issue: https://github.com/milvus-io/milvus/issues/35853
* BM25 Function now takes no params, k1, b should be passed via index
params
* support BM25 full text search when metric type is not present in
search request
* add more strict validation with functions at collection creation time
Signed-off-by: Buqian Zheng <zhengbuqian@gmail.com>
issue: #35922
add an enable_tokenizer param to varchar field: must be set to true so
that a varchar field can enable_match or used as input of BM25 function
---------
Signed-off-by: Buqian Zheng <zhengbuqian@gmail.com>
Before executing the import, partition IDs should be reordered according
to partition names. Otherwise, the data might be hashed to the wrong
partition during import. This PR corrects this error.
issue: https://github.com/milvus-io/milvus/issues/33237
---------
Signed-off-by: bigsheeper <yihao.dai@zilliz.com>
1. add nullable in model.Field
help to read nullable accurately.
2. check valid_data
a. if user pass default_value or the field is nullable, the length of
valid_data must be num_rows.
b. if passed valid_data, the length of passed field data must equal to
the number of 'true' in valid_data.
c. after fill default_value, only nullable field will still has
valid_data.
3. fill data in two situation
a. has no default_value, if nullable,
will fill nullValue when passed num_rows not equal to expected num_rows.
b. has default_value,
will fill default_value when passed num_rows not equal to expected
num_rows.
c. after fill data, the length of all field will equal to passed
num_rows.
#31728
---------
Signed-off-by: lixinguo <xinguo.li@zilliz.com>
Co-authored-by: lixinguo <xinguo.li@zilliz.com>
add sparse float vector support to different milvus components,
including proxy, data node to receive and write sparse float vectors to
binlog, query node to handle search requests, index node to build index
for sparse float column, etc.
https://github.com/milvus-io/milvus/issues/29419
---------
Signed-off-by: Buqian Zheng <zhengbuqian@gmail.com>
issue: #30102#30225
we should read MetricType from SearchResult,
because query node never
1. read metricType from LoadMeta
2. store to collection
3. set SearchRequest.MetricType
Signed-off-by: PowderLi <min.li@zilliz.com>
See also #29113
The collection schema is crucial when performing search/query but some
of the information is calculated for every request.
This PR change schema field of cached collection info into a utility
`schemaInfo` type to store some stable result, say pk field,
partitionKeyEnabled, etc. And provided field name to id map for
search/query services.
---------
Signed-off-by: Congqi Xia <congqi.xia@zilliz.com>
issue: https://github.com/milvus-io/milvus/issues/27704
Add inverted index for some data types in Milvus. This index type can
save a lot of memory compared to loading all data into RAM and speed up
the term query and range query.
Supported: `INT8`, `INT16`, `INT32`, `INT64`, `FLOAT`, `DOUBLE`, `BOOL`
and `VARCHAR`.
Not supported: `ARRAY` and `JSON`.
Note:
- The inverted index for `VARCHAR` is not designed to serve full-text
search now. We will treat every row as a whole keyword instead of
tokenizing it into multiple terms.
- The inverted index don't support retrieval well, so if you create
inverted index for field, those operations which depend on the raw data
will fallback to use chunk storage, which will bring some performance
loss. For example, comparisons between two columns and retrieval of
output fields.
The inverted index is very easy to be used.
Taking below collection as an example:
```python
fields = [
FieldSchema(name="pk", dtype=DataType.VARCHAR, is_primary=True, auto_id=False, max_length=100),
FieldSchema(name="int8", dtype=DataType.INT8),
FieldSchema(name="int16", dtype=DataType.INT16),
FieldSchema(name="int32", dtype=DataType.INT32),
FieldSchema(name="int64", dtype=DataType.INT64),
FieldSchema(name="float", dtype=DataType.FLOAT),
FieldSchema(name="double", dtype=DataType.DOUBLE),
FieldSchema(name="bool", dtype=DataType.BOOL),
FieldSchema(name="varchar", dtype=DataType.VARCHAR, max_length=1000),
FieldSchema(name="random", dtype=DataType.DOUBLE),
FieldSchema(name="embeddings", dtype=DataType.FLOAT_VECTOR, dim=dim),
]
schema = CollectionSchema(fields)
collection = Collection("demo", schema)
```
Then we can simply create inverted index for field via:
```python
index_type = "INVERTED"
collection.create_index("int8", {"index_type": index_type})
collection.create_index("int16", {"index_type": index_type})
collection.create_index("int32", {"index_type": index_type})
collection.create_index("int64", {"index_type": index_type})
collection.create_index("float", {"index_type": index_type})
collection.create_index("double", {"index_type": index_type})
collection.create_index("bool", {"index_type": index_type})
collection.create_index("varchar", {"index_type": index_type})
```
Then, term query and range query on the field can be speed up
automatically by the inverted index:
```python
result = collection.query(expr='int64 in [1, 2, 3]', output_fields=["pk"])
result = collection.query(expr='int64 < 5', output_fields=["pk"])
result = collection.query(expr='int64 > 2997', output_fields=["pk"])
result = collection.query(expr='1 < int64 < 5', output_fields=["pk"])
```
---------
Signed-off-by: longjiquan <jiquan.long@zilliz.com>
support enable/disable mmap for index, the user could alter the index's
mode by `AlterIndex` method
related: https://github.com/milvus-io/milvus/issues/21866
---------
Signed-off-by: yah01 <yah2er0ne@outlook.com>
Signed-off-by: yah01 <yang.cen@zilliz.com>
See also #29113
- Unify partition info refresh logic
- Prevent parse partition names for each partition key search request
---------
Signed-off-by: Congqi Xia <congqi.xia@zilliz.com>
issue: #28781#28329
1. There is no need to call `DescribeCollection`, if the collection's
schema is found in the globalMetaCache
2. did `GetProperties` to check the access to Azure Blob Service while
construct the ChunkManager
Signed-off-by: PowderLi <min.li@zilliz.com>
Patrick Weizhi Xu
foxspy
Buqian Zheng
jaime
cai.zhang
aoiasd
congqixia
zhagnlu
Chun Han
SimFG
smellthemoon
wei liu
yihao.dai
Cai Yudong
Jiquan Long
Xiaofan
PowderLi
xige-16
Xu Tong
yah01