This application aims to provide a translation service for Bible verses. It uses a few-shot learning approach to generate translations based on a few examples. The application is designed to work with different languages, identified by their ISO 639-3 codes.

Architecture

The application has several components:

1. Backend (backend.py): This part of the application handles data processing tasks such as fetching and preparing data, creating and querying databases, and building translation prompts. It also includes functions for evaluating the quality of translations.

2. API (index.py): This is the interface of the application. It provides endpoints for fetching verses, getting unique tokens for a language, populating the database, querying the database, and building translation prompts.

3. Frontend (various page.tsx routes and components such as FewShotPrompt.tsx): These files handle the user interface of the application. They display the translation prompts and the generated translations, and allow users to interact with the application.

The application fetches data from different sources and loads them into dataframes, including the Berean Standard Bible (bsb_bible_df), the Macula Greek/Hebrew Bible (macula_df), and a target language Bible (target_vref_df). It uses this data to generate translation prompts, which are then passed to a language model for translation. The application also includes functionality for evaluating the quality of the generated translations.

Usage notes

Generate triplets for a given ebible target language: http://localhost:3000/api/bible?language_code=tpi&file_suffix=OTNT&force=True

Split the triplet file into chunks: python3.10 notebooks/split_json_data.py --data_path=./data/bible/tpiOTNT.json --chunk_size=5000

Pass in one of the chunks directly (run this in several terminals as needed, with a different chunk each time): python3.10 notebooks/run_align.py --run_name=tpiOTNT-gpt35i --data_path='/Users/ryderwishart/translators-copilot/data/bible/tpiOTNT.json_1.json' --n=1

To identify incomplete alignments (those which errored out) from a previous run or split run, use:

# in the directory where the alignment output files are stored
grep -h "Error: Maximum"  alig* > tbp.txt

Then subsequently you can run

python3.10 notebooks/align_with_pseudo_english.py --run_name=tpiOTNT-pseudo_english --data_path='/Users/ryderwishart/translators-copilot/data/bible/tpiOTNT.json_1.json' --model='gpt-3.5-turbo-instruct' --ids_file_path=/Users/ryderwishart/translators-copilot/data/alignments/tpiOTNT-pseudo_english/tbp.txt

Note that the ids_file_path is passed in, and you will have to specify which split you are working on (e.g., tpiOTNT.json_1.json').

Running in parallel

brew install parallel if needed.

mkdir chunks
split -l 1000 tbp.jsonl chunks/tbp_chunk_
# Add .jsonl suffix to split files
for file in chunks/tbp_chunk_*; do
    mv "$file" "$file.jsonl"
done

Then run the following using parallel:

ls chunks/tbp_chunk_* | parallel -j+0 python3.10 notebooks/run_align.py --run_name=fraLSG --data_path={} --model='gpt-3.5-turbo-instruct'

To consolidate errors, run:

cat alignments* | grep -v "Error" > complete_bible_fraLSG.jsonl
cat alignments* | grep "Error" > remaining_errors.jsonl

Identifying string ranges and Macula IDs from alignment output

To map the output back to the original Macula data, run:

python3 scripts/find-ranges-for-alignments/find-ranges-for-alignments.py /Users/ryderwishart/translators-copilot/data/alignments/fraLSG/complete_bible_fraLSG_missing_a_few_vrefs.jsonl