Add support for milvus lite vector database in the RAG module of AgentScope

examples/functionality/vector_store/milvus_lite/README.md

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+# MilvusLite Vector Store
+
+This example demonstrates how to use **MilvusLiteStore** for vector storage and semantic search in AgentScope.
+It includes four test scenarios covering CRUD operations, metadata filtering, document chunking, and distance metrics.
+
+### Quick Start
+
+Install agentscope first, and then the MilvusLite dependency:
+
+```bash
+# In MacOS/Linux
+pip install pymilvus\[milvus_lite\]
+
+# In Windows
+pip install pymilvus[milvus_lite]
+```
+
+Run the example script, which showcases adding, searching with/without filters in MilvusLite vector store:
+
+```bash
+python milvuslite_store.py
+```
+
+> **Note:** The script creates `.db` files in the current directory. You can delete them after testing.
+
+## Usage
+
+### Initialize Store
+```python
+from agentscope.rag import MilvusLiteStore
+
+store = MilvusLiteStore(
+    uri="./milvus_test.db",
+    collection_name="test_collection",
+    dimensions=768,              # Match your embedding model
+    distance="COSINE",           # COSINE, L2, or IP
+)
+```
+
+### 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, ...],  # Your embedding vector
+)
+
+await store.add([doc])
+```
+
+### Search
+
+```python
+results = await store.search(
+    query_embedding=[0.15, 0.25, ...],
+    limit=5,
+    score_threshold=0.9,                # Optional
+    filter='doc_id like "prefix%"',     # Optional
+)
+```
+
+### Delete
+
+```python
+await store.delete(filter_expr='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
+
+```python
+# Exact match
+filter='doc_id == "doc_1"'
+
+# Pattern matching
+filter='doc_id like "prefix%"'
+
+# Numeric and logical operators
+filter='chunk_id >= 0 and total_chunks > 1'
+```
+
+## 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 delete the database?**
+Delete the `.db` file specified in the `uri` parameter.
+
+**Is this suitable for production?**
+MilvusLite works well for development and small-scale applications. For production at scale, consider Milvus standalone or cluster mode.
+
+## References
+
+- [Milvus Documentation](https://milvus.io/docs)
+- [AgentScope RAG Tutorial](https://doc.agentscope.io/tutorial/task_rag.html)