Currently, the index type AiSAQ RAM usage estimation is not being
calculated correctly.
AiSAQ index type consumes less RAM usage while loading the index than
DISKANN does, and the query node module is missing the implementation of
the RAM usage estimation for that AiSAQ index type.
We suggest that the AiSAQ RAM usage estimation calculation should be as
follows:
UsedDiskMemoryRatioAisaq = 1024 (contrary to the UsedDiskMemoryRatio,
which is 4)
neededMemSize = indexInfo.IndexSize / UsedDiskMemoryRatioAisaq
neededDiskSize = indexInfo.IndexSize
Reported issue is #45247
---------
Signed-off-by: Lior Friedman <lior.friedman@il.kioxia.com>
Signed-off-by: friedl <lior.friedman@kioxia.com>
Co-authored-by: friedl <lior.friedman@kioxia.com>
Ref https://github.com/milvus-io/milvus/issues/42148
This PR supports create index for vector array (now, only for
`DataType.FLOAT_VECTOR`) and search on it.
The index type supported in this PR is `EMB_LIST_HNSW` and the metric
type is `MAX_SIM` only.
The way to use it:
```python
milvus_client = MilvusClient("xxx:19530")
schema = milvus_client.create_schema(enable_dynamic_field=True, auto_id=True)
...
struct_schema = milvus_client.create_struct_array_field_schema("struct_array_field")
...
struct_schema.add_field("struct_float_vec", DataType.ARRAY_OF_VECTOR, element_type=DataType.FLOAT_VECTOR, dim=128, max_capacity=1000)
...
schema.add_struct_array_field(struct_schema)
index_params = milvus_client.prepare_index_params()
index_params.add_index(field_name="struct_float_vec", index_type="EMB_LIST_HNSW", metric_type="MAX_SIM", index_params={"nlist": 128})
...
milvus_client.create_index(COLLECTION_NAME, schema=schema, index_params=index_params)
```
Note: This PR uses `Lims` to convey offsets of the vector array to
knowhere where vectors of multiple vector arrays are concatenated and we
need offsets to specify which vectors belong to which vector array.
---------
Signed-off-by: SpadeA <tangchenjie1210@gmail.com>
Signed-off-by: SpadeA-Tang <tangchenjie1210@gmail.com>
