The Scalable Vector Database for AI

### User Query
query = 'How is Acme Inc managing its loan losses as of late?'

### Create the dense query vector
dense_query = [embedding_model.encode(query).tolist()]

### Create the sparse query vector
sparse_query = [dict(Counter(ids)) for ids in tokenizer([query], 
add_special_tokens=False, max_length=None)['input_ids']]

### Hybrid search
table.search(
    vectors={"sparse_index": sparse_query,
    "dense_index": dense_query},
    index_params={"sparse_index":{'weight':0.5} ,
    "dense_index":{'weight':0.5}}, n=5
)
			

table.search (
	      dense_query,
	      n=3,
	      filter=[
		      ("=", "sym", "AAA"),
              (">", "price", "100.00"),
		    ]
	      )
			

### Time Series Window Query Vector
q = AAA_df['price'][100].tolist()

### Search for Similar Time Series Windows
table.search(vectors={'price': [q]}, n=10, type="tss")

### Outlier Search for Anomaly Detection
res = table.search(vectors={'price': [q]}, n=-10, type="tss")[0]

### Multi Search over Multiple Temporal Windows Simultaneously
table.search(vectors={'price': [q, q2, q3]}, n=10, type="tss")