https://github.com/milvus-io/milvus/issues/35528
This PR adds json index support for json and dynamic fields. Now you can
only do unary query like 'a["b"] > 1' using this index. We will support
more filter type later.
basic usage:
```
collection.create_index("json_field", {"index_type": "INVERTED",
"params": {"json_cast_type": DataType.STRING, "json_path":
'json_field["a"]["b"]'}})
```
There are some limits to use this index:
1. If a record does not have the json path you specify, it will be
ignored and there will not be an error.
2. If a value of the json path fails to be cast to the type you specify,
it will be ignored and there will not be an error.
3. A specific json path can have only one json index.
4. If you try to create more than one json indexes for one json field,
sdk(pymilvus<=2.4.7) may return immediately because of internal
implementation. This will be fixed in a later version.
---------
Signed-off-by: sunby <sunbingyi1992@gmail.com>
The error message was malformated or missing some meta info, say field
name. This PR recitfies some message format and add field name in error
message when type param check fails.
---------
Signed-off-by: Congqi Xia <congqi.xia@zilliz.com>
enhance :
1. alterindex delete properties
We have introduced a new parameter deleteKeys to the alterindex
functionality, which allows for the deletion of properties within an
index. This enhancement provides users with the flexibility to manage
index properties more effectively by removing specific keys as needed.
2. altercollection delete properties
We have introduced a new parameter deleteKeys to the altercollection
functionality, which allows for the deletion of properties within an
collection. This enhancement provides users with the flexibility to
manage collection properties more effectively by removing specific keys
as needed.
3.support altercollectionfield
We currently support modifying the fieldparams of a field in a
collection using altercollectionfield, which only allows changes to the
max-length attribute.
Key Points:
- New Parameter - deleteKeys: This new parameter enables the deletion of
specified properties from an index. By passing a list of keys to
deleteKeys, users can remove the corresponding properties from the
index.
- Mutual Exclusivity: The deleteKeys parameter cannot be used in
conjunction with the extraParams parameter. Users must choose one
parameter to pass based on their requirement. If deleteKeys is provided,
it indicates an intent to delete properties; if extraParams is provided,
it signifies the addition or update of properties.
issue: https://github.com/milvus-io/milvus/issues/37436
---------
Signed-off-by: Xianhui.Lin <xianhui.lin@zilliz.com>
issue: https://github.com/milvus-io/milvus/issues/36835
currently searching BM25 output field using IP will end up in an error
in segcore which is hard to understand. now returning error in query
node delegator and provide more useful error message
Signed-off-by: Buqian Zheng <zhengbuqian@gmail.com>
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: #34304
cosine is more widely used in float vectors, and cosine and hamming
distance are 'metrics' which have good geometric properties
Signed-off-by: chasingegg <chao.gao@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>
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>
congqixia
Bingyi Sun
SimFG
Cai Yudong
Xianhui Lin
Zhen Ye
tinswzy
foxspy
Buqian Zheng
aoiasd
zhagnlu
Jiquan Long
Gao
cqy123456
cai.zhang
yah01
Xu Tong
xige-16
yah01