feat(rag/store): add OceanBaseStore as a vdb choice

examples/functionality/vector_store/oceanbase/README.md

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+# OceanBase Vector Store
+
+This example demonstrates how to use **OceanBaseStore** for vector storage and semantic search in AgentScope.
+It includes CRUD operations, metadata filtering, document chunking, and distance metric tests.
+
+### Quick Start
+
+Install dependencies (including `pyobvector`):
+
+```bash
+pip install -e .[full]
+```
+
+Start seekdb (a minimal OceanBase-compatible instance):
+
+```bash
+docker run -d -p 2881:2881 oceanbase/seekdb
+```
+
+Run the example script:
+
+```bash
+python main.py
+```
+
+> **Note:** The script defaults to `127.0.0.1:2881`, user `root`, database `test`.
+> If you use a multi-tenant OceanBase account (e.g., `root@test`), override via environment variables.
+
+## Usage
+
+### Initialize Store
+
+```python
+from agentscope.rag import OceanBaseStore
+
+store = OceanBaseStore(
+    collection_name="test_collection",
+    dimensions=768,
+    distance="COSINE",
+    client_kwargs={
+        "uri": "127.0.0.1:2881",
+        "user": "root",
+        "password": "",
+        "db_name": "test",
+    },
+)
+```
+
+### Add Documents
+
+```python
+from agentscope.rag import Document, DocMetadata
+from agentscope.message import TextBlock
+
+doc = Document(
+    metadata=DocMetadata(
+        content=TextBlock(type="text", text="Your document text"),
+        doc_id="doc_1",
+        chunk_id=0,
+        total_chunks=1,
+    ),
+    embedding=[0.1, 0.2, 0.3],
+)
+
+await store.add([doc])
+```
+
+### Search
+
+```python
+results = await store.search(
+    query_embedding=[0.1, 0.2, 0.3],
+    limit=5,
+    score_threshold=0.9,
+)
+```
+
+### Filter Search
+
+```python
+client = store.get_client()
+table = client.load_table(collection_name="test_collection")
+
+results = await store.search(
+    query_embedding=[0.1, 0.2, 0.3],
+    limit=5,
+    flter=[table.c["doc_id"].like("doc%")],
+)
+```
+
+### Delete
+
+```python
+client = store.get_client()
+table = client.load_table(collection_name="test_collection")
+
+await store.delete(where=[table.c["doc_id"] == "doc_1"])
+```
+
+## Distance Metrics
+
+| Metric | Description | Best For |
+|--------|-------------|----------|
+| **COSINE** | Cosine similarity | Text embeddings (recommended) |
+| **L2** | Euclidean distance | Spatial data |
+| **IP** | Inner product | Recommendation systems |
+
+## Filter Expressions
+
+Build filters using SQLAlchemy expressions and pass them via `flter`:
+
+```python
+table = store.get_client().load_table("test_collection")
+
+filters = [
+    table.c["doc_id"] == "doc_1",
+    table.c["doc_id"].like("prefix%"),
+    table.c["chunk_id"] >= 0,
+]
+```
+
+## Advanced Usage
+
+### Access Underlying Client
+
+```python
+client = store.get_client()
+stats = client.get_collection_stats(collection_name="test_collection")
+```
+
+### Document Metadata
+
+- `content`: Text content (TextBlock)
+- `doc_id`: Unique document identifier
+- `chunk_id`: Chunk position (0-indexed)
+- `total_chunks`: Total chunks in document
+
+## FAQ
+
+**What embedding dimension should I use?**
+Match your embedding model's output dimension (e.g., 768 for BERT, 1536 for OpenAI ada-002).
+
+**Can I change the distance metric after creation?**
+No, create a new collection with the desired metric.
+
+**How do I clean up test data?**
+Drop the collection via the underlying client or remove the seekdb container volume.
+
+## Environment Variables
+
+The script supports the following environment variables to override connection settings:
+
+```bash
+export OCEANBASE_URI="127.0.0.1:2881"
+export OCEANBASE_USER="root"
+export OCEANBASE_PASSWORD=""
+export OCEANBASE_DB="test"
+```
+
+## References
+
+- [OceanBase Vector Store](https://github.com/oceanbase/pyobvector)
+- [AgentScope RAG Tutorial](https://doc.agentscope.io/tutorial/task_rag.html)