Copywrite

• Contact for inquiry: Sarah Daeun Kim (daeun@narratear.com)

• documents: This folder contains input files (pdf)
• demos: This folder contains the demo short video
• upload.py: This code reads the file or files in the default folder('documents'), and indexes and stores the file/files in a vector DB ('Pipecone').
• requirement.txt: A list of packages or libraries needed to work on a project that can all be installed with the file.

Getting start

1. conda activate 'rag'

2. python3 upload.py / query.py

3. Upload pdf: parameters - chunk size and chunk overlap

4. Request the query: parameter - top-k

Arguments

parser = argparse.ArgumentParser(description= 'Process the pdf file for uploading the file to Pinecone (Vector DB)')
parser.add_argument('--file_name', type=str, default= None, help='A path of input file')
parser.add_argument('--chunck_size', type=int, default=200, help='Enter the chunck size over 100 range')
parser.add_argument('--chunck_overlap', type=float, default=0.25, help='The portion of the overlap chunks: 25% = 0.25 range[0,1]')
parser.parse_args()

1. Single file upload

The file have to store in 'documents' folder.

conda activate rag python3 upload.py --file_name sample.pdf

2. With optional arguments

python upload.py --name_space test_case --chunck_size 100 --chunk_overlap 0.20 # overlap = 100*0.2 = 20.

• '--file_name': [Optional] Enter the PDF file name. The file have to store in 'documents/'. If do not enter it, the code will Reads the files in the documents folder

  python upload.py --file_name sample.pdf

• '--chunk_size': [Optional] Enter the chunk size as integer. The default is 200

  python upload.py --chunk_size 100

• '--chunk_overlap': [Optional] Enter the ratio of chunk overlap ranging [0,1]. The default is 0.25

  python upload.py --chunck_overlap 0.25

Steps of the sub-task

1. Read the query : Read a query + embedding the query with the same model
2. Retrieval k-top chunks: The default of k-top: 5
3. Language Model: a small pre-trained language model by using Llma-CPP with Hugging face- llama-2-13b-chat.Q4_0.gguf

Arguments

parser.add_argument('--question', type=str, default= None, required=True, help='A query')
parser.add_argument('--top_k', type=int, default=5, help='top_k')

Embedding output dimension = 3072 : large (latest one)

1. Answer generation with (top-k= 5) based on the query

python query.py --question "What is ~?"

2. Answer generation with (user-defined top-k= 5) based on the query

python query.py --question "What is the attention model?" --top_k 8