mirror of
https://github.com/infiniflow/ragflow.git
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Feat: message manage (#12083)
### What problem does this PR solve? Message CRUD. Issue #4213 ### Type of change - [x] New Feature (non-breaking change which adds functionality)
This commit is contained in:
Lynn
0
common/doc_store/__init__.py
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0
common/doc_store/__init__.py
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270
common/doc_store/doc_store_base.py
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270
common/doc_store/doc_store_base.py
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@ -0,0 +1,270 @@
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#
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# Copyright 2025 The InfiniFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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from abc import ABC, abstractmethod
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from dataclasses import dataclass
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import numpy as np
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DEFAULT_MATCH_VECTOR_TOPN = 10
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DEFAULT_MATCH_SPARSE_TOPN = 10
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VEC = list | np.ndarray
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@dataclass
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class SparseVector:
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indices: list[int]
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values: list[float] | list[int] | None = None
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def __post_init__(self):
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assert (self.values is None) or (len(self.indices) == len(self.values))
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def to_dict_old(self):
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d = {"indices": self.indices}
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if self.values is not None:
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d["values"] = self.values
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return d
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def to_dict(self):
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if self.values is None:
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raise ValueError("SparseVector.values is None")
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result = {}
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for i, v in zip(self.indices, self.values):
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result[str(i)] = v
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return result
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@staticmethod
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def from_dict(d):
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return SparseVector(d["indices"], d.get("values"))
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def __str__(self):
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return f"SparseVector(indices={self.indices}{'' if self.values is None else f', values={self.values}'})"
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def __repr__(self):
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return str(self)
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class MatchTextExpr:
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def __init__(
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self,
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fields: list[str],
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matching_text: str,
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topn: int,
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extra_options: dict | None = None,
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):
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self.fields = fields
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self.matching_text = matching_text
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self.topn = topn
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self.extra_options = extra_options
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class MatchDenseExpr:
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def __init__(
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self,
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vector_column_name: str,
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embedding_data: VEC,
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embedding_data_type: str,
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distance_type: str,
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topn: int = DEFAULT_MATCH_VECTOR_TOPN,
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extra_options: dict | None = None,
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):
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self.vector_column_name = vector_column_name
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self.embedding_data = embedding_data
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self.embedding_data_type = embedding_data_type
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self.distance_type = distance_type
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self.topn = topn
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self.extra_options = extra_options
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class MatchSparseExpr:
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def __init__(
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self,
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vector_column_name: str,
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sparse_data: SparseVector | dict,
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distance_type: str,
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topn: int,
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opt_params: dict | None = None,
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):
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self.vector_column_name = vector_column_name
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self.sparse_data = sparse_data
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self.distance_type = distance_type
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self.topn = topn
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self.opt_params = opt_params
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class MatchTensorExpr:
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def __init__(
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self,
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column_name: str,
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query_data: VEC,
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query_data_type: str,
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topn: int,
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extra_option: dict | None = None,
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):
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self.column_name = column_name
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self.query_data = query_data
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self.query_data_type = query_data_type
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self.topn = topn
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self.extra_option = extra_option
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class FusionExpr:
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def __init__(self, method: str, topn: int, fusion_params: dict | None = None):
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self.method = method
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self.topn = topn
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self.fusion_params = fusion_params
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MatchExpr = MatchTextExpr | MatchDenseExpr | MatchSparseExpr | MatchTensorExpr | FusionExpr
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class OrderByExpr:
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def __init__(self):
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self.fields = list()
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def asc(self, field: str):
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self.fields.append((field, 0))
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return self
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def desc(self, field: str):
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self.fields.append((field, 1))
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return self
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def fields(self):
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return self.fields
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class DocStoreConnection(ABC):
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"""
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Database operations
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"""
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@abstractmethod
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def db_type(self) -> str:
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"""
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Return the type of the database.
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"""
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raise NotImplementedError("Not implemented")
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@abstractmethod
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def health(self) -> dict:
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"""
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Return the health status of the database.
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"""
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raise NotImplementedError("Not implemented")
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"""
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Table operations
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"""
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@abstractmethod
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def create_idx(self, index_name: str, dataset_id: str, vector_size: int):
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"""
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Create an index with given name
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"""
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raise NotImplementedError("Not implemented")
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@abstractmethod
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def delete_idx(self, index_name: str, dataset_id: str):
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"""
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Delete an index with given name
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"""
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raise NotImplementedError("Not implemented")
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@abstractmethod
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def index_exist(self, index_name: str, dataset_id: str) -> bool:
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"""
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Check if an index with given name exists
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"""
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raise NotImplementedError("Not implemented")
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"""
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CRUD operations
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"""
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@abstractmethod
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def search(
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self, select_fields: list[str],
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highlight_fields: list[str],
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condition: dict,
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match_expressions: list[MatchExpr],
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order_by: OrderByExpr,
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offset: int,
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limit: int,
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index_names: str|list[str],
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dataset_ids: list[str],
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agg_fields: list[str] | None = None,
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rank_feature: dict | None = None
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):
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"""
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Search with given conjunctive equivalent filtering condition and return all fields of matched documents
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"""
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raise NotImplementedError("Not implemented")
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@abstractmethod
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def get(self, data_id: str, index_name: str, dataset_ids: list[str]) -> dict | None:
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"""
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Get single chunk with given id
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"""
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raise NotImplementedError("Not implemented")
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@abstractmethod
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def insert(self, rows: list[dict], index_name: str, dataset_id: str = None) -> list[str]:
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"""
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Update or insert a bulk of rows
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"""
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raise NotImplementedError("Not implemented")
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@abstractmethod
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def update(self, condition: dict, new_value: dict, index_name: str, dataset_id: str) -> bool:
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"""
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Update rows with given conjunctive equivalent filtering condition
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"""
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raise NotImplementedError("Not implemented")
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@abstractmethod
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def delete(self, condition: dict, index_name: str, dataset_id: str) -> int:
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"""
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Delete rows with given conjunctive equivalent filtering condition
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"""
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raise NotImplementedError("Not implemented")
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"""
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Helper functions for search result
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"""
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@abstractmethod
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def get_total(self, res):
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raise NotImplementedError("Not implemented")
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@abstractmethod
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def get_doc_ids(self, res):
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raise NotImplementedError("Not implemented")
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@abstractmethod
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def get_fields(self, res, fields: list[str]) -> dict[str, dict]:
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raise NotImplementedError("Not implemented")
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@abstractmethod
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def get_highlight(self, res, keywords: list[str], field_name: str):
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raise NotImplementedError("Not implemented")
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@abstractmethod
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def get_aggregation(self, res, field_name: str):
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raise NotImplementedError("Not implemented")
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"""
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SQL
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"""
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@abstractmethod
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def sql(self, sql: str, fetch_size: int, format: str):
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"""
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Run the sql generated by text-to-sql
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"""
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raise NotImplementedError("Not implemented")
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326
common/doc_store/es_conn_base.py
Normal file
326
common/doc_store/es_conn_base.py
Normal file
@ -0,0 +1,326 @@
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#
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# Copyright 2025 The InfiniFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
|
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# You may obtain a copy of the License at
|
||||
#
|
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# http://www.apache.org/licenses/LICENSE-2.0
|
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#
|
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# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
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# See the License for the specific language governing permissions and
|
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# limitations under the License.
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#
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import logging
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import re
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import json
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import time
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import os
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from abc import abstractmethod
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from elasticsearch import Elasticsearch, NotFoundError
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from elasticsearch_dsl import Index
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from elastic_transport import ConnectionTimeout
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from common.file_utils import get_project_base_directory
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from common.misc_utils import convert_bytes
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from common.doc_store.doc_store_base import DocStoreConnection, OrderByExpr, MatchExpr
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from rag.nlp import is_english, rag_tokenizer
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from common import settings
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ATTEMPT_TIME = 2
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class ESConnectionBase(DocStoreConnection):
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def __init__(self, mapping_file_name: str="mapping.json", logger_name: str='ragflow.es_conn'):
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self.logger = logging.getLogger(logger_name)
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self.info = {}
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self.logger.info(f"Use Elasticsearch {settings.ES['hosts']} as the doc engine.")
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for _ in range(ATTEMPT_TIME):
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try:
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if self._connect():
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break
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except Exception as e:
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self.logger.warning(f"{str(e)}. Waiting Elasticsearch {settings.ES['hosts']} to be healthy.")
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time.sleep(5)
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if not self.es.ping():
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msg = f"Elasticsearch {settings.ES['hosts']} is unhealthy in 120s."
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self.logger.error(msg)
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raise Exception(msg)
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v = self.info.get("version", {"number": "8.11.3"})
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v = v["number"].split(".")[0]
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if int(v) < 8:
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msg = f"Elasticsearch version must be greater than or equal to 8, current version: {v}"
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self.logger.error(msg)
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raise Exception(msg)
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fp_mapping = os.path.join(get_project_base_directory(), "conf", mapping_file_name)
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if not os.path.exists(fp_mapping):
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msg = f"Elasticsearch mapping file not found at {fp_mapping}"
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self.logger.error(msg)
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raise Exception(msg)
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self.mapping = json.load(open(fp_mapping, "r"))
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self.logger.info(f"Elasticsearch {settings.ES['hosts']} is healthy.")
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def _connect(self):
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self.es = Elasticsearch(
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settings.ES["hosts"].split(","),
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basic_auth=(settings.ES["username"], settings.ES[
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"password"]) if "username" in settings.ES and "password" in settings.ES else None,
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verify_certs= settings.ES.get("verify_certs", False),
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timeout=600 )
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if self.es:
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self.info = self.es.info()
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return True
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return False
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||||
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||||
"""
|
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Database operations
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||||
"""
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||||
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def db_type(self) -> str:
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||||
return "elasticsearch"
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||||
|
||||
def health(self) -> dict:
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health_dict = dict(self.es.cluster.health())
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health_dict["type"] = "elasticsearch"
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return health_dict
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def get_cluster_stats(self):
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"""
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||||
curl -XGET "http://{es_host}/_cluster/stats" -H "kbn-xsrf: reporting" to view raw stats.
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"""
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raw_stats = self.es.cluster.stats()
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self.logger.debug(f"ESConnection.get_cluster_stats: {raw_stats}")
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||||
try:
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||||
res = {
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'cluster_name': raw_stats['cluster_name'],
|
||||
'status': raw_stats['status']
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}
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||||
indices_status = raw_stats['indices']
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res.update({
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'indices': indices_status['count'],
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||||
'indices_shards': indices_status['shards']['total']
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})
|
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doc_info = indices_status['docs']
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res.update({
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'docs': doc_info['count'],
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||||
'docs_deleted': doc_info['deleted']
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||||
})
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||||
store_info = indices_status['store']
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res.update({
|
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'store_size': convert_bytes(store_info['size_in_bytes']),
|
||||
'total_dataset_size': convert_bytes(store_info['total_data_set_size_in_bytes'])
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||||
})
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||||
mappings_info = indices_status['mappings']
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||||
res.update({
|
||||
'mappings_fields': mappings_info['total_field_count'],
|
||||
'mappings_deduplicated_fields': mappings_info['total_deduplicated_field_count'],
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'mappings_deduplicated_size': convert_bytes(mappings_info['total_deduplicated_mapping_size_in_bytes'])
|
||||
})
|
||||
node_info = raw_stats['nodes']
|
||||
res.update({
|
||||
'nodes': node_info['count']['total'],
|
||||
'nodes_version': node_info['versions'],
|
||||
'os_mem': convert_bytes(node_info['os']['mem']['total_in_bytes']),
|
||||
'os_mem_used': convert_bytes(node_info['os']['mem']['used_in_bytes']),
|
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'os_mem_used_percent': node_info['os']['mem']['used_percent'],
|
||||
'jvm_versions': node_info['jvm']['versions'][0]['vm_version'],
|
||||
'jvm_heap_used': convert_bytes(node_info['jvm']['mem']['heap_used_in_bytes']),
|
||||
'jvm_heap_max': convert_bytes(node_info['jvm']['mem']['heap_max_in_bytes'])
|
||||
})
|
||||
return res
|
||||
|
||||
except Exception as e:
|
||||
self.logger.exception(f"ESConnection.get_cluster_stats: {e}")
|
||||
return None
|
||||
|
||||
"""
|
||||
Table operations
|
||||
"""
|
||||
|
||||
def create_idx(self, index_name: str, dataset_id: str, vector_size: int):
|
||||
if self.index_exist(index_name, dataset_id):
|
||||
return True
|
||||
try:
|
||||
from elasticsearch.client import IndicesClient
|
||||
return IndicesClient(self.es).create(index=index_name,
|
||||
settings=self.mapping["settings"],
|
||||
mappings=self.mapping["mappings"])
|
||||
except Exception:
|
||||
self.logger.exception("ESConnection.createIndex error %s" % index_name)
|
||||
|
||||
def delete_idx(self, index_name: str, dataset_id: str):
|
||||
if len(dataset_id) > 0:
|
||||
# The index need to be alive after any kb deletion since all kb under this tenant are in one index.
|
||||
return
|
||||
try:
|
||||
self.es.indices.delete(index=index_name, allow_no_indices=True)
|
||||
except NotFoundError:
|
||||
pass
|
||||
except Exception:
|
||||
self.logger.exception("ESConnection.deleteIdx error %s" % index_name)
|
||||
|
||||
def index_exist(self, index_name: str, dataset_id: str = None) -> bool:
|
||||
s = Index(index_name, self.es)
|
||||
for i in range(ATTEMPT_TIME):
|
||||
try:
|
||||
return s.exists()
|
||||
except ConnectionTimeout:
|
||||
self.logger.exception("ES request timeout")
|
||||
time.sleep(3)
|
||||
self._connect()
|
||||
continue
|
||||
except Exception as e:
|
||||
self.logger.exception(e)
|
||||
break
|
||||
return False
|
||||
|
||||
"""
|
||||
CRUD operations
|
||||
"""
|
||||
|
||||
def get(self, doc_id: str, index_name: str, dataset_ids: list[str]) -> dict | None:
|
||||
for i in range(ATTEMPT_TIME):
|
||||
try:
|
||||
res = self.es.get(index=index_name,
|
||||
id=doc_id, source=True, )
|
||||
if str(res.get("timed_out", "")).lower() == "true":
|
||||
raise Exception("Es Timeout.")
|
||||
doc = res["_source"]
|
||||
doc["id"] = doc_id
|
||||
return doc
|
||||
except NotFoundError:
|
||||
return None
|
||||
except Exception as e:
|
||||
self.logger.exception(f"ESConnection.get({doc_id}) got exception")
|
||||
raise e
|
||||
self.logger.error(f"ESConnection.get timeout for {ATTEMPT_TIME} times!")
|
||||
raise Exception("ESConnection.get timeout.")
|
||||
|
||||
@abstractmethod
|
||||
def search(
|
||||
self, select_fields: list[str],
|
||||
highlight_fields: list[str],
|
||||
condition: dict,
|
||||
match_expressions: list[MatchExpr],
|
||||
order_by: OrderByExpr,
|
||||
offset: int,
|
||||
limit: int,
|
||||
index_names: str | list[str],
|
||||
dataset_ids: list[str],
|
||||
agg_fields: list[str] | None = None,
|
||||
rank_feature: dict | None = None
|
||||
):
|
||||
raise NotImplementedError("Not implemented")
|
||||
|
||||
@abstractmethod
|
||||
def insert(self, documents: list[dict], index_name: str, dataset_id: str = None) -> list[str]:
|
||||
raise NotImplementedError("Not implemented")
|
||||
|
||||
@abstractmethod
|
||||
def update(self, condition: dict, new_value: dict, index_name: str, dataset_id: str) -> bool:
|
||||
raise NotImplementedError("Not implemented")
|
||||
|
||||
@abstractmethod
|
||||
def delete(self, condition: dict, index_name: str, dataset_id: str) -> int:
|
||||
raise NotImplementedError("Not implemented")
|
||||
|
||||
"""
|
||||
Helper functions for search result
|
||||
"""
|
||||
|
||||
def get_total(self, res):
|
||||
if isinstance(res["hits"]["total"], type({})):
|
||||
return res["hits"]["total"]["value"]
|
||||
return res["hits"]["total"]
|
||||
|
||||
def get_doc_ids(self, res):
|
||||
return [d["_id"] for d in res["hits"]["hits"]]
|
||||
|
||||
def _get_source(self, res):
|
||||
rr = []
|
||||
for d in res["hits"]["hits"]:
|
||||
d["_source"]["id"] = d["_id"]
|
||||
d["_source"]["_score"] = d["_score"]
|
||||
rr.append(d["_source"])
|
||||
return rr
|
||||
|
||||
@abstractmethod
|
||||
def get_fields(self, res, fields: list[str]) -> dict[str, dict]:
|
||||
raise NotImplementedError("Not implemented")
|
||||
|
||||
def get_highlight(self, res, keywords: list[str], field_name: str):
|
||||
ans = {}
|
||||
for d in res["hits"]["hits"]:
|
||||
highlights = d.get("highlight")
|
||||
if not highlights:
|
||||
continue
|
||||
txt = "...".join([a for a in list(highlights.items())[0][1]])
|
||||
if not is_english(txt.split()):
|
||||
ans[d["_id"]] = txt
|
||||
continue
|
||||
|
||||
txt = d["_source"][field_name]
|
||||
txt = re.sub(r"[\r\n]", " ", txt, flags=re.IGNORECASE | re.MULTILINE)
|
||||
txt_list = []
|
||||
for t in re.split(r"[.?!;\n]", txt):
|
||||
for w in keywords:
|
||||
t = re.sub(r"(^|[ .?/'\"\(\)!,:;-])(%s)([ .?/'\"\(\)!,:;-])" % re.escape(w), r"\1<em>\2</em>\3", t,
|
||||
flags=re.IGNORECASE | re.MULTILINE)
|
||||
if not re.search(r"<em>[^<>]+</em>", t, flags=re.IGNORECASE | re.MULTILINE):
|
||||
continue
|
||||
txt_list.append(t)
|
||||
ans[d["_id"]] = "...".join(txt_list) if txt_list else "...".join([a for a in list(highlights.items())[0][1]])
|
||||
|
||||
return ans
|
||||
|
||||
def get_aggregation(self, res, field_name: str):
|
||||
agg_field = "aggs_" + field_name
|
||||
if "aggregations" not in res or agg_field not in res["aggregations"]:
|
||||
return list()
|
||||
buckets = res["aggregations"][agg_field]["buckets"]
|
||||
return [(b["key"], b["doc_count"]) for b in buckets]
|
||||
|
||||
"""
|
||||
SQL
|
||||
"""
|
||||
|
||||
def sql(self, sql: str, fetch_size: int, format: str):
|
||||
self.logger.debug(f"ESConnection.sql get sql: {sql}")
|
||||
sql = re.sub(r"[ `]+", " ", sql)
|
||||
sql = sql.replace("%", "")
|
||||
replaces = []
|
||||
for r in re.finditer(r" ([a-z_]+_l?tks)( like | ?= ?)'([^']+)'", sql):
|
||||
fld, v = r.group(1), r.group(3)
|
||||
match = " MATCH({}, '{}', 'operator=OR;minimum_should_match=30%') ".format(
|
||||
fld, rag_tokenizer.fine_grained_tokenize(rag_tokenizer.tokenize(v)))
|
||||
replaces.append(
|
||||
("{}{}'{}'".format(
|
||||
r.group(1),
|
||||
r.group(2),
|
||||
r.group(3)),
|
||||
match))
|
||||
|
||||
for p, r in replaces:
|
||||
sql = sql.replace(p, r, 1)
|
||||
self.logger.debug(f"ESConnection.sql to es: {sql}")
|
||||
|
||||
for i in range(ATTEMPT_TIME):
|
||||
try:
|
||||
res = self.es.sql.query(body={"query": sql, "fetch_size": fetch_size}, format=format,
|
||||
request_timeout="2s")
|
||||
return res
|
||||
except ConnectionTimeout:
|
||||
self.logger.exception("ES request timeout")
|
||||
time.sleep(3)
|
||||
self._connect()
|
||||
continue
|
||||
except Exception as e:
|
||||
self.logger.exception(f"ESConnection.sql got exception. SQL:\n{sql}")
|
||||
raise Exception(f"SQL error: {e}\n\nSQL: {sql}")
|
||||
self.logger.error(f"ESConnection.sql timeout for {ATTEMPT_TIME} times!")
|
||||
return None
|
||||
451
common/doc_store/infinity_conn_base.py
Normal file
451
common/doc_store/infinity_conn_base.py
Normal file
@ -0,0 +1,451 @@
|
||||
#
|
||||
# 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.
|
||||
#
|
||||
|
||||
import logging
|
||||
import os
|
||||
import re
|
||||
import json
|
||||
import time
|
||||
from abc import abstractmethod
|
||||
|
||||
import infinity
|
||||
from infinity.common import ConflictType
|
||||
from infinity.index import IndexInfo, IndexType
|
||||
from infinity.connection_pool import ConnectionPool
|
||||
from infinity.errors import ErrorCode
|
||||
import pandas as pd
|
||||
from common.file_utils import get_project_base_directory
|
||||
from rag.nlp import is_english
|
||||
from common import settings
|
||||
from common.doc_store.doc_store_base import DocStoreConnection, MatchExpr, OrderByExpr
|
||||
|
||||
|
||||
class InfinityConnectionBase(DocStoreConnection):
|
||||
def __init__(self, mapping_file_name: str="infinity_mapping.json", logger_name: str="ragflow.infinity_conn"):
|
||||
self.dbName = settings.INFINITY.get("db_name", "default_db")
|
||||
self.mapping_file_name = mapping_file_name
|
||||
self.logger = logging.getLogger(logger_name)
|
||||
infinity_uri = settings.INFINITY["uri"]
|
||||
if ":" in infinity_uri:
|
||||
host, port = infinity_uri.split(":")
|
||||
infinity_uri = infinity.common.NetworkAddress(host, int(port))
|
||||
self.connPool = None
|
||||
self.logger.info(f"Use Infinity {infinity_uri} as the doc engine.")
|
||||
for _ in range(24):
|
||||
try:
|
||||
conn_pool = ConnectionPool(infinity_uri, max_size=4)
|
||||
inf_conn = conn_pool.get_conn()
|
||||
res = inf_conn.show_current_node()
|
||||
if res.error_code == ErrorCode.OK and res.server_status in ["started", "alive"]:
|
||||
self._migrate_db(inf_conn)
|
||||
self.connPool = conn_pool
|
||||
conn_pool.release_conn(inf_conn)
|
||||
break
|
||||
conn_pool.release_conn(inf_conn)
|
||||
self.logger.warning(f"Infinity status: {res.server_status}. Waiting Infinity {infinity_uri} to be healthy.")
|
||||
time.sleep(5)
|
||||
except Exception as e:
|
||||
self.logger.warning(f"{str(e)}. Waiting Infinity {infinity_uri} to be healthy.")
|
||||
time.sleep(5)
|
||||
if self.connPool is None:
|
||||
msg = f"Infinity {infinity_uri} is unhealthy in 120s."
|
||||
self.logger.error(msg)
|
||||
raise Exception(msg)
|
||||
self.logger.info(f"Infinity {infinity_uri} is healthy.")
|
||||
|
||||
def _migrate_db(self, inf_conn):
|
||||
inf_db = inf_conn.create_database(self.dbName, ConflictType.Ignore)
|
||||
fp_mapping = os.path.join(get_project_base_directory(), "conf", self.mapping_file_name)
|
||||
if not os.path.exists(fp_mapping):
|
||||
raise Exception(f"Mapping file not found at {fp_mapping}")
|
||||
schema = json.load(open(fp_mapping))
|
||||
table_names = inf_db.list_tables().table_names
|
||||
for table_name in table_names:
|
||||
inf_table = inf_db.get_table(table_name)
|
||||
index_names = inf_table.list_indexes().index_names
|
||||
if "q_vec_idx" not in index_names:
|
||||
# Skip tables not created by me
|
||||
continue
|
||||
column_names = inf_table.show_columns()["name"]
|
||||
column_names = set(column_names)
|
||||
for field_name, field_info in schema.items():
|
||||
if field_name in column_names:
|
||||
continue
|
||||
res = inf_table.add_columns({field_name: field_info})
|
||||
assert res.error_code == infinity.ErrorCode.OK
|
||||
self.logger.info(f"INFINITY added following column to table {table_name}: {field_name} {field_info}")
|
||||
if field_info["type"] != "varchar" or "analyzer" not in field_info:
|
||||
continue
|
||||
analyzers = field_info["analyzer"]
|
||||
if isinstance(analyzers, str):
|
||||
analyzers = [analyzers]
|
||||
for analyzer in analyzers:
|
||||
inf_table.create_index(
|
||||
f"ft_{re.sub(r'[^a-zA-Z0-9]', '_', field_name)}_{re.sub(r'[^a-zA-Z0-9]', '_', analyzer)}",
|
||||
IndexInfo(field_name, IndexType.FullText, {"ANALYZER": analyzer}),
|
||||
ConflictType.Ignore,
|
||||
)
|
||||
|
||||
"""
|
||||
Dataframe and fields convert
|
||||
"""
|
||||
|
||||
@staticmethod
|
||||
@abstractmethod
|
||||
def field_keyword(field_name: str):
|
||||
# judge keyword or not, such as "*_kwd" tag-like columns.
|
||||
raise NotImplementedError("Not implemented")
|
||||
|
||||
@abstractmethod
|
||||
def convert_select_fields(self, output_fields: list[str]) -> list[str]:
|
||||
# rm _kwd, _tks, _sm_tks, _with_weight suffix in field name.
|
||||
raise NotImplementedError("Not implemented")
|
||||
|
||||
@staticmethod
|
||||
@abstractmethod
|
||||
def convert_matching_field(field_weight_str: str) -> str:
|
||||
# convert matching field to
|
||||
raise NotImplementedError("Not implemented")
|
||||
|
||||
@staticmethod
|
||||
def list2str(lst: str | list, sep: str = " ") -> str:
|
||||
if isinstance(lst, str):
|
||||
return lst
|
||||
return sep.join(lst)
|
||||
|
||||
def equivalent_condition_to_str(self, condition: dict, table_instance=None) -> str | None:
|
||||
assert "_id" not in condition
|
||||
columns = {}
|
||||
if table_instance:
|
||||
for n, ty, de, _ in table_instance.show_columns().rows():
|
||||
columns[n] = (ty, de)
|
||||
|
||||
def exists(cln):
|
||||
nonlocal columns
|
||||
assert cln in columns, f"'{cln}' should be in '{columns}'."
|
||||
ty, de = columns[cln]
|
||||
if ty.lower().find("cha"):
|
||||
if not de:
|
||||
de = ""
|
||||
return f" {cln}!='{de}' "
|
||||
return f"{cln}!={de}"
|
||||
|
||||
cond = list()
|
||||
for k, v in condition.items():
|
||||
if not isinstance(k, str) or not v:
|
||||
continue
|
||||
if self.field_keyword(k):
|
||||
if isinstance(v, list):
|
||||
inCond = list()
|
||||
for item in v:
|
||||
if isinstance(item, str):
|
||||
item = item.replace("'", "''")
|
||||
inCond.append(f"filter_fulltext('{self.convert_matching_field(k)}', '{item}')")
|
||||
if inCond:
|
||||
strInCond = " or ".join(inCond)
|
||||
strInCond = f"({strInCond})"
|
||||
cond.append(strInCond)
|
||||
else:
|
||||
cond.append(f"filter_fulltext('{self.convert_matching_field(k)}', '{v}')")
|
||||
elif isinstance(v, list):
|
||||
inCond = list()
|
||||
for item in v:
|
||||
if isinstance(item, str):
|
||||
item = item.replace("'", "''")
|
||||
inCond.append(f"'{item}'")
|
||||
else:
|
||||
inCond.append(str(item))
|
||||
if inCond:
|
||||
strInCond = ", ".join(inCond)
|
||||
strInCond = f"{k} IN ({strInCond})"
|
||||
cond.append(strInCond)
|
||||
elif k == "must_not":
|
||||
if isinstance(v, dict):
|
||||
for kk, vv in v.items():
|
||||
if kk == "exists":
|
||||
cond.append("NOT (%s)" % exists(vv))
|
||||
elif isinstance(v, str):
|
||||
cond.append(f"{k}='{v}'")
|
||||
elif k == "exists":
|
||||
cond.append(exists(v))
|
||||
else:
|
||||
cond.append(f"{k}={str(v)}")
|
||||
return " AND ".join(cond) if cond else "1=1"
|
||||
|
||||
@staticmethod
|
||||
def concat_dataframes(df_list: list[pd.DataFrame], select_fields: list[str]) -> pd.DataFrame:
|
||||
df_list2 = [df for df in df_list if not df.empty]
|
||||
if df_list2:
|
||||
return pd.concat(df_list2, axis=0).reset_index(drop=True)
|
||||
|
||||
schema = []
|
||||
for field_name in select_fields:
|
||||
if field_name == "score()": # Workaround: fix schema is changed to score()
|
||||
schema.append("SCORE")
|
||||
elif field_name == "similarity()": # Workaround: fix schema is changed to similarity()
|
||||
schema.append("SIMILARITY")
|
||||
else:
|
||||
schema.append(field_name)
|
||||
return pd.DataFrame(columns=schema)
|
||||
|
||||
"""
|
||||
Database operations
|
||||
"""
|
||||
|
||||
def db_type(self) -> str:
|
||||
return "infinity"
|
||||
|
||||
def health(self) -> dict:
|
||||
"""
|
||||
Return the health status of the database.
|
||||
"""
|
||||
inf_conn = self.connPool.get_conn()
|
||||
res = inf_conn.show_current_node()
|
||||
self.connPool.release_conn(inf_conn)
|
||||
res2 = {
|
||||
"type": "infinity",
|
||||
"status": "green" if res.error_code == 0 and res.server_status in ["started", "alive"] else "red",
|
||||
"error": res.error_msg,
|
||||
}
|
||||
return res2
|
||||
|
||||
"""
|
||||
Table operations
|
||||
"""
|
||||
|
||||
def create_idx(self, index_name: str, dataset_id: str, vector_size: int):
|
||||
table_name = f"{index_name}_{dataset_id}"
|
||||
inf_conn = self.connPool.get_conn()
|
||||
inf_db = inf_conn.create_database(self.dbName, ConflictType.Ignore)
|
||||
|
||||
fp_mapping = os.path.join(get_project_base_directory(), "conf", self.mapping_file_name)
|
||||
if not os.path.exists(fp_mapping):
|
||||
raise Exception(f"Mapping file not found at {fp_mapping}")
|
||||
schema = json.load(open(fp_mapping))
|
||||
vector_name = f"q_{vector_size}_vec"
|
||||
schema[vector_name] = {"type": f"vector,{vector_size},float"}
|
||||
inf_table = inf_db.create_table(
|
||||
table_name,
|
||||
schema,
|
||||
ConflictType.Ignore,
|
||||
)
|
||||
inf_table.create_index(
|
||||
"q_vec_idx",
|
||||
IndexInfo(
|
||||
vector_name,
|
||||
IndexType.Hnsw,
|
||||
{
|
||||
"M": "16",
|
||||
"ef_construction": "50",
|
||||
"metric": "cosine",
|
||||
"encode": "lvq",
|
||||
},
|
||||
),
|
||||
ConflictType.Ignore,
|
||||
)
|
||||
for field_name, field_info in schema.items():
|
||||
if field_info["type"] != "varchar" or "analyzer" not in field_info:
|
||||
continue
|
||||
analyzers = field_info["analyzer"]
|
||||
if isinstance(analyzers, str):
|
||||
analyzers = [analyzers]
|
||||
for analyzer in analyzers:
|
||||
inf_table.create_index(
|
||||
f"ft_{re.sub(r'[^a-zA-Z0-9]', '_', field_name)}_{re.sub(r'[^a-zA-Z0-9]', '_', analyzer)}",
|
||||
IndexInfo(field_name, IndexType.FullText, {"ANALYZER": analyzer}),
|
||||
ConflictType.Ignore,
|
||||
)
|
||||
self.connPool.release_conn(inf_conn)
|
||||
self.logger.info(f"INFINITY created table {table_name}, vector size {vector_size}")
|
||||
return True
|
||||
|
||||
def delete_idx(self, index_name: str, dataset_id: str):
|
||||
table_name = f"{index_name}_{dataset_id}"
|
||||
inf_conn = self.connPool.get_conn()
|
||||
db_instance = inf_conn.get_database(self.dbName)
|
||||
db_instance.drop_table(table_name, ConflictType.Ignore)
|
||||
self.connPool.release_conn(inf_conn)
|
||||
self.logger.info(f"INFINITY dropped table {table_name}")
|
||||
|
||||
def index_exist(self, index_name: str, dataset_id: str) -> bool:
|
||||
table_name = f"{index_name}_{dataset_id}"
|
||||
try:
|
||||
inf_conn = self.connPool.get_conn()
|
||||
db_instance = inf_conn.get_database(self.dbName)
|
||||
_ = db_instance.get_table(table_name)
|
||||
self.connPool.release_conn(inf_conn)
|
||||
return True
|
||||
except Exception as e:
|
||||
self.logger.warning(f"INFINITY indexExist {str(e)}")
|
||||
return False
|
||||
|
||||
"""
|
||||
CRUD operations
|
||||
"""
|
||||
|
||||
@abstractmethod
|
||||
def search(
|
||||
self,
|
||||
select_fields: list[str],
|
||||
highlight_fields: list[str],
|
||||
condition: dict,
|
||||
match_expressions: list[MatchExpr],
|
||||
order_by: OrderByExpr,
|
||||
offset: int,
|
||||
limit: int,
|
||||
index_names: str | list[str],
|
||||
dataset_ids: list[str],
|
||||
agg_fields: list[str] | None = None,
|
||||
rank_feature: dict | None = None,
|
||||
) -> tuple[pd.DataFrame, int]:
|
||||
raise NotImplementedError("Not implemented")
|
||||
|
||||
@abstractmethod
|
||||
def get(self, doc_id: str, index_name: str, knowledgebase_ids: list[str]) -> dict | None:
|
||||
raise NotImplementedError("Not implemented")
|
||||
|
||||
@abstractmethod
|
||||
def insert(self, documents: list[dict], index_name: str, dataset_ids: str = None) -> list[str]:
|
||||
raise NotImplementedError("Not implemented")
|
||||
|
||||
@abstractmethod
|
||||
def update(self, condition: dict, new_value: dict, index_name: str, dataset_id: str) -> bool:
|
||||
raise NotImplementedError("Not implemented")
|
||||
|
||||
def delete(self, condition: dict, index_name: str, dataset_id: str) -> int:
|
||||
inf_conn = self.connPool.get_conn()
|
||||
db_instance = inf_conn.get_database(self.dbName)
|
||||
table_name = f"{index_name}_{dataset_id}"
|
||||
try:
|
||||
table_instance = db_instance.get_table(table_name)
|
||||
except Exception:
|
||||
self.logger.warning(f"Skipped deleting from table {table_name} since the table doesn't exist.")
|
||||
return 0
|
||||
filter = self.equivalent_condition_to_str(condition, table_instance)
|
||||
self.logger.debug(f"INFINITY delete table {table_name}, filter {filter}.")
|
||||
res = table_instance.delete(filter)
|
||||
self.connPool.release_conn(inf_conn)
|
||||
return res.deleted_rows
|
||||
|
||||
"""
|
||||
Helper functions for search result
|
||||
"""
|
||||
|
||||
def get_total(self, res: tuple[pd.DataFrame, int] | pd.DataFrame) -> int:
|
||||
if isinstance(res, tuple):
|
||||
return res[1]
|
||||
return len(res)
|
||||
|
||||
def get_doc_ids(self, res: tuple[pd.DataFrame, int] | pd.DataFrame) -> list[str]:
|
||||
if isinstance(res, tuple):
|
||||
res = res[0]
|
||||
return list(res["id"])
|
||||
|
||||
@abstractmethod
|
||||
def get_fields(self, res: tuple[pd.DataFrame, int] | pd.DataFrame, fields: list[str]) -> dict[str, dict]:
|
||||
raise NotImplementedError("Not implemented")
|
||||
|
||||
def get_highlight(self, res: tuple[pd.DataFrame, int] | pd.DataFrame, keywords: list[str], field_name: str):
|
||||
if isinstance(res, tuple):
|
||||
res = res[0]
|
||||
ans = {}
|
||||
num_rows = len(res)
|
||||
column_id = res["id"]
|
||||
if field_name not in res:
|
||||
return {}
|
||||
for i in range(num_rows):
|
||||
id = column_id[i]
|
||||
txt = res[field_name][i]
|
||||
if re.search(r"<em>[^<>]+</em>", txt, flags=re.IGNORECASE | re.MULTILINE):
|
||||
ans[id] = txt
|
||||
continue
|
||||
txt = re.sub(r"[\r\n]", " ", txt, flags=re.IGNORECASE | re.MULTILINE)
|
||||
txt_list = []
|
||||
for t in re.split(r"[.?!;\n]", txt):
|
||||
if is_english([t]):
|
||||
for w in keywords:
|
||||
t = re.sub(
|
||||
r"(^|[ .?/'\"\(\)!,:;-])(%s)([ .?/'\"\(\)!,:;-])" % re.escape(w),
|
||||
r"\1<em>\2</em>\3",
|
||||
t,
|
||||
flags=re.IGNORECASE | re.MULTILINE,
|
||||
)
|
||||
else:
|
||||
for w in sorted(keywords, key=len, reverse=True):
|
||||
t = re.sub(
|
||||
re.escape(w),
|
||||
f"<em>{w}</em>",
|
||||
t,
|
||||
flags=re.IGNORECASE | re.MULTILINE,
|
||||
)
|
||||
if not re.search(r"<em>[^<>]+</em>", t, flags=re.IGNORECASE | re.MULTILINE):
|
||||
continue
|
||||
txt_list.append(t)
|
||||
if txt_list:
|
||||
ans[id] = "...".join(txt_list)
|
||||
else:
|
||||
ans[id] = txt
|
||||
return ans
|
||||
|
||||
def get_aggregation(self, res: tuple[pd.DataFrame, int] | pd.DataFrame, field_name: str):
|
||||
"""
|
||||
Manual aggregation for tag fields since Infinity doesn't provide native aggregation
|
||||
"""
|
||||
from collections import Counter
|
||||
|
||||
# Extract DataFrame from result
|
||||
if isinstance(res, tuple):
|
||||
df, _ = res
|
||||
else:
|
||||
df = res
|
||||
|
||||
if df.empty or field_name not in df.columns:
|
||||
return []
|
||||
|
||||
# Aggregate tag counts
|
||||
tag_counter = Counter()
|
||||
|
||||
for value in df[field_name]:
|
||||
if pd.isna(value) or not value:
|
||||
continue
|
||||
|
||||
# Handle different tag formats
|
||||
if isinstance(value, str):
|
||||
# Split by ### for tag_kwd field or comma for other formats
|
||||
if field_name == "tag_kwd" and "###" in value:
|
||||
tags = [tag.strip() for tag in value.split("###") if tag.strip()]
|
||||
else:
|
||||
# Try comma separation as fallback
|
||||
tags = [tag.strip() for tag in value.split(",") if tag.strip()]
|
||||
|
||||
for tag in tags:
|
||||
if tag: # Only count non-empty tags
|
||||
tag_counter[tag] += 1
|
||||
elif isinstance(value, list):
|
||||
# Handle list format
|
||||
for tag in value:
|
||||
if tag and isinstance(tag, str):
|
||||
tag_counter[tag.strip()] += 1
|
||||
|
||||
# Return as list of [tag, count] pairs, sorted by count descending
|
||||
return [[tag, count] for tag, count in tag_counter.most_common()]
|
||||
|
||||
"""
|
||||
SQL
|
||||
"""
|
||||
|
||||
def sql(self, sql: str, fetch_size: int, format: str):
|
||||
raise NotImplementedError("Not implemented")
|
||||
Reference in New Issue
Block a user