Merge pull request #23 from longdafeng/master

Fixed #21, [Enhancement]: remove old python sdk demo in the Readme

Author: LINxiansheng

README.md

@@ -69,7 +69,7 @@
 ### Hybrid Search
 1. Supports multi-channel retrieval in a single SQL query, combining vector-based semantic search with keyword-based search for optimized recall.
 2. Query reranking supports weighted scores, Reciprocal Rank Fusion (RRF), and LLM-based reranking for enhanced results.
-3. Scalar filters are pushed down to storage for optimized performance, and multi-table joins allow related data retrieval.
+3. Relational filters are pushed down to storage for optimized performance, and multi-table joins allow related data retrieval.
 
 ### Vector & Full-Text Search
 1. Supports dense vectors and sparse vectors, with multiple distance metrics including Manhattan, Euclidean, inner product, and cosine similarity.
@@ -137,7 +137,7 @@ Quickly experience OceanBase Cloud on AWS Marketplace - a highly scalable distri
 Build a semantic search system in 5 minutes:
 
 <details>
-<summary><b>🗄️ 🐍 New Python SDK</b></summary>
+<summary><b>🗄️ 🐍 Python SDK</b></summary>
 
 ```bash
 # install sdk first
@@ -265,66 +265,6 @@ print(f"\nDeleted collection '{collection_name}'")
 Please refer to the [User Guide](https://github.com/oceanbase/pyseekdb) for more details.
 </details>
 
-<details>
-<summary><b>🗄️ 🐍 Old Python SDK</b></summary>
-
-```bash
-# install old sdk first, not recommended
-pip install -U pyobvector
-```
-
-```python
-from pyobvector import *
-
-# Don't support Embedded mode
-client = MilvusLikeClient(uri="127.0.0.1:2881", user="test@test")
-
-test_collection_name = "ann_test"
-# define the schema of collection with optional partitions
-range_part = ObRangePartition(False, range_part_infos = [
-    RangeListPartInfo('p0', 100),
-    RangeListPartInfo('p1', 'maxvalue'),
-], range_expr='id')
-schema = client.create_schema(partitions=range_part)
-# define field schema of collection
-schema.add_field(field_name="id", datatype=DataType.INT64, is_primary=True)
-schema.add_field(field_name="embedding", datatype=DataType.FLOAT_VECTOR, dim=3)
-schema.add_field(field_name="meta", datatype=DataType.JSON, nullable=True)
-# define index parameters
-idx_params = client.prepare_index_params()
-idx_params.add_index(
-    field_name='embedding',
-    index_type=VecIndexType.HNSW,
-    index_name='vidx',
-    metric_type="L2",
-    params={"M": 16, "efConstruction": 256},
-)
-# create collection
-client.create_collection(
-    collection_name=test_collection_name,
-    schema=schema,
-    index_params=idx_params,
-)
-
-# insert value
-# prepare data
-vector_value1 = [0.748479, 0.276979, 0.555195]
-vector_value2 = [0, 0, 0]
-data1 = [{'id': i, 'embedding': vector_value1} for i in range(10)]
-data1.extend([{'id': i, 'embedding': vector_value2} for i in range(10, 13)])
-data1.extend([{'id': i, 'embedding': vector_value2} for i in range(111, 113)])
-# insert data
-client.insert(collection_name=test_collection_name, data=data1)
-
-# do search
-res = client.search(collection_name=test_collection_name, data=[0, 0, 0], anns_field='embedding', limit=5, output_fields=['id'])
-# For example, the result will be:
-# [{'id': 112}, {'id': 111}, {'id': 10}, {'id': 11}, {'id': 12}]
-
-```
-Please refer to the [User Guide](https://github.com/oceanbase/pyobvector) for more details.
-</details>
-
 <details>
 <summary><b>🗄️ SQL</b></summary>
 

README_CN.md

@@ -146,7 +146,7 @@ rpm -ivh seekdb-1.x.x.x-xxxxxxx.el8.x86_64.rpm
 在 5 分钟内构建语义搜索系统：
 
 <details>
-<summary><b>🗄️ 🐍 新版 Python SDK</b></summary>
+<summary><b>🗄️ 🐍 Python SDK</b></summary>
 
 ```bash
 # install sdk first
@@ -273,66 +273,6 @@ print(f"\nDeleted collection '{collection_name}'")
 更多详情请参考[用户指南](https://github.com/oceanbase/pyseekdb)。
 </details>
 
-<details>
-<summary><b>🗄️ 🐍 旧版 Python SDK</b></summary>
-
-```bash
-# install old sdk first, not recommended
-pip install -U pyobvector
-```
-
-```python
-from pyobvector import *
-
-# Don't support Embedded mode
-client = MilvusLikeClient(uri="127.0.0.1:2881", user="test@test")
-
-test_collection_name = "ann_test"
-# define the schema of collection with optional partitions
-range_part = ObRangePartition(False, range_part_infos = [
-    RangeListPartInfo('p0', 100),
-    RangeListPartInfo('p1', 'maxvalue'),
-], range_expr='id')
-schema = client.create_schema(partitions=range_part)
-# define field schema of collection
-schema.add_field(field_name="id", datatype=DataType.INT64, is_primary=True)
-schema.add_field(field_name="embedding", datatype=DataType.FLOAT_VECTOR, dim=3)
-schema.add_field(field_name="meta", datatype=DataType.JSON, nullable=True)
-# define index parameters
-idx_params = client.prepare_index_params()
-idx_params.add_index(
-    field_name='embedding',
-    index_type=VecIndexType.HNSW,
-    index_name='vidx',
-    metric_type="L2",
-    params={"M": 16, "efConstruction": 256},
-)
-# create collection
-client.create_collection(
-    collection_name=test_collection_name,
-    schema=schema,
-    index_params=idx_params,
-)
-
-# insert value
-# prepare data
-vector_value1 = [0.748479, 0.276979, 0.555195]
-vector_value2 = [0, 0, 0]
-data1 = [{'id': i, 'embedding': vector_value1} for i in range(10)]
-data1.extend([{'id': i, 'embedding': vector_value2} for i in range(10, 13)])
-data1.extend([{'id': i, 'embedding': vector_value2} for i in range(111, 113)])
-# insert data
-client.insert(collection_name=test_collection_name, data=data1)
-
-# do search
-res = client.search(collection_name=test_collection_name, data=[0, 0, 0], anns_field='embedding', limit=5, output_fields=['id'])
-# For example, the result will be:
-# [{'id': 112}, {'id': 111}, {'id': 10}, {'id': 11}, {'id': 12}]
-
-```
-更多详情请参考[用户指南](https://github.com/oceanbase/pyobvector)。
-</details>
-
 <details>
 <summary><b>🗄️ SQL</b></summary>