[memory refactor][4/n] Update the client-sdk test for RAG (#834)

See https://github.com/meta-llama/llama-stack/issues/827 for the broader
design.

Update client-sdk tests

tests/client-sdk/agents/test_agents.py

@@ -286,19 +286,16 @@ def test_rag_agent(llama_stack_client, agent_config):
         )
         for i, url in enumerate(urls)
     ]
-    memory_bank_id = "test-memory-bank"
-    llama_stack_client.memory_banks.register(
-        memory_bank_id=memory_bank_id,
-        params={
-            "memory_bank_type": "vector",
-            "embedding_model": "all-MiniLM-L6-v2",
-            "chunk_size_in_tokens": 512,
-            "overlap_size_in_tokens": 64,
-        },
+    vector_db_id = "test-vector-db"
+    llama_stack_client.vector_dbs.register(
+        vector_db_id=vector_db_id,
+        embedding_model="all-MiniLM-L6-v2",
+        embedding_dimension=384,
     )
-    llama_stack_client.memory.insert(
-        bank_id=memory_bank_id,
+    llama_stack_client.tool_runtime.rag_tool.insert_documents(
         documents=documents,
+        vector_db_id=vector_db_id,
+        chunk_size_in_tokens=512,
     )
     agent_config = {
         **agent_config,
@@ -306,7 +303,7 @@ def test_rag_agent(llama_stack_client, agent_config):
             dict(
                 name="builtin::memory",
                 args={
-                    "memory_bank_ids": [memory_bank_id],
+                    "vector_db_ids": [vector_db_id],
                 },
             )
         ],
@@ -324,4 +321,4 @@ def test_rag_agent(llama_stack_client, agent_config):
         )
         logs = [str(log) for log in EventLogger().log(response) if log is not None]
         logs_str = "".join(logs)
-        assert "Tool:query_memory" in logs_str
+        assert "Tool:rag_tool.query_context" in logs_str

tests/client-sdk/tool_runtime/test_rag_tool.py

@@ -0,0 +1,180 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+import random
+
+import pytest
+
+from llama_stack_client.types.tool_runtime import DocumentParam
+
+
+@pytest.fixture(scope="function")
+def empty_vector_db_registry(llama_stack_client):
+    vector_dbs = [
+        vector_db.identifier for vector_db in llama_stack_client.vector_dbs.list()
+    ]
+    for vector_db_id in vector_dbs:
+        llama_stack_client.vector_dbs.unregister(vector_db_id=vector_db_id)
+
+
+@pytest.fixture(scope="function")
+def single_entry_vector_db_registry(llama_stack_client, empty_vector_db_registry):
+    vector_db_id = f"test_vector_db_{random.randint(1000, 9999)}"
+    llama_stack_client.vector_dbs.register(
+        vector_db_id=vector_db_id,
+        embedding_model="all-MiniLM-L6-v2",
+        embedding_dimension=384,
+        provider_id="faiss",
+    )
+    vector_dbs = [
+        vector_db.identifier for vector_db in llama_stack_client.vector_dbs.list()
+    ]
+    return vector_dbs
+
+
+@pytest.fixture(scope="session")
+def sample_documents():
+    return [
+        DocumentParam(
+            document_id="test-doc-1",
+            content="Python is a high-level programming language.",
+            metadata={"category": "programming", "difficulty": "beginner"},
+        ),
+        DocumentParam(
+            document_id="test-doc-2",
+            content="Machine learning is a subset of artificial intelligence.",
+            metadata={"category": "AI", "difficulty": "advanced"},
+        ),
+        DocumentParam(
+            document_id="test-doc-3",
+            content="Data structures are fundamental to computer science.",
+            metadata={"category": "computer science", "difficulty": "intermediate"},
+        ),
+        DocumentParam(
+            document_id="test-doc-4",
+            content="Neural networks are inspired by biological neural networks.",
+            metadata={"category": "AI", "difficulty": "advanced"},
+        ),
+    ]
+
+
+def assert_valid_response(response):
+    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)
+
+
+def test_vector_db_insert_inline_and_query(
+    llama_stack_client, single_entry_vector_db_registry, sample_documents
+):
+    vector_db_id = single_entry_vector_db_registry[0]
+    llama_stack_client.tool_runtime.rag_tool.insert_documents(
+        documents=sample_documents,
+        chunk_size_in_tokens=512,
+        vector_db_id=vector_db_id,
+    )
+
+    # Query with a direct match
+    query1 = "programming language"
+    response1 = llama_stack_client.vector_io.query(
+        vector_db_id=vector_db_id,
+        query=query1,
+    )
+    assert_valid_response(response1)
+    assert any("Python" in chunk.content for chunk in response1.chunks)
+
+    # Query with semantic similarity
+    query2 = "AI and brain-inspired computing"
+    response2 = llama_stack_client.vector_io.query(
+        vector_db_id=vector_db_id,
+        query=query2,
+    )
+    assert_valid_response(response2)
+    assert any("neural networks" in chunk.content.lower() for chunk in response2.chunks)
+
+    # Query with limit on number of results (max_chunks=2)
+    query3 = "computer"
+    response3 = llama_stack_client.vector_io.query(
+        vector_db_id=vector_db_id,
+        query=query3,
+        params={"max_chunks": 2},
+    )
+    assert_valid_response(response3)
+    assert len(response3.chunks) <= 2
+
+    # Query with threshold on similarity score
+    query4 = "computer"
+    response4 = llama_stack_client.vector_io.query(
+        vector_db_id=vector_db_id,
+        query=query4,
+        params={"score_threshold": 0.01},
+    )
+    assert_valid_response(response4)
+    assert all(score >= 0.01 for score in response4.scores)
+
+
+def test_vector_db_insert_from_url_and_query(
+    llama_stack_client, empty_vector_db_registry
+):
+    providers = [p for p in llama_stack_client.providers.list() if p.api == "vector_io"]
+    assert len(providers) > 0
+
+    vector_db_id = "test_vector_db"
+
+    llama_stack_client.vector_dbs.register(
+        vector_db_id=vector_db_id,
+        embedding_model="all-MiniLM-L6-v2",
+        embedding_dimension=384,
+        provider_id="faiss",
+    )
+
+    # list to check memory bank is successfully registered
+    available_vector_dbs = [
+        vector_db.identifier for vector_db in llama_stack_client.vector_dbs.list()
+    ]
+    assert vector_db_id in available_vector_dbs
+
+    # URLs of documents to insert
+    # TODO: Move to test/memory/resources then update the url to
+    # https://raw.githubusercontent.com/meta-llama/llama-stack/main/tests/memory/resources/{url}
+    urls = [
+        "memory_optimizations.rst",
+        "chat.rst",
+        "llama3.rst",
+    ]
+    documents = [
+        DocumentParam(
+            document_id=f"num-{i}",
+            content=f"https://raw.githubusercontent.com/pytorch/torchtune/main/docs/source/tutorials/{url}",
+            mime_type="text/plain",
+            metadata={},
+        )
+        for i, url in enumerate(urls)
+    ]
+
+    llama_stack_client.tool_runtime.rag_tool.insert_documents(
+        documents=documents,
+        vector_db_id=vector_db_id,
+        chunk_size_in_tokens=512,
+    )
+
+    # Query for the name of method
+    response1 = llama_stack_client.vector_io.query(
+        vector_db_id=vector_db_id,
+        query="What's the name of the fine-tunning method used?",
+    )
+    assert_valid_response(response1)
+    assert any("lora" in chunk.content.lower() for chunk in response1.chunks)
+
+    # Query for the name of model
+    response2 = llama_stack_client.vector_io.query(
+        vector_db_id=vector_db_id,
+        query="Which Llama model is mentioned?",
+    )
+    assert_valid_response(response2)
+    assert any("llama2" in chunk.content.lower() for chunk in response2.chunks)

tests/client-sdk/vector_io/test_vector_io.py

@@ -8,251 +8,82 @@ import random
 
 import pytest
 
-from llama_stack.apis.memory import MemoryBankDocument
-from llama_stack_client.types.memory_insert_params import Document
-
 
 @pytest.fixture(scope="function")
-def empty_memory_bank_registry(llama_stack_client):
-    memory_banks = [
-        memory_bank.identifier for memory_bank in llama_stack_client.memory_banks.list()
+def empty_vector_db_registry(llama_stack_client):
+    vector_dbs = [
+        vector_db.identifier for vector_db in llama_stack_client.vector_dbs.list()
     ]
-    for memory_bank_id in memory_banks:
-        llama_stack_client.memory_banks.unregister(memory_bank_id=memory_bank_id)
+    for vector_db_id in vector_dbs:
+        llama_stack_client.vector_dbs.unregister(vector_db_id=vector_db_id)
 
 
 @pytest.fixture(scope="function")
-def single_entry_memory_bank_registry(llama_stack_client, empty_memory_bank_registry):
-    memory_bank_id = f"test_bank_{random.randint(1000, 9999)}"
-    llama_stack_client.memory_banks.register(
-        memory_bank_id=memory_bank_id,
-        params={
-            "memory_bank_type": "vector",
-            "embedding_model": "all-MiniLM-L6-v2",
-            "chunk_size_in_tokens": 512,
-            "overlap_size_in_tokens": 64,
-        },
+def single_entry_vector_db_registry(llama_stack_client, empty_vector_db_registry):
+    vector_db_id = f"test_vector_db_{random.randint(1000, 9999)}"
+    llama_stack_client.vector_dbs.register(
+        vector_db_id=vector_db_id,
+        embedding_model="all-MiniLM-L6-v2",
+        embedding_dimension=384,
         provider_id="faiss",
     )
-    memory_banks = [
-        memory_bank.identifier for memory_bank in llama_stack_client.memory_banks.list()
+    vector_dbs = [
+        vector_db.identifier for vector_db in llama_stack_client.vector_dbs.list()
     ]
-    return memory_banks
+    return vector_dbs
 
 
-@pytest.fixture(scope="session")
-def sample_documents():
-    return [
-        MemoryBankDocument(
-            document_id="test-doc-1",
-            content="Python is a high-level programming language.",
-            metadata={"category": "programming", "difficulty": "beginner"},
-        ),
-        MemoryBankDocument(
-            document_id="test-doc-2",
-            content="Machine learning is a subset of artificial intelligence.",
-            metadata={"category": "AI", "difficulty": "advanced"},
-        ),
-        MemoryBankDocument(
-            document_id="test-doc-3",
-            content="Data structures are fundamental to computer science.",
-            metadata={"category": "computer science", "difficulty": "intermediate"},
-        ),
-        MemoryBankDocument(
-            document_id="test-doc-4",
-            content="Neural networks are inspired by biological neural networks.",
-            metadata={"category": "AI", "difficulty": "advanced"},
-        ),
-    ]
-
-
-def assert_valid_response(response):
-    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
-
-
-def test_memory_bank_retrieve(llama_stack_client, empty_memory_bank_registry):
+def test_vector_db_retrieve(llama_stack_client, empty_vector_db_registry):
     # Register a memory bank first
-    memory_bank_id = f"test_bank_{random.randint(1000, 9999)}"
-    llama_stack_client.memory_banks.register(
-        memory_bank_id=memory_bank_id,
-        params={
-            "memory_bank_type": "vector",
-            "embedding_model": "all-MiniLM-L6-v2",
-            "chunk_size_in_tokens": 512,
-            "overlap_size_in_tokens": 64,
-        },
+    vector_db_id = f"test_vector_db_{random.randint(1000, 9999)}"
+    llama_stack_client.vector_dbs.register(
+        vector_db_id=vector_db_id,
+        embedding_model="all-MiniLM-L6-v2",
+        embedding_dimension=384,
         provider_id="faiss",
     )
 
     # Retrieve the memory bank and validate its properties
-    response = llama_stack_client.memory_banks.retrieve(memory_bank_id=memory_bank_id)
+    response = llama_stack_client.vector_dbs.retrieve(vector_db_id=vector_db_id)
     assert response is not None
-    assert response.identifier == memory_bank_id
-    assert response.type == "memory_bank"
-    assert response.memory_bank_type == "vector"
+    assert response.identifier == vector_db_id
     assert response.embedding_model == "all-MiniLM-L6-v2"
-    assert response.chunk_size_in_tokens == 512
-    assert response.overlap_size_in_tokens == 64
     assert response.provider_id == "faiss"
-    assert response.provider_resource_id == memory_bank_id
+    assert response.provider_resource_id == vector_db_id
 
 
-def test_memory_bank_list(llama_stack_client, empty_memory_bank_registry):
-    memory_banks_after_register = [
-        memory_bank.identifier for memory_bank in llama_stack_client.memory_banks.list()
+def test_vector_db_list(llama_stack_client, empty_vector_db_registry):
+    vector_dbs_after_register = [
+        vector_db.identifier for vector_db in llama_stack_client.vector_dbs.list()
     ]
-    assert len(memory_banks_after_register) == 0
+    assert len(vector_dbs_after_register) == 0
 
 
-def test_memory_bank_register(llama_stack_client, empty_memory_bank_registry):
-    memory_provider_id = "faiss"
-    memory_bank_id = f"test_bank_{random.randint(1000, 9999)}"
-    llama_stack_client.memory_banks.register(
-        memory_bank_id=memory_bank_id,
-        params={
-            "memory_bank_type": "vector",
-            "embedding_model": "all-MiniLM-L6-v2",
-            "chunk_size_in_tokens": 512,
-            "overlap_size_in_tokens": 64,
-        },
-        provider_id=memory_provider_id,
+def test_vector_db_register(llama_stack_client, empty_vector_db_registry):
+    vector_db_id = f"test_vector_db_{random.randint(1000, 9999)}"
+    llama_stack_client.vector_dbs.register(
+        vector_db_id=vector_db_id,
+        embedding_model="all-MiniLM-L6-v2",
+        embedding_dimension=384,
+        provider_id="faiss",
     )
 
-    memory_banks_after_register = [
-        memory_bank.identifier for memory_bank in llama_stack_client.memory_banks.list()
+    vector_dbs_after_register = [
+        vector_db.identifier for vector_db in llama_stack_client.vector_dbs.list()
     ]
-    assert memory_banks_after_register == [memory_bank_id]
+    assert vector_dbs_after_register == [vector_db_id]
 
 
-def test_memory_bank_unregister(llama_stack_client, single_entry_memory_bank_registry):
-    memory_banks = [
-        memory_bank.identifier for memory_bank in llama_stack_client.memory_banks.list()
+def test_vector_db_unregister(llama_stack_client, single_entry_vector_db_registry):
+    vector_dbs = [
+        vector_db.identifier for vector_db in llama_stack_client.vector_dbs.list()
     ]
-    assert len(memory_banks) == 1
+    assert len(vector_dbs) == 1
 
-    memory_bank_id = memory_banks[0]
-    llama_stack_client.memory_banks.unregister(memory_bank_id=memory_bank_id)
+    vector_db_id = vector_dbs[0]
+    llama_stack_client.vector_dbs.unregister(vector_db_id=vector_db_id)
 
-    memory_banks = [
-        memory_bank.identifier for memory_bank in llama_stack_client.memory_banks.list()
+    vector_dbs = [
+        vector_db.identifier for vector_db in llama_stack_client.vector_dbs.list()
     ]
-    assert len(memory_banks) == 0
-
-
-def test_memory_bank_insert_inline_and_query(
-    llama_stack_client, single_entry_memory_bank_registry, sample_documents
-):
-    memory_bank_id = single_entry_memory_bank_registry[0]
-    llama_stack_client.memory.insert(
-        bank_id=memory_bank_id,
-        documents=sample_documents,
-    )
-
-    # Query with a direct match
-    query1 = "programming language"
-    response1 = llama_stack_client.memory.query(
-        bank_id=memory_bank_id,
-        query=query1,
-    )
-    assert_valid_response(response1)
-    assert any("Python" in chunk.content for chunk in response1.chunks)
-
-    # Query with semantic similarity
-    query2 = "AI and brain-inspired computing"
-    response2 = llama_stack_client.memory.query(
-        bank_id=memory_bank_id,
-        query=query2,
-    )
-    assert_valid_response(response2)
-    assert any("neural networks" in chunk.content.lower() for chunk in response2.chunks)
-
-    # Query with limit on number of results (max_chunks=2)
-    query3 = "computer"
-    response3 = llama_stack_client.memory.query(
-        bank_id=memory_bank_id,
-        query=query3,
-        params={"max_chunks": 2},
-    )
-    assert_valid_response(response3)
-    assert len(response3.chunks) <= 2
-
-    # Query with threshold on similarity score
-    query4 = "computer"
-    response4 = llama_stack_client.memory.query(
-        bank_id=memory_bank_id,
-        query=query4,
-        params={"score_threshold": 0.01},
-    )
-    assert_valid_response(response4)
-    assert all(score >= 0.01 for score in response4.scores)
-
-
-def test_memory_bank_insert_from_url_and_query(
-    llama_stack_client, empty_memory_bank_registry
-):
-    providers = [p for p in llama_stack_client.providers.list() if p.api == "memory"]
-    assert len(providers) > 0
-
-    memory_provider_id = providers[0].provider_id
-    memory_bank_id = "test_bank"
-
-    llama_stack_client.memory_banks.register(
-        memory_bank_id=memory_bank_id,
-        params={
-            "memory_bank_type": "vector",
-            "embedding_model": "all-MiniLM-L6-v2",
-            "chunk_size_in_tokens": 512,
-            "overlap_size_in_tokens": 64,
-        },
-        provider_id=memory_provider_id,
-    )
-
-    # list to check memory bank is successfully registered
-    available_memory_banks = [
-        memory_bank.identifier for memory_bank in llama_stack_client.memory_banks.list()
-    ]
-    assert memory_bank_id in available_memory_banks
-
-    # URLs of documents to insert
-    # TODO: Move to test/memory/resources then update the url to
-    # https://raw.githubusercontent.com/meta-llama/llama-stack/main/tests/memory/resources/{url}
-    urls = [
-        "memory_optimizations.rst",
-        "chat.rst",
-        "llama3.rst",
-    ]
-    documents = [
-        Document(
-            document_id=f"num-{i}",
-            content=f"https://raw.githubusercontent.com/pytorch/torchtune/main/docs/source/tutorials/{url}",
-            mime_type="text/plain",
-            metadata={},
-        )
-        for i, url in enumerate(urls)
-    ]
-
-    llama_stack_client.memory.insert(
-        bank_id=memory_bank_id,
-        documents=documents,
-    )
-
-    # Query for the name of method
-    response1 = llama_stack_client.memory.query(
-        bank_id=memory_bank_id,
-        query="What's the name of the fine-tunning method used?",
-    )
-    assert_valid_response(response1)
-    assert any("lora" in chunk.content.lower() for chunk in response1.chunks)
-
-    # Query for the name of model
-    response2 = llama_stack_client.memory.query(
-        bank_id=memory_bank_id,
-        query="Which Llama model is mentioned?",
-    )
-    assert_valid_response(response1)
-    assert any("llama2" in chunk.content.lower() for chunk in response2.chunks)
+    assert len(vector_dbs) == 0