langchain/libs/text-splitters/langchain_text_splitters/json.py

from __future__ import annotations

import copy
import json
from typing import Any, Dict, List, Optional

from langchain_core.documents import Document


class RecursiveJsonSplitter:
    def __init__(
        self, max_chunk_size: int = 2000, min_chunk_size: Optional[int] = None
    ):
        super().__init__()
        self.max_chunk_size = max_chunk_size
        self.min_chunk_size = (
            min_chunk_size
            if min_chunk_size is not None
            else max(max_chunk_size - 200, 50)
        )

    @staticmethod
    def _json_size(data: Dict) -> int:
        """Calculate the size of the serialized JSON object."""
        return len(json.dumps(data))

    @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: Dict[str, Any],
        current_path: Optional[List[str]] = None,
        chunks: Optional[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: Dict[str, Any],
        convert_lists: bool = False,
    ) -> List[Dict]:
        """Splits JSON into a list of JSON chunks"""

        if convert_lists:
            chunks = self._json_split(self._list_to_dict_preprocessing(json_data))
        else:
            chunks = self._json_split(json_data)

        # 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
    ) -> 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) for chunk in chunks]

    def create_documents(
        self,
        texts: List[Dict],
        convert_lists: bool = False,
        metadatas: Optional[List[dict]] = None,
    ) -> List[Document]:
        """Create documents from a list of json objects (Dict)."""
        _metadatas = metadatas or [{}] * len(texts)
        documents = []
        for i, text in enumerate(texts):
            for chunk in self.split_text(json_data=text, convert_lists=convert_lists):
                metadata = copy.deepcopy(_metadatas[i])
                new_doc = Document(page_content=chunk, metadata=metadata)
                documents.append(new_doc)
        return documents