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### 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")
Ability to handle GenAI complexities for modeling unstructured data such as text, video, audio, and images.
Unify multiple indexes at once for multi-layered embeddings creating flexibility and faster search.
Scale with purpose-built qHNSW and qFlat indexing to lower costs and memory requirements.
Perform search 17x faster with 12x less memory than HNSW without the need for embeddings for fast changing temporal data.
Reduce memory and on-disk storage by 100x for slow changing time-based data sets and accelerate search by 10x.
Combines similarity, exact, and literal search in a single query where query results remain relevant with content changes.
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New to vector databases? Master the basics and explore key use cases like semantic search, recommendation systems, and anomaly detection.
