txtai-langchain-bm25.py

##################################
# Data functions
##################################

import re

from datasets import load_dataset

def clean(text):
    text = text.replace("\n", " ").strip()
    return re.sub(r"\s{2,}", " ", text)

def stream():
    arxiv = load_dataset("arxiv_dataset", split="train[:1600000]")
    for result in arxiv:
        yield f"{clean(result['title'])}\n{clean(result['abstract'])}"

def batch(size):
    data = []
    for result in stream():
        data.append(result)
        if len(data) == size:
            yield data
            data = []

    if data:
        yield data

def queries(size):
    for text in next(batch(size)):
        # Get first 5 terms in title as query
        title = text.split("\n")[0]
        yield " ".join(title.split()[:5])

##################################
# LangChain
##################################

import time

from langchain_community.retrievers import BM25Retriever
from psutil import Process

start = time.time()

retriever = BM25Retriever.from_texts(list(stream()), k=3)

index = time.time() - start
start = time.time()

for query in queries(100):
    retriever.invoke(query)

memory = int(Process().memory_info().rss / (1024 * 1024))
print(f"INDEX TIME = {index:.2f}s")
print(f"SEARCH TIME = {time.time() - start:.2f}s")
print(f"MEMORY = {memory} MB")

##################################
# txtai
##################################

import time

from psutil import Process
from txtai import Embeddings

start = time.time()

embeddings = Embeddings(content=True, keyword=True)
embeddings.index(stream())

index = time.time() - start
start = time.time()

for query in queries(100):
    embeddings.search(query, 3)

memory = int(Process().memory_info().rss / (1024 * 1024))
print(f"INDEX TIME = {index:.2f}s")
print(f"SEARCH TIME = {time.time() - start:.2f}s")
print(f"MEMORY = {memory} MB")