issue: #44961
This PR fixes 3 geometry related bugs:
1. Implement `ToString` interface for GisFunctionFilter.
2. Ignore GisFunctionFilter `MoveCursor` for growing segment.
3. Don't skip null geometry for building R-Tree index, should be record
in null_offsets.
---------
Signed-off-by: Cai Zhang <cai.zhang@zilliz.com>
relate: https://github.com/milvus-io/milvus/issues/44758
Wrong code which should be `(result.seg_offsets_[i] >= 0 &&
result.seg_offsets_[j] < 0)`, but was `(result.seg_offsets_[j] >= 0 &&
result.seg_offsets_[j] < 0) ` now.
But because all placeholder which was offset -1, will fill with worst
distance value.
For IP, L2 or COSIN, it will be +inf or -inf. So sort distance was
enough.
But when use BM25, it will be NAN. Will case sort out of ordered.
Signed-off-by: aoiasd <zhicheng.yue@zilliz.com>
issue: #43427
The GISFunction asserts that the segment_offsets cannot be nullptr. When
size is 0, the segment_offsets is nullptr, so the loop is skiped.
Signed-off-by: Cai Zhang <cai.zhang@zilliz.com>
issue: #43427
This pr's main goal is merge #37417 to milvus 2.5 without conflicts.
# Main Goals
1. Create and describe collections with geospatial type
2. Insert geospatial data into the insert binlog
3. Load segments containing geospatial data into memory
4. Enable query and search can display geospatial data
5. Support using GIS funtions like ST_EQUALS in query
6. Support R-Tree index for geometry type
# Solution
1. **Add Type**: Modify the Milvus core by adding a Geospatial type in
both the C++ and Go code layers, defining the Geospatial data structure
and the corresponding interfaces.
2. **Dependency Libraries**: Introduce necessary geospatial data
processing libraries. In the C++ source code, use Conan package
management to include the GDAL library. In the Go source code, add the
go-geom library to the go.mod file.
3. **Protocol Interface**: Revise the Milvus protocol to provide
mechanisms for Geospatial message serialization and deserialization.
4. **Data Pipeline**: Facilitate interaction between the client and
proxy using the WKT format for geospatial data. The proxy will convert
all data into WKB format for downstream processing, providing column
data interfaces, segment encapsulation, segment loading, payload
writing, and cache block management.
5. **Query Operators**: Implement simple display and support for filter
queries. Initially, focus on filtering based on spatial relationships
for a single column of geospatial literal values, providing parsing and
execution for query expressions.Now only support brutal search
7. **Client Modification**: Enable the client to handle user input for
geospatial data and facilitate end-to-end testing.Check the modification
in pymilvus.
---------
Signed-off-by: Yinwei Li <yinwei.li@zilliz.com>
Signed-off-by: Cai Zhang <cai.zhang@zilliz.com>
Co-authored-by: ZhuXi <150327960+Yinwei-Yu@users.noreply.github.com>
And support set function mode and boost mode when run search with boost.
RandomScore support get random function score between [0, weight).
FunctionMode decide how to calculate boost score for multiple boost
function scores.
BoostMode decide how to calculate final score for origin score and boost
score.
relate: https://github.com/milvus-io/milvus/issues/43867
---------
Signed-off-by: aoiasd <zhicheng.yue@zilliz.com>
issue: https://github.com/milvus-io/milvus/issues/27467
>My plan is as follows.
>- [x] M1 Create collection with timestamptz field
>- [x] M2 Insert timestamptz field data
>- [x] M3 Retrieve timestamptz field data
>- [x] M4 Implement handoff
>- [x] M5 Implement compare operator
>- [x] M6 Implement extract operator
>- [x] M8 Support database/collection level default timezone
>- [x] M7 Support STL-SORT index for datatype timestamptz
---
The third PR of issue: https://github.com/milvus-io/milvus/issues/27467,
which completes M5, M6, M7, M8 described above.
## M8 Default Timezone
We will be able to use alter_collection() and alter_database() in a
future Python SDK release to modify the default timezone at the
collection or database level.
For insert requests, the timezone will be resolved using the following
order of precedence: String Literal-> Collection Default -> Database
Default.
For retrieval requests, the timezone will be resolved in this order:
Query Parameters -> Collection Default -> Database Default.
In both cases, the final fallback timezone is UTC.
## M5: Comparison Operators
We can now use the following expression format to filter on the
timestamptz field:
- `timestamptz_field [+/- INTERVAL 'interval_string'] {comparison_op}
ISO 'iso_string' `
- The interval_string follows the ISO 8601 duration format, for example:
P1Y2M3DT1H2M3S.
- The iso_string follows the ISO 8601 timestamp format, for example:
2025-01-03T00:00:00+08:00.
- Example expressions: "tsz + INTERVAL 'P0D' != ISO
'2025-01-03T00:00:00+08:00'" or "tsz != ISO
'2025-01-03T00:00:00+08:00'".
## M6: Extract
We will be able to extract sepecific time filed by kwargs in a future
Python SDK release.
The key is `time_fields`, and value should be one or more of "year,
month, day, hour, minute, second, microsecond", seperated by comma or
space. Then the result of each record would be an array of int64.
## M7: Indexing Support
Expressions without interval arithmetic can be accelerated using an
STL-SORT index. However, expressions that include interval arithmetic
cannot be indexed. This is because the result of an interval calculation
depends on the specific timestamp value. For example, adding one month
to a date in February results in a different number of added days than
adding one month to a date in March.
---
After this PR, the input / output type of timestamptz would be iso
string. Timestampz would be stored as timestamptz data, which is int64_t
finally.
> for more information, see https://en.wikipedia.org/wiki/ISO_8601
---------
Signed-off-by: xtx <xtianx@smail.nju.edu.cn>
Remove redundant `VectorPlanNode` subclasses and simplify the visitor
pattern by consolidating to a single `VectorPlanNode`.
The previous design used distinct `VectorPlanNode` subclasses and a
templated `VectorVisitorImpl` for type-directed dispatch. However, the
template parameter was not functionally used to implement different
logic for each vector type, making the subclasses redundant for their
intended purpose.
This PR is created by Cursor Agent and manually moved from
https://github.com/zhengbuqian/milvus/pull/14.
Signed-off-by: zhengbuqian <zhengbuqian@gmail.com>
Co-authored-by: Cursor Agent <cursoragent@cursor.com>
Co-authored-by: buqian.zheng <buqian.zheng@zilliz.com>
issue: #44212
Implement search/query storage usage statistics in go side(result
reduce), only record storage usage in vector search C++ path. Need to be
implemented in query c++ path in next prs.
---------
Signed-off-by: chasingegg <chao.gao@zilliz.com>
Signed-off-by: marcelo.chen <marcelo.chen@zilliz.com>
Co-authored-by: marcelo.chen <marcelo.chen@zilliz.com>
issue: #42942
This pr includes the following changes:
1. Added checks for index checker in querycoord to generate drop index
tasks
2. Added drop index interface to querynode
3. To avoid search failure after dropping the index, the querynode
allows the use of lazy mode (warmup=disable) to load raw data even when
indexes contain raw data.
4. In segcore, loading the index no longer deletes raw data; instead, it
evicts it.
5. In expr, the index is pinned to prevent concurrent errors.
---------
Signed-off-by: sunby <sunbingyi1992@gmail.com>
issue: https://github.com/milvus-io/milvus/issues/43917
1. fix ngrma index to be mistakenly used for unsopported operation
2. fix potential uaf problem
---------
Signed-off-by: SpadeA <tangchenjie1210@gmail.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>
