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#
# Copyright 2025 The InfiniFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
from abc import ABC, abstractmethod
from dataclasses import dataclass
import numpy as np
DEFAULT_MATCH_VECTOR_TOPN = 10
DEFAULT_MATCH_SPARSE_TOPN = 10
VEC = list | np.ndarray
@dataclass
class SparseVector:
indices: list[int]
values: list[float] | list[int] | None = None
def __post_init__(self):
assert (self.values is None) or (len(self.indices) == len(self.values))
def to_dict_old(self):
d = {"indices": self.indices}
if self.values is not None:
d["values"] = self.values
return d
def to_dict(self):
if self.values is None:
raise ValueError("SparseVector.values is None")
result = {}
for i, v in zip(self.indices, self.values):
result[str(i)] = v
return result
@staticmethod
def from_dict(d):
return SparseVector(d["indices"], d.get("values"))
def __str__(self):
return f"SparseVector(indices={self.indices}{'' if self.values is None else f', values={self.values}'})"
def __repr__(self):
return str(self)
class MatchTextExpr(ABC):
def __init__(
self,
fields: list[str],
matching_text: str,
topn: int,
extra_options: dict = dict(),
):
self.fields = fields
self.matching_text = matching_text
self.topn = topn
self.extra_options = extra_options
class MatchDenseExpr(ABC):
def __init__(
self,
vector_column_name: str,
embedding_data: VEC,
embedding_data_type: str,
distance_type: str,
topn: int = DEFAULT_MATCH_VECTOR_TOPN,
extra_options: dict = dict(),
):
self.vector_column_name = vector_column_name
self.embedding_data = embedding_data
self.embedding_data_type = embedding_data_type
self.distance_type = distance_type
self.topn = topn
self.extra_options = extra_options
class MatchSparseExpr(ABC):
def __init__(
self,
vector_column_name: str,
sparse_data: SparseVector | dict,
distance_type: str,
topn: int,
opt_params: dict | None = None,
):
self.vector_column_name = vector_column_name
self.sparse_data = sparse_data
self.distance_type = distance_type
self.topn = topn
self.opt_params = opt_params
class MatchTensorExpr(ABC):
def __init__(
self,
column_name: str,
query_data: VEC,
query_data_type: str,
topn: int,
extra_option: dict | None = None,
):
self.column_name = column_name
self.query_data = query_data
self.query_data_type = query_data_type
self.topn = topn
self.extra_option = extra_option
class FusionExpr(ABC):
def __init__(self, method: str, topn: int, fusion_params: dict | None = None):
self.method = method
self.topn = topn
self.fusion_params = fusion_params
MatchExpr = MatchTextExpr | MatchDenseExpr | MatchSparseExpr | MatchTensorExpr | FusionExpr
class OrderByExpr(ABC):
def __init__(self):
self.fields = list()
def asc(self, field: str):
self.fields.append((field, 0))
return self
def desc(self, field: str):
self.fields.append((field, 1))
return self
def fields(self):
return self.fields
class DocStoreConnection(ABC):
"""
Database operations
"""
@abstractmethod
def dbType(self) -> str:
"""
Return the type of the database.
"""
raise NotImplementedError("Not implemented")
@abstractmethod
def health(self) -> dict:
"""
Return the health status of the database.
"""
raise NotImplementedError("Not implemented")
"""
Table operations
"""
@abstractmethod
def createIdx(self, indexName: str, knowledgebaseId: str, vectorSize: int):
"""
Create an index with given name
"""
raise NotImplementedError("Not implemented")
@abstractmethod
def deleteIdx(self, indexName: str, knowledgebaseId: str):
"""
Delete an index with given name
"""
raise NotImplementedError("Not implemented")
@abstractmethod
def indexExist(self, indexName: str, knowledgebaseId: str) -> bool:
"""
Check if an index with given name exists
"""
raise NotImplementedError("Not implemented")
"""
CRUD operations
"""
@abstractmethod
def search(
self, selectFields: list[str],
highlightFields: list[str],
condition: dict,
matchExprs: list[MatchExpr],
orderBy: OrderByExpr,
offset: int,
limit: int,
indexNames: str|list[str],
knowledgebaseIds: list[str],
aggFields: list[str] = [],
rank_feature: dict | None = None
):
"""
Search with given conjunctive equivalent filtering condition and return all fields of matched documents
"""
raise NotImplementedError("Not implemented")
@abstractmethod
def get(self, chunkId: str, indexName: str, knowledgebaseIds: list[str]) -> dict | None:
"""
Get single chunk with given id
"""
raise NotImplementedError("Not implemented")
@abstractmethod
def insert(self, rows: list[dict], indexName: str, knowledgebaseId: str = None) -> list[str]:
"""
Update or insert a bulk of rows
"""
raise NotImplementedError("Not implemented")
@abstractmethod
def update(self, condition: dict, newValue: dict, indexName: str, knowledgebaseId: str) -> bool:
"""
Update rows with given conjunctive equivalent filtering condition
"""
raise NotImplementedError("Not implemented")
@abstractmethod
def delete(self, condition: dict, indexName: str, knowledgebaseId: str) -> int:
"""
Delete rows with given conjunctive equivalent filtering condition
"""
raise NotImplementedError("Not implemented")
"""
Helper functions for search result
"""
@abstractmethod
def getTotal(self, res):
raise NotImplementedError("Not implemented")
@abstractmethod
def getChunkIds(self, res):
raise NotImplementedError("Not implemented")
@abstractmethod
def getFields(self, res, fields: list[str]) -> dict[str, dict]:
raise NotImplementedError("Not implemented")
@abstractmethod
def getHighlight(self, res, keywords: list[str], fieldnm: str):
raise NotImplementedError("Not implemented")
@abstractmethod
def getAggregation(self, res, fieldnm: str):
raise NotImplementedError("Not implemented")
"""
SQL
"""
@abstractmethod
def sql(sql: str, fetch_size: int, format: str):
"""
Run the sql generated by text-to-sql
"""
raise NotImplementedError("Not implemented")
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