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collection.py
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collection.py
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# Copyright (C) 2019-2021 Zilliz. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except
# in compliance with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software distributed under the License
# is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express
# or implied. See the License for the specific language governing permissions and limitations under
# the License.
import copy
import json
from typing import Dict, List, Optional, Union
import pandas as pd
from pymilvus.client import utils
from pymilvus.client.abstract import BaseRanker, SearchResult
from pymilvus.client.constants import DEFAULT_CONSISTENCY_LEVEL
from pymilvus.client.types import (
CompactionPlans,
CompactionState,
Replica,
cmp_consistency_level,
get_consistency_level,
)
from pymilvus.exceptions import (
AutoIDException,
DataTypeNotMatchException,
DataTypeNotSupportException,
ExceptionsMessage,
IndexNotExistException,
PartitionAlreadyExistException,
SchemaNotReadyException,
)
from pymilvus.grpc_gen import schema_pb2
from pymilvus.settings import Config
from .connections import connections
from .constants import UNLIMITED
from .future import MutationFuture, SearchFuture
from .index import Index
from .iterator import QueryIterator, SearchIterator
from .mutation import MutationResult
from .partition import Partition
from .prepare import Prepare
from .schema import (
CollectionSchema,
FieldSchema,
check_insert_schema,
check_schema,
check_upsert_schema,
construct_fields_from_dataframe,
is_row_based,
is_valid_insert_data,
)
from .types import DataType
from .utility import _get_connection
class Collection:
def __init__(
self,
name: str,
schema: Optional[CollectionSchema] = None,
using: str = "default",
**kwargs,
) -> None:
"""Constructs a collection by name, schema and other parameters.
Args:
name (``str``): the name of collection
schema (``CollectionSchema``, optional): the schema of collection, defaults to None.
using (``str``, optional): Milvus connection alias name, defaults to 'default'.
**kwargs (``dict``):
* *num_shards (``int``, optional): how many shards will the insert data be divided.
* *shards_num (``int``, optional, deprecated):
how many shards will the insert data be divided.
* *consistency_level* (``int/ str``)
Which consistency level to use when searching in the collection.
Options of consistency level: Strong, Bounded, Eventually, Session, Customized.
Note: can be overwritten by the same parameter specified in search.
* *properties* (``dict``, optional)
Collection properties.
* *timeout* (``float``)
An optional duration of time in seconds to allow for the RPCs.
If timeout is not set, the client keeps waiting until the server
responds or an error occurs.
Raises:
SchemaNotReadyException: if the schema is wrong.
Examples:
>>> from pymilvus import Collection, FieldSchema, CollectionSchema, DataType
>>> fields = [
... FieldSchema("film_id", DataType.INT64, is_primary=True),
... FieldSchema("films", dtype=DataType.FLOAT_VECTOR, dim=128)
... ]
>>> schema = CollectionSchema(fields=fields)
>>> prop = {"collection.ttl.seconds": 1800}
>>> collection = Collection(name="test_collection_init", schema=schema, properties=prop)
>>> collection.name
'test_collection_init'
"""
self._name = name
self._using = using
self._kwargs = kwargs
self._num_shards = None
conn = self._get_connection()
has = conn.has_collection(self._name, **kwargs)
if has:
resp = conn.describe_collection(self._name, **kwargs)
s_consistency_level = resp.get("consistency_level", DEFAULT_CONSISTENCY_LEVEL)
arg_consistency_level = kwargs.get("consistency_level", s_consistency_level)
if not cmp_consistency_level(s_consistency_level, arg_consistency_level):
raise SchemaNotReadyException(
message=ExceptionsMessage.ConsistencyLevelInconsistent
)
server_schema = CollectionSchema.construct_from_dict(resp)
self._consistency_level = s_consistency_level
if schema is None:
self._schema = server_schema
else:
if not isinstance(schema, CollectionSchema):
raise SchemaNotReadyException(message=ExceptionsMessage.SchemaType)
if server_schema != schema:
raise SchemaNotReadyException(message=ExceptionsMessage.SchemaInconsistent)
self._schema = schema
else:
if schema is None:
raise SchemaNotReadyException(
message=ExceptionsMessage.CollectionNotExistNoSchema % name
)
if isinstance(schema, CollectionSchema):
schema.verify()
check_schema(schema)
consistency_level = get_consistency_level(
kwargs.get("consistency_level", DEFAULT_CONSISTENCY_LEVEL)
)
conn.create_collection(self._name, schema, **kwargs)
self._schema = schema
self._consistency_level = consistency_level
else:
raise SchemaNotReadyException(message=ExceptionsMessage.SchemaType)
self._schema_dict = self._schema.to_dict()
self._schema_dict["consistency_level"] = self._consistency_level
def __repr__(self) -> str:
_dict = {
"name": self.name,
"description": self.description,
"schema": self._schema,
}
r = ["<Collection>:\n-------------\n"]
s = "<{}>: {}\n"
for k, v in _dict.items():
r.append(s.format(k, v))
return "".join(r)
def _get_connection(self):
return connections._fetch_handler(self._using)
# TODO(SPARSE): support pd.SparseDtype
@classmethod
def construct_from_dataframe(cls, name: str, dataframe: pd.DataFrame, **kwargs):
if not isinstance(dataframe, pd.DataFrame):
raise SchemaNotReadyException(message=ExceptionsMessage.DataFrameType)
primary_field = kwargs.pop("primary_field", None)
if primary_field is None:
raise SchemaNotReadyException(message=ExceptionsMessage.NoPrimaryKey)
pk_index = -1
for i, field in enumerate(dataframe):
if field == primary_field:
pk_index = i
if pk_index == -1:
raise SchemaNotReadyException(message=ExceptionsMessage.PrimaryKeyNotExist)
if "auto_id" in kwargs and not isinstance(kwargs.get("auto_id"), bool):
raise AutoIDException(message=ExceptionsMessage.AutoIDType)
auto_id = kwargs.pop("auto_id", False)
if auto_id:
if dataframe[primary_field].isnull().all():
dataframe = dataframe.drop(primary_field, axis=1)
else:
raise SchemaNotReadyException(message=ExceptionsMessage.AutoIDWithData)
using = kwargs.get("using", Config.MILVUS_CONN_ALIAS)
conn = _get_connection(using)
if conn.has_collection(name, **kwargs):
resp = conn.describe_collection(name, **kwargs)
server_schema = CollectionSchema.construct_from_dict(resp)
schema = server_schema
else:
fields_schema = construct_fields_from_dataframe(dataframe)
if auto_id:
fields_schema.insert(
pk_index,
FieldSchema(
name=primary_field,
dtype=DataType.INT64,
is_primary=True,
auto_id=True,
**kwargs,
),
)
for field in fields_schema:
if auto_id is False and field.name == primary_field:
field.is_primary = True
field.auto_id = False
if field.dtype == DataType.VARCHAR:
field.params[Config.MaxVarCharLengthKey] = int(Config.MaxVarCharLength)
schema = CollectionSchema(fields=fields_schema)
check_schema(schema)
collection = cls(name, schema, **kwargs)
res = collection.insert(data=dataframe)
return collection, res
@property
def schema(self) -> CollectionSchema:
"""CollectionSchema: schema of the collection."""
return self._schema
@property
def aliases(self) -> list:
"""List[str]: all the aliases of the collection."""
conn = self._get_connection()
resp = conn.describe_collection(self._name)
return resp["aliases"]
@property
def description(self) -> str:
"""str: a text description of the collection."""
return self.schema.description
@property
def name(self) -> str:
"""str: the name of the collection."""
return self._name
@property
def is_empty(self) -> bool:
"""bool: whether the collection is empty or not."""
return self.num_entities == 0
@property
def num_shards(self) -> int:
"""int: number of shards used by the collection."""
if self._num_shards is None:
self._num_shards = self.describe().get("num_shards")
return self._num_shards
@property
def num_entities(self) -> int:
"""int: The number of entities in the collection, not real time.
Examples:
>>> from pymilvus import Collection, FieldSchema, CollectionSchema, DataType
>>> schema = CollectionSchema([
... FieldSchema("film_id", DataType.INT64, is_primary=True),
... FieldSchema("films", dtype=DataType.FLOAT_VECTOR, dim=2)
... ])
>>> collection = Collection("test_collection_num_entities", schema)
>>> collection.num_entities
0
>>> collection.insert([[1, 2], [[1.0, 2.0], [3.0, 4.0]]])
>>> collection.num_entities
0
>>> collection.flush()
>>> collection.num_entities
2
"""
conn = self._get_connection()
stats = conn.get_collection_stats(collection_name=self._name)
result = {stat.key: stat.value for stat in stats}
result["row_count"] = int(result["row_count"])
return result["row_count"]
@property
def primary_field(self) -> FieldSchema:
"""FieldSchema: the primary field of the collection."""
return self.schema.primary_field
def flush(self, timeout: Optional[float] = None, **kwargs):
"""Seal all segments in the collection. Inserts after flushing will be written into
new segments. Only sealed segments can be indexed.
Args:
timeout (float): an optional duration of time in seconds to allow for the RPCs.
If timeout is not set, the client keeps waiting until the server
responds or an error occurs.
Examples:
>>> from pymilvus import Collection, FieldSchema, CollectionSchema, DataType
>>> fields = [
... FieldSchema("film_id", DataType.INT64, is_primary=True),
... FieldSchema("films", dtype=DataType.FLOAT_VECTOR, dim=128)
... ]
>>> schema = CollectionSchema(fields=fields)
>>> collection = Collection(name="test_collection_flush", schema=schema)
>>> collection.insert([[1, 2], [[1.0, 2.0], [3.0, 4.0]]])
>>> collection.flush()
>>> collection.num_entities
2
"""
conn = self._get_connection()
conn.flush([self.name], timeout=timeout, **kwargs)
def drop(self, timeout: Optional[float] = None, **kwargs):
"""Drops the collection. The same as `utility.drop_collection()`
Args:
timeout (float, optional): an optional duration of time in seconds to allow
for the RPCs. If timeout is not set, the client keeps waiting until the
server responds or an error occurs.
Examples:
>>> from pymilvus import Collection, FieldSchema, CollectionSchema, DataType
>>> schema = CollectionSchema([
... FieldSchema("film_id", DataType.INT64, is_primary=True),
... FieldSchema("films", dtype=DataType.FLOAT_VECTOR, dim=2)
... ])
>>> collection = Collection("test_collection_drop", schema)
>>> utility.has_collection("test_collection_drop")
True
>>> collection.drop()
>>> utility.has_collection("test_collection_drop")
False
"""
conn = self._get_connection()
conn.drop_collection(self._name, timeout=timeout, **kwargs)
def set_properties(self, properties: dict, timeout: Optional[float] = None, **kwargs):
"""Set properties for the collection
Args:
properties (``dict``): collection properties.
support collection TTL with key `collection.ttl.seconds`
support collection replica number with key `collection.replica.number`
support collection resource groups with key `collection.resource_groups`.
timeout (float, optional): an optional duration of time in seconds to allow
for the RPCs. If timeout is not set, the client keeps waiting until the
server responds or an error occurs.
Examples:
>>> from pymilvus import Collection, FieldSchema, CollectionSchema, DataType
>>> fields = [
... FieldSchema("film_id", DataType.INT64, is_primary=True),
... FieldSchema("films", dtype=DataType.FLOAT_VECTOR, dim=128)
... ]
>>> schema = CollectionSchema(fields=fields)
>>> collection = Collection("test_set_properties", schema)
>>> collection.set_properties({"collection.ttl.seconds": 60})
"""
conn = self._get_connection()
conn.alter_collection(
self.name,
properties=properties,
timeout=timeout,
**kwargs,
)
def load(
self,
partition_names: Optional[list] = None,
replica_number: int = 0,
timeout: Optional[float] = None,
**kwargs,
):
"""Load the data into memory.
Args:
partition_names (``List[str]``): The specified partitions to load.
replica_number (``int``, optional): The replica number to load, defaults to 1.
timeout (float, optional): an optional duration of time in seconds to allow
for the RPCs. If timeout is not set, the client keeps waiting until the
server responds or an error occurs.
**kwargs (``dict``, optional):
* *_async*(``bool``)
Indicate if invoke asynchronously.
* *refresh*(``bool``)
Whether to renew the segment list of this collection before loading
* *resource_groups(``List[str]``)
Specify resource groups which can be used during loading.
* *load_fields(``List[str]``)
Specify load fields list needed during this load
* *_skip_load_dynamic_field(``bool``)
Specify whether this load shall skip dynamic schmea field
Raises:
MilvusException: If anything goes wrong.
Examples:
>>> from pymilvus import Collection, FieldSchema, CollectionSchema, DataType
>>> connections.connect()
>>> schema = CollectionSchema([
... FieldSchema("film_id", DataType.INT64, is_primary=True),
... FieldSchema("films", dtype=DataType.FLOAT_VECTOR, dim=2)
... ])
>>> collection = Collection("test_collection_load", schema)
>>> collection.insert([[1, 2], [[1.0, 2.0], [3.0, 4.0]]])
>>> index_param = {"index_type": "FLAT", "metric_type": "L2", "params": {}}
>>> collection.create_index("films", index_param)
>>> collection.load()
"""
conn = self._get_connection()
if partition_names is not None:
conn.load_partitions(
collection_name=self._name,
partition_names=partition_names,
replica_number=replica_number,
timeout=timeout,
**kwargs,
)
else:
conn.load_collection(
collection_name=self._name,
replica_number=replica_number,
timeout=timeout,
**kwargs,
)
def release(self, timeout: Optional[float] = None, **kwargs):
"""Releases the collection data from memory.
Args:
timeout (float, optional): an optional duration of time in seconds to allow
for the RPCs. If timeout is not set, the client keeps waiting until the
server responds or an error occurs.
Examples:
>>> from pymilvus import Collection, FieldSchema, CollectionSchema, DataType
>>> schema = CollectionSchema([
... FieldSchema("film_id", DataType.INT64, is_primary=True),
... FieldSchema("films", dtype=DataType.FLOAT_VECTOR, dim=2)
... ])
>>> collection = Collection("test_collection_release", schema)
>>> collection.insert([[1, 2], [[1.0, 2.0], [3.0, 4.0]]])
>>> index_param = {"index_type": "FLAT", "metric_type": "L2", "params": {}}
>>> collection.create_index("films", index_param)
>>> collection.load()
>>> collection.release()
"""
conn = self._get_connection()
conn.release_collection(self._name, timeout=timeout, **kwargs)
def insert(
self,
data: Union[List, pd.DataFrame, Dict, utils.SparseMatrixInputType],
partition_name: Optional[str] = None,
timeout: Optional[float] = None,
**kwargs,
) -> MutationResult:
"""Insert data into the collection.
Args:
data (``list/tuple/pandas.DataFrame/sparse types``): The specified data to insert
partition_name (``str``): The partition name which the data will be inserted to,
if partition name is not passed, then the data will be inserted
to default partition
timeout (float, optional): an optional duration of time in seconds to allow
for the RPCs. If timeout is not set, the client keeps waiting until the
server responds or an error occurs.
Returns:
MutationResult: contains 2 properties `insert_count`, and, `primary_keys`
`insert_count`: how may entites have been inserted into Milvus,
`primary_keys`: list of primary keys of the inserted entities
Raises:
MilvusException: If anything goes wrong.
Examples:
>>> from pymilvus import Collection, FieldSchema, CollectionSchema, DataType
>>> import random
>>> schema = CollectionSchema([
... FieldSchema("film_id", DataType.INT64, is_primary=True),
... FieldSchema("films", dtype=DataType.FLOAT_VECTOR, dim=2)
... ])
>>> collection = Collection("test_collection_insert", schema)
>>> data = [
... [random.randint(1, 100) for _ in range(10)],
... [[random.random() for _ in range(2)] for _ in range(10)],
... ]
>>> res = collection.insert(data)
>>> res.insert_count
10
"""
if not is_valid_insert_data(data):
raise DataTypeNotSupportException(
message="The type of data should be List, pd.DataFrame or Dict"
)
conn = self._get_connection()
if is_row_based(data):
return conn.insert_rows(
collection_name=self._name,
entities=data,
partition_name=partition_name,
timeout=timeout,
schema=self._schema_dict,
**kwargs,
)
check_insert_schema(self.schema, data)
entities = Prepare.prepare_data(data, self.schema)
return conn.batch_insert(
self._name,
entities,
partition_name,
timeout=timeout,
schema=self._schema_dict,
**kwargs,
)
def delete(
self,
expr: str,
partition_name: Optional[str] = None,
timeout: Optional[float] = None,
**kwargs,
):
"""Delete entities with an expression condition.
Args:
expr (``str``): The specified data to insert.
partition_names (``List[str]``): Name of partitions to delete entities.
timeout (float, optional): an optional duration of time in seconds to allow
for the RPCs. If timeout is not set, the client keeps waiting until the
server responds or an error occurs.
**kwargs (``dict``): Optional search params
* *consistency_level* (``str/int``, optional)
Which consistency level to use when searching in the collection.
Options of consistency level: Strong, Bounded, Eventually, Session, Customized.
Note: this parameter overwrites the same one specified when creating collection,
if no consistency level was specified, search will use the
consistency level when you create the collection.
Returns:
MutationResult:
contains `delete_count` properties represents how many entities might be deleted.
Raises:
MilvusException: If anything goes wrong.
Examples:
>>> from pymilvus import Collection, FieldSchema, CollectionSchema, DataType
>>> import random
>>> schema = CollectionSchema([
... FieldSchema("film_id", DataType.INT64, is_primary=True),
... FieldSchema("film_date", DataType.INT64),
... FieldSchema("films", dtype=DataType.FLOAT_VECTOR, dim=2),
... ])
>>> collection = Collection("test_collection_delete", schema)
>>> # insert
>>> data = [
... [i for i in range(10)],
... [i + 2000 for i in range(10)],
... [[random.random() for _ in range(2)] for _ in range(10)],
... ]
>>> collection.insert(data)
>>> res = collection.delete("film_id in [ 0, 1 ]")
>>> print(f"- Deleted entities: {res}")
- Delete results: [0, 1]
"""
conn = self._get_connection()
res = conn.delete(self._name, expr, partition_name, timeout=timeout, **kwargs)
if kwargs.get("_async", False):
return MutationFuture(res)
return MutationResult(res)
def upsert(
self,
data: Union[List, pd.DataFrame, Dict, utils.SparseMatrixInputType],
partition_name: Optional[str] = None,
timeout: Optional[float] = None,
**kwargs,
) -> MutationResult:
"""Upsert data into the collection.
Args:
data (``list/tuple/pandas.DataFrame/sparse types``): The specified data to upsert
partition_name (``str``): The partition name which the data will be upserted at,
if partition name is not passed, then the data will be upserted
in default partition
timeout (float, optional): an optional duration of time in seconds to allow
for the RPCs. If timeout is not set, the client keeps waiting until the
server responds or an error occurs.
Returns:
MutationResult: contains 2 properties `upsert_count`, and, `primary_keys`
`upsert_count`: how may entites have been upserted at Milvus,
`primary_keys`: list of primary keys of the upserted entities
Raises:
MilvusException: If anything goes wrong.
Examples:
>>> from pymilvus import Collection, FieldSchema, CollectionSchema, DataType
>>> import random
>>> schema = CollectionSchema([
... FieldSchema("film_id", DataType.INT64, is_primary=True),
... FieldSchema("films", dtype=DataType.FLOAT_VECTOR, dim=2)
... ])
>>> collection = Collection("test_collection_upsert", schema)
>>> data = [
... [random.randint(1, 100) for _ in range(10)],
... [[random.random() for _ in range(2)] for _ in range(10)],
... ]
>>> res = collection.upsert(data)
>>> res.upsert_count
10
"""
if not is_valid_insert_data(data):
raise DataTypeNotSupportException(
message="The type of data should be List, pd.DataFrame or Dict"
)
conn = self._get_connection()
if is_row_based(data):
res = conn.upsert_rows(
self._name,
data,
partition_name,
timeout=timeout,
schema=self._schema_dict,
**kwargs,
)
return MutationResult(res)
check_upsert_schema(self.schema, data)
entities = Prepare.prepare_data(data, self.schema, False)
res = conn.upsert(
self._name,
entities,
partition_name,
timeout=timeout,
schema=self._schema_dict,
**kwargs,
)
return MutationFuture(res) if kwargs.get("_async", False) else MutationResult(res)
def search(
self,
data: Union[List, utils.SparseMatrixInputType],
anns_field: str,
param: Dict,
limit: int,
expr: Optional[str] = None,
partition_names: Optional[List[str]] = None,
output_fields: Optional[List[str]] = None,
timeout: Optional[float] = None,
round_decimal: int = -1,
**kwargs,
):
"""Conducts a vector similarity search with an optional boolean expression as filter.
Args:
data (``List[List[float]]/sparse types``): The vectors of search data.
the length of data is number of query (nq),
and the dim of every vector in data must be equal to the vector field of collection.
anns_field (``str``): The name of the vector field used to search of collection.
param (``dict[str, Any]``):
The parameters of search. The followings are valid keys of param.
* *metric_type* (``str``)
similar metricy types, the value must be of type str.
* *offset* (``int``, optional)
offset for pagination.
* *params of index: *nprobe*, *ef*, *search_k*, etc
Corresponding search params for a certain index.
example for param::
{
"metric_type": "L2",
"offset": 10,
"params": {"nprobe": 12},
}
limit (``int``): The max number of returned record, also known as `topk`.
expr (``str``, Optional): The boolean expression used to filter attribute.
example for expr::
"id_field >= 0", "id_field in [1, 2, 3, 4]"
partition_names (``List[str]``, optional): The names of partitions to search on.
output_fields (``List[str]``, optional):
The name of fields to return in the search result. Can only get scalar fields.
round_decimal (``int``, optional):
The specified number of decimal places of returned distance.
Defaults to -1 means no round to returned distance.
timeout (``float``, optional): A duration of time in seconds to allow for the RPC.
If timeout is set to None, the client keeps waiting until the server
responds or an error occurs.
**kwargs (``dict``): Optional search params
* *_async* (``bool``, optional)
Indicate if invoke asynchronously.
Returns a SearchFuture if True, else returns results from server directly.
* *_callback* (``function``, optional)
The callback function which is invoked after server response successfully.
It functions only if _async is set to True.
* *offset* (``int``, optinal)
offset for pagination.
* *consistency_level* (``str/int``, optional)
Which consistency level to use when searching in the collection.
Options of consistency level: Strong, Bounded, Eventually, Session, Customized.
Note: this parameter overwrites the same one specified when creating collection,
if no consistency level was specified, search will use the
consistency level when you create the collection.
* *guarantee_timestamp* (``int``, optional)
Instructs Milvus to see all operations performed before this timestamp.
By default Milvus will search all operations performed to date.
Note: only valid in Customized consistency level.
* *graceful_time* (``int``, optional)
Search will use the (current_timestamp - the graceful_time) as the
`guarantee_timestamp`. By default with 5s.
Note: only valid in Bounded consistency level
Returns:
SearchResult:
Returns ``SearchResult`` if `_async` is False , otherwise ``SearchFuture``
.. _Metric type documentations:
https://milvus.io/docs/v2.2.x/metric.md
.. _Index documentations:
https://milvus.io/docs/v2.2.x/index.md
.. _How guarantee ts works:
https://github.com/milvus-io/milvus/blob/master/docs/developer_guides/how-guarantee-ts-works.md
Raises:
MilvusException: If anything goes wrong
Examples:
>>> from pymilvus import Collection, FieldSchema, CollectionSchema, DataType
>>> import random
>>> schema = CollectionSchema([
... FieldSchema("film_id", DataType.INT64, is_primary=True),
... FieldSchema("films", dtype=DataType.FLOAT_VECTOR, dim=2)
... ])
>>> collection = Collection("test_collection_search", schema)
>>> # insert
>>> data = [
... [i for i in range(10)],
... [[random.random() for _ in range(2)] for _ in range(10)],
... ]
>>> collection.insert(data)
>>> index_param = {"index_type": "FLAT", "metric_type": "L2", "params": {}}
>>> collection.create_index("films", index_param)
>>> collection.load()
>>> # search
>>> search_param = {
... "data": [[1.0, 1.0]],
... "anns_field": "films",
... "param": {"metric_type": "L2", "offset": 1},
... "limit": 2,
... "expr": "film_id > 0",
... }
>>> res = collection.search(**search_param)
>>> assert len(res) == 1
>>> hits = res[0]
>>> assert len(hits) == 2
>>> print(f"- Total hits: {len(hits)}, hits ids: {hits.ids} ")
- Total hits: 2, hits ids: [8, 5]
>>> print(f"- Top1 hit id: {hits[0].id}, score: {hits[0].score} ")
- Top1 hit id: 8, score: 0.10143111646175385
"""
if expr is not None and not isinstance(expr, str):
raise DataTypeNotMatchException(message=ExceptionsMessage.ExprType % type(expr))
empty_scipy_sparse = utils.SciPyHelper.is_scipy_sparse(data) and (data.shape[0] == 0)
if (isinstance(data, list) and len(data) == 0) or empty_scipy_sparse:
resp = SearchResult(schema_pb2.SearchResultData())
return SearchFuture(None) if kwargs.get("_async", False) else resp
conn = self._get_connection()
resp = conn.search(
self._name,
data,
anns_field,
param,
limit,
expr,
partition_names,
output_fields,
round_decimal,
timeout=timeout,
schema=self._schema_dict,
**kwargs,
)
return SearchFuture(resp) if kwargs.get("_async", False) else resp
def hybrid_search(
self,
reqs: List,
rerank: BaseRanker,
limit: int,
partition_names: Optional[List[str]] = None,
output_fields: Optional[List[str]] = None,
timeout: Optional[float] = None,
round_decimal: int = -1,
**kwargs,
):
"""Conducts multi vector similarity search with a rerank for rearrangement.
Args:
reqs (``List[AnnSearchRequest]``): The vector search requests.
rerank (``BaseRanker``): The reranker for rearrange nummer of limit results.
limit (``int``): The max number of returned record, also known as `topk`.
partition_names (``List[str]``, optional): The names of partitions to search on.
output_fields (``List[str]``, optional):
The name of fields to return in the search result. Can only get scalar fields.
round_decimal (``int``, optional):
The specified number of decimal places of returned distance.
Defaults to -1 means no round to returned distance.
timeout (``float``, optional): A duration of time in seconds to allow for the RPC.
If timeout is set to None, the client keeps waiting until the server
responds or an error occurs.
**kwargs (``dict``): Optional search params
* *_async* (``bool``, optional)
Indicate if invoke asynchronously.
Returns a SearchFuture if True, else returns results from server directly.
* *_callback* (``function``, optional)
The callback function which is invoked after server response successfully.
It functions only if _async is set to True.
* *offset* (``int``, optinal)
offset for pagination.
* *consistency_level* (``str/int``, optional)
Which consistency level to use when searching in the collection.
Options of consistency level: Strong, Bounded, Eventually, Session, Customized.
Note: this parameter overwrites the same one specified when creating collection,
if no consistency level was specified, search will use the
consistency level when you create the collection.
* *guarantee_timestamp* (``int``, optional)
Instructs Milvus to see all operations performed before this timestamp.
By default Milvus will search all operations performed to date.
Note: only valid in Customized consistency level.
* *graceful_time* (``int``, optional)
Search will use the (current_timestamp - the graceful_time) as the
`guarantee_timestamp`. By default with 5s.
Note: only valid in Bounded consistency level
Returns:
SearchResult:
Returns ``SearchResult`` if `_async` is False , otherwise ``SearchFuture``
.. _Metric type documentations:
https://milvus.io/docs/v2.2.x/metric.md
.. _Index documentations:
https://milvus.io/docs/v2.2.x/index.md
.. _How guarantee ts works:
https://github.com/milvus-io/milvus/blob/master/docs/developer_guides/how-guarantee-ts-works.md
Raises:
MilvusException: If anything goes wrong
Examples:
>>> from pymilvus import (Collection, FieldSchema, CollectionSchema, DataType,
>>> AnnSearchRequest, RRFRanker, WeightedRanker)
>>> import random
>>> schema = CollectionSchema([
... FieldSchema("film_id", DataType.INT64, is_primary=True),
... FieldSchema("films", dtype=DataType.FLOAT_VECTOR, dim=2),
... FieldSchema("poster", dtype=DataType.FLOAT_VECTOR, dim=2),
... ])
>>> collection = Collection("test_collection_search", schema)
>>> # insert
>>> data = [
... [i for i in range(10)],
... [[random.random() for _ in range(2)] for _ in range(10)],
... [[random.random() for _ in range(2)] for _ in range(10)],
... ]
>>> collection.insert(data)
>>> index_param = {"index_type": "FLAT", "metric_type": "L2", "params": {}}
>>> collection.create_index("films", index_param)
>>> collection.create_index("poster", index_param)
>>> collection.load()
>>> # search
>>> search_param1 = {
... "data": [[1.0, 1.0]],
... "anns_field": "films",
... "param": {"metric_type": "L2", "offset": 1},
... "limit": 2,
... "expr": "film_id > 0",
... }
>>> req1 = AnnSearchRequest(**search_param1)
>>> search_param2 = {
... "data": [[2.0, 2.0]],
... "anns_field": "poster",
... "param": {"metric_type": "L2", "offset": 1},
... "limit": 2,
... "expr": "film_id > 0",
... }
>>> req2 = AnnSearchRequest(**search_param2)
>>> res = collection.hybrid_search([req1, req2], WeightedRanker(0.9, 0.1), 2)
>>> assert len(res) == 1
>>> hits = res[0]
>>> assert len(hits) == 2
>>> print(f"- Total hits: {len(hits)}, hits ids: {hits.ids} ")
- Total hits: 2, hits ids: [8, 5]
>>> print(f"- Top1 hit id: {hits[0].id}, score: {hits[0].score} ")
- Top1 hit id: 8, score: 0.10143111646175385
"""
if isinstance(reqs, list) and len(reqs) == 0:
resp = SearchResult(schema_pb2.SearchResultData())
return SearchFuture(None) if kwargs.get("_async", False) else resp
conn = self._get_connection()
resp = conn.hybrid_search(
self._name,
reqs,
rerank,
limit,
partition_names,
output_fields,
round_decimal,
timeout=timeout,
schema=self._schema_dict,
**kwargs,
)
return SearchFuture(resp) if kwargs.get("_async", False) else resp
def search_iterator(
self,
data: Union[List, utils.SparseMatrixInputType],
anns_field: str,
param: Dict,
batch_size: Optional[int] = 1000,
limit: Optional[int] = UNLIMITED,
expr: Optional[str] = None,
partition_names: Optional[List[str]] = None,
output_fields: Optional[List[str]] = None,
timeout: Optional[float] = None,
round_decimal: int = -1,
**kwargs,
):
if expr is not None and not isinstance(expr, str):
raise DataTypeNotMatchException(message=ExceptionsMessage.ExprType % type(expr))
return SearchIterator(
connection=self._get_connection(),
collection_name=self._name,
data=data,
ann_field=anns_field,
param=param,
batch_size=batch_size,
limit=limit,
expr=expr,
partition_names=partition_names,
output_fields=output_fields,
timeout=timeout,
round_decimal=round_decimal,
schema=self._schema_dict,
**kwargs,
)
def query(
self,
expr: str,
output_fields: Optional[List[str]] = None,
partition_names: Optional[List[str]] = None,
timeout: Optional[float] = None,
**kwargs,
):