impls -> inline, adapters -> remote (#381)

llama_stack/providers/inline/meta_reference/memory/__init__.py

@@ -0,0 +1,19 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+from .config import FaissImplConfig
+
+
+async def get_provider_impl(config: FaissImplConfig, _deps):
+    from .faiss import FaissMemoryImpl
+
+    assert isinstance(
+        config, FaissImplConfig
+    ), f"Unexpected config type: {type(config)}"
+
+    impl = FaissMemoryImpl(config)
+    await impl.initialize()
+    return impl

llama_stack/providers/inline/meta_reference/memory/config.py

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+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+from llama_models.schema_utils import json_schema_type
+from pydantic import BaseModel
+
+from llama_stack.distribution.utils.config_dirs import RUNTIME_BASE_DIR
+from llama_stack.providers.utils.kvstore.config import (
+    KVStoreConfig,
+    SqliteKVStoreConfig,
+)
+
+
+@json_schema_type
+class FaissImplConfig(BaseModel):
+    kvstore: KVStoreConfig = SqliteKVStoreConfig(
+        db_path=(RUNTIME_BASE_DIR / "faiss_store.db").as_posix()
+    )  # Uses SQLite config specific to FAISS storage

llama_stack/providers/inline/meta_reference/memory/faiss.py

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+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+import logging
+
+from typing import Any, Dict, List, Optional
+
+import faiss
+import numpy as np
+from numpy.typing import NDArray
+
+from llama_models.llama3.api.datatypes import *  # noqa: F403
+
+from llama_stack.apis.memory import *  # noqa: F403
+from llama_stack.providers.datatypes import MemoryBanksProtocolPrivate
+from llama_stack.providers.utils.kvstore import kvstore_impl
+
+from llama_stack.providers.utils.memory.vector_store import (
+    ALL_MINILM_L6_V2_DIMENSION,
+    BankWithIndex,
+    EmbeddingIndex,
+)
+from llama_stack.providers.utils.telemetry import tracing
+
+from .config import FaissImplConfig
+
+logger = logging.getLogger(__name__)
+
+MEMORY_BANKS_PREFIX = "memory_banks:"
+
+
+class FaissIndex(EmbeddingIndex):
+    id_by_index: Dict[int, str]
+    chunk_by_index: Dict[int, str]
+
+    def __init__(self, dimension: int):
+        self.index = faiss.IndexFlatL2(dimension)
+        self.id_by_index = {}
+        self.chunk_by_index = {}
+
+    @tracing.span(name="add_chunks")
+    async def add_chunks(self, chunks: List[Chunk], embeddings: NDArray):
+        indexlen = len(self.id_by_index)
+        for i, chunk in enumerate(chunks):
+            self.chunk_by_index[indexlen + i] = chunk
+            self.id_by_index[indexlen + i] = chunk.document_id
+
+        self.index.add(np.array(embeddings).astype(np.float32))
+
+    async def query(
+        self, embedding: NDArray, k: int, score_threshold: float
+    ) -> QueryDocumentsResponse:
+        distances, indices = self.index.search(
+            embedding.reshape(1, -1).astype(np.float32), k
+        )
+
+        chunks = []
+        scores = []
+        for d, i in zip(distances[0], indices[0]):
+            if i < 0:
+                continue
+            chunks.append(self.chunk_by_index[int(i)])
+            scores.append(1.0 / float(d))
+
+        return QueryDocumentsResponse(chunks=chunks, scores=scores)
+
+
+class FaissMemoryImpl(Memory, MemoryBanksProtocolPrivate):
+    def __init__(self, config: FaissImplConfig) -> None:
+        self.config = config
+        self.cache = {}
+        self.kvstore = None
+
+    async def initialize(self) -> None:
+        self.kvstore = await kvstore_impl(self.config.kvstore)
+        # Load existing banks from kvstore
+        start_key = MEMORY_BANKS_PREFIX
+        end_key = f"{MEMORY_BANKS_PREFIX}\xff"
+        stored_banks = await self.kvstore.range(start_key, end_key)
+
+        for bank_data in stored_banks:
+            bank = VectorMemoryBankDef.model_validate_json(bank_data)
+            index = BankWithIndex(
+                bank=bank, index=FaissIndex(ALL_MINILM_L6_V2_DIMENSION)
+            )
+            self.cache[bank.identifier] = index
+
+    async def shutdown(self) -> None:
+        # Cleanup if needed
+        pass
+
+    async def register_memory_bank(
+        self,
+        memory_bank: MemoryBankDef,
+    ) -> None:
+        assert (
+            memory_bank.type == MemoryBankType.vector.value
+        ), f"Only vector banks are supported {memory_bank.type}"
+
+        # Store in kvstore
+        key = f"{MEMORY_BANKS_PREFIX}{memory_bank.identifier}"
+        await self.kvstore.set(
+            key=key,
+            value=memory_bank.json(),
+        )
+
+        # Store in cache
+        index = BankWithIndex(
+            bank=memory_bank, index=FaissIndex(ALL_MINILM_L6_V2_DIMENSION)
+        )
+        self.cache[memory_bank.identifier] = index
+
+    async def list_memory_banks(self) -> List[MemoryBankDef]:
+        return [i.bank for i in self.cache.values()]
+
+    async def insert_documents(
+        self,
+        bank_id: str,
+        documents: List[MemoryBankDocument],
+        ttl_seconds: Optional[int] = None,
+    ) -> None:
+        index = self.cache.get(bank_id)
+        if index is None:
+            raise ValueError(f"Bank {bank_id} not found")
+
+        await index.insert_documents(documents)
+
+    async def query_documents(
+        self,
+        bank_id: str,
+        query: InterleavedTextMedia,
+        params: Optional[Dict[str, Any]] = None,
+    ) -> QueryDocumentsResponse:
+        index = self.cache.get(bank_id)
+        if index is None:
+            raise ValueError(f"Bank {bank_id} not found")
+
+        return await index.query_documents(query, params)

llama_stack/providers/inline/meta_reference/memory/tests/test_faiss.py

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+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+import tempfile
+
+import pytest
+from llama_stack.apis.memory import MemoryBankType, VectorMemoryBankDef
+from llama_stack.providers.inline.meta_reference.memory.config import FaissImplConfig
+
+from llama_stack.providers.inline.meta_reference.memory.faiss import FaissMemoryImpl
+from llama_stack.providers.utils.kvstore.config import SqliteKVStoreConfig
+
+
+class TestFaissMemoryImpl:
+    @pytest.fixture
+    def faiss_impl(self):
+        # Create a temporary SQLite database file
+        temp_db = tempfile.NamedTemporaryFile(suffix=".db", delete=False)
+        config = FaissImplConfig(kvstore=SqliteKVStoreConfig(db_path=temp_db.name))
+        return FaissMemoryImpl(config)
+
+    @pytest.mark.asyncio
+    async def test_initialize(self, faiss_impl):
+        # Test empty initialization
+        await faiss_impl.initialize()
+        assert len(faiss_impl.cache) == 0
+
+        # Test initialization with existing banks
+        bank = VectorMemoryBankDef(
+            identifier="test_bank",
+            type=MemoryBankType.vector.value,
+            embedding_model="all-MiniLM-L6-v2",
+            chunk_size_in_tokens=512,
+            overlap_size_in_tokens=64,
+        )
+
+        # Register a bank and reinitialize to test loading
+        await faiss_impl.register_memory_bank(bank)
+
+        # Create new instance to test initialization with existing data
+        new_impl = FaissMemoryImpl(faiss_impl.config)
+        await new_impl.initialize()
+
+        assert len(new_impl.cache) == 1
+        assert "test_bank" in new_impl.cache
+
+    @pytest.mark.asyncio
+    async def test_register_memory_bank(self, faiss_impl):
+        bank = VectorMemoryBankDef(
+            identifier="test_bank",
+            type=MemoryBankType.vector.value,
+            embedding_model="all-MiniLM-L6-v2",
+            chunk_size_in_tokens=512,
+            overlap_size_in_tokens=64,
+        )
+
+        await faiss_impl.initialize()
+        await faiss_impl.register_memory_bank(bank)
+
+        assert "test_bank" in faiss_impl.cache
+        assert faiss_impl.cache["test_bank"].bank == bank
+
+        # Verify persistence
+        new_impl = FaissMemoryImpl(faiss_impl.config)
+        await new_impl.initialize()
+        assert "test_bank" in new_impl.cache
+
+
+if __name__ == "__main__":
+    pytest.main([__file__])