chore: initialize sandbox and overwrite remote content

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)

examples/functionality/vector_store/milvus_lite/main.py

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+# -*- coding: utf-8 -*-
+"""Example of using MilvusLiteStore in AgentScope RAG system."""
+import asyncio
+from agentscope.rag import (
+    MilvusLiteStore,
+    Document,
+    DocMetadata,
+)
+from agentscope.message import TextBlock
+
+
+async def example_basic_operations() -> None:
+    """The example of basic CRUD operations with MilvusLiteStore."""
+    print("\n" + "=" * 60)
+    print("Test 1: Basic CRUD Operations")
+    print("=" * 60)
+
+    # Initialize MilvusLiteStore with a local file
+    store = MilvusLiteStore(
+        uri="./milvus_test.db",
+        collection_name="test_collection",
+        dimensions=4,  # Small dimension for testing
+        distance="COSINE",
+    )
+
+    print("✓ MilvusLiteStore initialized")
+
+    # Create test documents with embeddings
+    test_docs = [
+        Document(
+            metadata=DocMetadata(
+                content=TextBlock(
+                    text="Artificial Intelligence is the future",
+                ),
+                doc_id="doc_1",
+                chunk_id=0,
+                total_chunks=1,
+            ),
+            embedding=[0.1, 0.2, 0.3, 0.4],
+        ),
+        Document(
+            metadata=DocMetadata(
+                content=TextBlock(text="Machine Learning is a subset of AI"),
+                doc_id="doc_2",
+                chunk_id=0,
+                total_chunks=1,
+            ),
+            embedding=[0.2, 0.3, 0.4, 0.5],
+        ),
+        Document(
+            metadata=DocMetadata(
+                content=TextBlock(text="Deep Learning uses neural networks"),
+                doc_id="doc_3",
+                chunk_id=0,
+                total_chunks=1,
+            ),
+            embedding=[0.3, 0.4, 0.5, 0.6],
+        ),
+    ]
+
+    # Test add operation
+    await store.add(test_docs)
+    print(f"✓ Added {len(test_docs)} documents to the store")
+
+    # Test search operation
+    query_embedding = [0.15, 0.25, 0.35, 0.45]
+    results = await store.search(
+        query_embedding=query_embedding,
+        limit=2,
+    )
+
+    print(f"\n✓ Search completed, found {len(results)} results:")
+    for i, result in enumerate(results, 1):
+        print(f"  {i}. Score: {result.score:.4f}")
+        print(f"     Content: {result.metadata.content}")
+        print(f"     Doc ID: {result.metadata.doc_id}")
+
+    # Test search with score threshold
+    results_filtered = await store.search(
+        query_embedding=query_embedding,
+        limit=5,
+        score_threshold=0.9,
+    )
+    print(f"\n✓ Search with threshold (>0.9): {len(results_filtered)} results")
+
+    # Test delete operation
+    # Note: We need to use filter expression to delete by doc_id
+    await store.delete(filter='doc_id == "doc_2"')
+    print("\n✓ Deleted document with doc_id='doc_2'")
+
+    # Verify deletion
+    results_after_delete = await store.search(
+        query_embedding=query_embedding,
+        limit=5,
+    )
+    print(f"✓ After deletion: {len(results_after_delete)} documents remain")
+
+    # Get client for advanced operations
+    client = store.get_client()
+    print(f"\n✓ Got MilvusClient: {type(client).__name__}")
+
+
+async def example_filter_search() -> None:
+    """The example of search with metadata filtering."""
+    print("\n" + "=" * 60)
+    print("Test 2: Search with Metadata Filtering")
+    print("=" * 60)
+
+    store = MilvusLiteStore(
+        uri="./milvus_filter_test.db",
+        collection_name="filter_collection",
+        dimensions=4,
+        distance="COSINE",
+    )
+
+    # Create documents with different categories
+    docs = [
+        Document(
+            metadata=DocMetadata(
+                content=TextBlock(text="Python is a programming language"),
+                doc_id="prog_1",
+                chunk_id=0,
+                total_chunks=1,
+            ),
+            embedding=[0.1, 0.2, 0.3, 0.4],
+        ),
+        Document(
+            metadata=DocMetadata(
+                content=TextBlock(
+                    text="Java is used for enterprise applications",
+                ),
+                doc_id="prog_2",
+                chunk_id=0,
+                total_chunks=1,
+            ),
+            embedding=[0.2, 0.3, 0.4, 0.5],
+        ),
+        Document(
+            metadata=DocMetadata(
+                content=TextBlock(text="Neural networks are used in AI"),
+                doc_id="ai_1",
+                chunk_id=0,
+                total_chunks=1,
+            ),
+            embedding=[0.3, 0.4, 0.5, 0.6],
+        ),
+        Document(
+            metadata=DocMetadata(
+                content=TextBlock(text="Deep learning requires GPUs"),
+                doc_id="ai_2",
+                chunk_id=0,
+                total_chunks=1,
+            ),
+            embedding=[0.4, 0.5, 0.6, 0.7],
+        ),
+    ]
+
+    await store.add(docs)
+    print(f"✓ Added {len(docs)} documents with different doc_id prefixes")
+
+    # Search without filter
+    query_embedding = [0.25, 0.35, 0.45, 0.55]
+    all_results = await store.search(
+        query_embedding=query_embedding,
+        limit=4,
+    )
+    print(f"\n✓ Search without filter: {len(all_results)} results")
+    for i, result in enumerate(all_results, 1):
+        doc_id = result.metadata.doc_id
+        score = result.score
+        print(f"  {i}. Doc ID: {doc_id}, Score: {score:.4f}")
+
+    # Search with filter for programming docs
+    prog_results = await store.search(
+        query_embedding=query_embedding,
+        limit=4,
+        filter='doc_id like "prog%"',
+    )
+    filter_msg = "doc_id like 'prog%'"
+    print(f"\n✓ Search with filter ({filter_msg}): {len(prog_results)}")
+    for i, result in enumerate(prog_results, 1):
+        doc_id = result.metadata.doc_id
+        score = result.score
+        print(f"  {i}. Doc ID: {doc_id}, Score: {score:.4f}")
+
+    # Search with filter for AI docs
+    ai_results = await store.search(
+        query_embedding=query_embedding,
+        limit=4,
+        filter='doc_id like "ai%"',
+    )
+    filter_msg = "doc_id like 'ai%'"
+    print(f"\n✓ Search with filter ({filter_msg}): {len(ai_results)}")
+    for i, result in enumerate(ai_results, 1):
+        doc_id = result.metadata.doc_id
+        score = result.score
+        print(f"  {i}. Doc ID: {doc_id}, Score: {score:.4f}")
+
+
+async def example_multiple_chunks() -> None:
+    """The example of documents with multiple chunks."""
+    print("\n" + "=" * 60)
+    print("Test 3: Documents with Multiple Chunks")
+    print("=" * 60)
+
+    store = MilvusLiteStore(
+        uri="./milvus_chunks_test.db",
+        collection_name="chunks_collection",
+        dimensions=4,
+        distance="COSINE",
+    )
+
+    # Create a document split into multiple chunks
+    chunks = [
+        Document(
+            metadata=DocMetadata(
+                content=TextBlock(text="Chapter 1: Introduction to AI"),
+                doc_id="book_1",
+                chunk_id=0,
+                total_chunks=3,
+            ),
+            embedding=[0.1, 0.2, 0.3, 0.4],
+        ),
+        Document(
+            metadata=DocMetadata(
+                content=TextBlock(text="Chapter 2: Machine Learning Basics"),
+                doc_id="book_1",
+                chunk_id=1,
+                total_chunks=3,
+            ),
+            embedding=[0.2, 0.3, 0.4, 0.5],
+        ),
+        Document(
+            metadata=DocMetadata(
+                content=TextBlock(text="Chapter 3: Deep Learning Advanced"),
+                doc_id="book_1",
+                chunk_id=2,
+                total_chunks=3,
+            ),
+            embedding=[0.3, 0.4, 0.5, 0.6],
+        ),
+    ]
+
+    await store.add(chunks)
+    print(f"✓ Added document with {len(chunks)} chunks")
+
+    # Search and verify chunk information
+    query_embedding = [0.2, 0.3, 0.4, 0.5]
+    results = await store.search(
+        query_embedding=query_embedding,
+        limit=3,
+    )
+
+    print("\n✓ Search results for multi-chunk document:")
+    for i, result in enumerate(results, 1):
+        chunk_info = (
+            f"{result.metadata.chunk_id}/{result.metadata.total_chunks}"
+        )
+        print(f"  {i}. Chunk {chunk_info}")
+        print(f"     Content: {result.metadata.content}")
+        print(f"     Score: {result.score:.4f}")
+
+
+async def example_distance_metrics() -> None:
+    """The example of different distance metrics."""
+    print("\n" + "=" * 60)
+    print("Test 4: Different Distance Metrics")
+    print("=" * 60)
+
+    # Test with different metrics
+    metrics = ["COSINE", "L2", "IP"]
+
+    for metric in metrics:
+        print(f"\n--- Testing {metric} metric ---")
+        store = MilvusLiteStore(
+            uri=f"./milvus_{metric.lower()}_test.db",
+            collection_name=f"{metric.lower()}_collection",
+            dimensions=4,
+            distance=metric,
+        )
+
+        docs = [
+            Document(
+                metadata=DocMetadata(
+                    content=TextBlock(text=f"Test doc for {metric}"),
+                    doc_id=f"doc_{metric}_1",
+                    chunk_id=0,
+                    total_chunks=1,
+                ),
+                embedding=[0.1, 0.2, 0.3, 0.4],
+            ),
+        ]
+
+        await store.add(docs)
+        results = await store.search(
+            query_embedding=[0.1, 0.2, 0.3, 0.4],
+            limit=1,
+        )
+
+        print(f"✓ {metric} metric: Score = {results[0].score:.4f}")
+
+
+async def main() -> None:
+    """Run all example."""
+    print("\n" + "=" * 60)
+    print("MilvusLiteStore Comprehensive Test Suite")
+    print("=" * 60)
+
+    try:
+        await example_basic_operations()
+        await example_filter_search()
+        await example_multiple_chunks()
+        await example_distance_metrics()
+
+        print("\n" + "=" * 60)
+        print("✓ All tests completed successfully!")
+        print("=" * 60)
+
+    except Exception as e:
+        print(f"\n✗ Test failed with error: {e}")
+        import traceback
+
+        traceback.print_exc()
+
+
+if __name__ == "__main__":
+    asyncio.run(main())