fix: #39711
Unlike English sentence where each words are parsed exactly once and one
after one with position length 1, one Chinese word may be parsed to
multiple words with position length larger than 1.
For example, "badminton and skiing" will be parsed to Token{ start: 0,
length: 1, text: "badminton" }, Token{ start: 1, length: 1, text: "and"
}, and Token{ start: 2, length: 1, text: "tennis" }.
While for exmaple for Chinsese: "羽毛球和滑雪" may be parsed to Token{ start:
0, length: 2, text: "羽毛" }, Token{ start: 0, length: 3, text: "羽毛球" },
Token{ start: 3, length: 1, text: "和" }, and Token{ start: 4, length: 2,
text: "滑雪" }.
This PR fix that the code not recognizes this situation.
---------
Signed-off-by: SpadeA <tangchenjie1210@gmail.com>
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>
issue: #37851
- make a global thread pool at tantivy temporally.
- set 1 but not 4 threads for inverted text index.
Signed-off-by: chyezh <chyezh@outlook.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>
issue: https://github.com/milvus-io/milvus/issues/29793
The custom `VecCollector` have already transformed the results into
vector of offsets, no need to copy them twice.
Signed-off-by: longjiquan <jiquan.long@zilliz.com>
issue: #29988
This pr adds full-support for wildcard pattern matching from end to end.
Before this pr, the users can only use prefix match in their expression,
for example, "like 'prefix%'". With this pr, more flexible syntax can be
combined.
To do so, this pr makes these changes:
- 1. support regex query both on index and raw data;
- 2. translate the pattern matching to regex query, so that it can be
handled by the regex query logic;
- 3. loose the limit of the expression parsing, which allows general
pattern matching syntax;
With the support of regex query in segcore backend, we can also add
mysql-like `REGEXP` syntax later easily.
---------
Signed-off-by: longjiquan <jiquan.long@zilliz.com>
issue: #29793
Use `DocSetCollector` instead of `TopDocsCollector`, which will avoid
scoring and sorting.
---------
Signed-off-by: longjiquan <jiquan.long@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>
