Binciken vector (binciken ma'ana)
Gajeriyar ma'ana
Binciken vector wata hanya ce ta dawo da bayanai wacce ke dawo da abubuwan da embeddings ɗinsu suka fi kusa da embedding na tambaya, don haka sakamakon yana dacewa da ma'ana maimakon ta hanyar kalmomi masu raba. Shi ne injin da aka saba na binciken ma'ana.
A wannan shafin
Yadda binciken vector ke aiki
Kowane takarda, passage ko samfur ana canza shi zuwa embedding kuma ana adana shi a cikin index. A lokacin tambaya, tambayar tana embedding tare da samfurin iri ɗaya, kuma index yana dawo da vectors mafi kusa, yawanci ana auna su ta hanyar cosine similarity. Abokin ciniki wanda ke tambayar “wani abu mai dumi don yin kankara” na iya samun jacket wanda aka bayyana a matsayin “insulated, waterproof winter shell” ko da ba a sami kalma da ta dace ba.
Kwatanta tambaya tare da miliyoyin vectors ɗaya bayan ɗaya zai kasance mai jinkiri, don haka indexes suna amfani da algorithms na kusan makwabta mafi kusa. HNSW, wani zane mai layi wanda Malkov da Yashunin suka bayyana a 2016, yana ɗaya daga cikin waɗanda aka fi amfani da su; yana musanya ƙaramin asarar daidaito don bincike mai sauri. Ƙarin kyauta kamar pgvector suna kawo waɗannan indexes zuwa PostgreSQL.
Binciken ma'ana vs binciken kalmomi
Binciken kalmomi (BM25, cikakken rubutu, daidaitawar trigram) yana da daidaito akan kalmomi masu daidaito: alamar samfur, alama, lambar kuskure. Yana kasa akan synonyms da paraphrases. Binciken vector shine akasin haka: mai ƙarfi akan ma'ana, rauni akan strings daidaito da kuma rare identifiers.
Wannan shine dalilin da ya sa yawancin tsarin samarwa ke amfani da binciken haɗaka: gudanar da duka, sannan haɗa ko sake tsara sakamakon.
Kurakurai
Binciken vector koyaushe yana dawo da wani abu, ko da lokacin da babu wani abu mai mahimmanci, don haka tsarin yana buƙatar ƙayyadadden dacewa ko duba kafin amfani da sakamakon. Ba ya fahimci lambobi ko ƙuntatawa: “kasa da euros 50, a cikin stock, girman M” yana buƙatar ainihin tacewa akan bayanan tsari. Kuma inganci yana dogara da yadda aka raba abun ciki: ɓangarorin da suka yi tsawo sosai suna ɓata ma'anar, ɓangarorin da suka yi gajere suna rasa mahallin.
Binciken vector a cikin AI SmartTalk
AI SmartTalk yana adana embeddings a cikin PostgreSQL tare da pgvector, tare da babban jeri na HNSW akan takardun samfur, kuma yana haɗa kamanceceniya na vector tare da binciken trigram da cikakken rubutu. Don samfuran, kayan aikin binciken mai taimako yana ƙara tacewa na tsari akan farashi, stock, alama, rukuni da halayen bambance-bambancen, don haka tambayar ma'ana da ƙayyadadden daidaito za a iya amsa tare.
Tambayoyi da aka yawan yi
Shin binciken ma'ana yana daidai da binciken vector?
Binciken ma'ana shine burin — samun sakamako ta hanyar ma'ana. Binciken vector akan embeddings shine hanya mafi yawan amfani don cimma wannan, yawanci an haɗa shi tare da binciken kalmomi.
Shin ina buƙatar wani bayanan vector na musamman?
Ba lallai ba ne. Ƙarin abubuwa kamar pgvector suna ƙara jeri na vector zuwa PostgreSQL, wanda ya isa ga aikace-aikace da yawa kuma yana riƙe vectors a kusa da sauran bayanan.
Menene binciken haɗin gwiwa?
Bincike wanda ke gudanar da binciken vector da binciken kalmomi tare kuma yana haɗa sakamakon, don haka yana kula da tambayoyin da aka maimaita da takamaiman sharuɗɗan kamar ambaton samfur.