forked from phoenix-oss/llama-stack-mirror
191cd28831
* Simplified Telemetry API and tying it to logger * small update which adds a METRIC type * move span events one level down into structured log events --------- Co-authored-by: Ashwin Bharambe <ashwin@meta.com>
124 lines
3.7 KiB
Python
124 lines
3.7 KiB
Python
# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the terms described in the LICENSE file in
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# the root directory of this source tree.
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import logging
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import uuid
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from typing import Any, Dict, List, Optional
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import faiss
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import numpy as np
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from numpy.typing import NDArray
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from llama_models.llama3.api.datatypes import * # noqa: F403
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from llama_toolchain.memory.api import * # noqa: F403
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from llama_toolchain.memory.common.vector_store import (
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ALL_MINILM_L6_V2_DIMENSION,
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BankWithIndex,
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EmbeddingIndex,
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)
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from llama_toolchain.telemetry import tracing
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from .config import FaissImplConfig
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logger = logging.getLogger(__name__)
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class FaissIndex(EmbeddingIndex):
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id_by_index: Dict[int, str]
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chunk_by_index: Dict[int, str]
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def __init__(self, dimension: int):
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self.index = faiss.IndexFlatL2(dimension)
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self.id_by_index = {}
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self.chunk_by_index = {}
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@tracing.span(name="add_chunks")
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async def add_chunks(self, chunks: List[Chunk], embeddings: NDArray):
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indexlen = len(self.id_by_index)
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for i, chunk in enumerate(chunks):
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self.chunk_by_index[indexlen + i] = chunk
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logger.info(f"Adding chunk #{indexlen + i} tokens={chunk.token_count}")
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self.id_by_index[indexlen + i] = chunk.document_id
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self.index.add(np.array(embeddings).astype(np.float32))
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async def query(self, embedding: NDArray, k: int) -> QueryDocumentsResponse:
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distances, indices = self.index.search(
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embedding.reshape(1, -1).astype(np.float32), k
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)
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chunks = []
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scores = []
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for d, i in zip(distances[0], indices[0]):
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if i < 0:
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continue
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chunks.append(self.chunk_by_index[int(i)])
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scores.append(1.0 / float(d))
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return QueryDocumentsResponse(chunks=chunks, scores=scores)
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class FaissMemoryImpl(Memory):
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def __init__(self, config: FaissImplConfig) -> None:
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self.config = config
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self.cache = {}
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async def initialize(self) -> None: ...
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async def shutdown(self) -> None: ...
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async def create_memory_bank(
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self,
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name: str,
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config: MemoryBankConfig,
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url: Optional[URL] = None,
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) -> MemoryBank:
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assert url is None, "URL is not supported for this implementation"
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assert (
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config.type == MemoryBankType.vector.value
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), f"Only vector banks are supported {config.type}"
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bank_id = str(uuid.uuid4())
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bank = MemoryBank(
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bank_id=bank_id,
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name=name,
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config=config,
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url=url,
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)
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index = BankWithIndex(bank=bank, index=FaissIndex(ALL_MINILM_L6_V2_DIMENSION))
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self.cache[bank_id] = index
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return bank
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async def get_memory_bank(self, bank_id: str) -> Optional[MemoryBank]:
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index = self.cache.get(bank_id)
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if index is None:
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return None
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return index.bank
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async def insert_documents(
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self,
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bank_id: str,
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documents: List[MemoryBankDocument],
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ttl_seconds: Optional[int] = None,
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) -> None:
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index = self.cache.get(bank_id)
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if index is None:
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raise ValueError(f"Bank {bank_id} not found")
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await index.insert_documents(documents)
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async def query_documents(
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self,
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bank_id: str,
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query: InterleavedTextMedia,
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params: Optional[Dict[str, Any]] = None,
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) -> QueryDocumentsResponse:
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index = self.cache.get(bank_id)
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if index is None:
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raise ValueError(f"Bank {bank_id} not found")
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return await index.query_documents(query, params)
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