diff --git a/llama_stack/providers/adapters/memory/chroma/chroma.py b/llama_stack/providers/adapters/memory/chroma/chroma.py
index f720159a5..f8af9ac5c 100644
--- a/llama_stack/providers/adapters/memory/chroma/chroma.py
+++ b/llama_stack/providers/adapters/memory/chroma/chroma.py
@@ -99,7 +99,7 @@ class ChromaMemoryAdapter(Memory):
             memory_bank.type == MemoryBankType.vector.value
         ), f"Only vector banks are supported {memory_bank.type}"
 
-        collection = await self.client.create_collection(
+        collection = await self.client.get_or_create_collection(
             name=memory_bank.identifier,
         )
         bank_index = BankWithIndex(
diff --git a/llama_stack/providers/tests/memory/test_memory.py b/llama_stack/providers/tests/memory/test_memory.py
index 4f6dadb14..1e9db2161 100644
--- a/llama_stack/providers/tests/memory/test_memory.py
+++ b/llama_stack/providers/tests/memory/test_memory.py
@@ -22,13 +22,29 @@ async def memory_impl():
 
 
 @pytest.fixture
-def sample_document():
-    return MemoryBankDocument(
-        document_id="doc1",
-        content="This is a sample document for testing.",
-        mime_type="text/plain",
-        metadata={"author": "Test Author"},
-    )
+def sample_documents():
+    return [
+        MemoryBankDocument(
+            document_id="doc1",
+            content="Python is a high-level programming language.",
+            metadata={"category": "programming", "difficulty": "beginner"},
+        ),
+        MemoryBankDocument(
+            document_id="doc2",
+            content="Machine learning is a subset of artificial intelligence.",
+            metadata={"category": "AI", "difficulty": "advanced"},
+        ),
+        MemoryBankDocument(
+            document_id="doc3",
+            content="Data structures are fundamental to computer science.",
+            metadata={"category": "computer science", "difficulty": "intermediate"},
+        ),
+        MemoryBankDocument(
+            document_id="doc4",
+            content="Neural networks are inspired by biological neural networks.",
+            metadata={"category": "AI", "difficulty": "advanced"},
+        ),
+    ]
 
 
 async def register_memory_bank(memory_impl: Memory):
@@ -44,17 +60,44 @@ async def register_memory_bank(memory_impl: Memory):
 
 
 @pytest.mark.asyncio
-async def test_query_documents(memory_impl, sample_document):
+async def test_query_documents(memory_impl, sample_documents):
     with pytest.raises(ValueError):
-        await memory_impl.insert_documents("test_bank", [sample_document])
+        await memory_impl.insert_documents("test_bank", sample_documents)
 
     await register_memory_bank(memory_impl)
-    await memory_impl.insert_documents("test_bank", [sample_document])
+    await memory_impl.insert_documents("test_bank", sample_documents)
 
-    query = ["sample ", "document"]
-    response = await memory_impl.query_documents("test_bank", query)
+    query1 = "programming language"
+    response1 = await memory_impl.query_documents("test_bank", query1)
+    assert_valid_response(response1)
+    assert any("Python" in chunk.content for chunk in response1.chunks)
 
+    # Test case 3: Query with semantic similarity
+    query3 = "AI and brain-inspired computing"
+    response3 = await memory_impl.query_documents("test_bank", query3)
+    assert_valid_response(response3)
+    assert any("neural networks" in chunk.content.lower() for chunk in response3.chunks)
+
+    # Test case 4: Query with limit on number of results
+    query4 = "computer"
+    params4 = {"max_chunks": 2}
+    response4 = await memory_impl.query_documents("test_bank", query4, params4)
+    assert_valid_response(response4)
+    assert len(response4.chunks) <= 2
+
+    # Test case 5: Query with threshold on similarity score
+    query5 = "quantum computing"  # Not directly related to any document
+    params5 = {"score_threshold": 0.5}
+    response5 = await memory_impl.query_documents("test_bank", query5, params5)
+    assert_valid_response(response5)
+    assert all(score >= 0.5 for score in response5.scores)
+
+
+def assert_valid_response(response: QueryDocumentsResponse):
     assert isinstance(response, QueryDocumentsResponse)
     assert len(response.chunks) > 0
     assert len(response.scores) > 0
     assert len(response.chunks) == len(response.scores)
+    for chunk in response.chunks:
+        assert isinstance(chunk.content, str)
+        assert chunk.document_id is not None