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from __future__ import annotations | |
import logging | |
from llama_index import Prompt | |
from typing import List, Tuple | |
import mdtex2html | |
from presets import * | |
from llama_func import * | |
def compact_text_chunks(self, prompt: Prompt, text_chunks: List[str]) -> List[str]: | |
logging.debug("Compacting text chunks...πππ") | |
combined_str = [c.strip() for c in text_chunks if c.strip()] | |
combined_str = [f"[{index+1}] {c}" for index, c in enumerate(combined_str)] | |
combined_str = "\n\n".join(combined_str) | |
# resplit based on self.max_chunk_overlap | |
text_splitter = self.get_text_splitter_given_prompt(prompt, 1, padding=1) | |
return text_splitter.split_text(combined_str) | |
def postprocess( | |
self, y: List[Tuple[str | None, str | None]] | |
) -> List[Tuple[str | None, str | None]]: | |
""" | |
Parameters: | |
y: List of tuples representing the message and response pairs. Each message and response should be a string, which may be in Markdown format. | |
Returns: | |
List of tuples representing the message and response. Each message and response will be a string of HTML. | |
""" | |
if y is None or y == []: | |
return [] | |
tag_regex = re.compile(r"^<\w+>[^<]+</\w+>") | |
if tag_regex.search(y[-1][1]): | |
y[-1] = (y[-1][0].replace("\n", "<br>"), y[-1][1]) | |
else: | |
y[-1] = (y[-1][0].replace("\n", "<br>"), convert_mdtext(y[-1][1])) | |
return y | |