystack.git
synced 2025-07-23 08:52:16 +00:00
72b637ae6d
* dpr training and inference code refactored with FARM modules * dpr test cases modified * docstring and default arguments updated * dpr training docstring updated * bugfix in dense retriever inference, DPR tutorials modified * Bump FARM to 0.5.0 * update README for DPR * dpr training and inference code refactored with FARM modules * dpr test cases modified * docstring and default arguments updated * dpr training docstring updated * bugfix in dense retriever inference, DPR tutorials modified * Bump FARM to 0.5.0 * update README for DPR * mypy errors fix * DPR instantiation bugfix * Fix DPR init in RAG Tutorial Co-authored-by: Malte Pietsch <malte.pietsch@deepset.ai>
66 lines
3.2 KiB
Python
Executable File
66 lines
3.2 KiB
Python
Executable File
from haystack import Finder
|
|
from haystack.document_store.faiss import FAISSDocumentStore
|
|
from haystack.preprocessor.cleaning import clean_wiki_text
|
|
from haystack.preprocessor.utils import convert_files_to_dicts, fetch_archive_from_http
|
|
from haystack.reader.farm import FARMReader
|
|
from haystack.utils import print_answers
|
|
from haystack.retriever.dense import DensePassageRetriever
|
|
|
|
|
|
# FAISS is a library for efficient similarity search on a cluster of dense vectors.
|
|
# The FAISSDocumentStore uses a SQL(SQLite in-memory be default) document store under-the-hood
|
|
# to store the document text and other meta data. The vector embeddings of the text are
|
|
# indexed on a FAISS Index that later is queried for searching answers.
|
|
document_store = FAISSDocumentStore()
|
|
|
|
# ## Preprocessing of documents
|
|
# Let's first get some documents that we want to query
|
|
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
|
|
dicts = convert_files_to_dicts(dir_path=doc_dir, clean_func=clean_wiki_text, split_paragraphs=True)
|
|
|
|
# Now, let's write the docs to our DB.
|
|
document_store.write_documents(dicts)
|
|
|
|
### Retriever
|
|
retriever = DensePassageRetriever(document_store=document_store,
|
|
query_embedding_model="facebook/dpr-question_encoder-single-nq-base",
|
|
passage_embedding_model="facebook/dpr-ctx_encoder-single-nq-base",
|
|
max_seq_len_query=64,
|
|
max_seq_len_passage=256,
|
|
batch_size=2,
|
|
use_gpu=True,
|
|
embed_title=True,
|
|
use_fast_tokenizers=True
|
|
)
|
|
|
|
# Important:
|
|
# Now that after we have the DPR initialized, we need to call update_embeddings() to iterate over all
|
|
# previously indexed documents and update their embedding representation.
|
|
# While this can be a time consuming operation (depending on corpus size), it only needs to be done once.
|
|
# At query time, we only need to embed the query and compare it the existing doc embeddings which is very fast.
|
|
document_store.update_embeddings(retriever)
|
|
|
|
### Reader
|
|
# Load a local model or any of the QA models on
|
|
# Hugging Face's model hub (https://huggingface.co/models)
|
|
reader = FARMReader(model_name_or_path="deepset/roberta-base-squad2", use_gpu=True)
|
|
|
|
### Finder
|
|
# The Finder sticks together reader and retriever in a pipeline to answer our actual questions.
|
|
finder = Finder(reader, retriever)
|
|
|
|
### Voilà! Ask a question!
|
|
# You can configure how many candidates the reader and retriever shall return
|
|
# The higher top_k_retriever, the better (but also the slower) your answers.
|
|
prediction = finder.get_answers(question="Who is the father of Arya Stark?", top_k_retriever=10, top_k_reader=5)
|
|
|
|
|
|
# prediction = finder.get_answers(question="Who created the Dothraki vocabulary?", top_k_reader=5)
|
|
# prediction = finder.get_answers(question="Who is the sister of Sansa?", top_k_reader=5)
|
|
|
|
print_answers(prediction, details="minimal")
|