forked from phoenix-oss/llama-stack-mirror
115 lines
3.4 KiB
Python
115 lines
3.4 KiB
Python
# 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.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__)
|
|
|
|
|
|
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 = {}
|
|
|
|
async def initialize(self) -> None: ...
|
|
|
|
async def shutdown(self) -> None: ...
|
|
|
|
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}"
|
|
|
|
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)
|