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Rename question parameter to query (#614)

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Tanay Soni 2020-11-30 17:50:04 +01:00 committed by GitHub
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13 changed files with 85 additions and 72 deletions
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6
haystack/document_store/elasticsearch.py
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@ -455,10 +455,10 @@ class ElasticsearchDocumentStore(BaseDocumentStore):
body["query"]["bool"]["filter"] = filter_clause
# Retrieval via custom query
elif custom_query: # substitute placeholder for question and filters for the custom_query template string
elif custom_query: # substitute placeholder for query and filters for the custom_query template string
template = Template(custom_query)
# replace all "${question}" placeholder(s) with query
substitutions = {"question": query}
# replace all "${query}" placeholder(s) with query
substitutions = {"query": query}
# For each filter we got passed, we'll try to find & replace the corresponding placeholder in the template
# Example: filters={"years":[2018]} => replaces {$years} in custom_query with '[2018]'
if filters:

23
haystack/finder.py
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@ -28,8 +28,14 @@ class Finder:
:param reader: Reader instance
:param retriever: Retriever instance
"""
logger.warning("The 'Finder' class will be deprecated in the next Haystack release in favour of the new"
"`Pipeline` class.")
logger.warning(
"""DEPRECATION WARNINGS:
1. The 'Finder' class will be deprecated in the next Haystack release in
favour of a new `Pipeline` class that supports building custom search pipelines using Haystack components
including Retriever, Readers, and Generators.
For more details, please refer to the issue: https://github.com/deepset-ai/haystack/issues/544
2. The `question` parameter in search requests & results is renamed to `query`."""
)
self.retriever = retriever
self.reader = reader
if self.reader is None and self.retriever is None:
@ -48,6 +54,14 @@ class Finder:
:return:
"""
logger.warning(
"""DEPRECATION WARNINGS:
1. The 'Finder' class will be deprecated in the next Haystack release in
favour of a new `Pipeline` class that supports building custom search pipelines using Haystack components
including Retriever, Readers, and Generators.
For more details, please refer to the issue: https://github.com/deepset-ai/haystack/issues/544
2. The `question` parameter in search requests & results is renamed to `query`."""
)
if self.retriever is None or self.reader is None:
raise AttributeError("Finder.get_answers requires self.retriever AND self.reader")
@ -65,9 +79,10 @@ class Finder:
len_chars = sum([len(d.text) for d in documents])
logger.info(f"Reader is looking for detailed answer in {len_chars} chars ...")
results = self.reader.predict(question=question,
results = self.reader.predict(query=question,
documents=documents,
top_k=top_k_reader) # type: Dict[str, Any]
results["question"] = results["query"]
# Add corresponding document_name and more meta data, if an answer contains the document_id
for ans in results["answers"]:
@ -364,7 +379,7 @@ class Finder:
self.reader.return_no_answers = True
reader_start_time = time.time()
predictions = self.reader.predict_batch(questions_with_correct_doc,
top_k_per_question=top_k_reader, batch_size=batch_size)
top_k=top_k_reader, batch_size=batch_size)
reader_total_time = time.time() - reader_start_time
for pred in predictions:

10
haystack/pipeline.py
View File

@ -127,8 +127,8 @@ class ExtractiveQAPipeline:
self.pipeline.add_node(component=retriever, name="Retriever", inputs=["Query"])
self.pipeline.add_node(component=reader, name="Reader", inputs=["Retriever"])
def run(self, question, top_k_retriever=5, top_k_reader=5):
output = self.pipeline.run(question=question,
def run(self, query, top_k_retriever=5, top_k_reader=5):
output = self.pipeline.run(query=query,
top_k_retriever=top_k_retriever,
top_k_reader=top_k_reader)
return output
@ -150,8 +150,8 @@ class DocumentSearchPipeline:
self.pipeline = Pipeline()
self.pipeline.add_node(component=retriever, name="Retriever", inputs=["Query"])
def run(self, question, top_k_retriever=5):
output = self.pipeline.run(question=question, top_k_retriever=top_k_retriever)
def run(self, query, top_k_retriever=5):
output = self.pipeline.run(query=query, top_k_retriever=top_k_retriever)
document_dicts = [doc.to_dict() for doc in output["documents"]]
output["documents"] = document_dicts
return output
@ -183,7 +183,7 @@ class JoinDocuments:
for i, _ in inputs:
documents.extend(i["documents"])
output = {
"question": inputs[0][0]["question"],
"query": inputs[0][0]["query"],
"documents": documents
}
return output, "output_1"

9
haystack/reader/base.py
View File

@ -12,12 +12,11 @@ class BaseReader(ABC):
outgoing_edges = 1
@abstractmethod
def predict(self, question: str, documents: List[Document], top_k: Optional[int] = None):
def predict(self, query: str, documents: List[Document], top_k: Optional[int] = None):
pass
@abstractmethod
def predict_batch(self, question_doc_list: List[dict], top_k_per_question: Optional[int] = None,
batch_size: Optional[int] = None):
def predict_batch(self, query_doc_list: List[dict], top_k: Optional[int] = None, batch_size: Optional[int] = None):
pass
@staticmethod
@ -47,9 +46,9 @@ class BaseReader(ABC):
"meta": None,}
return no_ans_prediction, max_no_ans_gap
def run(self, question: str, documents: List[Document], top_k: Optional[int] = None):
def run(self, query: str, documents: List[Document], top_k: Optional[int] = None):
if documents:
results = self.predict(question=question, documents=documents, top_k=top_k)
results = self.predict(query=query, documents=documents, top_k=top_k)
else:
results = {"answers": []}

42
haystack/reader/farm.py
View File

@ -243,16 +243,16 @@ class FARMReader(BaseReader):
self.inferencer.model.save(directory)
self.inferencer.processor.save(directory)
def predict_batch(self, question_doc_list: List[dict], top_k_per_question: int = None, batch_size: int = None):
def predict_batch(self, query_doc_list: List[dict], top_k: int = None, batch_size: int = None):
"""
Use loaded QA model to find answers for a list of questions in each question's supplied list of Document.
Use loaded QA model to find answers for a list of queries in each query's supplied list of Document.
Returns list of dictionaries containing answers sorted by (desc.) probability
:param question_doc_list: List of dictionaries containing questions with their retrieved documents
:param top_k_per_question: The maximum number of answers to return for each question
:param query_doc_list: List of dictionaries containing queries with their retrieved documents
:param top_k: The maximum number of answers to return for each query
:param batch_size: Number of samples the model receives in one batch for inference
:return: List of dictionaries containing question and answers
:return: List of dictionaries containing query and answers
"""
# convert input to FARM format
@ -261,20 +261,20 @@ class FARMReader(BaseReader):
labels = []
# build input objects for inference_from_objects
for question_with_docs in question_doc_list:
documents = question_with_docs["docs"]
question = question_with_docs["question"]
labels.append(question)
for query_with_docs in query_doc_list:
documents = query_with_docs["docs"]
query = query_with_docs["question"]
labels.append(query)
number_of_docs.append(len(documents))
for doc in documents:
cur = QAInput(doc_text=doc.text,
questions=Question(text=question.question,
questions=Question(text=query.question,
uid=doc.id))
inputs.append(cur)
self.inferencer.batch_size = batch_size
# make predictions on all document-question pairs
# make predictions on all document-query pairs
predictions = self.inferencer.inference_from_objects(
objects=inputs, return_json=False, multiprocessing_chunksize=1
)
@ -290,11 +290,11 @@ class FARMReader(BaseReader):
result = []
for idx, group in enumerate(grouped_predictions):
answers, max_no_ans_gap = self._extract_answers_of_predictions(group, top_k_per_question)
question = group[0].question
answers, max_no_ans_gap = self._extract_answers_of_predictions(group, top_k)
query = group[0].question
cur_label = labels[idx]
result.append({
"question": question,
"query": query,
"no_ans_gap": max_no_ans_gap,
"answers": answers,
"label": cur_label
@ -302,15 +302,15 @@ class FARMReader(BaseReader):
return result
def predict(self, question: str, documents: List[Document], top_k: Optional[int] = None):
def predict(self, query: str, documents: List[Document], top_k: Optional[int] = None):
"""
Use loaded QA model to find answers for a question in the supplied list of Document.
Use loaded QA model to find answers for a query in the supplied list of Document.
Returns dictionaries containing answers sorted by (desc.) probability.
Example:
```python
|{
| 'question': 'Who is the father of Arya Stark?',
| 'query': 'Who is the father of Arya Stark?',
| 'answers':[
| {'answer': 'Eddard,',
| 'context': " She travels with her father, Eddard, to King's Landing when he is ",
@ -324,17 +324,17 @@ class FARMReader(BaseReader):
|}
```
:param question: Question string
:param query: Query string
:param documents: List of Document in which to search for the answer
:param top_k: The maximum number of answers to return
:return: Dict containing question and answers
:return: Dict containing query and answers
"""
# convert input to FARM format
inputs = []
for doc in documents:
cur = QAInput(doc_text=doc.text,
questions=Question(text=question,
questions=Question(text=query,
uid=doc.id))
inputs.append(cur)
@ -345,7 +345,7 @@ class FARMReader(BaseReader):
)
# assemble answers from all the different documents & format them.
answers, max_no_ans_gap = self._extract_answers_of_predictions(predictions, top_k)
result = {"question": question,
result = {"query": query,
"no_ans_gap": max_no_ans_gap,
"answers": answers}

21
haystack/reader/transformers.py
View File

@ -62,16 +62,16 @@ class TransformersReader(BaseReader):
# TODO context_window_size behaviour different from behavior in FARMReader
def predict(self, question: str, documents: List[Document], top_k: Optional[int] = None):
def predict(self, query: str, documents: List[Document], top_k: Optional[int] = None):
"""
Use loaded QA model to find answers for a question in the supplied list of Document.
Use loaded QA model to find answers for a query in the supplied list of Document.
Returns dictionaries containing answers sorted by (desc.) probability.
Example:
```python
|{
| 'question': 'Who is the father of Arya Stark?',
| 'query': 'Who is the father of Arya Stark?',
| 'answers':[
| {'answer': 'Eddard,',
| 'context': " She travels with her father, Eddard, to King's Landing when he is ",
@ -85,10 +85,10 @@ class TransformersReader(BaseReader):
|}
```
:param question: Question string
:param query: Query string
:param documents: List of Document in which to search for the answer
:param top_k: The maximum number of answers to return
:return: Dict containing question and answers
:return: Dict containing query and answers
"""
# get top-answers for each candidate passage
@ -96,8 +96,8 @@ class TransformersReader(BaseReader):
no_ans_gaps = []
best_overall_score = 0
for doc in documents:
query = {"context": doc.text, "question": question}
predictions = self.model(query,
transformers_query = {"context": doc.text, "question": query}
predictions = self.model(transformers_query,
topk=self.top_k_per_candidate,
handle_impossible_answer=self.return_no_answers,
max_seq_len=self.max_seq_len,
@ -146,12 +146,11 @@ class TransformersReader(BaseReader):
)
answers = answers[:top_k]
results = {"question": question,
results = {"query": query,
"answers": answers}
return results
def predict_batch(self, question_doc_list: List[dict], top_k_per_question: Optional[int] = None,
batch_size: Optional[int] = None):
def predict_batch(self, query_doc_list: List[dict], top_k: Optional[int] = None, batch_size: Optional[int] = None):
raise NotImplementedError("Batch prediction not yet available in TransformersReader.")

12
haystack/retriever/base.py
View File

@ -52,7 +52,7 @@ class BaseRetriever(ABC):
) -> dict:
"""
Performs evaluation on the Retriever.
Retriever is evaluated based on whether it finds the correct document given the question string and at which
Retriever is evaluated based on whether it finds the correct document given the query string and at which
position in the ranking of documents the correct document is.
| Returns a dict containing the following metrics:
@ -68,7 +68,7 @@ class BaseRetriever(ABC):
:param label_index: Index/Table in DocumentStore where labeled questions are stored
:param doc_index: Index/Table in DocumentStore where documents that are used for evaluation are stored
:param top_k: How many documents to return per question
:param top_k: How many documents to return per query
:param open_domain: If ``True``, retrieval will be evaluated by checking if the answer string to a question is
contained in the retrieved docs (common approach in open-domain QA).
If ``False``, retrieval uses a stricter evaluation that checks if the retrieved document ids
@ -170,17 +170,17 @@ class BaseRetriever(ABC):
def run(
self,
question: str,
query: str,
filters: Optional[dict] = None,
top_k_retriever: Optional[int] = None,
top_k_reader: Optional[int] = None,
):
if top_k_retriever:
documents = self.retrieve(query=question, filters=filters, top_k=top_k_retriever)
documents = self.retrieve(query=query, filters=filters, top_k=top_k_retriever)
else:
documents = self.retrieve(query=question, filters=filters)
documents = self.retrieve(query=query, filters=filters)
output = {
"question": question,
"query": query,
"documents": documents,
"top_k": top_k_reader
}

4
haystack/retriever/sparse.py
View File

@ -18,7 +18,7 @@ class ElasticsearchRetriever(BaseRetriever):
def __init__(self, document_store: ElasticsearchDocumentStore, custom_query: str = None):
"""
:param document_store: an instance of a DocumentStore to retrieve documents from.
:param custom_query: query string as per Elasticsearch DSL with a mandatory question placeholder($question).
:param custom_query: query string as per Elasticsearch DSL with a mandatory query placeholder(query).
Optionally, ES `filter` clause can be added where the values of `terms` are placeholders
that get substituted during runtime. The placeholder(${filter_name_1}, ${filter_name_2}..)
@ -32,7 +32,7 @@ class ElasticsearchRetriever(BaseRetriever):
| "query": {
| "bool": {
| "should": [{"multi_match": {
| "query": "${question}", // mandatory $question placeholder
| "query": "${query}", // mandatory query placeholder
| "type": "most_fields",
| "fields": ["text", "title"]}}],
| "filter": [ // optional custom filters

6
haystack/utils.py
View File

@ -40,11 +40,11 @@ def export_answers_to_csv(agg_results: list, output_file):
if isinstance(agg_results, dict):
agg_results = [agg_results]
assert "question" in agg_results[0], f"Wrong format used for {agg_results[0]}"
assert "query" in agg_results[0], f"Wrong format used for {agg_results[0]}"
assert "answers" in agg_results[0], f"Wrong format used for {agg_results[0]}"
data = {} # type: Dict[str, List[Any]]
data["question"] = []
data["query"] = []
data["prediction"] = []
data["prediction_rank"] = []
data["prediction_context"] = []
@ -52,7 +52,7 @@ def export_answers_to_csv(agg_results: list, output_file):
for res in agg_results:
for i in range(len(res["answers"])):
temp = res["answers"][i]
data["question"].append(res["question"])
data["query"].append(res["query"])
data["prediction"].append(temp["answer"])
data["prediction_rank"].append(i + 1)
data["prediction_context"].append(temp["context"])

4
test/conftest.py
View File

@ -231,14 +231,14 @@ def no_answer_reader(request, transformers_roberta, farm_roberta):
@pytest.fixture()
def prediction(reader, test_docs_xs):
docs = [Document.from_dict(d) if isinstance(d, dict) else d for d in test_docs_xs]
prediction = reader.predict(question="Who lives in Berlin?", documents=docs, top_k=5)
prediction = reader.predict(query="Who lives in Berlin?", documents=docs, top_k=5)
return prediction
@pytest.fixture()
def no_answer_prediction(no_answer_reader, test_docs_xs):
docs = [Document.from_dict(d) if isinstance(d, dict) else d for d in test_docs_xs]
prediction = no_answer_reader.predict(question="What is the meaning of life?", documents=docs, top_k=5)
prediction = no_answer_reader.predict(query="What is the meaning of life?", documents=docs, top_k=5)
return prediction

2
test/test_finder.py
View File

@ -10,7 +10,7 @@ def test_finder_get_answers(reader, retriever_with_docs, document_store_with_doc
prediction = finder.get_answers(question="Who lives in Berlin?", top_k_retriever=10,
top_k_reader=3)
assert prediction is not None
assert prediction["question"] == "Who lives in Berlin?"
assert prediction["query"] == "Who lives in Berlin?"
assert prediction["answers"][0]["answer"] == "Carla"
assert prediction["answers"][0]["probability"] <= 1
assert prediction["answers"][0]["probability"] >= 0

8
test/test_pipeline.py
View File

@ -25,9 +25,9 @@ def test_graph_creation(reader, retriever_with_docs, document_store_with_docs):
@pytest.mark.parametrize("retriever_with_docs", ["tfidf"], indirect=True)
def test_extractive_qa_answers(reader, retriever_with_docs, document_store_with_docs):
pipeline = ExtractiveQAPipeline(reader=reader, retriever=retriever_with_docs)
prediction = pipeline.run(question="Who lives in Berlin?", top_k_retriever=10, top_k_reader=3)
prediction = pipeline.run(query="Who lives in Berlin?", top_k_retriever=10, top_k_reader=3)
assert prediction is not None
assert prediction["question"] == "Who lives in Berlin?"
assert prediction["query"] == "Who lives in Berlin?"
assert prediction["answers"][0]["answer"] == "Carla"
assert prediction["answers"][0]["probability"] <= 1
assert prediction["answers"][0]["probability"] >= 0
@ -41,7 +41,7 @@ def test_extractive_qa_answers(reader, retriever_with_docs, document_store_with_
@pytest.mark.parametrize("retriever_with_docs", ["tfidf"], indirect=True)
def test_extractive_qa_offsets(reader, retriever_with_docs, document_store_with_docs):
pipeline = ExtractiveQAPipeline(reader=reader, retriever=retriever_with_docs)
prediction = pipeline.run(question="Who lives in Berlin?", top_k_retriever=10, top_k_reader=5)
prediction = pipeline.run(query="Who lives in Berlin?", top_k_retriever=10, top_k_reader=5)
assert prediction["answers"][0]["offset_start"] == 11
assert prediction["answers"][0]["offset_end"] == 16
@ -56,7 +56,7 @@ def test_extractive_qa_offsets(reader, retriever_with_docs, document_store_with_
def test_extractive_qa_answers_single_result(reader, retriever_with_docs, document_store_with_docs):
pipeline = ExtractiveQAPipeline(reader=reader, retriever=retriever_with_docs)
query = "testing finder"
prediction = pipeline.run(question=query, top_k_retriever=1, top_k_reader=1)
prediction = pipeline.run(query=query, top_k_retriever=1, top_k_reader=1)
assert prediction is not None
assert len(prediction["answers"]) == 1

10
test/test_reader.py
View File

@ -14,7 +14,7 @@ def test_reader_basic(reader):
def test_output(prediction):
assert prediction is not None
assert prediction["question"] == "Who lives in Berlin?"
assert prediction["query"] == "Who lives in Berlin?"
assert prediction["answers"][0]["answer"] == "Carla"
assert prediction["answers"][0]["offset_start"] == 11
assert prediction["answers"][0]["offset_end"] == 16
@ -27,7 +27,7 @@ def test_output(prediction):
@pytest.mark.slow
def test_no_answer_output(no_answer_prediction):
assert no_answer_prediction is not None
assert no_answer_prediction["question"] == "What is the meaning of life?"
assert no_answer_prediction["query"] == "What is the meaning of life?"
assert math.isclose(no_answer_prediction["no_ans_gap"], -13.048564434051514, rel_tol=0.0001)
assert no_answer_prediction["answers"][0]["answer"] is None
assert no_answer_prediction["answers"][0]["offset_start"] == 0
@ -48,7 +48,7 @@ def test_no_answer_output(no_answer_prediction):
@pytest.mark.slow
def test_prediction_attributes(prediction):
# TODO FARM's prediction also has no_ans_gap
attributes_gold = ["question", "answers"]
attributes_gold = ["query", "answers"]
for ag in attributes_gold:
assert ag in prediction
@ -73,7 +73,7 @@ def test_context_window_size(reader, test_docs_xs, window_size):
old_window_size = reader.inferencer.model.prediction_heads[0].context_window_size
reader.inferencer.model.prediction_heads[0].context_window_size = window_size
prediction = reader.predict(question="Who lives in Berlin?", documents=docs, top_k=5)
prediction = reader.predict(query="Who lives in Berlin?", documents=docs, top_k=5)
for answer in prediction["answers"]:
# If the extracted answer is larger than the context window, the context window is expanded.
# If the extracted answer is odd in length, the resulting context window is one less than context_window_size
@ -106,7 +106,7 @@ def test_top_k(reader, test_docs_xs, top_k):
except:
print("WARNING: Could not set `top_k_per_sample` in FARM. Please update FARM version.")
prediction = reader.predict(question="Who lives in Berlin?", documents=docs, top_k=top_k)
prediction = reader.predict(query="Who lives in Berlin?", documents=docs, top_k=top_k)
assert len(prediction["answers"]) == top_k
reader.top_k_per_candidate = old_top_k_per_candidate