Vecotr Search Engine

based on pretrained sentence embeddings

Author

Affiliation

Xinzhuo Huang

HKUST SOSC

Let’s utilize a pre-trained sentence model to convert text into sentence vectors, then perfrom vector search. We will use CUDA to accerate the process. I wil call Python in R through reticulate.

Package Mangement

require(pacman)
p_load(reticulate, tidyverse, tictoc)

Call Python in R and check if CUDA is available.

Code

reticulate::use_python("C:\\Users\\xhuangcb\\anaconda3\\envs\\pytorch_gpu\\python.exe") 

torch <- reticulate::import("torch")

torch$cuda$is_available()

[1] TRUE

Utilize pretrained model GanymedeNil/text2vec-large-chinese.

Code

text2vec <- reticulate::import("text2vec")

model <- text2vec$SentenceModel("GanymedeNil/text2vec-large-chinese")

Sample data is from Chinese court document. Let’s vonvert the text into a 1024-dimensional vector. It takes 24 seconds to process 2000 court documents, using a RTX 3080.

Code

judgements <- rio::import("D:/OneDrive - HKUST Connect/search_engine/legal.csv", setclass = "tibble")

tic()
vec <- judgements %>% 
    mutate(vecotr = model$encode(content))
toc()

[1] "24.27 sec elapsed"

Code

vec$vector <- vec$vector %>% 
    as_tibble()

vec <- vec %>%  
    nest(data = vector) %>% 
    mutate(data = map(data, ~ unlist(.) |> unname()))

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	V1019	V1020	V1021	V1022	V1023	V1024
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2	-0.7767202	0.2098723	0.4147573	0.1512255	-0.3720939	0.0636261	1.1831774	-0.6993153	-1.1723713	-0.1721279	-0.6260084	0.4650337	0.7011845	1.3038018	1.2234302	1.0019288	-0.0126232	0.4833007	-1.6387281	-0.7921288	0.1423948	-0.5081744	0.6849892	0.8473518	0.3236441	-1.0343987	1.1444592	-0.6310747	-0.8645843	0.6971349	-0.3423012	0.6101743	-0.5222071	-0.3362309	-1.5412116	0.9837184	0.2351980	0.2440530	-0.8134343	-0.9231049	0.0115133	0.4385045	0.3665770	-0.0228702	0.9612855	0.4380586	-0.9686128	0.9233942	0.0066329	-0.7390410	0.8038399	-0.2446096	-0.0858632	0.6646044	0.5900694	-0.6782909	-0.4250847	-0.7722499	1.1994760	-0.2119833	0.7805612	0.7009464	-1.5209064	0.7903733	-0.1718653	0.5084716	-0.0006538	-0.6025160	0.1650962	-0.4125753	-0.9821480

Build a vector search engine.

Code

annoy <- reticulate::import("annoy")

index <- annoy$AnnoyIndex(1024L, "angular")

add_item <- \(x, y) {
    index$add_item(x, y)
}

walk2(
    .x = 1L:1895L,
    .y = vec$data,
    possibly(add_item)
)

index$build(10L)

index$save("E:/OneDrive - HKUST Connect/search_engine/search_index.ann")

Code

search_engine <- \(df, keyword, top = 10L, ann = "E:/OneDrive - HKUST Connect/search_engine/search_index.ann") {
    search$load(ann) 

    indexing <- search$get_nns_by_vector(model$encode(keyword), 10L)

    df %>% 
        slice(indexing)

}

search_engine(df = vec, keyword = "土地所有权纠纷")

title
海丰县鲘门镇红泉村委会港尾村村民小组一审行政裁定书
马宫镇长沙经济联合社、张超如农村土地承包合同纠纷二审民事判决书
王贤玉与范碧英房屋租赁合同纠纷二审民事判决书
王岳芹与王国强管辖裁定书
陈优萍与王国强债务转移合同纠纷二审民事裁定书
袁钱林与王国强管辖裁定书
原告东营市地震工程咨询中心与被告孙渊房屋租赁合同纠纷一审民事裁定书
钱国允与王国强管辖裁定书
丁星月、青岛交运保险代理有限公司与中国人民财产保险股份有限公司青岛分公司市南支公司保险合同纠纷一审民事调解书
明从香与刘登毅、刘登勇、第三人中国平安财产保险股份有限公司成都市蜀都支公司机动车交通事故责任纠纷

Citation

BibTeX citation:

@online{xinzhuo2023,
  author = {Xinzhuo, Huang},
  title = {Vecotr {Search} {Engine}},
  date = {2023-11-05},
  url = {https://xinzhuo.work/blog/vector search},
  langid = {en}
}

For attribution, please cite this work as:

Xinzhuo, Huang. 2023. “Vecotr Search Engine.” November 5, 2023. https://xinzhuo.work/blog/vector search.