def test_dpr_inmemory_retrieval(document_store):
documents = [
Document(
text="""Aaron Aaron ( or ; ""Ahärôn"") is a prophet, high priest, and the brother of Moses in the Abrahamic religions. Knowledge of Aaron, along with his brother Moses, comes exclusively from religious texts, such as the Bible and Quran. The Hebrew Bible relates that, unlike Moses, who grew up in the Egyptian royal court, Aaron and his elder sister Miriam remained with their kinsmen in the eastern border-land of Egypt (Goshen). When Moses first confronted the Egyptian king about the Israelites, Aaron served as his brother's spokesman (""prophet"") to the Pharaoh. Part of the Law (Torah) that Moses received from""",
meta={"name": "0"}
),
Document(
text="""Democratic Republic of the Congo to the south. Angola's capital, Luanda, lies on the Atlantic coast in the northwest of the country. Angola, although located in a tropical zone, has a climate that is not characterized for this region, due to the confluence of three factors: As a result, Angola's climate is characterized by two seasons: rainfall from October to April and drought, known as ""Cacimbo"", from May to August, drier, as the name implies, and with lower temperatures. On the other hand, while the coastline has high rainfall rates, decreasing from North to South and from to , with""",
),
Document(
text="""Schopenhauer, describing him as an ultimately shallow thinker: ""Schopenhauer has quite a crude mind ... where real depth starts, his comes to an end."" His friend Bertrand Russell had a low opinion on the philosopher, and attacked him in his famous ""History of Western Philosophy"" for hypocritically praising asceticism yet not acting upon it. On the opposite isle of Russell on the foundations of mathematics, the Dutch mathematician L. E. J. Brouwer incorporated the ideas of Kant and Schopenhauer in intuitionism, where mathematics is considered a purely mental activity, instead of an analytic activity wherein objective properties of reality are""",
meta={"name": "1"}
),
Document(
text="""The Dothraki vocabulary was created by David J. Peterson well in advance of the adaptation. HBO hired the Language Creatio""",
meta={"name": "2"}
),
Document(
text="""The title of the episode refers to the Great Sept of Baelor, the main religious building in King's Landing, where the episode's pivotal scene takes place. In the world created by George R. R. Martin""",
meta={}
)
]
document_store.delete_all_documents(index="test_dpr")
document_store.write_documents(documents, index="test_dpr")
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",
use_gpu=True, embed_title=True,
remove_sep_tok_from_untitled_passages=True)
document_store.update_embeddings(retriever=retriever, index="test_dpr")
time.sleep(2)
docs_with_emb = document_store.get_all_documents(index="test_dpr")
# FAISSDocumentStore doesn't return embeddings, so these tests only work with ElasticsearchDocumentStore
if isinstance(document_store, ElasticsearchDocumentStore):
assert (len(docs_with_emb[0].embedding) == 768)
assert (abs(docs_with_emb[0].embedding[0] - (-0.30634)) < 0.001)
assert (abs(docs_with_emb[1].embedding[0] - (-0.37449)) < 0.001)
assert (abs(docs_with_emb[2].embedding[0] - (-0.24695)) < 0.001)
assert (abs(docs_with_emb[3].embedding[0] - (-0.08017)) < 0.001)
assert (abs(docs_with_emb[4].embedding[0] - (-0.01534)) < 0.001)
res = retriever.retrieve(query="Which philosopher attacked Schopenhauer?", index="test_dpr")
assert res[0].meta["name"] == "1"
# clean up
document_store.delete_all_documents(index="test_dpr")
def test_dpr_inmemory_retrieval():
document_store = InMemoryDocumentStore()
documents = [
{
'name':
'0',
'text':
"""Aaron Aaron ( or ; ""Ahärôn"") is a prophet, high priest, and the brother of Moses in the Abrahamic religions. Knowledge of Aaron, along with his brother Moses, comes exclusively from religious texts, such as the Bible and Quran. The Hebrew Bible relates that, unlike Moses, who grew up in the Egyptian royal court, Aaron and his elder sister Miriam remained with their kinsmen in the eastern border-land of Egypt (Goshen). When Moses first confronted the Egyptian king about the Israelites, Aaron served as his brother's spokesman (""prophet"") to the Pharaoh. Part of the Law (Torah) that Moses received from"""
},
{
'name':
'1',
'text':
"""Schopenhauer, describing him as an ultimately shallow thinker: ""Schopenhauer has quite a crude mind ... where real depth starts, his comes to an end."" His friend Bertrand Russell had a low opinion on the philosopher, and attacked him in his famous ""History of Western Philosophy"" for hypocritically praising asceticism yet not acting upon it. On the opposite isle of Russell on the foundations of mathematics, the Dutch mathematician L. E. J. Brouwer incorporated the ideas of Kant and Schopenhauer in intuitionism, where mathematics is considered a purely mental activity, instead of an analytic activity wherein objective properties of reality are"""
},
{
'name':
'2',
'text':
"""Democratic Republic of the Congo to the south. Angola's capital, Luanda, lies on the Atlantic coast in the northwest of the country. Angola, although located in a tropical zone, has a climate that is not characterized for this region, due to the confluence of three factors: As a result, Angola's climate is characterized by two seasons: rainfall from October to April and drought, known as ""Cacimbo"", from May to August, drier, as the name implies, and with lower temperatures. On the other hand, while the coastline has high rainfall rates, decreasing from North to South and from to , with"""
},
]
retriever = DensePassageRetriever(document_store=document_store,
embedding_model="dpr-bert-base-nq",
use_gpu=False)
embedded = []
for doc in documents:
embedding = retriever.embed_passages([doc['text']])[0]
doc['embedding'] = embedding
embedded.append(doc)
assert (embedding.shape[0] == 768)
assert (embedding[0] - 0.52872 < 0.001)
document_store.write_documents(embedded)
res = retriever.retrieve(query="Which philosopher attacked Schopenhauer?")
assert res[0].text == documents[1]["text"]
"which mode is used for short wave broadcast service",
"who is the owner of reading football club",
"when is the next scandal episode coming out",
"when is the last time the philadelphia won the superbowl",
"what is the most current adobe flash player version",
"how many episodes are there in dragon ball z",
"what is the first step in the evolution of the eye",
"where is gall bladder situated in human body",
"what is the main mineral in lithium batteries",
"who is the president of usa right now",
"where do the greasers live in the outsiders",
"panda is a national animal of which country",
"what is the name of manchester united stadium",
]
# Now generate answer for question
for question in QUESTIONS:
# Retrieve related documents from retriever
retriever_results = retriever.retrieve(query=question)
# Now generate answer from question and retrieved documents
predicted_result = generator.predict(query=question,
documents=retriever_results,
top_k=1)
# Print you answer
answers = predicted_result["answers"]
print(
f'Generated answer is \'{answers[0]["answer"]}\' for the question = \'{question}\''
)
def tutorial7_rag_generator():
# Add documents from which you want generate answers
# Download a csv containing some sample documents data
# Here some sample documents data
temp = requests.get("https://raw.githubusercontent.com/deepset-ai/haystack/master/tutorials/small_generator_dataset.csv")
open('small_generator_dataset.csv', 'wb').write(temp.content)
# Get dataframe with columns "title", and "text"
df = pd.read_csv("small_generator_dataset.csv", sep=',')
# Minimal cleaning
df.fillna(value="", inplace=True)
print(df.head())
titles = list(df["title"].values)
texts = list(df["text"].values)
# Create to haystack document format
documents: List[Document] = []
for title, text in zip(titles, texts):
documents.append(
Document(
text=text,
meta={
"name": title or ""
}
)
)
# Initialize FAISS document store to documents and corresponding index for embeddings
# Set `return_embedding` to `True`, so generator doesn't have to perform re-embedding
document_store = FAISSDocumentStore(
faiss_index_factory_str="Flat",
return_embedding=True
)
# Initialize DPR Retriever to encode documents, encode question and query documents
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",
use_gpu=True,
embed_title=True,
)
# Initialize RAG Generator
generator = RAGenerator(
model_name_or_path="facebook/rag-token-nq",
use_gpu=True,
top_k_answers=1,
max_length=200,
min_length=2,
embed_title=True,
num_beams=2,
)
# Delete existing documents in documents store
document_store.delete_all_documents()
# Write documents to document store
document_store.write_documents(documents)
# Add documents embeddings to index
document_store.update_embeddings(
retriever=retriever
)
# Now ask your questions
# We have some sample questions
QUESTIONS = [
"who got the first nobel prize in physics",
"when is the next deadpool movie being released",
"which mode is used for short wave broadcast service",
"who is the owner of reading football club",
"when is the next scandal episode coming out",
"when is the last time the philadelphia won the superbowl",
"what is the most current adobe flash player version",
"how many episodes are there in dragon ball z",
"what is the first step in the evolution of the eye",
"where is gall bladder situated in human body",
"what is the main mineral in lithium batteries",
"who is the president of usa right now",
"where do the greasers live in the outsiders",
"panda is a national animal of which country",
"what is the name of manchester united stadium",
]
# Now generate answer for question
for question in QUESTIONS:
# Retrieve related documents from retriever
retriever_results = retriever.retrieve(
query=question
)
# Now generate answer from question and retrieved documents
predicted_result = generator.predict(
query=question,
documents=retriever_results,
top_k=1
)
# Print you answer
answers = predicted_result["answers"]
print(f'Generated answer is \'{answers[0]["answer"]}\' for the question = \'{question}\'')