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migrate memory banks to Resource and new registration (#411)

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* migrate memory banks to Resource and new registration

* address feedback

* address feedback

* fix tests

* pgvector fix

* pgvector fix v2

* remove auto discovery

* change register signature to make params required

* update client

* client fix

* use annotated union to parse

* remove base MemoryBank inheritence

---------

Co-authored-by: Dinesh Yeduguru <dineshyv@fb.com>
This commit is contained in:
Dinesh Yeduguru 2024-11-11 17:10:44 -08:00 • committed by GitHub
parent 6b9850e11b
commit 38cce97597
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GPG key ID: B5690EEEBB952194
19 changed files with 240 additions and 129 deletions
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10
llama_stack/providers/remote/memory/chroma/chroma.py
View file

@ -98,11 +98,11 @@ class ChromaMemoryAdapter(Memory, MemoryBanksProtocolPrivate):
async def register_memory_bank(
self,
memory_bank: MemoryBankDef,
memory_bank: MemoryBank,
) -> None:
assert (
memory_bank.type == MemoryBankType.vector.value
), f"Only vector banks are supported {memory_bank.type}"
memory_bank.memory_bank_type == MemoryBankType.vector.value
), f"Only vector banks are supported {memory_bank.memory_bank_type}"
collection = await self.client.get_or_create_collection(
name=memory_bank.identifier,
@ -113,12 +113,12 @@ class ChromaMemoryAdapter(Memory, MemoryBanksProtocolPrivate):
)
self.cache[memory_bank.identifier] = bank_index
async def list_memory_banks(self) -> List[MemoryBankDef]:
async def list_memory_banks(self) -> List[MemoryBank]:
collections = await self.client.list_collections()
for collection in collections:
try:
data = json.loads(collection.metadata["bank"])
bank = parse_obj_as(MemoryBankDef, data)
bank = parse_obj_as(VectorMemoryBank, data)
except Exception:
import traceback

13
llama_stack/providers/remote/memory/pgvector/pgvector.py
View file

@ -52,7 +52,7 @@ def load_models(cur, cls):
class PGVectorIndex(EmbeddingIndex):
def __init__(self, bank: MemoryBankDef, dimension: int, cursor):
def __init__(self, bank: VectorMemoryBank, dimension: int, cursor):
self.cursor = cursor
self.table_name = f"vector_store_{bank.identifier}"
@ -121,6 +121,7 @@ class PGVectorMemoryAdapter(Memory, MemoryBanksProtocolPrivate):
self.cache = {}
async def initialize(self) -> None:
print(f"Initializing PGVector memory adapter with config: {self.config}")
try:
self.conn = psycopg2.connect(
host=self.config.host,
@ -157,11 +158,11 @@ class PGVectorMemoryAdapter(Memory, MemoryBanksProtocolPrivate):
async def register_memory_bank(
self,
memory_bank: MemoryBankDef,
memory_bank: MemoryBank,
) -> None:
assert (
memory_bank.type == MemoryBankType.vector.value
), f"Only vector banks are supported {memory_bank.type}"
memory_bank.memory_bank_type == MemoryBankType.vector.value
), f"Only vector banks are supported {memory_bank.memory_bank_type}"
upsert_models(
self.cursor,
@ -176,8 +177,8 @@ class PGVectorMemoryAdapter(Memory, MemoryBanksProtocolPrivate):
)
self.cache[memory_bank.identifier] = index
async def list_memory_banks(self) -> List[MemoryBankDef]:
banks = load_models(self.cursor, MemoryBankDef)
async def list_memory_banks(self) -> List[MemoryBank]:
banks = load_models(self.cursor, VectorMemoryBank)
for bank in banks:
if bank.identifier not in self.cache:
index = BankWithIndex(

9
llama_stack/providers/remote/memory/qdrant/qdrant.py
View file

@ -12,6 +12,7 @@ from numpy.typing import NDArray
from qdrant_client import AsyncQdrantClient, models
from qdrant_client.models import PointStruct
from llama_stack.apis.memory_banks import * # noqa: F403
from llama_stack.providers.datatypes import MemoryBanksProtocolPrivate
from llama_stack.apis.memory import * # noqa: F403
@ -112,11 +113,11 @@ class QdrantVectorMemoryAdapter(Memory, MemoryBanksProtocolPrivate):
async def register_memory_bank(
self,
memory_bank: MemoryBankDef,
memory_bank: MemoryBank,
) -> None:
assert (
memory_bank.type == MemoryBankType.vector.value
), f"Only vector banks are supported {memory_bank.type}"
memory_bank.memory_bank_type == MemoryBankType.vector
), f"Only vector banks are supported {memory_bank.memory_bank_type}"
index = BankWithIndex(
bank=memory_bank,
@ -125,7 +126,7 @@ class QdrantVectorMemoryAdapter(Memory, MemoryBanksProtocolPrivate):
self.cache[memory_bank.identifier] = index
async def list_memory_banks(self) -> List[MemoryBankDef]:
async def list_memory_banks(self) -> List[MemoryBank]:
# Qdrant doesn't have collection level metadata to store the bank properties
# So we only return from the cache value
return [i.bank for i in self.cache.values()]

12
llama_stack/providers/remote/memory/weaviate/weaviate.py
View file

@ -114,11 +114,11 @@ class WeaviateMemoryAdapter(
async def register_memory_bank(
self,
memory_bank: MemoryBankDef,
memory_bank: MemoryBank,
) -> None:
assert (
memory_bank.type == MemoryBankType.vector.value
), f"Only vector banks are supported {memory_bank.type}"
memory_bank.memory_bank_type == MemoryBankType.vector
), f"Only vector banks are supported {memory_bank.memory_bank_type}"
client = self._get_client()
@ -141,7 +141,7 @@ class WeaviateMemoryAdapter(
)
self.cache[memory_bank.identifier] = index
async def list_memory_banks(self) -> List[MemoryBankDef]:
async def list_memory_banks(self) -> List[MemoryBank]:
# TODO: right now the Llama Stack is the source of truth for these banks. That is
# not ideal. It should be Weaviate which is the source of truth. Unfortunately,
# list() happens at Stack startup when the Weaviate client (credentials) is not
@ -157,8 +157,8 @@ class WeaviateMemoryAdapter(
raise ValueError(f"Bank {bank_id} not found")
client = self._get_client()
if not client.collections.exists(bank_id):
raise ValueError(f"Collection with name `{bank_id}` not found")
if not client.collections.exists(bank.identifier):
raise ValueError(f"Collection with name `{bank.identifier}` not found")
index = BankWithIndex(
bank=bank,