forked from phoenix-oss/llama-stack-mirror
ec4fc800cc
This is yet another of those large PRs (hopefully we will have less and less of them as things mature fast). This one introduces substantial improvements and some simplifications to the stack. Most important bits: * Agents reference implementation now has support for session / turn persistence. The default implementation uses sqlite but there's also support for using Redis. * We have re-architected the structure of the Stack APIs to allow for more flexible routing. The motivating use cases are: - routing model A to ollama and model B to a remote provider like Together - routing shield A to local impl while shield B to a remote provider like Bedrock - routing a vector memory bank to Weaviate while routing a keyvalue memory bank to Redis * Support for provider specific parameters to be passed from the clients. A client can pass data using `x_llamastack_provider_data` parameter which can be type-checked and provided to the Adapter implementations.
124 lines
3.6 KiB
Python
124 lines
3.6 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_stack.apis.memory import * # noqa: F403
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from llama_stack.providers.utils.memory.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_stack.providers.utils.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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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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