ragflow/rag/utils/doc_store_conn.py

from abc import ABC, abstractmethod
from typing import Optional, Union
from dataclasses import dataclass
import numpy as np
import polars as pl
from typing import List, Dict

DEFAULT_MATCH_VECTOR_TOPN = 10
DEFAULT_MATCH_SPARSE_TOPN = 10
VEC = Union[list, np.ndarray]


@dataclass
class SparseVector:
    indices: list[int]
    values: Union[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: 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: Optional[dict] = 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: Optional[dict] = 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: Optional[dict] = None):
        self.method = method
        self.topn = topn
        self.fusion_params = fusion_params


MatchExpr = Union[
    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], highlight: list[str], condition: dict, matchExprs: list[MatchExpr], orderBy: OrderByExpr, offset: int, limit: int, indexNames: str|list[str], knowledgebaseIds: list[str]
    ) -> list[dict] | pl.DataFrame:
        """
        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) -> 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")