num_docs,

chunks_per_doc=5,

embed_dim=10,

jitter=1,

start_id=0,

embedding=True,

sparse_embedding=False,

text='hello world',

) -> DocumentArray:

da = DocumentArray()

next_chunk_doc_id = start_id + num_docs

for j in range(num_docs):

doc_id = str(start_id + j)

d = Document(id=doc_id)

d.text = text

d.tags['id'] = f'myself id is: {doc_id}'

if embedding:

if sparse_embedding:

from scipy.sparse import coo_matrix

d.embedding = coo_matrix(

(np.array([1, 1, 1]), (np.array([0, 1, 2]), np.array([1, 2, 1])))

)

else:

d.embedding = np.random.random(

[embed_dim + np.random.randint(0, jitter)]

)

for _ in range(chunks_per_doc):

chunk_doc_id = str(next_chunk_doc_id)

c = Document(id=chunk_doc_id)

c.text = 'i\'m chunk %s from doc %s' % (chunk_doc_id, doc_id)

if embedding:

c.embedding = np.random.random(

[embed_dim + np.random.randint(0, jitter)]

)

c.tags['parent_id'] = f'my parent is: {id}'

c.tags['id'] = f'myself id is: {doc_id}'

d.chunks.append(c)

next_chunk_doc_id += 1

da.append(d)