[memory refactor][5/n] Migrate all vector_io providers (#835)

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

This PR finishes off all the stragglers and migrates everything to the
new naming.

llama_stack/providers/tests/eval/fixtures.py

@@ -53,7 +53,7 @@ async def eval_stack(
         "inference",
         "agents",
         "safety",
-        "memory",
+        "vector_io",
         "tool_runtime",
     ]:
         fixture = request.getfixturevalue(f"{key}_{fixture_dict[key]}")
@@ -69,7 +69,7 @@ async def eval_stack(
             Api.scoring,
             Api.agents,
             Api.safety,
-            Api.memory,
+            Api.vector_io,
             Api.tool_runtime,
         ],
         providers,

llama_stack/providers/tests/tools/fixtures.py

@@ -83,7 +83,7 @@ async def tools_stack(
 
     providers = {}
     provider_data = {}
-    for key in ["inference", "memory", "tool_runtime"]:
+    for key in ["inference", "vector_io", "tool_runtime"]:
         fixture = request.getfixturevalue(f"{key}_{fixture_dict[key]}")
         providers[key] = fixture.providers
         if key == "inference":
@@ -117,7 +117,12 @@ async def tools_stack(
     )
 
     test_stack = await construct_stack_for_test(
-        [Api.tool_groups, Api.inference, Api.memory, Api.tool_runtime],
+        [
+            Api.tool_groups,
+            Api.inference,
+            Api.vector_io,
+            Api.tool_runtime,
+        ],
         providers,
         provider_data,
         models=models,

llama_stack/providers/tests/tools/test_tools.py

@@ -8,10 +8,7 @@ import os
 
 import pytest
 
-from llama_stack.apis.inference import UserMessage
-from llama_stack.apis.memory import MemoryBankDocument
-from llama_stack.apis.memory_banks import VectorMemoryBankParams
-from llama_stack.apis.tools import ToolInvocationResult
+from llama_stack.apis.tools import RAGDocument, RAGQueryResult, ToolInvocationResult
 from llama_stack.providers.datatypes import Api
 
 
@@ -36,7 +33,7 @@ def sample_documents():
         "lora_finetune.rst",
     ]
     return [
-        MemoryBankDocument(
+        RAGDocument(
             document_id=f"num-{i}",
             content=f"https://raw.githubusercontent.com/pytorch/torchtune/main/docs/source/tutorials/{url}",
             mime_type="text/plain",
@@ -57,7 +54,7 @@ class TestTools:
 
         # Execute the tool
         response = await tools_impl.invoke_tool(
-            tool_name="web_search", args={"query": sample_search_query}
+            tool_name="web_search", kwargs={"query": sample_search_query}
         )
 
         # Verify the response
@@ -75,7 +72,7 @@ class TestTools:
         tools_impl = tools_stack.impls[Api.tool_runtime]
 
         response = await tools_impl.invoke_tool(
-            tool_name="wolfram_alpha", args={"query": sample_wolfram_alpha_query}
+            tool_name="wolfram_alpha", kwargs={"query": sample_wolfram_alpha_query}
         )
 
         # Verify the response
@@ -85,43 +82,33 @@ class TestTools:
         assert isinstance(response.content, str)
 
     @pytest.mark.asyncio
-    async def test_memory_tool(self, tools_stack, sample_documents):
+    async def test_rag_tool(self, tools_stack, sample_documents):
         """Test the memory tool functionality."""
-        memory_banks_impl = tools_stack.impls[Api.memory_banks]
-        memory_impl = tools_stack.impls[Api.memory]
+        vector_dbs_impl = tools_stack.impls[Api.vector_dbs]
         tools_impl = tools_stack.impls[Api.tool_runtime]
 
         # Register memory bank
-        await memory_banks_impl.register_memory_bank(
-            memory_bank_id="test_bank",
-            params=VectorMemoryBankParams(
-                embedding_model="all-MiniLM-L6-v2",
-                chunk_size_in_tokens=512,
-                overlap_size_in_tokens=64,
-            ),
+        await vector_dbs_impl.register(
+            vector_db_id="test_bank",
+            embedding_model="all-MiniLM-L6-v2",
+            embedding_dimension=384,
             provider_id="faiss",
         )
 
         # Insert documents into memory
-        await memory_impl.insert_documents(
-            bank_id="test_bank",
+        await tools_impl.rag_tool.insert_documents(
             documents=sample_documents,
+            vector_db_id="test_bank",
+            chunk_size_in_tokens=512,
         )
 
         # Execute the memory tool
-        response = await tools_impl.invoke_tool(
-            tool_name="memory",
-            args={
-                "messages": [
-                    UserMessage(
-                        content="What are the main topics covered in the documentation?",
-                    )
-                ],
-                "memory_bank_ids": ["test_bank"],
-            },
+        response = await tools_impl.rag_tool.query_context(
+            content="What are the main topics covered in the documentation?",
+            vector_db_ids=["test_bank"],
         )
 
         # Verify the response
-        assert isinstance(response, ToolInvocationResult)
+        assert isinstance(response, RAGQueryResult)
         assert response.content is not None
         assert len(response.content) > 0