Datasets:
MohamedAAK
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test

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```bash
python ./rag.py --index "bm25" --dataset "hotpotqa-train" --similarity bertscore \
--maxKnowledge 80 --maxParagraph 100 --maxQuestion 80 --topk 3 \
--modelname "meta-llama/Llama-3.1-8B-Instruct" --randomSeed 0 \
--output "./rag_results.txt"
```
### Note:
#### `--maxKnowledge` parameter notice:
> [!NOTE]
> Approximate Tokens count corresponding to knowledge document size of "squad-train" and "hotpotqa-train" dataset.
> datasets=("squad-train")
> - when k = 3, tokens = 21,000
> - when k = 4, tokens = 32,000
> - when k = 7, tokens = 50,000
>
> datasets=("hotpotqa-train")
> - all k = 7405 article, tokens = 10,038,084
> - when k = 1, tokens = 1,400
> - when k = 16, tokens = 22,400
> - when k = 24, tokens = 33,667
> - when k = 32, tokens = 44,800
> - when k = 48, tokens = 64,000
> - when k = 64, tokens = 85,000
> - when k = 80, tokens = 106,000
#### `--maxQuestion` parameter notice:
> - when using "squad-train" dataset, 1 knowledge has average 150 questions
> - when using "hotpotqa-train" dataset, 1 knowledge has 1 question
> [!TIP]
> Since 1 document in "hotpoqa-train" dataset has only 1 question, it may not satisfy large-scale evaluation.
> Multiple evaluation could be a relatively better approach.
>
## Citation
```
@misc{chan2024dontragcacheaugmentedgeneration,
title={Don't Do RAG: When Cache-Augmented Generation is All You Need for Knowledge Tasks},
author={Brian J Chan and Chao-Ting Chen and Jui-Hung Cheng and Hen-Hsen Huang},
year={2024},
eprint={2412.15605},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2412.15605},
}
```
#!/bin/bash
# squad dataset
curl -L -o ./datasets/squad/stanford-question-answering-dataset.zip\
ERROR: type should be string, got " https://www.kaggle.com/api/v1/datasets/download/stanfordu/stanford-question-answering-dataset"
unzip ./datasets/squad/stanford-question-answering-dataset.zip -d ./datasets/squad/
rm ./datasets/squad/stanford-question-answering-dataset.zip
# hotpotqa dataset
curl -L -o ./datasets/hotpotqa/hotpotqa-question-answering-dataset.zip\
ERROR: type should be string, got " https://www.kaggle.com/api/v1/datasets/download/jeromeblanchet/hotpotqa-question-answering-dataset"
unzip ./datasets/hotpotqa/hotpotqa-question-answering-dataset.zip -d ./datasets/hotpotqa/
rm ./datasets/hotpotqa/hotpotqa-question-answering-dataset.zip
import torch
import pandas as pd
import argparse
import os
import json
from time import time
from sentence_transformers import SentenceTransformer, util
from transformers import BitsAndBytesConfig, AutoTokenizer, AutoModelForCausalLM
from transformers.cache_utils import DynamicCache
import random
def get_env():
env_dict = {}
with open(file=".env" if os.path.exists(".env") else "env", mode="r") as f:
for line in f:
key, value = line.strip().split("=")
env_dict[key] = value.strip('"')
return env_dict
"""Hugging Face Llama model"""
HF_TOKEN = get_env()["HF_TOKEN"]
global model_name, model, tokenizer
global rand_seed
def generate(
model,
input_ids: torch.Tensor,
past_key_values,
max_new_tokens: int = 300
) -> torch.Tensor:
"""
Generate text with greedy decoding.