def query(self, docs: DocumentArray, **kwargs):
for doc in docs:
new_matches = DocumentArray()
for match in doc.matches:
extracted_doc = self._docs[match.parent_id]
extracted_doc.scores = match.scores
new_matches.append(extracted_doc)
doc.matches = new_matches
def _add_docs(req, batch, data_type, _kwargs):
da = DocumentArray()
for content in batch:
if isinstance(content, tuple) and len(content) == 2:
d, data_type = _new_doc_from_data(content[0], data_type, **_kwargs)
gt, _ = _new_doc_from_data(content[1], data_type, **_kwargs)
da.append(d)
else:
d, data_type = _new_doc_from_data(content, data_type, **_kwargs)
da.append(d)
req.data.docs = da
def print_result(groundtruths, resp):
from docarray import DocumentArray
global top_k
global evaluation_value
"""
Callback function to receive results.
:param resp: returned response with data
"""
queries = DocumentArray()
queries.extend(resp.docs)
gts = DocumentArray()
for query in queries:
gt = groundtruths[query.tags['query_label']]
gts.append(gt)
queries.evaluate(gts,
metric='recall_at_k',
hash_fn=lambda d: d.tags['id'],
top_k=50)
queries.evaluate(gts,
metric='precision_at_k',
hash_fn=lambda d: d.tags['id'],
top_k=50)
for query in queries:
vi = query.uri
result_html.append(f'<tr><td><img src="{vi}"/></td><td>')
top_k = len(query.matches)
for kk in query.matches:
kmi = kk.uri
result_html.append(
f'<img src="{kmi}" style="opacity:{kk.scores["cosine"].value}"/>'
)
result_html.append('</td></tr>\n')
# update evaluation values
# as evaluator set to return running avg, here we can simply replace the value
for k, evaluation in query.evaluations.items():
evaluation_value[k] = evaluation.value
def rank(
self, docs_matrix: List['DocumentArray'], parameters: Dict, **kwargs
) -> 'DocumentArray':
"""
:param docs_matrix: list of :class:`DocumentArray` on multiple requests to
get bubbled up matches.
:param parameters: the parameters passed into the ranker, in this case stores :attr`top_k`
to filter k results based on score.
:param kwargs: not used (kept to maintain interface)
"""
result_da = DocumentArray() # length: 1 as every time there is only one query
for d_mod1, d_mod2 in zip(*docs_matrix):
final_matches = {} # type: Dict[str, Document]
for m in d_mod1.matches:
relevance_score = m.scores['cosine'].value * d_mod1.weight
m.scores['relevance'].value = relevance_score
final_matches[m.parent_id] = Document(m, copy=True)
for m in d_mod2.matches:
if m.parent_id in final_matches:
final_matches[m.parent_id].scores[
'relevance'
].value = final_matches[m.parent_id].scores['relevance'].value + (
m.scores['cosine'].value * d_mod2.weight
)
else:
m.scores['relevance'].value = (
m.scores['cosine'].value * d_mod2.weight
)
final_matches[m.parent_id] = Document(m, copy=True)
da = DocumentArray(list(final_matches.values()))
da = sorted(da, key=lambda ma: ma.scores['relevance'].value, reverse=True)
d = Document(matches=da[: int(parameters['top_k'])])
result_da.append(d)
return result_da
def index(self, docs: DocumentArray, **kwargs):
docs.append(Document(text='added'))
return docs
def _get_example_data():
from docarray import Document, DocumentArray
docs = DocumentArray()
docs.append(Document(text='hello, world!'))
return docs.to_list()