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langchain/tests/integration_tests/vectorstores
Davit Buniatyan b4914888a7
Deep Lake upgrade to include attribute search, distance metrics, returning scores and MMR (#2455)
### Features include

- Metadata based embedding search
- Choice of distance metric function (`L2` for Euclidean, `L1` for
Nuclear, `max` L-infinity distance, `cos` for cosine similarity, 'dot'
for dot product. Defaults to `L2`
- Returning scores
- Max Marginal Relevance Search
- Deleting samples from the dataset

### Notes
- Added numerous tests, let me know if you would like to shorten them or
make smarter

---------

Co-authored-by: Davit Buniatyan <d@activeloop.ai>
1 year ago
..
docker-compose fix: elasticsearch (#2402) 1 year ago
fixtures fix: elasticsearch (#2402) 1 year ago
__init__.py
fake_embeddings.py Propagate "filter" arg in Chroma similarity_search (#1869) 2 years ago
test_atlas.py
test_chroma.py Propagate "filter" arg in Chroma similarity_search (#1869) 2 years ago
test_deeplake.py Deep Lake upgrade to include attribute search, distance metrics, returning scores and MMR (#2455) 1 year ago
test_elasticsearch.py fix: elasticsearch (#2402) 1 year ago
test_faiss.py
test_milvus.py
test_opensearch.py
test_pgvector.py Removed duplicate BaseModel dependencies (#2471) 1 year ago
test_pinecone.py
test_qdrant.py Qdrant update to 1.1.1 & docs polishing (#2388) 1 year ago
test_redis.py