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- # -*- coding: utf-8 -*-
- # The following documents are mainly referenced, and only adaptation modifications have been made
- # from https://github.com/langchain-ai/langchain/blob/master/libs/text-splitters/langchain_text_splitters/json.py
-
- import json
- from typing import Any
-
- from rag.nlp import find_codec
- class RAGFlowJsonParser:
- def __init__(
- self, max_chunk_size: int = 2000, min_chunk_size: int | None = None
- ):
- super().__init__()
- self.max_chunk_size = max_chunk_size * 2
- self.min_chunk_size = (
- min_chunk_size
- if min_chunk_size is not None
- else max(max_chunk_size - 200, 50)
- )
-
- def __call__(self, binary):
- encoding = find_codec(binary)
- txt = binary.decode(encoding, errors="ignore")
- json_data = json.loads(txt)
- chunks = self.split_json(json_data, True)
- sections = [json.dumps(line, ensure_ascii=False) for line in chunks if line]
- return sections
-
- @staticmethod
- def _json_size(data: dict) -> int:
- """Calculate the size of the serialized JSON object."""
- return len(json.dumps(data, ensure_ascii=False))
-
- @staticmethod
- def _set_nested_dict(d: dict, path: list[str], value: Any) -> None:
- """Set a value in a nested dictionary based on the given path."""
- for key in path[:-1]:
- d = d.setdefault(key, {})
- d[path[-1]] = value
-
- def _list_to_dict_preprocessing(self, data: Any) -> Any:
- if isinstance(data, dict):
- # Process each key-value pair in the dictionary
- return {k: self._list_to_dict_preprocessing(v) for k, v in data.items()}
- elif isinstance(data, list):
- # Convert the list to a dictionary with index-based keys
- return {
- str(i): self._list_to_dict_preprocessing(item)
- for i, item in enumerate(data)
- }
- else:
- # Base case: the item is neither a dict nor a list, so return it unchanged
- return data
-
- def _json_split(
- self,
- data,
- current_path: list[str] | None,
- chunks: list[dict] | None,
- ) -> list[dict]:
- """
- Split json into maximum size dictionaries while preserving structure.
- """
- current_path = current_path or []
- chunks = chunks or [{}]
- if isinstance(data, dict):
- for key, value in data.items():
- new_path = current_path + [key]
- chunk_size = self._json_size(chunks[-1])
- size = self._json_size({key: value})
- remaining = self.max_chunk_size - chunk_size
-
- if size < remaining:
- # Add item to current chunk
- self._set_nested_dict(chunks[-1], new_path, value)
- else:
- if chunk_size >= self.min_chunk_size:
- # Chunk is big enough, start a new chunk
- chunks.append({})
-
- # Iterate
- self._json_split(value, new_path, chunks)
- else:
- # handle single item
- self._set_nested_dict(chunks[-1], current_path, data)
- return chunks
-
- def split_json(
- self,
- json_data,
- convert_lists: bool = False,
- ) -> list[dict]:
- """Splits JSON into a list of JSON chunks"""
-
- if convert_lists:
- preprocessed_data = self._list_to_dict_preprocessing(json_data)
- chunks = self._json_split(preprocessed_data, None, None)
- else:
- chunks = self._json_split(json_data, None, None)
-
- # Remove the last chunk if it's empty
- if not chunks[-1]:
- chunks.pop()
- return chunks
-
- def split_text(
- self,
- json_data: dict[str, Any],
- convert_lists: bool = False,
- ensure_ascii: bool = True,
- ) -> list[str]:
- """Splits JSON into a list of JSON formatted strings"""
-
- chunks = self.split_json(json_data=json_data, convert_lists=convert_lists)
-
- # Convert to string
- return [json.dumps(chunk, ensure_ascii=ensure_ascii) for chunk in chunks]
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