def test_body_function(self):
def body_function(query):
body = {
"yql": "select * from sources * where userQuery();",
"query": query,
"type": "any",
"ranking": {
"profile": "bm25",
"listFeatures": "true"
},
}
return body
query_model = QueryModel(body_function=body_function)
self.assertDictEqual(
query_model.create_body(query=self.query),
{
"yql": "select * from sources * where userQuery();",
"query": "this is a test",
"type": "any",
"ranking": {
"profile": "bm25",
"listFeatures": "true"
},
},
)
def test_default(self):
query = QueryModel()
self.assertDictEqual(
query.create_body(query=self.query),
{
"yql":
'select * from sources * where (userInput("this is a test"));',
"ranking": {
"profile": "default",
"listFeatures": "false"
},
},
)
def bert_model_input_and_output(self, app, schema_name, fields_to_send,
model_config):
#
# Feed a data point
#
response = app.feed_data_point(
schema=schema_name,
data_id=fields_to_send["id"],
fields=fields_to_send,
)
self.assertEqual(
response.json["id"],
"id:{}:{}::{}".format(schema_name, schema_name,
fields_to_send["id"]),
)
#
# Run a test query
#
result = app.query(
query="this is a test",
query_model=QueryModel(
query_properties=[
QueryRankingFeature(
name=model_config.query_token_ids_name,
mapping=model_config.query_tensor_mapping,
)
],
match_phase=OR(),
rank_profile=Ranking(name="pretrained_bert_tiny"),
),
)
vespa_input_ids = self._parse_vespa_tensor(
result.hits[0], "rankingExpression(input_ids)")
vespa_attention_mask = self._parse_vespa_tensor(
result.hits[0], "rankingExpression(attention_mask)")
vespa_token_type_ids = self._parse_vespa_tensor(
result.hits[0], "rankingExpression(token_type_ids)")
expected_inputs = model_config.create_encodings(
queries=["this is a test"], docs=[fields_to_send["title"]])
self.assertEqual(vespa_input_ids, expected_inputs["input_ids"][0])
self.assertEqual(vespa_attention_mask,
expected_inputs["attention_mask"][0])
self.assertEqual(vespa_token_type_ids,
expected_inputs["token_type_ids"][0])
expected_logits = model_config.predict(queries=["this is a test"],
docs=[fields_to_send["title"]])
self.assertAlmostEqual(
result.hits[0]["fields"]["summaryfeatures"]
["rankingExpression(logit0)"],
expected_logits[0][0],
5,
)
self.assertAlmostEqual(
result.hits[0]["fields"]["summaryfeatures"]
["rankingExpression(logit1)"],
expected_logits[0][1],
5,
)
def feed_batch_synchronous_mode(self, app, schema_name, fields_to_send):
"""
Sync feed a batch of data to the application
:param app: Vespa instance holding the connection to the application
:param schema_name: Schema name containing the document we want to send and retrieve data
:param fields_to_send: List of Dicts where keys are field names and values are field values. Must
contain 'id' field.
:return:
"""
#
# Create and feed documents
#
num_docs = len(fields_to_send)
docs = []
schema = schema_name
for fields in fields_to_send:
docs.append({"id": fields["id"], "fields": fields})
app.feed_batch(schema=schema, batch=docs, asynchronous=False)
# Verify that all documents are fed
result = app.query(query="sddocname:{}".format(schema_name),
query_model=QueryModel())
self.assertEqual(result.number_documents_indexed, num_docs)
def test_collect_training_data_point(self):
self.app.query = Mock(side_effect=[
VespaResult(self.raw_vespa_result_recall),
VespaResult(self.raw_vespa_result_additional),
])
query_model = QueryModel(rank_profile=RankProfile(list_features=True))
data = self.app.collect_training_data_point(
query="this is a query",
query_id="123",
relevant_id="abc",
id_field="vespa_id_field",
query_model=query_model,
number_additional_docs=2,
fields=["rankfeatures", "title"],
timeout="15s",
)
self.assertEqual(self.app.query.call_count, 2)
self.app.query.assert_has_calls([
call(
query="this is a query",
query_model=query_model,
recall=("vespa_id_field", ["abc"]),
timeout="15s",
),
call(
query="this is a query",
query_model=query_model,
hits=2,
timeout="15s",
),
])
expected_data = [
{
"document_id": "abc",
"query_id": "123",
"label": 1,
"a": 1,
"b": 2,
"title": "this is a title",
},
{
"document_id": "def",
"query_id": "123",
"label": 0,
"a": 3,
"b": 4,
"title": "this is a title 2",
},
{
"document_id": "ghi",
"query_id": "123",
"label": 0,
"a": 5,
"b": 6,
"title": "this is a title 3",
},
]
self.assertEqual(data, expected_data)
def test_evaluate_query(self):
self.app.query = Mock(return_value={})
eval_metric = Mock()
eval_metric.evaluate_query = Mock(return_value={"metric": 1})
eval_metric2 = Mock()
eval_metric2.evaluate_query = Mock(return_value={"metric_2": 2})
query_model = QueryModel()
evaluation = self.app.evaluate_query(
eval_metrics=[eval_metric, eval_metric2],
query_model=query_model,
query_id="0",
query="this is a test",
id_field="vespa_id_field",
relevant_docs=self.labeled_data[0]["relevant_docs"],
default_score=0,
hits=10,
)
self.assertEqual(self.app.query.call_count, 1)
self.app.query.assert_has_calls([
call(query="this is a test", query_model=query_model, hits=10),
])
self.assertEqual(eval_metric.evaluate_query.call_count, 1)
eval_metric.evaluate_query.assert_has_calls([
call({}, self.labeled_data[0]["relevant_docs"], "vespa_id_field",
0),
])
self.assertDictEqual(evaluation, {
"query_id": "0",
"metric": 1,
"metric_2": 2
})
def test_rank_input_output(self):
#
# Feed a data point
#
fields = {
"cord_uid": "1",
"title": "this is my first title",
}
fields.update(self.bert_config.doc_fields(text=str(fields["title"])))
response = self.app.feed_data_point(
schema="cord19",
data_id="1",
fields=fields,
)
self.assertEqual(response.json()["id"], "id:cord19:cord19::1")
#
# Run a test query
#
result = self.app.query(
query="this is a test",
query_model=QueryModel(
query_properties=[
QueryRankingFeature(
name=self.bert_config.query_token_ids_name,
mapping=self.bert_config.query_tensor_mapping,
)
],
match_phase=OR(),
rank_profile=Ranking(name="pretrained_bert_tiny"),
),
)
vespa_input_ids = self._parse_vespa_tensor(
result.hits[0], "rankingExpression(input_ids)")
vespa_attention_mask = self._parse_vespa_tensor(
result.hits[0], "rankingExpression(attention_mask)")
vespa_token_type_ids = self._parse_vespa_tensor(
result.hits[0], "rankingExpression(token_type_ids)")
expected_inputs = self.bert_config.create_encodings(
queries=["this is a test"], docs=["this is my first title"])
self.assertEqual(vespa_input_ids, expected_inputs["input_ids"][0])
self.assertEqual(vespa_attention_mask,
expected_inputs["attention_mask"][0])
self.assertEqual(vespa_token_type_ids,
expected_inputs["token_type_ids"][0])
expected_logits = self.bert_config.predict(
queries=["this is a test"], docs=["this is my first title"])
self.assertAlmostEqual(
result.hits[0]["fields"]["summaryfeatures"]
["rankingExpression(logit0)"],
expected_logits[0][0],
5,
)
self.assertAlmostEqual(
result.hits[0]["fields"]["summaryfeatures"]
["rankingExpression(logit1)"],
expected_logits[0][1],
5,
)
def test_collect_training_data(self):
app = Vespa(url="https://api.cord19.vespa.ai")
query_model = QueryModel(match_phase=OR(),
rank_profile=Ranking(name="bm25",
list_features=True))
labeled_data = [
{
"query_id": 0,
"query":
"Intrauterine virus infections and congenital heart disease",
"relevant_docs": [{
"id": 0,
"score": 1
}, {
"id": 3,
"score": 1
}],
},
{
"query_id": 1,
"query":
"Clinical and immunologic studies in identical twins discordant for systemic lupus erythematosus",
"relevant_docs": [{
"id": 1,
"score": 1
}, {
"id": 5,
"score": 1
}],
},
]
training_data_batch = app.collect_training_data(
labeled_data=labeled_data,
id_field="id",
query_model=query_model,
number_additional_docs=2,
fields=["rankfeatures"],
)
self.assertEqual(training_data_batch.shape[0], 12)
# It should have at least one rank feature in addition to document_id, query_id and label
self.assertTrue(training_data_batch.shape[1] > 3)
training_data = []
for query_data in labeled_data:
for doc_data in query_data["relevant_docs"]:
training_data_point = app.collect_training_data_point(
query=query_data["query"],
query_id=query_data["query_id"],
relevant_id=doc_data["id"],
id_field="id",
query_model=query_model,
number_additional_docs=2,
fields=["rankfeatures"],
)
training_data.extend(training_data_point)
training_data = DataFrame.from_records(training_data)
self.assertEqual(training_data.shape[0], 12)
# It should have at least one rank feature in addition to document_id, query_id and label
self.assertTrue(training_data.shape[1] > 3)
def test_query(self):
app = Vespa(url="http://localhost", port=8080)
body = {"yql": "select * from sources * where test"}
self.assertDictEqual(
app.query(body=body, debug_request=True).request_body, body)
self.assertDictEqual(
app.query(
query="this is a test",
query_model=QueryModel(match_phase=OR(),
rank_profile=RankProfile()),
debug_request=True,
hits=10,
).request_body,
{
"yql":
'select * from sources * where ([{"grammar": "any"}]userInput("this is a test"));',
"ranking": {
"profile": "default",
"listFeatures": "false"
},
"hits": 10,
},
)
self.assertDictEqual(
app.query(
query="this is a test",
query_model=QueryModel(match_phase=OR(),
rank_profile=RankProfile()),
debug_request=True,
hits=10,
recall=("id", [1, 5]),
).request_body,
{
"yql":
'select * from sources * where ([{"grammar": "any"}]userInput("this is a test"));',
"ranking": {
"profile": "default",
"listFeatures": "false"
},
"hits": 10,
"recall": "+(id:1 id:5)",
},
)
def test_collect_training_data(self):
mock_return_value = [
{
"document_id": "abc",
"query_id": "123",
"relevant": 1,
"a": 1,
"b": 2,
},
{
"document_id": "def",
"query_id": "123",
"relevant": 0,
"a": 3,
"b": 4,
},
{
"document_id": "ghi",
"query_id": "123",
"relevant": 0,
"a": 5,
"b": 6,
},
]
self.app.collect_training_data_point = Mock(
return_value=mock_return_value)
labeled_data = [{
"query_id": 123,
"query": "this is a query",
"relevant_docs": [{
"id": "abc",
"score": 1
}],
}]
query_model = QueryModel(rank_profile=RankProfile(list_features=True))
data = self.app.collect_training_data(
labeled_data=labeled_data,
id_field="vespa_id_field",
query_model=query_model,
number_additional_docs=2,
timeout="15s",
)
self.app.collect_training_data_point.assert_has_calls([
call(
query="this is a query",
query_id=123,
relevant_id="abc",
id_field="vespa_id_field",
query_model=query_model,
number_additional_docs=2,
relevant_score=1,
default_score=0,
timeout="15s",
)
])
assert_frame_equal(data, DataFrame.from_records(mock_return_value))
def test_collect_training_data_point_0_recall_hits(self):
self.raw_vespa_result_recall = {
"root": {
"id": "toplevel",
"relevance": 1.0,
"fields": {
"totalCount": 0
},
"coverage": {
"coverage": 100,
"documents": 62529,
"full": True,
"nodes": 2,
"results": 1,
"resultsFull": 1,
},
}
}
self.app.query = Mock(side_effect=[
VespaQueryResponse(
self.raw_vespa_result_recall, status_code=None, url=None),
VespaQueryResponse(
self.raw_vespa_result_additional, status_code=None, url=None),
])
query_model = QueryModel(rank_profile=RankProfile(list_features=True))
data = self.app.collect_training_data_point(
query="this is a query",
query_id="123",
relevant_id="abc",
id_field="vespa_id_field",
query_model=query_model,
number_additional_docs=2,
fields=["rankfeatures"],
timeout="15s",
)
self.assertEqual(self.app.query.call_count, 1)
self.app.query.assert_has_calls([
call(
query="this is a query",
query_model=query_model,
recall=("vespa_id_field", ["abc"]),
timeout="15s",
),
])
expected_data = []
self.assertEqual(data, expected_data)
def test_query_properties_match_and_rank(self):
query_model = QueryModel(
query_properties=[
QueryRankingFeature(name="query_vector",
mapping=lambda x: [1, 2, 3])
],
match_phase=OR(),
rank_profile=RankProfile(name="bm25", list_features=True),
)
self.assertDictEqual(
query_model.create_body(query=self.query),
{
"yql":
'select * from sources * where ([{"grammar": "any"}]userInput("this is a test"));',
"ranking": {
"profile": "bm25",
"listFeatures": "true"
},
"ranking.features.query(query_vector)": "[1, 2, 3]",
},
)
query_model = QueryModel(
query_properties=[
QueryRankingFeature(name="query_vector",
mapping=lambda x: [1, 2, 3])
],
match_phase=ANN(
doc_vector="doc_vector",
query_vector="query_vector",
hits=10,
label="label",
),
rank_profile=RankProfile(name="bm25", list_features=True),
)
self.assertDictEqual(
query_model.create_body(query=self.query),
{
"yql":
'select * from sources * where ([{"targetNumHits": 10, "label": "label", "approximate": true}]nearestNeighbor(doc_vector, query_vector));',
"ranking": {
"profile": "bm25",
"listFeatures": "true"
},
"ranking.features.query(query_vector)": "[1, 2, 3]",
},
)
def test_query_with_body_function(self):
app = Vespa(url="http://localhost", port=8080)
def body_function(query):
body = {
"yql": "select * from sources * where userQuery();",
"query": query,
"type": "any",
"ranking": {
"profile": "bm25",
"listFeatures": "true"
},
}
return body
query_model = QueryModel(body_function=body_function)
self.assertDictEqual(
app.query(
query="this is a test",
query_model=query_model,
debug_request=True,
hits=10,
recall=("id", [1, 5]),
).request_body,
{
"yql": "select * from sources * where userQuery();",
"query": "this is a test",
"type": "any",
"ranking": {
"profile": "bm25",
"listFeatures": "true"
},
"hits": 10,
"recall": "+(id:1 id:5)",
},
)
async def execute_async_data_operations(
self,
app,
schema_name,
fields_to_send,
fields_to_update,
expected_fields_from_get_operation,
):
"""
Async feed, get, update and delete data to/from the application
:param app: Vespa instance holding the connection to the application
:param schema_name: Schema name containing the document we want to send and retrieve data
:param fields_to_send: List of Dicts where keys are field names and values are field values. Must
contain 'id' field.
:param fields_to_update: Dict where keys are field names and values are field values.
:param expected_fields_from_get_operation: Dict containing fields as returned by Vespa get operation.
There are cases where fields returned from Vespa are different than inputs, e.g. when dealing with Tensors.
:return:
"""
async with app.asyncio() as async_app:
#
# Get data that does not exist
#
response = await async_app.get_data(
schema=schema_name, data_id=fields_to_send[0]["id"])
self.assertEqual(response.status_code, 404)
#
# Feed some data points
#
feed = []
for fields in fields_to_send:
feed.append(
asyncio.create_task(
async_app.feed_data_point(
schema=schema_name,
data_id=fields["id"],
fields=fields,
)))
await asyncio.wait(feed, return_when=asyncio.ALL_COMPLETED)
result = feed[0].result().json
self.assertEqual(
result["id"],
"id:{}:{}::{}".format(schema_name, schema_name,
fields_to_send[0]["id"]),
)
self.assertEqual(
await async_app.feed_data_point(
schema="msmarco",
data_id="1",
fields={
"id": "1",
"title": "this is title 1",
"body": "this is body 1",
},
),
app.feed_data_point(
schema="msmarco",
data_id="1",
fields={
"id": "1",
"title": "this is title 1",
"body": "this is body 1",
},
),
)
#
# Get data that exists
#
response = await async_app.get_data(
schema=schema_name, data_id=fields_to_send[0]["id"])
self.assertEqual(response.status_code, 200)
result = response.json
self.assertDictEqual(
result,
{
"fields":
expected_fields_from_get_operation[0],
"id":
"id:{}:{}::{}".format(schema_name, schema_name,
fields_to_send[0]["id"]),
"pathId":
"/document/v1/{}/{}/docid/{}".format(
schema_name, schema_name, fields_to_send[0]["id"]),
},
)
#
# Update data
#
response = await async_app.update_data(
schema=schema_name,
data_id=fields_to_send[0]["id"],
fields=fields_to_update,
)
result = response.json
self.assertEqual(
result["id"],
"id:{}:{}::{}".format(schema_name, schema_name,
fields_to_send[0]["id"]),
)
#
# Get the updated data point
#
response = await async_app.get_data(
schema=schema_name, data_id=fields_to_send[0]["id"])
self.assertEqual(response.status_code, 200)
result = response.json
expected_result = {
k: v
for k, v in expected_fields_from_get_operation[0].items()
}
expected_result.update(fields_to_update)
self.assertDictEqual(
result,
{
"fields":
expected_result,
"id":
"id:{}:{}::{}".format(schema_name, schema_name,
fields_to_send[0]["id"]),
"pathId":
"/document/v1/{}/{}/docid/{}".format(
schema_name, schema_name, fields_to_send[0]["id"]),
},
)
#
# Delete a data point
#
response = await async_app.delete_data(
schema=schema_name, data_id=fields_to_send[0]["id"])
result = response.json
self.assertEqual(
result["id"],
"id:{}:{}::{}".format(schema_name, schema_name,
fields_to_send[0]["id"]),
)
#
# Deleted data should be gone
#
response = await async_app.get_data(
schema=schema_name, data_id=fields_to_send[0]["id"])
self.assertEqual(response.status_code, 404)
#
# Issue a bunch of queries in parallel
#
queries = []
for i in range(10):
queries.append(
asyncio.create_task(
async_app.query(
query="sddocname:{}".format(schema_name),
query_model=QueryModel(),
timeout=5000,
)))
await asyncio.wait(queries, return_when=asyncio.ALL_COMPLETED)
self.assertEqual(queries[0].result().number_documents_indexed,
len(fields_to_send) - 1)
def test_workflow(self):
#
# Connect to a running Vespa Application
#
app = Vespa(url="https://api.cord19.vespa.ai")
#
# Define a query model
#
match_phase = Union(
WeakAnd(hits=10),
ANN(
doc_vector="title_embedding",
query_vector="title_vector",
hits=10,
label="title",
),
)
rank_profile = Ranking(name="bm25", list_features=True)
query_model = QueryModel(
name="ANN_bm25",
query_properties=[
QueryRankingFeature(
name="title_vector",
mapping=lambda x: [random() for x in range(768)],
)
],
match_phase=match_phase,
rank_profile=rank_profile,
)
#
# Query Vespa app
#
query_result = app.query(
query="Is remdesivir an effective treatment for COVID-19?",
query_model=query_model,
)
self.assertTrue(query_result.number_documents_retrieved > 0)
self.assertEqual(len(query_result.hits), 10)
#
# Define labelled data
#
labeled_data = [
{
"query_id": 0,
"query":
"Intrauterine virus infections and congenital heart disease",
"relevant_docs": [{
"id": 0,
"score": 1
}, {
"id": 3,
"score": 1
}],
},
{
"query_id": 1,
"query":
"Clinical and immunologic studies in identical twins discordant for systemic lupus erythematosus",
"relevant_docs": [{
"id": 1,
"score": 1
}, {
"id": 5,
"score": 1
}],
},
]
# equivalent data in df format
labeled_data_df = DataFrame(
data={
"qid": [0, 0, 1, 1],
"query":
["Intrauterine virus infections and congenital heart disease"]
* 2 + [
"Clinical and immunologic studies in identical twins discordant for systemic lupus erythematosus"
] * 2,
"doc_id": [0, 3, 1, 5],
"relevance": [1, 1, 1, 1],
})
#
# Collect training data
#
training_data_batch = app.collect_training_data(
labeled_data=labeled_data,
id_field="id",
query_model=query_model,
number_additional_docs=2,
fields=["rankfeatures"],
)
self.assertTrue(training_data_batch.shape[0] > 0)
self.assertEqual(
len({"document_id", "query_id",
"label"}.intersection(set(training_data_batch.columns))),
3,
)
#
# Evaluate a query model
#
eval_metrics = [MatchRatio(), Recall(at=10), ReciprocalRank(at=10)]
evaluation = app.evaluate(
labeled_data=labeled_data,
eval_metrics=eval_metrics,
query_model=query_model,
id_field="id",
)
self.assertEqual(evaluation.shape, (9, 1))
#
# AssertionError - two models with the same name
#
with self.assertRaises(AssertionError):
_ = app.evaluate(
labeled_data=labeled_data,
eval_metrics=eval_metrics,
query_model=[QueryModel(),
QueryModel(), query_model],
id_field="id",
)
evaluation = app.evaluate(
labeled_data=labeled_data,
eval_metrics=eval_metrics,
query_model=[QueryModel(), query_model],
id_field="id",
)
self.assertEqual(evaluation.shape, (9, 2))
evaluation = app.evaluate(
labeled_data=labeled_data_df,
eval_metrics=eval_metrics,
query_model=query_model,
id_field="id",
detailed_metrics=True,
)
self.assertEqual(evaluation.shape, (15, 1))
evaluation = app.evaluate(
labeled_data=labeled_data_df,
eval_metrics=eval_metrics,
query_model=query_model,
id_field="id",
detailed_metrics=True,
per_query=True,
)
self.assertEqual(evaluation.shape, (2, 7))
