38e7dc6246
I've also added __slots__ less so to improve efficiency and more to be sure there are no typos on assignments. There remain a few untyped parts where I could not find documentation of the types. These things are in particular: - Agent.Dsl - Agent.create_session() - DataSet.ParserConfig - I'm not sure if the documented parameters are complete. - Session.ask() - kwargs specific to agent/chat
112 lines
3.2 KiB
Python
112 lines
3.2 KiB
Python
#
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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 typing import Any, NotRequired, Optional, TYPE_CHECKING, TypedDict
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from .base import Base
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if TYPE_CHECKING:
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from ..ragflow import RAGFlow
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__all__ = 'Chunk',
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class UpdateMessage(TypedDict):
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content: NotRequired[str]
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important_keywords: NotRequired[list[str]]
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available: NotRequired[bool]
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class ChunkUpdateError(Exception):
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__slots__ = (
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'code',
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'message',
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'details',
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)
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code: Optional[int]
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message: Optional[str]
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details: Optional[str]
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def __init__(self, code: Optional[int]=None, message: Optional[str]=None, details: Optional[str]=None):
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self.code = code
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self.message = message
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self.details = details
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super().__init__(message)
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class Chunk(Base):
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__slots__ = (
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'id',
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'content',
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'important_keywords',
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'questions',
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'create_time',
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'create_timestamp',
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'dataset_id',
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'document_name',
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'document_id',
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'available',
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'similarity',
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'vector_similarity',
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'term_similarity',
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'positions',
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'doc_type',
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)
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id: str
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content: str
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important_keywords: list[str]
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questions: list[str]
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create_time: str
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create_timestamp: float
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dataset_id: Optional[str]
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document_name: str
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document_id: str
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available: bool
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similarity: float
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vector_similarity: float
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term_similarity: float
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positions: list[str]
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doc_type: str
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def __init__(self, rag: "RAGFlow", res_dict: dict[str, Any]) -> None:
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self.id = ""
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self.content = ""
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self.important_keywords = []
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self.questions = []
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self.create_time = ""
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self.create_timestamp = 0.0
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self.dataset_id = None
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self.document_name = ""
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self.document_id = ""
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self.available = True
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# Additional fields for retrieval results
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self.similarity = 0.0
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self.vector_similarity = 0.0
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self.term_similarity = 0.0
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self.positions = []
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self.doc_type = ""
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for k in list(res_dict.keys()):
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if not hasattr(self, k):
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res_dict.pop(k)
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super().__init__(rag, res_dict)
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def update(self, update_message: UpdateMessage) -> None:
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res = self.put(f"/datasets/{self.dataset_id}/documents/{self.document_id}/chunks/{self.id}", update_message)
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res = res.json()
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if res.get("code") != 0:
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raise ChunkUpdateError(
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code=res.get("code"),
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message=res.get("message"),
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details=res.get("details")
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)
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