automatic import of python-pymilvus-distributed

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+/pymilvus-distributed-0.0.66.tar.gz
diff --git a/python-pymilvus-distributed.spec b/python-pymilvus-distributed.spec
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+%global _empty_manifest_terminate_build 0
+Name:		python-pymilvus-distributed
+Version:	0.0.66
+Release:	1
+Summary:	Python Sdk for Milvus-Distributed
+License:	Apache-2.0
+URL:		https://github.com/milvus-io/pymilvus
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/33/ad/42ebfd56e1154675d18a193d164a67c65ca6ac978efd7ad93dee46ce8197/pymilvus-distributed-0.0.66.tar.gz
+BuildArch:	noarch
+
+Requires:	python3-grpcio
+Requires:	python3-grpcio-tools
+Requires:	python3-requests
+Requires:	python3-ujson
+Requires:	python3-mmh3
+
+%description
+
+# Milvus Python SDK
+
+[![version](https://img.shields.io/pypi/v/pymilvus.svg?color=blue)](https://pypi.org/project/pymilvus/)
+[![Downloads](https://pepy.tech/badge/pymilvus)](https://pepy.tech/project/pymilvus)
+[![Downloads](https://pepy.tech/badge/pymilvus/month)](https://pepy.tech/project/pymilvus/month)
+[![Downloads](https://pepy.tech/badge/pymilvus/week)](https://pepy.tech/project/pymilvus/week)
+[![license](https://img.shields.io/hexpm/l/plug.svg?color=green)](https://github.com/milvus-io/pymilvus/blob/master/LICENSE)
+
+Python SDK for [Milvus](https://github.com/milvus-io/milvus). To contribute code to this project, please read our [contribution guidelines](https://github.com/milvus-io/milvus/blob/master/CONTRIBUTING.md) first.
+
+For detailed SDK documentation, refer to [API Documentation](https://milvus-io.github.io/milvus-sdk-python/pythondoc/v0.3.0/index.html).
+
+
+<!-- TOC -->
+
+- [New features](#new-features)
+- [Get started](#get-started)
+    - [Prerequisites](#prerequisites)
+    - [Install pymilvus](#install-pymilvus)
+    - [Examples](#examples)
+- [Basic operations](#basic-operations)
+    - [Connect to the Milvus server](#connect-to-the-milvus-server)
+- [Create/Drop collections](#createdrop-collections)
+    - [Create a collection](#create-a-collection)
+    - [Drop a collection](#drop-a-collection)
+- [Create/Drop partitions in a collection](#createdrop-partitions-in-a-collection)
+    - [Create a partition](#create-a-partition)
+    - [Drop a partition](#drop-a-partition)
+- [Create/Drop indexes in a collection](#createdrop-indexes-in-a-collection)
+    - [Create an index](#create-an-index)
+    - [Drop an index](#drop-an-index)
+- [Insert/Delete vectors in collections/partitions](#insertdelete-entities-in-collectionspartitions)
+    - [Insert entities in a collection](#insert-entities-in-a-collection)
+    - [Insert entities in a partition](#insert-entities-in-a-partition)
+    - [Delete entities by ID](#delete-entities-by-id)
+- [Flush data in one or multiple collections to disk](#flush-data-in-one-or-multiple-collections-to-disk)
+- [Compact all segments in a collection](#compact-all-segments-in-a-collection)
+- [Search entities in collections/partitions](#search-entities-in-collectionspartitions)
+    - [Search entities in a collection](#search-entities-in-a-collection)
+    - [Search entities in a partition](#search-entities-in-a-partition)
+- [Disconnect from the Milvus server](#close-client)
+- [FAQ](#faq)
+
+<!-- /TOC -->
+
+## New features
+
+- remove `get_index_info`, the index info can be obtained by `get_collection_info`
+
+- add `get_collection_stats`, more detailed collection stats.
+
+## Get started
+
+### Prerequisites
+
+pymilvus only supports Python 3.5 or higher.
+
+### Install pymilvus
+
+You can install pymilvus via `pip` or `pip3` for Python3:
+
+```shell
+$ pip3 install pymilvus
+```
+
+The following collection shows Milvus versions and recommended pymilvus versions:
+
+|Milvus version| Recommended pymilvus version |
+|:-----:|:-----:|
+| 0.3.0 | 0.1.13|
+| 0.3.1 | 0.1.25|
+| 0.4.0 | 0.2.2 |
+| 0.5.0 | 0.2.3 |
+| 0.5.1 | 0.2.3 |
+| 0.5.2 | 0.2.3 |
+| 0.5.3 | 0.2.5 |
+| 0.6.0 | 0.2.6, 0.2.7 |
+| 0.7.0 | 0.2.8 |
+| 0.7.1 | 0.2.9 |
+| 0.8.0 | 0.2.10 |
+| 0.9.0 | 0.2.11 |
+| 0.9.1 | 0.2.12 |
+| 0.10.0 | 0.2.13 |
+| >=0.10.1, <0.11.0 | 0.2.14 |
+| 0.11.0 | 0.3.0 |
+
+
+You can install a specific version of pymilvus by:
+
+```shell
+$ pip install pymilvus==0.3.0
+```
+
+You can upgrade `pymilvus` to the latest version by:
+
+```shell
+$ pip install --upgrade pymilvus
+```
+
+### Examples
+
+Refer to [examples](/examples) for more example programs.
+
+## Basic operations
+
+### Connect to the Milvus server
+
+1. Import pymilvus.
+
+```python
+# Import pymilvus
+>>> from milvus import Milvus, DataType
+```
+
+2. Create a client to Milvus server using one of the following methods:
+
+```python
+# Connect to Milvus server
+>>> client = Milvus(host='localhost', port='19530')
+```
+
+   > Note: In the above code, default values are used for `host` and `port` parameters.
+Feel free to change them to the IP address and port you set for Milvus server.
+
+```python
+>>> client = Milvus(uri='tcp://localhost:19530')
+```
+
+## Create/Drop collections
+
+### Create a collection
+
+1. Prepare collection parameters.
+
+```python
+# create collection name
+>>> collection_name = 'test01'
+
+# create a collection of 4 fields, fields A, B and C are int type fields
+# and Vec is a float vector field.
+# segment_row_limit is default as 524288 if not specified
+>>> collection_param = {
+...    "fields": [
+...        {"name": "A", "type": DataType.INT32},
+...        {"name": "B", "type": DataType.INT32},
+...        {"name": "C", "type": DataType.INT64},
+...        {"name": "Vec", "type": DataType.FLOAT_VECTOR, "params": {"dim": 128}}
+...    ],
+...    "segment_row_limit": 4096,
+...    "auto_id": True
+... }
+```
+
+2. Create collection `test01` with dimension of 128, size of the data file for Milvus to automatically
+create indexes as 4096. If `metric_type` isn't offered, default metric type is Euclidean distance (L2).
+For `FLOAT_VECTOR` field, `dim` is a must.
+
+```python
+# Create a collection
+>>> client.create_collection(collection_name, collection_param)
+```
+
+3. You can check collection info by `get_collection_info`
+```python
+>>> info = client.get_collection_info('test01')
+>>> info
+{'fields': [
+    {'name': 'A', 'type': <DataType.INT32: 4>, 'params': {}, 'indexes': [{}]},
+    {'name': 'C', 'type': <DataType.INT64: 5>, 'params': {}, 'indexes': [{}]},
+    {'name': 'B', 'type': <DataType.INT32: 4>, 'params': {}, 'indexes': [{}]},
+    {'name': 'Vec', 'type': <DataType.FLOAT_VECTOR: 101>, 'params': {'dim': 128},
+     'indexes': [{}]}
+    ],
+ 'auto_id': True,
+ 'segment_row_limit': 4096
+}
+```
+
+You can see from the info, there is an `auto_id` option in collection info, and its `True` by default.
+So if you have your own ids and don't want auto generated ids, you may want to set `auto_id` to `False`
+while creating collections.
+
+
+
+### Drop a collection
+
+```python
+# Drop collection
+>>> status = client.drop_collection('test01')
+>>> status
+Status(code=0, message='OK')
+```
+
+## Create/Drop partitions in a collection
+
+### Create a partition
+
+You can split collections into partitions by partition tags for improved search performance.
+
+```python
+# Create partition
+>>> client.create_partition(collection_name='test01', partition_tag='tag01')
+```
+
+Use `list_partitions()` to verify whether the partition is created.
+
+```python
+# Show partitions
+>>> partitions = client.list_partitions(collection_name='test01')
+>>> partitions
+['_default', 'tag01']
+```
+
+### Drop a partition
+
+```python
+# Drop partitions
+>>> status = client.drop_partition(collection_name='test01', partition_tag='tag01')
+Status(code=0, message='OK')
+```
+
+
+## Create/Drop indexes in a collection
+
+### Create an index
+
+> Note: In production, it is recommended to create indexes before inserting vectors into the collection.
+Index is automatically built when vectors are being imported. However, you need to create the same index
+again after the vector insertion process is completed because some data files may not meet the
+`index_file_size` and index will not be automatically built for these data files.
+
+1. Create an index of `IVF_FLAT` with `nlist = 100` for the collection.
+
+```python
+# Create index
+>>> status = client.create_index('test01', "Vec", {"index_type": "IVF_FLAT", "metric_type": "L2", "params": {"nlist": 100}})
+>>> status
+Status(code=0, message='OK')   
+```
+
+2. You can check index info by `get_collection_info`
+
+```python
+>>> info = client.get_collection_info('test01')
+>>> info
+{'fields': [
+    {'name': 'A', 'type': <DataType.INT32: 4>, 'params': {}, 'indexes': [{}]},
+    {'name': 'C', 'type': <DataType.INT64: 5>, 'params': {}, 'indexes': [{}]},
+    {'name': 'B', 'type': <DataType.INT32: 4>, 'params': {}, 'indexes': [{}]},
+    {'name': 'Vec',
+        'type': <DataType.FLOAT_VECTOR: 101>,
+        'params': {'dim': 128, 'metric_type': 'L2'},
+        'indexes': [{'index_type': 'IVF_FLAT', 'metric_type': 'L2', 'params': {'nlist': 100}}]}],
+ 'auto_id': True,
+ 'segment_row_limit': 4096
+}
+```
+
+### Drop an index
+
+```python
+# Drop an index of a specific field "Vec"
+>>> status = client.drop_index('test01', "Vec")
+Status(code=0, message='OK')
+```
+
+## Insert/Delete entities in collections/partitions
+
+### Insert entities in a collection
+
+1. Generate 5000 vectors of 128 dimension and an integer list.
+
+```python
+>>> import random
+>>> num = 5000
+
+# Generate a list of integer.
+>>> list_of_int = [random.randint(0, 255) for _ in range(num)]
+# Generate 20 vectors of 128 dimension
+>>> vectors = [[random.random() for _ in range(128)] for _ in range(num)]
+```
+
+2. Create hybrid entities
+
+```python
+>>> hybrid_entities = [
+   {"name": "A", "values": list_of_int, "type": DataType.INT32},
+   {"name": "B", "values": list_of_int, "type": DataType.INT32},
+   {"name": "C", "values": list_of_int, "type": DataType.INT64},
+   {"name": "Vec", "values": vectors, "type":DataType.FLOAT_VECTOR}
+]
+```
+
+3. Insert the hybrid entities.
+
+If you create a new collection with `auto_id = True`, Milvus automatically generates IDs for the vectors.
+
+```python
+# Insert vectors
+>>> ids = client.insert('test01', hybrid_entities)
+```
+
+If you create a new collection with `auto_id = False`, you have to provide user-defined vector ids:
+
+```python
+# Generate fake custom ids
+>>> vector_ids = [id for id in range(num)]
+# Insert to the non-auto-id collection
+>>> ids = client.insert('test01', hybrid_entities, ids=vector_ids)
+```
+
+**The examples below are based on collection with `auto_id = True`.**
+
+### Insert entities in a partition
+
+```python
+>>> inserted_vector_ids = client.insert('test01', hybrid_entities, partition_tag="tag01")
+```
+
+To verify the entities you have inserted, use `get_entity_by_id()`. 
+
+```python
+>>> entities = client.get_entity_by_id(collection_name='test01', ids=inserted_vector_ids[:10])
+```
+
+### Delete entities by ID
+
+You can delete these entities by:
+
+```python
+>>> status = client.delete_entity_by_id('test01', ids[:10])
+>>> status
+Status(code=0, message='OK')
+```
+
+## Flush data in one or multiple collections to disk
+
+When performing operations related to data changes, you can flush the data from memory to disk to avoid possible data loss. Milvus also supports automatic flushing, which runs at a fixed interval to flush the data in all collections to disk. You can use the [Milvus server configuration file](https://milvus.io/docs/reference/milvus_config.md) to set the interval.
+
+```python
+>>> client.flush(['test01'])
+```
+
+## Compact all segments in a collection
+
+A segment is a data file that Milvus automatically creates by merging inserted vector data. A collection can contain multiple segments. If some vectors are deleted from a segment, the space taken by the deleted vectors cannot be released automatically. You can compact segments in a collection to release space.
+
+```python
+>>> status = client.compact('test01')
+>>> status
+Status(code=0, message='OK')
+```
+
+## Search entities in collections/partitions
+
+### Search entities in a collection
+
+1. Prepare search parameters. `"term"` and `"range"` is optional, `"params"` in `"vector"` stands for index params.
+
+
+```python
+# This dsl will search topk `entities` that are
+# close to vectors[:1] searched by `IVF_FLAT` index with `nprobe = 10` and `metric_type = L2`,
+# AND field "A" in [1, 2, 5],
+# AND field "B" greater than 1 less than 100
+>>> dsl = {
+...     "bool": {
+...         "must":[
+...             {
+...                 "term": {"A": [1, 2, 5]}
+...             },
+...             {
+...                 "range": {"B": {"GT": 1, "LT": 100}}
+...             },
+...             {
+...                 "vector": {
+...                    "Vec": {"topk": 10, "query": vectors[:1], "metric_type": "L2", "params": {"nprobe": 10}}
+...                 }
+...             }
+...         ]
+...     }
+... }
+
+```
+A search without hybrid conditions with `IVF_FLAT` index would be like:
+
+```python
+>>> dsl = {
+...     "bool": {
+...         "must":[
+...             {
+...                 "vector": {
+...                    "Vec": {"topk": 10, "query": vectors[:1], "metric_type": "L2", "params": {"nprobe": 10}}
+...                 }
+...             }
+...         ]
+...     }
+... }
+
+```
+
+A `FLAT` search doesn't need index params, so the query would be like:
+
+```python
+>>> dsl = {
+...     "bool": {
+...         "must":[
+...             {
+...                 "vector": {
+...                    "Vec": {"topk": 10, "query": vectors[0], "metric_type": "L2"}
+...                 }
+...             }
+...         ]
+...     }
+... }
+
+```
+
+
+2. Search entities.
+
+With `fields=["B"]`, not only can you get entity ids and distances, but also values of a spacific field B.
+
+```python
+# search entities and get entity field B back
+>>> results = client.search('test01', dsl, fields=["B"])
+```
+
+You can obtain ids, distances and fields by entities in results.
+
+```python
+# Results consist of number-of-query entities
+>>> entities = results[0]
+
+# Entities consists of topk entity
+>>> entity = entities[0]
+
+# You can get all ids and distances by entities
+>>> all_ids = entities.ids
+>>> all_distances = entities.distances
+
+# Or you can get them one by one by entity
+>>> a_id = entity.id
+>>> a_distance = entity.distance
+>>> a_field = entity.entity.B # getattr(entity.entity, "B")
+```
+
+> Note: If you don't provide fields in search, you will only get ids and distances.
+
+### Search entities in a partition
+
+```python
+# Search entities in a partition `tag01`
+>>> client.search(collection_name='test01', dsl=dsl, partition_tags=['tag01'])
+```
+
+> Note: If you do not specify `partition_tags`, Milvus searches the whole collection.
+
+## Close client
+
+```python
+>>> client.close()
+```
+
+## FAQ
+
+> I'm getting random "socket operation on non-socket" errors from gRPC when connecting to Milvus from an application served on Gunicorn
+
+Make sure to set the environment variable `GRPC_ENABLE_FORK_SUPPORT=1`. For reference, see https://zhuanlan.zhihu.com/p/136619485
+
+
+
+
+%package -n python3-pymilvus-distributed
+Summary:	Python Sdk for Milvus-Distributed
+Provides:	python-pymilvus-distributed
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-pymilvus-distributed
+
+# Milvus Python SDK
+
+[![version](https://img.shields.io/pypi/v/pymilvus.svg?color=blue)](https://pypi.org/project/pymilvus/)
+[![Downloads](https://pepy.tech/badge/pymilvus)](https://pepy.tech/project/pymilvus)
+[![Downloads](https://pepy.tech/badge/pymilvus/month)](https://pepy.tech/project/pymilvus/month)
+[![Downloads](https://pepy.tech/badge/pymilvus/week)](https://pepy.tech/project/pymilvus/week)
+[![license](https://img.shields.io/hexpm/l/plug.svg?color=green)](https://github.com/milvus-io/pymilvus/blob/master/LICENSE)
+
+Python SDK for [Milvus](https://github.com/milvus-io/milvus). To contribute code to this project, please read our [contribution guidelines](https://github.com/milvus-io/milvus/blob/master/CONTRIBUTING.md) first.
+
+For detailed SDK documentation, refer to [API Documentation](https://milvus-io.github.io/milvus-sdk-python/pythondoc/v0.3.0/index.html).
+
+
+<!-- TOC -->
+
+- [New features](#new-features)
+- [Get started](#get-started)
+    - [Prerequisites](#prerequisites)
+    - [Install pymilvus](#install-pymilvus)
+    - [Examples](#examples)
+- [Basic operations](#basic-operations)
+    - [Connect to the Milvus server](#connect-to-the-milvus-server)
+- [Create/Drop collections](#createdrop-collections)
+    - [Create a collection](#create-a-collection)
+    - [Drop a collection](#drop-a-collection)
+- [Create/Drop partitions in a collection](#createdrop-partitions-in-a-collection)
+    - [Create a partition](#create-a-partition)
+    - [Drop a partition](#drop-a-partition)
+- [Create/Drop indexes in a collection](#createdrop-indexes-in-a-collection)
+    - [Create an index](#create-an-index)
+    - [Drop an index](#drop-an-index)
+- [Insert/Delete vectors in collections/partitions](#insertdelete-entities-in-collectionspartitions)
+    - [Insert entities in a collection](#insert-entities-in-a-collection)
+    - [Insert entities in a partition](#insert-entities-in-a-partition)
+    - [Delete entities by ID](#delete-entities-by-id)
+- [Flush data in one or multiple collections to disk](#flush-data-in-one-or-multiple-collections-to-disk)
+- [Compact all segments in a collection](#compact-all-segments-in-a-collection)
+- [Search entities in collections/partitions](#search-entities-in-collectionspartitions)
+    - [Search entities in a collection](#search-entities-in-a-collection)
+    - [Search entities in a partition](#search-entities-in-a-partition)
+- [Disconnect from the Milvus server](#close-client)
+- [FAQ](#faq)
+
+<!-- /TOC -->
+
+## New features
+
+- remove `get_index_info`, the index info can be obtained by `get_collection_info`
+
+- add `get_collection_stats`, more detailed collection stats.
+
+## Get started
+
+### Prerequisites
+
+pymilvus only supports Python 3.5 or higher.
+
+### Install pymilvus
+
+You can install pymilvus via `pip` or `pip3` for Python3:
+
+```shell
+$ pip3 install pymilvus
+```
+
+The following collection shows Milvus versions and recommended pymilvus versions:
+
+|Milvus version| Recommended pymilvus version |
+|:-----:|:-----:|
+| 0.3.0 | 0.1.13|
+| 0.3.1 | 0.1.25|
+| 0.4.0 | 0.2.2 |
+| 0.5.0 | 0.2.3 |
+| 0.5.1 | 0.2.3 |
+| 0.5.2 | 0.2.3 |
+| 0.5.3 | 0.2.5 |
+| 0.6.0 | 0.2.6, 0.2.7 |
+| 0.7.0 | 0.2.8 |
+| 0.7.1 | 0.2.9 |
+| 0.8.0 | 0.2.10 |
+| 0.9.0 | 0.2.11 |
+| 0.9.1 | 0.2.12 |
+| 0.10.0 | 0.2.13 |
+| >=0.10.1, <0.11.0 | 0.2.14 |
+| 0.11.0 | 0.3.0 |
+
+
+You can install a specific version of pymilvus by:
+
+```shell
+$ pip install pymilvus==0.3.0
+```
+
+You can upgrade `pymilvus` to the latest version by:
+
+```shell
+$ pip install --upgrade pymilvus
+```
+
+### Examples
+
+Refer to [examples](/examples) for more example programs.
+
+## Basic operations
+
+### Connect to the Milvus server
+
+1. Import pymilvus.
+
+```python
+# Import pymilvus
+>>> from milvus import Milvus, DataType
+```
+
+2. Create a client to Milvus server using one of the following methods:
+
+```python
+# Connect to Milvus server
+>>> client = Milvus(host='localhost', port='19530')
+```
+
+   > Note: In the above code, default values are used for `host` and `port` parameters.
+Feel free to change them to the IP address and port you set for Milvus server.
+
+```python
+>>> client = Milvus(uri='tcp://localhost:19530')
+```
+
+## Create/Drop collections
+
+### Create a collection
+
+1. Prepare collection parameters.
+
+```python
+# create collection name
+>>> collection_name = 'test01'
+
+# create a collection of 4 fields, fields A, B and C are int type fields
+# and Vec is a float vector field.
+# segment_row_limit is default as 524288 if not specified
+>>> collection_param = {
+...    "fields": [
+...        {"name": "A", "type": DataType.INT32},
+...        {"name": "B", "type": DataType.INT32},
+...        {"name": "C", "type": DataType.INT64},
+...        {"name": "Vec", "type": DataType.FLOAT_VECTOR, "params": {"dim": 128}}
+...    ],
+...    "segment_row_limit": 4096,
+...    "auto_id": True
+... }
+```
+
+2. Create collection `test01` with dimension of 128, size of the data file for Milvus to automatically
+create indexes as 4096. If `metric_type` isn't offered, default metric type is Euclidean distance (L2).
+For `FLOAT_VECTOR` field, `dim` is a must.
+
+```python
+# Create a collection
+>>> client.create_collection(collection_name, collection_param)
+```
+
+3. You can check collection info by `get_collection_info`
+```python
+>>> info = client.get_collection_info('test01')
+>>> info
+{'fields': [
+    {'name': 'A', 'type': <DataType.INT32: 4>, 'params': {}, 'indexes': [{}]},
+    {'name': 'C', 'type': <DataType.INT64: 5>, 'params': {}, 'indexes': [{}]},
+    {'name': 'B', 'type': <DataType.INT32: 4>, 'params': {}, 'indexes': [{}]},
+    {'name': 'Vec', 'type': <DataType.FLOAT_VECTOR: 101>, 'params': {'dim': 128},
+     'indexes': [{}]}
+    ],
+ 'auto_id': True,
+ 'segment_row_limit': 4096
+}
+```
+
+You can see from the info, there is an `auto_id` option in collection info, and its `True` by default.
+So if you have your own ids and don't want auto generated ids, you may want to set `auto_id` to `False`
+while creating collections.
+
+
+
+### Drop a collection
+
+```python
+# Drop collection
+>>> status = client.drop_collection('test01')
+>>> status
+Status(code=0, message='OK')
+```
+
+## Create/Drop partitions in a collection
+
+### Create a partition
+
+You can split collections into partitions by partition tags for improved search performance.
+
+```python
+# Create partition
+>>> client.create_partition(collection_name='test01', partition_tag='tag01')
+```
+
+Use `list_partitions()` to verify whether the partition is created.
+
+```python
+# Show partitions
+>>> partitions = client.list_partitions(collection_name='test01')
+>>> partitions
+['_default', 'tag01']
+```
+
+### Drop a partition
+
+```python
+# Drop partitions
+>>> status = client.drop_partition(collection_name='test01', partition_tag='tag01')
+Status(code=0, message='OK')
+```
+
+
+## Create/Drop indexes in a collection
+
+### Create an index
+
+> Note: In production, it is recommended to create indexes before inserting vectors into the collection.
+Index is automatically built when vectors are being imported. However, you need to create the same index
+again after the vector insertion process is completed because some data files may not meet the
+`index_file_size` and index will not be automatically built for these data files.
+
+1. Create an index of `IVF_FLAT` with `nlist = 100` for the collection.
+
+```python
+# Create index
+>>> status = client.create_index('test01', "Vec", {"index_type": "IVF_FLAT", "metric_type": "L2", "params": {"nlist": 100}})
+>>> status
+Status(code=0, message='OK')   
+```
+
+2. You can check index info by `get_collection_info`
+
+```python
+>>> info = client.get_collection_info('test01')
+>>> info
+{'fields': [
+    {'name': 'A', 'type': <DataType.INT32: 4>, 'params': {}, 'indexes': [{}]},
+    {'name': 'C', 'type': <DataType.INT64: 5>, 'params': {}, 'indexes': [{}]},
+    {'name': 'B', 'type': <DataType.INT32: 4>, 'params': {}, 'indexes': [{}]},
+    {'name': 'Vec',
+        'type': <DataType.FLOAT_VECTOR: 101>,
+        'params': {'dim': 128, 'metric_type': 'L2'},
+        'indexes': [{'index_type': 'IVF_FLAT', 'metric_type': 'L2', 'params': {'nlist': 100}}]}],
+ 'auto_id': True,
+ 'segment_row_limit': 4096
+}
+```
+
+### Drop an index
+
+```python
+# Drop an index of a specific field "Vec"
+>>> status = client.drop_index('test01', "Vec")
+Status(code=0, message='OK')
+```
+
+## Insert/Delete entities in collections/partitions
+
+### Insert entities in a collection
+
+1. Generate 5000 vectors of 128 dimension and an integer list.
+
+```python
+>>> import random
+>>> num = 5000
+
+# Generate a list of integer.
+>>> list_of_int = [random.randint(0, 255) for _ in range(num)]
+# Generate 20 vectors of 128 dimension
+>>> vectors = [[random.random() for _ in range(128)] for _ in range(num)]
+```
+
+2. Create hybrid entities
+
+```python
+>>> hybrid_entities = [
+   {"name": "A", "values": list_of_int, "type": DataType.INT32},
+   {"name": "B", "values": list_of_int, "type": DataType.INT32},
+   {"name": "C", "values": list_of_int, "type": DataType.INT64},
+   {"name": "Vec", "values": vectors, "type":DataType.FLOAT_VECTOR}
+]
+```
+
+3. Insert the hybrid entities.
+
+If you create a new collection with `auto_id = True`, Milvus automatically generates IDs for the vectors.
+
+```python
+# Insert vectors
+>>> ids = client.insert('test01', hybrid_entities)
+```
+
+If you create a new collection with `auto_id = False`, you have to provide user-defined vector ids:
+
+```python
+# Generate fake custom ids
+>>> vector_ids = [id for id in range(num)]
+# Insert to the non-auto-id collection
+>>> ids = client.insert('test01', hybrid_entities, ids=vector_ids)
+```
+
+**The examples below are based on collection with `auto_id = True`.**
+
+### Insert entities in a partition
+
+```python
+>>> inserted_vector_ids = client.insert('test01', hybrid_entities, partition_tag="tag01")
+```
+
+To verify the entities you have inserted, use `get_entity_by_id()`. 
+
+```python
+>>> entities = client.get_entity_by_id(collection_name='test01', ids=inserted_vector_ids[:10])
+```
+
+### Delete entities by ID
+
+You can delete these entities by:
+
+```python
+>>> status = client.delete_entity_by_id('test01', ids[:10])
+>>> status
+Status(code=0, message='OK')
+```
+
+## Flush data in one or multiple collections to disk
+
+When performing operations related to data changes, you can flush the data from memory to disk to avoid possible data loss. Milvus also supports automatic flushing, which runs at a fixed interval to flush the data in all collections to disk. You can use the [Milvus server configuration file](https://milvus.io/docs/reference/milvus_config.md) to set the interval.
+
+```python
+>>> client.flush(['test01'])
+```
+
+## Compact all segments in a collection
+
+A segment is a data file that Milvus automatically creates by merging inserted vector data. A collection can contain multiple segments. If some vectors are deleted from a segment, the space taken by the deleted vectors cannot be released automatically. You can compact segments in a collection to release space.
+
+```python
+>>> status = client.compact('test01')
+>>> status
+Status(code=0, message='OK')
+```
+
+## Search entities in collections/partitions
+
+### Search entities in a collection
+
+1. Prepare search parameters. `"term"` and `"range"` is optional, `"params"` in `"vector"` stands for index params.
+
+
+```python
+# This dsl will search topk `entities` that are
+# close to vectors[:1] searched by `IVF_FLAT` index with `nprobe = 10` and `metric_type = L2`,
+# AND field "A" in [1, 2, 5],
+# AND field "B" greater than 1 less than 100
+>>> dsl = {
+...     "bool": {
+...         "must":[
+...             {
+...                 "term": {"A": [1, 2, 5]}
+...             },
+...             {
+...                 "range": {"B": {"GT": 1, "LT": 100}}
+...             },
+...             {
+...                 "vector": {
+...                    "Vec": {"topk": 10, "query": vectors[:1], "metric_type": "L2", "params": {"nprobe": 10}}
+...                 }
+...             }
+...         ]
+...     }
+... }
+
+```
+A search without hybrid conditions with `IVF_FLAT` index would be like:
+
+```python
+>>> dsl = {
+...     "bool": {
+...         "must":[
+...             {
+...                 "vector": {
+...                    "Vec": {"topk": 10, "query": vectors[:1], "metric_type": "L2", "params": {"nprobe": 10}}
+...                 }
+...             }
+...         ]
+...     }
+... }
+
+```
+
+A `FLAT` search doesn't need index params, so the query would be like:
+
+```python
+>>> dsl = {
+...     "bool": {
+...         "must":[
+...             {
+...                 "vector": {
+...                    "Vec": {"topk": 10, "query": vectors[0], "metric_type": "L2"}
+...                 }
+...             }
+...         ]
+...     }
+... }
+
+```
+
+
+2. Search entities.
+
+With `fields=["B"]`, not only can you get entity ids and distances, but also values of a spacific field B.
+
+```python
+# search entities and get entity field B back
+>>> results = client.search('test01', dsl, fields=["B"])
+```
+
+You can obtain ids, distances and fields by entities in results.
+
+```python
+# Results consist of number-of-query entities
+>>> entities = results[0]
+
+# Entities consists of topk entity
+>>> entity = entities[0]
+
+# You can get all ids and distances by entities
+>>> all_ids = entities.ids
+>>> all_distances = entities.distances
+
+# Or you can get them one by one by entity
+>>> a_id = entity.id
+>>> a_distance = entity.distance
+>>> a_field = entity.entity.B # getattr(entity.entity, "B")
+```
+
+> Note: If you don't provide fields in search, you will only get ids and distances.
+
+### Search entities in a partition
+
+```python
+# Search entities in a partition `tag01`
+>>> client.search(collection_name='test01', dsl=dsl, partition_tags=['tag01'])
+```
+
+> Note: If you do not specify `partition_tags`, Milvus searches the whole collection.
+
+## Close client
+
+```python
+>>> client.close()
+```
+
+## FAQ
+
+> I'm getting random "socket operation on non-socket" errors from gRPC when connecting to Milvus from an application served on Gunicorn
+
+Make sure to set the environment variable `GRPC_ENABLE_FORK_SUPPORT=1`. For reference, see https://zhuanlan.zhihu.com/p/136619485
+
+
+
+
+%package help
+Summary:	Development documents and examples for pymilvus-distributed
+Provides:	python3-pymilvus-distributed-doc
+%description help
+
+# Milvus Python SDK
+
+[![version](https://img.shields.io/pypi/v/pymilvus.svg?color=blue)](https://pypi.org/project/pymilvus/)
+[![Downloads](https://pepy.tech/badge/pymilvus)](https://pepy.tech/project/pymilvus)
+[![Downloads](https://pepy.tech/badge/pymilvus/month)](https://pepy.tech/project/pymilvus/month)
+[![Downloads](https://pepy.tech/badge/pymilvus/week)](https://pepy.tech/project/pymilvus/week)
+[![license](https://img.shields.io/hexpm/l/plug.svg?color=green)](https://github.com/milvus-io/pymilvus/blob/master/LICENSE)
+
+Python SDK for [Milvus](https://github.com/milvus-io/milvus). To contribute code to this project, please read our [contribution guidelines](https://github.com/milvus-io/milvus/blob/master/CONTRIBUTING.md) first.
+
+For detailed SDK documentation, refer to [API Documentation](https://milvus-io.github.io/milvus-sdk-python/pythondoc/v0.3.0/index.html).
+
+
+<!-- TOC -->
+
+- [New features](#new-features)
+- [Get started](#get-started)
+    - [Prerequisites](#prerequisites)
+    - [Install pymilvus](#install-pymilvus)
+    - [Examples](#examples)
+- [Basic operations](#basic-operations)
+    - [Connect to the Milvus server](#connect-to-the-milvus-server)
+- [Create/Drop collections](#createdrop-collections)
+    - [Create a collection](#create-a-collection)
+    - [Drop a collection](#drop-a-collection)
+- [Create/Drop partitions in a collection](#createdrop-partitions-in-a-collection)
+    - [Create a partition](#create-a-partition)
+    - [Drop a partition](#drop-a-partition)
+- [Create/Drop indexes in a collection](#createdrop-indexes-in-a-collection)
+    - [Create an index](#create-an-index)
+    - [Drop an index](#drop-an-index)
+- [Insert/Delete vectors in collections/partitions](#insertdelete-entities-in-collectionspartitions)
+    - [Insert entities in a collection](#insert-entities-in-a-collection)
+    - [Insert entities in a partition](#insert-entities-in-a-partition)
+    - [Delete entities by ID](#delete-entities-by-id)
+- [Flush data in one or multiple collections to disk](#flush-data-in-one-or-multiple-collections-to-disk)
+- [Compact all segments in a collection](#compact-all-segments-in-a-collection)
+- [Search entities in collections/partitions](#search-entities-in-collectionspartitions)
+    - [Search entities in a collection](#search-entities-in-a-collection)
+    - [Search entities in a partition](#search-entities-in-a-partition)
+- [Disconnect from the Milvus server](#close-client)
+- [FAQ](#faq)
+
+<!-- /TOC -->
+
+## New features
+
+- remove `get_index_info`, the index info can be obtained by `get_collection_info`
+
+- add `get_collection_stats`, more detailed collection stats.
+
+## Get started
+
+### Prerequisites
+
+pymilvus only supports Python 3.5 or higher.
+
+### Install pymilvus
+
+You can install pymilvus via `pip` or `pip3` for Python3:
+
+```shell
+$ pip3 install pymilvus
+```
+
+The following collection shows Milvus versions and recommended pymilvus versions:
+
+|Milvus version| Recommended pymilvus version |
+|:-----:|:-----:|
+| 0.3.0 | 0.1.13|
+| 0.3.1 | 0.1.25|
+| 0.4.0 | 0.2.2 |
+| 0.5.0 | 0.2.3 |
+| 0.5.1 | 0.2.3 |
+| 0.5.2 | 0.2.3 |
+| 0.5.3 | 0.2.5 |
+| 0.6.0 | 0.2.6, 0.2.7 |
+| 0.7.0 | 0.2.8 |
+| 0.7.1 | 0.2.9 |
+| 0.8.0 | 0.2.10 |
+| 0.9.0 | 0.2.11 |
+| 0.9.1 | 0.2.12 |
+| 0.10.0 | 0.2.13 |
+| >=0.10.1, <0.11.0 | 0.2.14 |
+| 0.11.0 | 0.3.0 |
+
+
+You can install a specific version of pymilvus by:
+
+```shell
+$ pip install pymilvus==0.3.0
+```
+
+You can upgrade `pymilvus` to the latest version by:
+
+```shell
+$ pip install --upgrade pymilvus
+```
+
+### Examples
+
+Refer to [examples](/examples) for more example programs.
+
+## Basic operations
+
+### Connect to the Milvus server
+
+1. Import pymilvus.
+
+```python
+# Import pymilvus
+>>> from milvus import Milvus, DataType
+```
+
+2. Create a client to Milvus server using one of the following methods:
+
+```python
+# Connect to Milvus server
+>>> client = Milvus(host='localhost', port='19530')
+```
+
+   > Note: In the above code, default values are used for `host` and `port` parameters.
+Feel free to change them to the IP address and port you set for Milvus server.
+
+```python
+>>> client = Milvus(uri='tcp://localhost:19530')
+```
+
+## Create/Drop collections
+
+### Create a collection
+
+1. Prepare collection parameters.
+
+```python
+# create collection name
+>>> collection_name = 'test01'
+
+# create a collection of 4 fields, fields A, B and C are int type fields
+# and Vec is a float vector field.
+# segment_row_limit is default as 524288 if not specified
+>>> collection_param = {
+...    "fields": [
+...        {"name": "A", "type": DataType.INT32},
+...        {"name": "B", "type": DataType.INT32},
+...        {"name": "C", "type": DataType.INT64},
+...        {"name": "Vec", "type": DataType.FLOAT_VECTOR, "params": {"dim": 128}}
+...    ],
+...    "segment_row_limit": 4096,
+...    "auto_id": True
+... }
+```
+
+2. Create collection `test01` with dimension of 128, size of the data file for Milvus to automatically
+create indexes as 4096. If `metric_type` isn't offered, default metric type is Euclidean distance (L2).
+For `FLOAT_VECTOR` field, `dim` is a must.
+
+```python
+# Create a collection
+>>> client.create_collection(collection_name, collection_param)
+```
+
+3. You can check collection info by `get_collection_info`
+```python
+>>> info = client.get_collection_info('test01')
+>>> info
+{'fields': [
+    {'name': 'A', 'type': <DataType.INT32: 4>, 'params': {}, 'indexes': [{}]},
+    {'name': 'C', 'type': <DataType.INT64: 5>, 'params': {}, 'indexes': [{}]},
+    {'name': 'B', 'type': <DataType.INT32: 4>, 'params': {}, 'indexes': [{}]},
+    {'name': 'Vec', 'type': <DataType.FLOAT_VECTOR: 101>, 'params': {'dim': 128},
+     'indexes': [{}]}
+    ],
+ 'auto_id': True,
+ 'segment_row_limit': 4096
+}
+```
+
+You can see from the info, there is an `auto_id` option in collection info, and its `True` by default.
+So if you have your own ids and don't want auto generated ids, you may want to set `auto_id` to `False`
+while creating collections.
+
+
+
+### Drop a collection
+
+```python
+# Drop collection
+>>> status = client.drop_collection('test01')
+>>> status
+Status(code=0, message='OK')
+```
+
+## Create/Drop partitions in a collection
+
+### Create a partition
+
+You can split collections into partitions by partition tags for improved search performance.
+
+```python
+# Create partition
+>>> client.create_partition(collection_name='test01', partition_tag='tag01')
+```
+
+Use `list_partitions()` to verify whether the partition is created.
+
+```python
+# Show partitions
+>>> partitions = client.list_partitions(collection_name='test01')
+>>> partitions
+['_default', 'tag01']
+```
+
+### Drop a partition
+
+```python
+# Drop partitions
+>>> status = client.drop_partition(collection_name='test01', partition_tag='tag01')
+Status(code=0, message='OK')
+```
+
+
+## Create/Drop indexes in a collection
+
+### Create an index
+
+> Note: In production, it is recommended to create indexes before inserting vectors into the collection.
+Index is automatically built when vectors are being imported. However, you need to create the same index
+again after the vector insertion process is completed because some data files may not meet the
+`index_file_size` and index will not be automatically built for these data files.
+
+1. Create an index of `IVF_FLAT` with `nlist = 100` for the collection.
+
+```python
+# Create index
+>>> status = client.create_index('test01', "Vec", {"index_type": "IVF_FLAT", "metric_type": "L2", "params": {"nlist": 100}})
+>>> status
+Status(code=0, message='OK')   
+```
+
+2. You can check index info by `get_collection_info`
+
+```python
+>>> info = client.get_collection_info('test01')
+>>> info
+{'fields': [
+    {'name': 'A', 'type': <DataType.INT32: 4>, 'params': {}, 'indexes': [{}]},
+    {'name': 'C', 'type': <DataType.INT64: 5>, 'params': {}, 'indexes': [{}]},
+    {'name': 'B', 'type': <DataType.INT32: 4>, 'params': {}, 'indexes': [{}]},
+    {'name': 'Vec',
+        'type': <DataType.FLOAT_VECTOR: 101>,
+        'params': {'dim': 128, 'metric_type': 'L2'},
+        'indexes': [{'index_type': 'IVF_FLAT', 'metric_type': 'L2', 'params': {'nlist': 100}}]}],
+ 'auto_id': True,
+ 'segment_row_limit': 4096
+}
+```
+
+### Drop an index
+
+```python
+# Drop an index of a specific field "Vec"
+>>> status = client.drop_index('test01', "Vec")
+Status(code=0, message='OK')
+```
+
+## Insert/Delete entities in collections/partitions
+
+### Insert entities in a collection
+
+1. Generate 5000 vectors of 128 dimension and an integer list.
+
+```python
+>>> import random
+>>> num = 5000
+
+# Generate a list of integer.
+>>> list_of_int = [random.randint(0, 255) for _ in range(num)]
+# Generate 20 vectors of 128 dimension
+>>> vectors = [[random.random() for _ in range(128)] for _ in range(num)]
+```
+
+2. Create hybrid entities
+
+```python
+>>> hybrid_entities = [
+   {"name": "A", "values": list_of_int, "type": DataType.INT32},
+   {"name": "B", "values": list_of_int, "type": DataType.INT32},
+   {"name": "C", "values": list_of_int, "type": DataType.INT64},
+   {"name": "Vec", "values": vectors, "type":DataType.FLOAT_VECTOR}
+]
+```
+
+3. Insert the hybrid entities.
+
+If you create a new collection with `auto_id = True`, Milvus automatically generates IDs for the vectors.
+
+```python
+# Insert vectors
+>>> ids = client.insert('test01', hybrid_entities)
+```
+
+If you create a new collection with `auto_id = False`, you have to provide user-defined vector ids:
+
+```python
+# Generate fake custom ids
+>>> vector_ids = [id for id in range(num)]
+# Insert to the non-auto-id collection
+>>> ids = client.insert('test01', hybrid_entities, ids=vector_ids)
+```
+
+**The examples below are based on collection with `auto_id = True`.**
+
+### Insert entities in a partition
+
+```python
+>>> inserted_vector_ids = client.insert('test01', hybrid_entities, partition_tag="tag01")
+```
+
+To verify the entities you have inserted, use `get_entity_by_id()`. 
+
+```python
+>>> entities = client.get_entity_by_id(collection_name='test01', ids=inserted_vector_ids[:10])
+```
+
+### Delete entities by ID
+
+You can delete these entities by:
+
+```python
+>>> status = client.delete_entity_by_id('test01', ids[:10])
+>>> status
+Status(code=0, message='OK')
+```
+
+## Flush data in one or multiple collections to disk
+
+When performing operations related to data changes, you can flush the data from memory to disk to avoid possible data loss. Milvus also supports automatic flushing, which runs at a fixed interval to flush the data in all collections to disk. You can use the [Milvus server configuration file](https://milvus.io/docs/reference/milvus_config.md) to set the interval.
+
+```python
+>>> client.flush(['test01'])
+```
+
+## Compact all segments in a collection
+
+A segment is a data file that Milvus automatically creates by merging inserted vector data. A collection can contain multiple segments. If some vectors are deleted from a segment, the space taken by the deleted vectors cannot be released automatically. You can compact segments in a collection to release space.
+
+```python
+>>> status = client.compact('test01')
+>>> status
+Status(code=0, message='OK')
+```
+
+## Search entities in collections/partitions
+
+### Search entities in a collection
+
+1. Prepare search parameters. `"term"` and `"range"` is optional, `"params"` in `"vector"` stands for index params.
+
+
+```python
+# This dsl will search topk `entities` that are
+# close to vectors[:1] searched by `IVF_FLAT` index with `nprobe = 10` and `metric_type = L2`,
+# AND field "A" in [1, 2, 5],
+# AND field "B" greater than 1 less than 100
+>>> dsl = {
+...     "bool": {
+...         "must":[
+...             {
+...                 "term": {"A": [1, 2, 5]}
+...             },
+...             {
+...                 "range": {"B": {"GT": 1, "LT": 100}}
+...             },
+...             {
+...                 "vector": {
+...                    "Vec": {"topk": 10, "query": vectors[:1], "metric_type": "L2", "params": {"nprobe": 10}}
+...                 }
+...             }
+...         ]
+...     }
+... }
+
+```
+A search without hybrid conditions with `IVF_FLAT` index would be like:
+
+```python
+>>> dsl = {
+...     "bool": {
+...         "must":[
+...             {
+...                 "vector": {
+...                    "Vec": {"topk": 10, "query": vectors[:1], "metric_type": "L2", "params": {"nprobe": 10}}
+...                 }
+...             }
+...         ]
+...     }
+... }
+
+```
+
+A `FLAT` search doesn't need index params, so the query would be like:
+
+```python
+>>> dsl = {
+...     "bool": {
+...         "must":[
+...             {
+...                 "vector": {
+...                    "Vec": {"topk": 10, "query": vectors[0], "metric_type": "L2"}
+...                 }
+...             }
+...         ]
+...     }
+... }
+
+```
+
+
+2. Search entities.
+
+With `fields=["B"]`, not only can you get entity ids and distances, but also values of a spacific field B.
+
+```python
+# search entities and get entity field B back
+>>> results = client.search('test01', dsl, fields=["B"])
+```
+
+You can obtain ids, distances and fields by entities in results.
+
+```python
+# Results consist of number-of-query entities
+>>> entities = results[0]
+
+# Entities consists of topk entity
+>>> entity = entities[0]
+
+# You can get all ids and distances by entities
+>>> all_ids = entities.ids
+>>> all_distances = entities.distances
+
+# Or you can get them one by one by entity
+>>> a_id = entity.id
+>>> a_distance = entity.distance
+>>> a_field = entity.entity.B # getattr(entity.entity, "B")
+```
+
+> Note: If you don't provide fields in search, you will only get ids and distances.
+
+### Search entities in a partition
+
+```python
+# Search entities in a partition `tag01`
+>>> client.search(collection_name='test01', dsl=dsl, partition_tags=['tag01'])
+```
+
+> Note: If you do not specify `partition_tags`, Milvus searches the whole collection.
+
+## Close client
+
+```python
+>>> client.close()
+```
+
+## FAQ
+
+> I'm getting random "socket operation on non-socket" errors from gRPC when connecting to Milvus from an application served on Gunicorn
+
+Make sure to set the environment variable `GRPC_ENABLE_FORK_SUPPORT=1`. For reference, see https://zhuanlan.zhihu.com/p/136619485
+
+
+
+
+%prep
+%autosetup -n pymilvus-distributed-0.0.66
+
+%build
+%py3_build
+
+%install
+%py3_install
+install -d -m755 %{buildroot}/%{_pkgdocdir}
+if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
+if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
+if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
+if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
+pushd %{buildroot}
+if [ -d usr/lib ]; then
+	find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/lib64 ]; then
+	find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/bin ]; then
+	find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/sbin ]; then
+	find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+touch doclist.lst
+if [ -d usr/share/man ]; then
+	find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
+fi
+popd
+mv %{buildroot}/filelist.lst .
+mv %{buildroot}/doclist.lst .
+
+%files -n python3-pymilvus-distributed -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Mon May 15 2023 Python_Bot <Python_Bot@openeuler.org> - 0.0.66-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
index 0000000..8c52088
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+3378e701fc89797b78213f971400690d  pymilvus-distributed-0.0.66.tar.gz