"""Abstraction for a vector that can be stored in the system."""
from enum import Enum
from typing import Any, Optional
from uuid import UUID
from pydantic import BaseModel, Field
from .base import R2RSerializable
class VectorType(str, Enum):
FIXED = "FIXED"
class IndexMethod(str, Enum):
"""An enum representing the index methods available.
This class currently only supports the 'ivfflat' method but may
expand in the future.
Attributes:
auto (str): Automatically choose the best available index method.
ivfflat (str): The ivfflat index method.
hnsw (str): The hnsw index method.
"""
auto = "auto"
ivfflat = "ivfflat"
hnsw = "hnsw"
def __str__(self) -> str:
return self.value
class IndexMeasure(str, Enum):
"""An enum representing the types of distance measures available for
indexing.
Attributes:
cosine_distance (str): The cosine distance measure for indexing.
l2_distance (str): The Euclidean (L2) distance measure for indexing.
max_inner_product (str): The maximum inner product measure for indexing.
"""
l2_distance = "l2_distance"
max_inner_product = "max_inner_product"
cosine_distance = "cosine_distance"
l1_distance = "l1_distance"
hamming_distance = "hamming_distance"
jaccard_distance = "jaccard_distance"
def __str__(self) -> str:
return self.value
@property
def ops(self) -> str:
return {
IndexMeasure.l2_distance: "_l2_ops",
IndexMeasure.max_inner_product: "_ip_ops",
IndexMeasure.cosine_distance: "_cosine_ops",
IndexMeasure.l1_distance: "_l1_ops",
IndexMeasure.hamming_distance: "_hamming_ops",
IndexMeasure.jaccard_distance: "_jaccard_ops",
}[self]
@property
def pgvector_repr(self) -> str:
return {
IndexMeasure.l2_distance: "<->",
IndexMeasure.max_inner_product: "<#>",
IndexMeasure.cosine_distance: "<=>",
IndexMeasure.l1_distance: "<+>",
IndexMeasure.hamming_distance: "<~>",
IndexMeasure.jaccard_distance: "<%>",
}[self]
class IndexArgsIVFFlat(R2RSerializable):
"""A class for arguments that can optionally be supplied to the index
creation method when building an IVFFlat type index.
Attributes:
nlist (int): The number of IVF centroids that the index should use
"""
n_lists: int
class IndexArgsHNSW(R2RSerializable):
"""A class for arguments that can optionally be supplied to the index
creation method when building an HNSW type index.
Ref: https://github.com/pgvector/pgvector#index-options
Both attributes are Optional in case the user only wants to specify one and
leave the other as default
Attributes:
m (int): Maximum number of connections per node per layer (default: 16)
ef_construction (int): Size of the dynamic candidate list for
constructing the graph (default: 64)
"""
m: Optional[int] = 16
ef_construction: Optional[int] = 64
class VectorTableName(str, Enum):
"""This enum represents the different tables where we store vectors."""
CHUNKS = "chunks"
ENTITIES_DOCUMENT = "documents_entities"
GRAPHS_ENTITIES = "graphs_entities"
# TODO: Add support for relationships
# TRIPLES = "relationship"
COMMUNITIES = "graphs_communities"
def __str__(self) -> str:
return self.value
class VectorQuantizationType(str, Enum):
"""An enum representing the types of quantization available for vectors.
Attributes:
FP32 (str): 32-bit floating point quantization.
FP16 (str): 16-bit floating point quantization.
INT1 (str): 1-bit integer quantization.
SPARSE (str): Sparse vector quantization.
"""
FP32 = "FP32"
FP16 = "FP16"
INT1 = "INT1"
SPARSE = "SPARSE"
def __str__(self) -> str:
return self.value
@property
def db_type(self) -> str:
db_type_mapping = {
"FP32": "vector",
"FP16": "halfvec",
"INT1": "bit",
"SPARSE": "sparsevec",
}
return db_type_mapping[self.value]
class VectorQuantizationSettings(R2RSerializable):
quantization_type: VectorQuantizationType = Field(
default=VectorQuantizationType.FP32
)
class Vector(R2RSerializable):
"""A vector with the option to fix the number of elements."""
data: list[float]
type: VectorType = Field(default=VectorType.FIXED)
length: int = Field(default=-1)
def __init__(self, **data):
super().__init__(**data)
if (
self.type == VectorType.FIXED
and self.length > 0
and len(self.data) != self.length
):
raise ValueError(
f"Vector must be exactly {self.length} elements long."
)
def __repr__(self) -> str:
return (
f"Vector(data={self.data}, type={self.type}, length={self.length})"
)
class VectorEntry(R2RSerializable):
"""A vector entry that can be stored directly in supported vector
databases."""
id: UUID
document_id: UUID
owner_id: UUID
collection_ids: list[UUID]
vector: Vector
text: str
metadata: dict[str, Any]
def __str__(self) -> str:
"""Return a string representation of the VectorEntry."""
return (
f"VectorEntry("
f"chunk_id={self.id}, "
f"document_id={self.document_id}, "
f"owner_id={self.owner_id}, "
f"collection_ids={self.collection_ids}, "
f"vector={self.vector}, "
f"text={self.text}, "
f"metadata={self.metadata})"
)
def __repr__(self) -> str:
"""Return an unambiguous string representation of the VectorEntry."""
return self.__str__()
class StorageResult(R2RSerializable):
"""A result of a storage operation."""
success: bool
document_id: UUID
num_chunks: int = 0
error_message: Optional[str] = None
def __str__(self) -> str:
"""Return a string representation of the StorageResult."""
return f"StorageResult(success={self.success}, error_message={self.error_message})"
def __repr__(self) -> str:
"""Return an unambiguous string representation of the StorageResult."""
return self.__str__()
class IndexConfig(BaseModel):
name: Optional[str] = Field(default=None)
table_name: Optional[str] = Field(default=VectorTableName.CHUNKS)
index_method: Optional[str] = Field(default=IndexMethod.hnsw)
index_measure: Optional[str] = Field(default=IndexMeasure.cosine_distance)
index_arguments: Optional[IndexArgsIVFFlat | IndexArgsHNSW] = Field(
default=None
)
index_name: Optional[str] = Field(default=None)
index_column: Optional[str] = Field(default=None)
concurrently: Optional[bool] = Field(default=True)
