import os.path
from itertools import chain
from typing import Tuple, List, Dict, Any, Optional
from llama_index.core import Document
from llama_index.core.node_parser.interface import NodeParser
from autorag.utils.util import process_batch, get_event_loop
from autorag.data.chunk.base import chunker_node, add_file_name
from autorag.data.utils.util import (
add_essential_metadata_llama_text_node,
get_start_end_idx,
)
[docs]
@chunker_node
def llama_index_chunk(
texts: List[str],
chunker: NodeParser,
file_name_language: Optional[str] = None,
metadata_list: Optional[List[Dict[str, str]]] = None,
batch: int = 8,
) -> Tuple[
List[str], List[str], List[str], List[Tuple[int, int]], List[Dict[str, Any]]
]:
"""
Chunk texts from the parsed result to use llama index chunk method
:param texts: The list of texts to chunk from the parsed result
:param chunker: A llama index NodeParser(Chunker) instance.
:param file_name_language: The language to use 'add_file_name' feature.
You need to set one of 'English' and 'Korean'
The 'add_file_name' feature is to add a file_name to chunked_contents.
This is used to prevent hallucination by retrieving contents from the wrong document.
Default form of 'English' is "file_name: {file_name}\n contents: {content}"
:param metadata_list: The list of dict of metadata from the parsed result
:param batch: The batch size for chunk texts. Default is 8
:return: tuple of lists containing the chunked doc_id, contents, path, start_idx, end_idx and metadata
"""
tasks = [
llama_index_chunk_pure(text, chunker, file_name_language, meta)
for text, meta in zip(texts, metadata_list)
]
loop = get_event_loop()
results = loop.run_until_complete(process_batch(tasks, batch))
doc_id, contents, path, start_end_idx, metadata = (
list(chain.from_iterable(item)) for item in zip(*results)
)
return list(doc_id), list(contents), list(path), list(start_end_idx), list(metadata)
[docs]
async def llama_index_chunk_pure(
text: str,
chunker: NodeParser,
file_name_language: Optional[str] = None,
_metadata: Optional[Dict[str, str]] = None,
):
# set document
document = [Document(text=text, metadata=_metadata)]
# chunk document
chunk_results = await chunker.aget_nodes_from_documents(documents=document)
# make doc_id
doc_id = list(map(lambda node: node.node_id, chunk_results))
# make path
path_lst = list(map(lambda x: x.metadata.get("path", ""), chunk_results))
# make contents and start_end_idx
if file_name_language:
chunked_file_names = list(map(lambda x: os.path.basename(x), path_lst))
chunked_texts = list(map(lambda x: x.text, chunk_results))
start_end_idx = list(
map(
lambda x: get_start_end_idx(text, x),
chunked_texts,
)
)
contents = add_file_name(file_name_language, chunked_file_names, chunked_texts)
else:
contents = list(map(lambda x: x.text, chunk_results))
start_end_idx = list(map(lambda x: get_start_end_idx(text, x), contents))
metadata = list(
map(
lambda node: add_essential_metadata_llama_text_node(
node.metadata, node.relationships
),
chunk_results,
)
)
return doc_id, contents, path_lst, start_end_idx, metadata