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import uuid
from abc import ABC
from typing import Any, Callable, Dict, Generic, Type, TypeVar
from uuid import UUID
import numpy as np
from loguru import logger
from pydantic import UUID4, BaseModel, Field
from qdrant_client.http import exceptions
from qdrant_client.http.models import Distance, VectorParams
from qdrant_client.models import CollectionInfo, PointStruct, Record
from rag_demo.infra.qdrant import connection
T = TypeVar("T", bound="VectorBaseDocument")
EMBEDDING_SIZE = 1024
class VectorBaseDocument(BaseModel, Generic[T], ABC):
id: UUID4 = Field(default_factory=uuid.uuid4)
def __eq__(self, value: object) -> bool:
if not isinstance(value, self.__class__):
return False
return self.id == value.id
def __hash__(self) -> int:
return hash(self.id)
@classmethod
def from_record(cls: Type[T], point: Record) -> T:
_id = UUID(point.id, version=4)
payload = point.payload or {}
attributes = {
"id": _id,
**payload,
}
if cls._has_class_attribute("embedding"):
attributes["embedding"] = point.vector or None
return cls(**attributes)
def to_point(self: T, **kwargs) -> PointStruct:
exclude_unset = kwargs.pop("exclude_unset", False)
by_alias = kwargs.pop("by_alias", True)
payload = self.model_dump(
exclude_unset=exclude_unset, by_alias=by_alias, **kwargs
)
_id = str(payload.pop("id"))
vector = payload.pop("embedding", {})
if vector and isinstance(vector, np.ndarray):
vector = vector.tolist()
return PointStruct(id=_id, vector=vector, payload=payload)
def model_dump(self: T, **kwargs) -> dict:
dict_ = super().model_dump(**kwargs)
dict_ = self._uuid_to_str(dict_)
return dict_
def _uuid_to_str(self, item: Any) -> Any:
if isinstance(item, dict):
for key, value in item.items():
if isinstance(value, UUID):
item[key] = str(value)
elif isinstance(value, list):
item[key] = [self._uuid_to_str(v) for v in value]
elif isinstance(value, dict):
item[key] = {k: self._uuid_to_str(v) for k, v in value.items()}
return item
@classmethod
def bulk_insert(cls: Type[T], documents: list["VectorBaseDocument"]) -> bool:
try:
cls._bulk_insert(documents)
logger.info(
f"Successfully inserted {len(documents)} documents into {cls.get_collection_name()}"
)
except Exception as e:
logger.error(f"Error inserting documents: {e}")
logger.info(
f"Collection '{cls.get_collection_name()}' does not exist. Trying to create the collection and reinsert the documents."
)
cls.create_collection()
try:
cls._bulk_insert(documents)
except Exception as e:
logger.error(f"Error inserting documents: {e}")
logger.error(
f"Failed to insert documents in '{cls.get_collection_name()}'."
)
return False
return True
@classmethod
def _bulk_insert(cls: Type[T], documents: list["VectorBaseDocument"]) -> None:
points = [doc.to_point() for doc in documents]
connection.upsert(collection_name=cls.get_collection_name(), points=points)
@classmethod
def bulk_find(
cls: Type[T], limit: int = 10, **kwargs
) -> tuple[list[T], UUID | None]:
try:
documents, next_offset = cls._bulk_find(limit=limit, **kwargs)
except exceptions.UnexpectedResponse:
logger.error(
f"Failed to search documents in '{cls.get_collection_name()}'."
)
documents, next_offset = [], None
return documents, next_offset
@classmethod
def _bulk_find(
cls: Type[T], limit: int = 10, **kwargs
) -> tuple[list[T], UUID | None]:
collection_name = cls.get_collection_name()
offset = kwargs.pop("offset", None)
offset = str(offset) if offset else None
records, next_offset = connection.scroll(
collection_name=collection_name,
limit=limit,
with_payload=kwargs.pop("with_payload", True),
with_vectors=kwargs.pop("with_vectors", False),
offset=offset,
**kwargs,
)
documents = [cls.from_record(record) for record in records]
if next_offset is not None:
next_offset = UUID(next_offset, version=4)
return documents, next_offset
@classmethod
def search(cls: Type[T], query_vector: list, limit: int = 10, **kwargs) -> list[T]:
try:
documents = cls._search(query_vector=query_vector, limit=limit, **kwargs)
except exceptions.UnexpectedResponse:
logger.error(
f"Failed to search documents in '{cls.get_collection_name()}'."
)
documents = []
return documents
@classmethod
def _search(cls: Type[T], query_vector: list, limit: int = 10, **kwargs) -> list[T]:
collection_name = cls.get_collection_name()
records = connection.search(
collection_name=collection_name,
query_vector=query_vector,
limit=limit,
with_payload=kwargs.pop("with_payload", True),
with_vectors=kwargs.pop("with_vectors", False),
**kwargs,
)
documents = [cls.from_record(record) for record in records]
return documents
@classmethod
def get_or_create_collection(cls: Type[T]) -> CollectionInfo:
collection_name = cls.get_collection_name()
try:
return connection.get_collection(collection_name=collection_name)
except exceptions.UnexpectedResponse:
use_vector_index = cls.get_use_vector_index()
collection_created = cls._create_collection(
collection_name=collection_name, use_vector_index=use_vector_index
)
if collection_created is False:
raise RuntimeError(
f"Couldn't create collection {collection_name}"
) from None
return connection.get_collection(collection_name=collection_name)
@classmethod
def create_collection(cls: Type[T]) -> bool:
collection_name = cls.get_collection_name()
use_vector_index = cls.get_use_vector_index()
logger.info(
f"Creating collection {collection_name} with use_vector_index={use_vector_index}"
)
return cls._create_collection(
collection_name=collection_name, use_vector_index=use_vector_index
)
@classmethod
def _create_collection(
cls, collection_name: str, use_vector_index: bool = True
) -> bool:
if use_vector_index is True:
vectors_config = VectorParams(size=EMBEDDING_SIZE, distance=Distance.COSINE)
else:
vectors_config = {}
return connection.create_collection(
collection_name=collection_name, vectors_config=vectors_config
)
@classmethod
def get_collection_name(cls: Type[T]) -> str:
if not hasattr(cls, "Config") or not hasattr(cls.Config, "name"):
raise Exception(
f"The class {cls} should define a Config class with the 'name' property that reflects the collection's name."
)
return cls.Config.name
@classmethod
def get_use_vector_index(cls: Type[T]) -> bool:
if not hasattr(cls, "Config") or not hasattr(cls.Config, "use_vector_index"):
return True
return cls.Config.use_vector_index
@classmethod
def group_by_class(
cls: Type["VectorBaseDocument"], documents: list["VectorBaseDocument"]
) -> Dict["VectorBaseDocument", list["VectorBaseDocument"]]:
return cls._group_by(documents, selector=lambda doc: doc.__class__)
@classmethod
def _group_by(
cls: Type[T], documents: list[T], selector: Callable[[T], Any]
) -> Dict[Any, list[T]]:
grouped = {}
for doc in documents:
key = selector(doc)
if key not in grouped:
grouped[key] = []
grouped[key].append(doc)
return grouped
@classmethod
def collection_name_to_class(
cls: Type["VectorBaseDocument"], collection_name: str
) -> type["VectorBaseDocument"]:
for subclass in cls.__subclasses__():
try:
if subclass.get_collection_name() == collection_name:
return subclass
except Exception:
pass
try:
return subclass.collection_name_to_class(collection_name)
except ValueError:
continue
raise ValueError(f"No subclass found for collection name: {collection_name}")
@classmethod
def _has_class_attribute(cls: Type[T], attribute_name: str) -> bool:
if attribute_name in cls.__annotations__:
return True
for base in cls.__bases__:
if hasattr(base, "_has_class_attribute") and base._has_class_attribute(
attribute_name
):
return True
return False
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