yujunjun/haystack
1
0
Fork 0
mirror of https://github.com/deepset-ai/haystack.git synced 2025-11-15 17:43:55 +00:00
Code Issues Packages Projects Releases Wiki Activity

Fix faiss test tolerance

Browse Source
This commit is contained in:
Malte Pietsch 2020-09-18 13:57:29 +02:00
parent 7fdb85d63a
commit d69133966d
1 changed files with 2 additions and 2 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

4
test/test_faiss.py
View File

@ -28,7 +28,7 @@ def test_faiss_write_docs(document_store, index_buffer_size):
original_doc = [d for d in documents if d["text"] == doc.text][0] original_doc = [d for d in documents if d["text"] == doc.text][0]
stored_emb = document_store.faiss_index.reconstruct(int(doc.meta["vector_id"])) stored_emb = document_store.faiss_index.reconstruct(int(doc.meta["vector_id"]))
# compare original input vec with stored one (ignore extra dim added by hnsw) # compare original input vec with stored one (ignore extra dim added by hnsw)
assert np.allclose(original_doc["embedding"], stored_emb[:-1]) assert np.allclose(original_doc["embedding"], stored_emb[:-1], rtol=0.0001)
# test insertion of documents in an existing index fails # test insertion of documents in an existing index fails
with pytest.raises(Exception): with pytest.raises(Exception):
@ -80,6 +80,6 @@ def test_faiss_update_docs(document_store, index_buffer_size):
updated_embedding = retriever.embed_passages([Document.from_dict(original_doc)]) updated_embedding = retriever.embed_passages([Document.from_dict(original_doc)])
stored_emb = document_store.faiss_index.reconstruct(int(doc.meta["vector_id"])) stored_emb = document_store.faiss_index.reconstruct(int(doc.meta["vector_id"]))
# compare original input vec with stored one (ignore extra dim added by hnsw) # compare original input vec with stored one (ignore extra dim added by hnsw)
assert np.allclose(updated_embedding, stored_emb[:-1]) assert np.allclose(updated_embedding, stored_emb[:-1], rtol=0.0001)