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599 lines
20 KiB
Python
599 lines
20 KiB
Python
from __future__ import annotations
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import logging
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import uuid
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import warnings
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from typing import (
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TYPE_CHECKING,
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Any,
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Callable,
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Iterable,
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List,
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Optional,
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Tuple,
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TypeVar,
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Union,
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)
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import numpy as np
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from langchain_core.documents import Document
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from langchain_core.embeddings import Embeddings
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from langchain_core.vectorstores import VectorStore
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from langchain_community.vectorstores.utils import (
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DistanceStrategy,
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maximal_marginal_relevance,
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)
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if TYPE_CHECKING:
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from aerospike_vector_search import Client
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from aerospike_vector_search.types import Neighbor, VectorDistanceMetric
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logger = logging.getLogger(__name__)
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def _import_aerospike() -> Any:
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try:
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from aerospike_vector_search import Client
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except ImportError as e:
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raise ImportError(
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"Could not import aerospike_vector_search python package. "
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"Please install it with `pip install aerospike_vector`."
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) from e
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return Client
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AVST = TypeVar("AVST", bound="Aerospike")
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class Aerospike(VectorStore):
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"""`Aerospike` vector store.
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To use, you should have the ``aerospike_vector_search`` python package installed.
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"""
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def __init__(
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self,
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client: Client,
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embedding: Union[Embeddings, Callable],
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namespace: str,
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index_name: Optional[str] = None,
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vector_key: str = "_vector",
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text_key: str = "_text",
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id_key: str = "_id",
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set_name: Optional[str] = None,
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distance_strategy: Optional[
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Union[DistanceStrategy, VectorDistanceMetric]
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] = DistanceStrategy.EUCLIDEAN_DISTANCE,
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):
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"""Initialize with Aerospike client.
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Args:
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client: Aerospike client.
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embedding: Embeddings object or Callable (deprecated) to embed text.
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namespace: Namespace to use for storing vectors. This should match
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index_name: Name of the index previously created in Aerospike. This
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vector_key: Key to use for vector in metadata. This should match the
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key used during index creation.
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text_key: Key to use for text in metadata.
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id_key: Key to use for id in metadata.
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set_name: Default set name to use for storing vectors.
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distance_strategy: Distance strategy to use for similarity search
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This should match the distance strategy used during index creation.
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"""
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aerospike = _import_aerospike()
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if not isinstance(embedding, Embeddings):
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warnings.warn(
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"Passing in `embedding` as a Callable is deprecated. Please pass in an"
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" Embeddings object instead."
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)
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if not isinstance(client, aerospike):
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raise ValueError(
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f"client should be an instance of aerospike_vector_search.Client, "
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f"got {type(client)}"
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)
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self._client = client
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self._embedding = embedding
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self._text_key = text_key
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self._vector_key = vector_key
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self._id_key = id_key
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self._index_name = index_name
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self._namespace = namespace
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self._set_name = set_name
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self._distance_strategy = self.convert_distance_strategy(distance_strategy)
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@property
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def embeddings(self) -> Optional[Embeddings]:
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"""Access the query embedding object if available."""
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if isinstance(self._embedding, Embeddings):
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return self._embedding
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return None
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def _embed_documents(self, texts: Iterable[str]) -> List[List[float]]:
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"""Embed search docs."""
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if isinstance(self._embedding, Embeddings):
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return self._embedding.embed_documents(list(texts))
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return [self._embedding(t) for t in texts]
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def _embed_query(self, text: str) -> List[float]:
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"""Embed query text."""
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if isinstance(self._embedding, Embeddings):
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return self._embedding.embed_query(text)
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return self._embedding(text)
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@staticmethod
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def convert_distance_strategy(
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distance_strategy: Union[VectorDistanceMetric, DistanceStrategy],
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) -> DistanceStrategy:
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"""
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Convert Aerospikes distance strategy to langchains DistanceStrategy
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enum. This is a convenience method to allow users to pass in the same
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distance metric used to create the index.
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"""
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from aerospike_vector_search.types import VectorDistanceMetric
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if isinstance(distance_strategy, DistanceStrategy):
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return distance_strategy
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if distance_strategy == VectorDistanceMetric.COSINE:
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return DistanceStrategy.COSINE
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if distance_strategy == VectorDistanceMetric.DOT_PRODUCT:
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return DistanceStrategy.DOT_PRODUCT
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if distance_strategy == VectorDistanceMetric.SQUARED_EUCLIDEAN:
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return DistanceStrategy.EUCLIDEAN_DISTANCE
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raise ValueError(
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"Unknown distance strategy, must be cosine, dot_product" ", or euclidean"
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)
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def add_texts(
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self,
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texts: Iterable[str],
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metadatas: Optional[List[dict]] = None,
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ids: Optional[List[str]] = None,
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set_name: Optional[str] = None,
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embedding_chunk_size: int = 1000,
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index_name: Optional[str] = None,
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wait_for_index: bool = True,
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**kwargs: Any,
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) -> List[str]:
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"""Run more texts through the embeddings and add to the vectorstore.
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Args:
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texts: Iterable of strings to add to the vectorstore.
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metadatas: Optional list of metadatas associated with the texts.
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ids: Optional list of ids to associate with the texts.
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set_name: Optional aerospike set name to add the texts to.
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batch_size: Batch size to use when adding the texts to the vectorstore.
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embedding_chunk_size: Chunk size to use when embedding the texts.
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index_name: Optional aerospike index name used for waiting for index
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completion. If not provided, the default index_name will be used.
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wait_for_index: If True, wait for the all the texts to be indexed
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before returning. Requires index_name to be provided. Defaults
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to True.
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**kwargs: Additional keyword arguments to pass to the client upsert call.
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Returns:
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List of ids from adding the texts into the vectorstore.
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"""
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if set_name is None:
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set_name = self._set_name
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if index_name is None:
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index_name = self._index_name
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if wait_for_index and index_name is None:
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raise ValueError("if wait_for_index is True, index_name must be provided")
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texts = list(texts)
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ids = ids or [str(uuid.uuid4()) for _ in texts]
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# We need to shallow copy so that we can add the vector and text keys
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if metadatas:
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metadatas = [m.copy() for m in metadatas]
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else:
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metadatas = metadatas or [{} for _ in texts]
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for i in range(0, len(texts), embedding_chunk_size):
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chunk_texts = texts[i : i + embedding_chunk_size]
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chunk_ids = ids[i : i + embedding_chunk_size]
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chunk_metadatas = metadatas[i : i + embedding_chunk_size]
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embeddings = self._embed_documents(chunk_texts)
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for metadata, embedding, text in zip(
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chunk_metadatas, embeddings, chunk_texts
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):
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metadata[self._vector_key] = embedding
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metadata[self._text_key] = text
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for id, metadata in zip(chunk_ids, chunk_metadatas):
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metadata[self._id_key] = id
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self._client.upsert(
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namespace=self._namespace,
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key=id,
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set_name=set_name,
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record_data=metadata,
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**kwargs,
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)
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if wait_for_index:
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self._client.wait_for_index_completion(
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namespace=self._namespace,
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name=index_name,
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)
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return ids
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def delete(
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self,
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ids: Optional[List[str]] = None,
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set_name: Optional[str] = None,
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**kwargs: Any,
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) -> Optional[bool]:
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"""Delete by vector ID or other criteria.
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Args:
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ids: List of ids to delete.
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**kwargs: Other keyword arguments to pass to client delete call.
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Returns:
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Optional[bool]: True if deletion is successful,
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False otherwise, None if not implemented.
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"""
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from aerospike_vector_search import AVSServerError
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if ids:
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for id in ids:
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try:
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self._client.delete(
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namespace=self._namespace,
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key=id,
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set_name=set_name,
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**kwargs,
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)
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except AVSServerError:
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return False
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return True
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def similarity_search_with_score(
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self,
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query: str,
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k: int = 4,
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metadata_keys: Optional[List[str]] = None,
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index_name: Optional[str] = None,
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**kwargs: Any,
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) -> List[Tuple[Document, float]]:
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"""Return aerospike documents most similar to query, along with scores.
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Args:
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query: Text to look up documents similar to.
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k: Number of Documents to return. Defaults to 4.
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metadata_keys: List of metadata keys to return with the documents.
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If None, all metadata keys will be returned. Defaults to None.
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index_name: Name of the index to search. Overrides the default
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index_name.
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kwargs: Additional keyword arguments to pass to the search method.
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Returns:
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List of Documents most similar to the query and associated scores.
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"""
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return self.similarity_search_by_vector_with_score(
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self._embed_query(query),
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k=k,
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metadata_keys=metadata_keys,
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index_name=index_name,
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**kwargs,
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)
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def similarity_search_by_vector_with_score(
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self,
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embedding: List[float],
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k: int = 4,
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metadata_keys: Optional[List[str]] = None,
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index_name: Optional[str] = None,
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**kwargs: Any,
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) -> List[Tuple[Document, float]]:
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"""Return aerospike documents most similar to embedding, along with scores.
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Args:
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embedding: Embedding to look up documents similar to.
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k: Number of Documents to return. Defaults to 4.
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metadata_keys: List of metadata keys to return with the documents.
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If None, all metadata keys will be returned. Defaults to None.
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index_name: Name of the index to search. Overrides the default
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index_name.
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kwargs: Additional keyword arguments to pass to the client
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vector_search method.
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Returns:
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List of Documents most similar to the query and associated scores.
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"""
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docs = []
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if metadata_keys and self._text_key not in metadata_keys:
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metadata_keys = [self._text_key] + metadata_keys
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if index_name is None:
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index_name = self._index_name
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if index_name is None:
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raise ValueError("index_name must be provided")
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results: list[Neighbor] = self._client.vector_search(
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index_name=index_name,
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namespace=self._namespace,
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query=embedding,
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limit=k,
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field_names=metadata_keys,
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**kwargs,
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)
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for result in results:
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metadata = result.fields
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if self._text_key in metadata:
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text = metadata.pop(self._text_key)
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score = result.distance
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docs.append((Document(page_content=text, metadata=metadata), score))
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else:
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logger.warning(
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f"Found document with no `{self._text_key}` key. Skipping."
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)
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continue
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return docs
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def similarity_search_by_vector(
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self,
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embedding: List[float],
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k: int = 4,
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metadata_keys: Optional[List[str]] = None,
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index_name: Optional[str] = None,
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**kwargs: Any,
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) -> List[Document]:
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"""Return docs most similar to embedding vector.
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Args:
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embedding: Embedding to look up documents similar to.
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k: Number of Documents to return. Defaults to 4.
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metadata_keys: List of metadata keys to return with the documents.
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If None, all metadata keys will be returned. Defaults to None.
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index_name: Name of the index to search. Overrides the default
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index_name.
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kwargs: Additional keyword arguments to pass to the search method.
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Returns:
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List of Documents most similar to the query vector.
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"""
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return [
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doc
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for doc, _ in self.similarity_search_by_vector_with_score(
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embedding,
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k=k,
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metadata_keys=metadata_keys,
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index_name=index_name,
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**kwargs,
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)
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]
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def similarity_search(
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self,
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query: str,
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k: int = 4,
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metadata_keys: Optional[List[str]] = None,
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index_name: Optional[str] = None,
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**kwargs: Any,
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) -> List[Document]:
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"""Return aerospike documents most similar to query.
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Args:
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query: Text to look up documents similar to.
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k: Number of Documents to return. Defaults to 4.
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metadata_keys: List of metadata keys to return with the documents.
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If None, all metadata keys will be returned. Defaults to None.
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index_name: Optional name of the index to search. Overrides the
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default index_name.
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Returns:
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List of Documents most similar to the query and score for each
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"""
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docs_and_scores = self.similarity_search_with_score(
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query, k=k, metadata_keys=metadata_keys, index_name=index_name, **kwargs
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)
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return [doc for doc, _ in docs_and_scores]
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def _select_relevance_score_fn(self) -> Callable[[float], float]:
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"""
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The 'correct' relevance function
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may differ depending on a few things, including:
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- the distance / similarity metric used by the VectorStore
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- the scale of your embeddings (OpenAI's are unit normed. Many others are not!)
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- embedding dimensionality
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- etc.
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0 is dissimilar, 1 is similar.
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Aerospike's relevance_fn assume euclidean and dot product embeddings are
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normalized to unit norm.
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"""
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if self._distance_strategy == DistanceStrategy.COSINE:
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return self._cosine_relevance_score_fn
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elif self._distance_strategy == DistanceStrategy.DOT_PRODUCT:
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return self._max_inner_product_relevance_score_fn
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elif self._distance_strategy == DistanceStrategy.EUCLIDEAN_DISTANCE:
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return self._euclidean_relevance_score_fn
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else:
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raise ValueError(
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"Unknown distance strategy, must be cosine, dot_product"
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", or euclidean"
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)
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@staticmethod
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def _cosine_relevance_score_fn(score: float) -> float:
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"""Aerospike returns cosine distance scores between [0,2]
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0 is dissimilar, 1 is similar.
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"""
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return 1 - (score / 2)
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|
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def max_marginal_relevance_search_by_vector(
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self,
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embedding: List[float],
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k: int = 4,
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fetch_k: int = 20,
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lambda_mult: float = 0.5,
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metadata_keys: Optional[List[str]] = None,
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index_name: Optional[str] = None,
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**kwargs: Any,
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) -> List[Document]:
|
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"""Return docs selected using the maximal marginal relevance.
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Maximal marginal relevance optimizes for similarity to query AND diversity
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among selected documents.
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Args:
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embedding: Embedding to look up documents similar to.
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k: Number of Documents to return. Defaults to 4.
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fetch_k: Number of Documents to fetch to pass to MMR algorithm.
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lambda_mult: Number between 0 and 1 that determines the degree of
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diversity among the results with 0 corresponding to maximum
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diversity and 1 to minimum diversity. Defaults to 0.5.
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metadata_keys: List of metadata keys to return with the documents.
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If None, all metadata keys will be returned. Defaults to None.
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index_name: Optional name of the index to search. Overrides the
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default index_name.
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Returns:
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List of Documents selected by maximal marginal relevance.
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"""
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if metadata_keys and self._vector_key not in metadata_keys:
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metadata_keys = [self._vector_key] + metadata_keys
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docs = self.similarity_search_by_vector(
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embedding,
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k=fetch_k,
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metadata_keys=metadata_keys,
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index_name=index_name,
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**kwargs,
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)
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mmr_selected = maximal_marginal_relevance(
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np.array([embedding], dtype=np.float32),
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[doc.metadata[self._vector_key] for doc in docs],
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k=k,
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lambda_mult=lambda_mult,
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)
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if metadata_keys and self._vector_key in metadata_keys:
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for i in mmr_selected:
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docs[i].metadata.pop(self._vector_key)
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return [docs[i] for i in mmr_selected]
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|
|
def max_marginal_relevance_search(
|
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self,
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query: str,
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k: int = 4,
|
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fetch_k: int = 20,
|
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lambda_mult: float = 0.5,
|
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metadata_keys: Optional[List[str]] = None,
|
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index_name: Optional[str] = None,
|
|
**kwargs: Any,
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|
) -> List[Document]:
|
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"""Return docs selected using the maximal marginal relevance.
|
|
|
|
Maximal marginal relevance optimizes for similarity to query AND diversity
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among selected documents.
|
|
|
|
Args:
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query: Text to look up documents similar to.
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k: Number of Documents to return. Defaults to 4.
|
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fetch_k: Number of Documents to fetch to pass to MMR algorithm.
|
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lambda_mult: Number between 0 and 1 that determines the degree
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of diversity among the results with 0 corresponding
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to maximum diversity and 1 to minimum diversity.
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Defaults to 0.5.
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index_name: Name of the index to search.
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Returns:
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List of Documents selected by maximal marginal relevance.
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"""
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embedding = self._embed_query(query)
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return self.max_marginal_relevance_search_by_vector(
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embedding,
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k,
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fetch_k,
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lambda_mult,
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metadata_keys=metadata_keys,
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index_name=index_name,
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**kwargs,
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)
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|
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@classmethod
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|
def from_texts(
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cls,
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texts: List[str],
|
|
embedding: Embeddings,
|
|
metadatas: Optional[List[dict]] = None,
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|
client: Client = None,
|
|
namespace: str = "test",
|
|
index_name: Optional[str] = None,
|
|
ids: Optional[List[str]] = None,
|
|
embeddings_chunk_size: int = 1000,
|
|
client_kwargs: Optional[dict] = None,
|
|
**kwargs: Any,
|
|
) -> Aerospike:
|
|
"""
|
|
This is a user friendly interface that:
|
|
1. Embeds text.
|
|
2. Converts the texts into documents.
|
|
3. Adds the documents to a provided Aerospike index
|
|
|
|
This is intended to be a quick way to get started.
|
|
|
|
Example:
|
|
.. code-block:: python
|
|
|
|
from langchain_community.vectorstores import Aerospike
|
|
from langchain_openai import OpenAIEmbeddings
|
|
from aerospike_vector_search import Client, HostPort
|
|
|
|
client = Client(seeds=HostPort(host="localhost", port=5000))
|
|
aerospike = Aerospike.from_texts(
|
|
["foo", "bar", "baz"],
|
|
embedder,
|
|
client,
|
|
"namespace",
|
|
index_name="index",
|
|
vector_key="vector",
|
|
distance_strategy=MODEL_DISTANCE_CALC,
|
|
)
|
|
"""
|
|
aerospike = cls(
|
|
client,
|
|
embedding,
|
|
namespace,
|
|
**kwargs,
|
|
)
|
|
|
|
aerospike.add_texts(
|
|
texts,
|
|
metadatas=metadatas,
|
|
ids=ids,
|
|
index_name=index_name,
|
|
embedding_chunk_size=embeddings_chunk_size,
|
|
**(client_kwargs or {}),
|
|
)
|
|
return aerospike
|