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

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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.
This commit is contained in:
Ashwin Bharambe 2025-01-22 10:17:59 -08:00 • committed by GitHub
parent 63f37f9b7c
commit c9e5578151
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GPG key ID: B5690EEEBB952194
78 changed files with 504 additions and 623 deletions
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26
llama_stack/providers/inline/agents/meta_reference/agent_instance.py
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@ -413,8 +413,8 @@ class ChatAgent(ShieldRunnerMixin):
session_info = await self.storage.get_session_info(session_id)
# if the session has a memory bank id, let the memory tool use it
if session_info.memory_bank_id:
vector_db_ids.append(session_info.memory_bank_id)
if session_info.vector_db_id:
vector_db_ids.append(session_info.vector_db_id)
yield AgentTurnResponseStreamChunk(
event=AgentTurnResponseEvent(
@ -829,7 +829,7 @@ 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_memory_bank(session_id, documents)
await self.add_to_session_vector_db(session_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
@ -838,7 +838,7 @@ 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_memory_bank(session_id, documents)
await self.add_to_session_vector_db(session_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
@ -848,31 +848,31 @@ class ChatAgent(ShieldRunnerMixin):
+ await load_data_from_urls(url_items)
)
async def _ensure_memory_bank(self, session_id: str) -> str:
async def _ensure_vector_db(self, session_id: str) -> str:
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.memory_bank_id is None:
bank_id = f"memory_bank_{session_id}"
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=bank_id,
vector_db_id=vector_db_id,
embedding_model="all-MiniLM-L6-v2",
)
await self.storage.add_memory_bank_to_session(session_id, bank_id)
await self.storage.add_vector_db_to_session(session_id, vector_db_id)
else:
bank_id = session_info.memory_bank_id
vector_db_id = session_info.vector_db_id
return bank_id
return vector_db_id
async def add_to_session_memory_bank(
async def add_to_session_vector_db(
self, session_id: str, data: List[Document]
) -> None:
vector_db_id = await self._ensure_memory_bank(session_id)
vector_db_id = await self._ensure_vector_db(session_id)
documents = [
RAGDocument(
document_id=str(uuid.uuid4()),