renamed insert_vector_db_id to documents_db_id, removed vector_db_id from session info

Signed-off-by: Daniele Martinoli <dmartino@redhat.com>

docs/source/building_applications/rag.md

@@ -93,7 +93,14 @@ agent_config = AgentConfig(
         {
             "name": "builtin::rag/knowledge_search",
             "args": {
-                "vector_db_ids": [vector_db_id],
+                # 'documents_db_id' holds the ID of the registered vector database
+                # where the provided documents will be ingested. This argument is mandatory
+                # when the 'documents' parameter is provided in a 'create_turn' invocation.
+                # When provided, 'documents_db_id' will also be used to extract contextual information
+                # for the query.
+                "documents_db_id": vector_db_id,
+                # Optionally, the 'vector_db_ids' argument can specify additional vector databases
+                # to use at query time.
             },
         }
     ],
@@ -109,7 +116,7 @@ response = agent.create_turn(
     ],
     documents=[
         {
-            "content": "https://raw.githubusercontent.com/example/doc.rst",
+            "content": "https://raw.githubusercontent.com/pytorch/torchtune/main/docs/source/tutorials/memory_optimizations.rst",
             "mime_type": "text/plain",
         }
     ],

llama_stack/providers/inline/agents/meta_reference/agent_instance.py

@@ -911,7 +911,7 @@ class ChatAgent(ShieldRunnerMixin):
 
     async def handle_documents(
         self,
-        session_id: str,
+        documents_db_id: str,
         documents: List[Document],
         input_messages: List[Message],
         tool_defs: Dict[str, ToolDefinition],
@@ -943,7 +943,7 @@ class ChatAgent(ShieldRunnerMixin):
             msg = await attachment_message(self.tempdir, url_items)
             input_messages.append(msg)
             # Since memory is present, add all the data to the memory bank
-            await self.add_to_session_vector_db(session_id, documents)
+            await self.add_to_session_vector_db(documents_db_id, documents)
         elif code_interpreter_tool:
             # if only code_interpreter is available, we download the URLs to a tempdir
             # and attach the path to them as a message to inference with the
@@ -952,7 +952,7 @@ class ChatAgent(ShieldRunnerMixin):
             input_messages.append(msg)
         elif memory_tool:
             # if only memory is available, we load the data from the URLs and content items to the memory bank
-            await self.add_to_session_vector_db(session_id, documents)
+            await self.add_to_session_vector_db(documents_db_id, documents)
         else:
             # if no memory or code_interpreter tool is available,
             # we try to load the data from the URLs and content items as a message to inference
@@ -961,29 +961,7 @@ class ChatAgent(ShieldRunnerMixin):
                 [doc.content for doc in content_items] + await load_data_from_urls(url_items)
             )
 
-    async def _ensure_vector_db(self, session_id: str) -> str:
+    async def add_to_session_vector_db(self, vector_db_id: str, data: List[Document]) -> None:
-        session_info = await self.storage.get_session_info(session_id)
-        if session_info is None:
-            raise ValueError(f"Session {session_id} not found")
-
-        if session_info.vector_db_id is None:
-            vector_db_id = f"vector_db_{session_id}"
-
-            # TODO: the semantic for registration is definitely not "creation"
-            # so we need to fix it if we expect the agent to create a new vector db
-            # for each session
-            await self.vector_io_api.register_vector_db(
-                vector_db_id=vector_db_id,
-                embedding_model="all-MiniLM-L6-v2",
-            )
-            await self.storage.add_vector_db_to_session(session_id, vector_db_id)
-        else:
-            vector_db_id = session_info.vector_db_id
-
-        return vector_db_id
-
-    async def add_to_session_vector_db(self, session_id: str, data: List[Document]) -> None:
-        vector_db_id = await self._ensure_vector_db(session_id)
         documents = [
             RAGDocument(
                 document_id=str(uuid.uuid4()),

llama_stack/providers/inline/agents/meta_reference/persistence.py

@@ -53,17 +53,6 @@ class AgentPersistence:
 
         return AgentSessionInfo(**json.loads(value))
 
-    async def add_vector_db_to_session(self, session_id: str, vector_db_id: str):
-        session_info = await self.get_session_info(session_id)
-        if session_info is None:
-            raise ValueError(f"Session {session_id} not found")
-
-        session_info.vector_db_id = vector_db_id
-        await self.kvstore.set(
-            key=f"session:{self.agent_id}:{session_id}",
-            value=session_info.model_dump_json(),
-        )
-
     async def add_turn_to_session(self, session_id: str, turn: Turn):
         await self.kvstore.set(
             key=f"session:{self.agent_id}:{session_id}:{turn.turn_id}",