@ -1,14 +1,11 @@
"""
This script performs two main tasks :
1. It loads a markdown document , splits it into chunks , generates embeddings ,
and builds a FAISS index ( which is saved locally ) .
and builds a FAISS index with the original and paraphrased chunks ( which is saved locally ) .
2. It generates QA pairs from the document using llama .
For each chunk ( using a sliding window for context ) , it generates multiple question - answer pairs
with different difficulties . The generation is performed in batch with one retry for failed prompts .
Successfully generated QA pairs are saved to " data/questions.json " .
Requirements :
pip install langchain faiss - cpu unsloth vllm
Successfully generated QA pairs are saved to " data/questions.jsonl " .
"""
import json
@ -21,12 +18,12 @@ project_root = Path(__file__).resolve().parent.parent
sys . path . append ( str ( project_root ) )
import pandas as pd
from langchain . schema import Document
from langchain . text_splitter import RecursiveCharacterTextSplitter
# ========= Part 1: Document Processing and Embedding Generation =========
# Load and split the markdown document using LangChain
from langchain_community . document_loaders import UnstructuredMarkdownLoader
from langchain_community . vectorstores import FAISS
from unsloth import FastLanguageModel
from vllm import SamplingParams
from config import DATA_DIR , logger
from src . embeddings import CustomHuggingFaceEmbeddings
@ -57,13 +54,10 @@ vectorstore = FAISS.from_documents(chunks, embeddings)
vectorstore . save_local ( str ( DATA_DIR ) )
logger . info ( f " Saved FAISS index to { DATA_DIR } " )
# TODO: add the paraphrased chunks to the vector store
# ========= Part 2: QA Generation using Llama Backend =========
# Setup Llama backend via unsloth and vLLM
from unsloth import FastLanguageModel
from vllm import SamplingParams
# Load the Llama model (adjust parameters as needed)
model , tokenizer = FastLanguageModel . from_pretrained (
@ -81,6 +75,127 @@ sampling_params = SamplingParams(
max_tokens = 4096 ,
)
# Define paraphrasing styles and parameters
PARAPHRASE_PROMPTS = [
""" Rewrite this text in a formal, scholarly tone. Keep it very concise - summarize in 1-2 short sentences. Only output the paraphrased text:
TEXT : { text } """ ,
""" Rewrite this text in a clear, simple way that ' s easy to understand. Provide a medium-length explanation with key details. Only output the paraphrased text:
TEXT : { text } """ ,
""" Rewrite this text in a vivid, engaging style. Expand on the details and provide a comprehensive, detailed version. Only output the paraphrased text:
TEXT : { text } """ ,
]
# Sampling parameters for different lengths
sampling_params_short = SamplingParams (
temperature = 0.3 ,
top_p = 0.95 ,
max_tokens = 64 , # Short responses
)
sampling_params_medium = SamplingParams (
temperature = 0.3 ,
top_p = 0.95 ,
max_tokens = 256 , # Medium responses
)
sampling_params_long = SamplingParams (
temperature = 0.3 ,
top_p = 0.95 ,
max_tokens = 512 , # Long responses
)
def generate_paraphrases ( text : str ) - > list :
"""
Generate three different paraphrased versions with varying lengths .
Args :
text : Text to paraphrase
Returns :
List of three paraphrased versions ( short , medium , long )
"""
responses = [ ]
sampling_params_list = [
sampling_params_short ,
sampling_params_medium ,
sampling_params_long ,
]
for prompt_template , sampling_params in zip ( PARAPHRASE_PROMPTS , sampling_params_list ) :
formatted_prompt = tokenizer . apply_chat_template (
[ { " role " : " user " , " content " : prompt_template . format ( text = text ) } ] ,
tokenize = False ,
add_generation_prompt = True ,
)
output = model . fast_generate ( [ formatted_prompt ] , sampling_params = sampling_params )
responses . append ( output [ 0 ] . outputs [ 0 ] . text )
return responses
# Paraphrase all chunks and add to vector store
logger . info ( " Paraphrasing chunks and adding to vector store... " )
all_paraphrased = [ ]
chunk_ids = [ ]
for i , chunk in enumerate ( chunks ) :
# Get paragraphs from chunk
paragraphs = [ p . strip ( ) for p in chunk . page_content . split ( " \n \n " ) if p . strip ( ) ]
for paragraph in paragraphs :
# Generate 3 paraphrased versions
paraphrased_versions = generate_paraphrases ( paragraph )
# Save original paragraph ID for reference
for version in paraphrased_versions :
all_paraphrased . append ( { " chunk_id " : i + 1 , " original_paragraph " : paragraph , " paraphrased_text " : version } )
# Save paraphrased chunks to CSV for inspection
paraphrased_df = pd . DataFrame ( all_paraphrased )
paraphrased_csv_path = DATA_DIR / " paragraphs_noise.csv "
paraphrased_df . to_csv ( paraphrased_csv_path , index = False )
logger . info ( f " Saved { len ( all_paraphrased ) } paraphrased paragraphs to { paraphrased_csv_path } " )
paraphrased_docs = [
Document ( page_content = item [ " paraphrased_text " ] , metadata = { " chunk_id " : item [ " chunk_id " ] , " is_paraphrase " : True } )
for item in all_paraphrased
]
# Process embeddings in smaller batches to avoid OOM
logger . info ( f " Creating FAISS index with { len ( paraphrased_docs ) } documents in batches " )
batch_size = 100 # Process 100 documents at a time
paraphrased_vectorstore = None
for i in range ( 0 , len ( paraphrased_docs ) , batch_size ) :
batch = paraphrased_docs [ i : i + batch_size ]
logger . info ( f " Processing batch { i / / batch_size + 1 } / { ( len ( paraphrased_docs ) + batch_size - 1 ) / / batch_size } " )
# Create a new FAISS index for this batch
batch_vectorstore = FAISS . from_documents ( batch , embeddings )
# Merge with existing index or create a new one
if paraphrased_vectorstore is None :
paraphrased_vectorstore = batch_vectorstore
else :
paraphrased_vectorstore . merge_from ( batch_vectorstore )
# Merge with main vectorstore
if paraphrased_vectorstore is not None :
vectorstore . merge_from ( paraphrased_vectorstore )
logger . info ( f " Updated FAISS index with { len ( paraphrased_docs ) } paraphrased paragraphs " )
# Save the updated vector store
vectorstore . save_local ( str ( DATA_DIR ) )
logger . info ( f " Saved updated FAISS index to { DATA_DIR } " )
else :
logger . warning ( " No paraphrased documents were processed successfully " )
def batch_generate ( prompts : list ) - > list :
"""
@ -148,9 +263,7 @@ def parse_multiple_qa_output(output: str) -> list:
return qa_pairs
def generate_question_batch_for_chunks (
chunks : list , num_questions : int = 2 , difficulty = None
) - > list :
def generate_question_batch_for_chunks ( chunks : list , num_questions : int = 2 , difficulty = None ) - > list :
"""
Generates QA pairs for multiple chunks in batch .
@ -233,9 +346,7 @@ def generate_question_batch_for_chunks(
results [ idx ] = valid_pairs [ : num_questions ]
else :
results [ idx ] = None
logger . warning (
f " Retry failed for chunk { idx + 1 } : not enough valid QA pairs "
)
logger . warning ( f " Retry failed for chunk { idx + 1 } : not enough valid QA pairs " )
else :
results [ idx ] = None
logger . warning ( f " Retry failed for chunk { idx + 1 } : parsing failed " )
@ -245,13 +356,15 @@ def generate_question_batch_for_chunks(
for i , qa_list in enumerate ( results ) :
if qa_list is not None :
for qa in qa_list :
# Get supporting paragraphs by splitting chunk content into paragraphs
supporting_paragraphs = [ p . strip ( ) for p in chunk_contents [ i ] . split ( " \n \n " ) if p . strip ( ) ]
final_questions . append (
{
" chunk_id " : chunk_ids [ i ] ,
" " : str ( chunk_ids [ i ] ) ,
" question " : qa [ 0 ] ,
" answer " : qa [ 1 ] ,
" difficulty " : qa [ 2 ] ,
" chunk_content " : chunk_contents [ i ] ,
" supporting_paragraphs " : supporting_paragraphs ,
}
)
@ -260,13 +373,13 @@ def generate_question_batch_for_chunks(
# Generate QA pairs in batch (using a sliding window over the chunks)
all_questions = generate_question_batch_for_chunks (
chunks , num_questions = 2 , difficulty = " medium "
)
logger . info ( " Generating question-answer pairs... " )
all_questions = generate_question_batch_for_chunks ( chunks , num_questions = 2 , difficulty = " medium " )
logger . info ( f " Generated { len ( all_questions ) } QA pairs. " )
# Save the QA pairs to a JSON file
questions_path = DATA_DIR / " questions.json "
# Save the QA pairs to a JSON L file
questions_path = DATA_DIR / " questions.json l "
with open ( questions_path , " w " ) as f :
json . dump ( all_questions , f , indent = 2 )
for question in all_questions :
f . write ( json . dumps ( question ) + " \n " )
logger . info ( f " Saved questions to { questions_path } " )
thinhlpg
3 weeks ago