phoenix-oss/llama-stack-mirror
0
0
Fork 1
mirror of https://github.com/meta-llama/llama-stack.git synced 2025-07-14 17:16:09 +00:00
Code Issues Projects Releases Packages Wiki Activity Actions

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

Browse source 
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
No known key found for this signature in database
GPG key ID: B5690EEEBB952194
78 changed files with 504 additions and 623 deletions
Download patch file Download diff file Expand all files Collapse all files

26
llama_stack/providers/inline/agents/meta_reference/agent_instance.py
View file

@ -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()),

6
llama_stack/providers/inline/agents/meta_reference/persistence.py
View file

@ -21,7 +21,7 @@ log = logging.getLogger(__name__)
class AgentSessionInfo(BaseModel):
session_id: str
session_name: str
memory_bank_id: Optional[str] = None
vector_db_id: Optional[str] = None
started_at: datetime
@ -52,12 +52,12 @@ class AgentPersistence:
return AgentSessionInfo(**json.loads(value))
async def add_memory_bank_to_session(self, session_id: str, bank_id: str):
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.memory_bank_id = bank_id
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(),

116
llama_stack/providers/inline/agents/meta_reference/tests/test_chat_agent.py
View file

@ -29,10 +29,9 @@ from llama_stack.apis.inference import (
SamplingParams,
ToolChoice,
ToolDefinition,
ToolPromptFormat,
UserMessage,
)
from llama_stack.apis.memory import MemoryBank
from llama_stack.apis.memory_banks import BankParams, VectorMemoryBank
from llama_stack.apis.safety import RunShieldResponse
from llama_stack.apis.tools import (
Tool,
@ -40,8 +39,9 @@ from llama_stack.apis.tools import (
ToolGroup,
ToolHost,
ToolInvocationResult,
ToolPromptFormat,
)
from llama_stack.apis.vector_io import QueryChunksResponse
from llama_stack.providers.inline.agents.meta_reference.agent_instance import (
MEMORY_QUERY_TOOL,
)
@ -110,68 +110,22 @@ class MockSafetyAPI:
return RunShieldResponse(violation=None)
class MockMemoryAPI:
class MockVectorIOAPI:
def __init__(self):
self.memory_banks = {}
self.documents = {}
self.chunks = {}
async def create_memory_bank(self, name, config, url=None):
bank_id = f"bank_{len(self.memory_banks)}"
bank = MemoryBank(bank_id, name, config, url)
self.memory_banks[bank_id] = bank
self.documents[bank_id] = {}
return bank
async def insert_chunks(self, vector_db_id, chunks, ttl_seconds=None):
for chunk in chunks:
metadata = chunk.metadata
self.chunks[vector_db_id][metadata["document_id"]] = chunk
async def list_memory_banks(self):
return list(self.memory_banks.values())
async def query_chunks(self, vector_db_id, query, params=None):
if vector_db_id not in self.chunks:
raise ValueError(f"Bank {vector_db_id} not found")
async def get_memory_bank(self, bank_id):
return self.memory_banks.get(bank_id)
async def drop_memory_bank(self, bank_id):
if bank_id in self.memory_banks:
del self.memory_banks[bank_id]
del self.documents[bank_id]
return bank_id
async def insert_documents(self, bank_id, documents, ttl_seconds=None):
if bank_id not in self.documents:
raise ValueError(f"Bank {bank_id} not found")
for doc in documents:
self.documents[bank_id][doc.document_id] = doc
async def update_documents(self, bank_id, documents):
if bank_id not in self.documents:
raise ValueError(f"Bank {bank_id} not found")
for doc in documents:
if doc.document_id in self.documents[bank_id]:
self.documents[bank_id][doc.document_id] = doc
async def query_documents(self, bank_id, query, params=None):
if bank_id not in self.documents:
raise ValueError(f"Bank {bank_id} not found")
# Simple mock implementation: return all documents
chunks = [
{"content": doc.content, "token_count": 10, "document_id": doc.document_id}
for doc in self.documents[bank_id].values()
]
chunks = list(self.chunks[vector_db_id].values())
scores = [1.0] * len(chunks)
return {"chunks": chunks, "scores": scores}
async def get_documents(self, bank_id, document_ids):
if bank_id not in self.documents:
raise ValueError(f"Bank {bank_id} not found")
return [
self.documents[bank_id][doc_id]
for doc_id in document_ids
if doc_id in self.documents[bank_id]
]
async def delete_documents(self, bank_id, document_ids):
if bank_id not in self.documents:
raise ValueError(f"Bank {bank_id} not found")
for doc_id in document_ids:
self.documents[bank_id].pop(doc_id, None)
return QueryChunksResponse(chunks=chunks, scores=scores)
class MockToolGroupsAPI:
@ -241,31 +195,6 @@ class MockToolRuntimeAPI:
return ToolInvocationResult(content={"result": "Mock tool result"})
class MockMemoryBanksAPI:
async def list_memory_banks(self) -> List[MemoryBank]:
return []
async def get_memory_bank(self, memory_bank_id: str) -> Optional[MemoryBank]:
return None
async def register_memory_bank(
self,
memory_bank_id: str,
params: BankParams,
provider_id: Optional[str] = None,
provider_memory_bank_id: Optional[str] = None,
) -> MemoryBank:
return VectorMemoryBank(
identifier=memory_bank_id,
provider_resource_id=provider_memory_bank_id or memory_bank_id,
embedding_model="mock_model",
chunk_size_in_tokens=512,
)
async def unregister_memory_bank(self, memory_bank_id: str) -> None:
pass
@pytest.fixture
def mock_inference_api():
return MockInferenceAPI()
@ -277,8 +206,8 @@ def mock_safety_api():
@pytest.fixture
def mock_memory_api():
return MockMemoryAPI()
def mock_vector_io_api():
return MockVectorIOAPI()
@pytest.fixture
@ -291,17 +220,11 @@ def mock_tool_runtime_api():
return MockToolRuntimeAPI()
@pytest.fixture
def mock_memory_banks_api():
return MockMemoryBanksAPI()
@pytest.fixture
async def get_agents_impl(
mock_inference_api,
mock_safety_api,
mock_memory_api,
mock_memory_banks_api,
mock_vector_io_api,
mock_tool_runtime_api,
mock_tool_groups_api,
):
@ -314,8 +237,7 @@ async def get_agents_impl(
),
inference_api=mock_inference_api,
safety_api=mock_safety_api,
memory_api=mock_memory_api,
memory_banks_api=mock_memory_banks_api,
vector_io_api=mock_vector_io_api,
tool_runtime_api=mock_tool_runtime_api,
tool_groups_api=mock_tool_groups_api,
)
@ -484,7 +406,7 @@ async def test_chat_agent_tools(
toolgroups_for_turn=[
AgentToolGroupWithArgs(
name=MEMORY_TOOLGROUP,
args={"memory_banks": ["test_memory_bank"]},
args={"vector_dbs": ["test_vector_db"]},
)
]
)