Eserese Ọmụma GraphRAG
GraphRAG na-akwalite ọgbọ eweghachite-agbakwunyere site n'ịmepụta eserese ihe ọmụma nke ụlọ ọrụ na mmekọrịta site na nchịkọta akwụkwọ, wee weghachite ihe owuwu ahụ kama ịchịkọta ederede dịpụrụ adịpụ.
Nchịkọta
It matters because it answers broad, connect-the-dots questions that flat vector search cannot.
Ime miri emi
RAG nkịtị na-ekewa dọkụmentị ka ọ bụrụ nkewa, tinye ya, wee weghachite ndị kacha nso na ajụjụ. Nke ahụ na-arụ ọrụ maka nyocha eziokwu dị warara mana ọ na-ada na ajụjụ zuru oke dị ka 'gịnị bụ isi isiokwu n'ofe nke a dum dataset?' GraphRAG, nke Microsoft Nchọpụta na-ewu ewu na 2024, kama na-eji usoro asụsụ wepụta ụlọ ọrụ, njirimara ha, na mmekọrịta dị n'etiti ha, na-achịkọta eserese ọmụma. Ọ na-arụkwa algọridim nchọta obodo dị ka Leiden ka ọ na-achịkọta ihe ndị metụtara ya wee wepụta nchịkọta maka obodo ọ bụla. N'oge njụ-ajụjụ, usoro ahụ nwere ike mebie mmekọrịta yana chịkọta nchịkọta obodo ndị a, na-eme ka echiche multi-hop na nghọta zuru ụwa ọnụ. Nsonaazụ bụ azịza ka mma maka ajụjụ ndị ihe akaebe gbasasịrị n'ọtụtụ akwụkwọ ma jikọọ naanị site na ụlọ ọrụ etiti.
Nghọta nka nka
GraphRAG nwere usoro abụọ. Indexing: LLM na-agụ chunks wee wepụta ụzọ atọ a haziri ahazi (njikọ, njikọ, otu) gbakwunyere nkọwa, nke ewepụtara na eserese; nchịkọta (dịka ọmụmaatụ, Leiden) otu ọnụ ụzọ banye n'ime obodo ndị ọkwa ọkwa, nke ọ bụla LLM chịkọtara. Ajụjụ: Ọchụchọ 'mpaghara' na-agbasa site na ụlọ ọrụ dakọtara ajụjụ n'akụkụ ha, ebe maapụ ọchụchọ 'ụwa' na-ebelata karịa nchịkọta obodo iji zaa ajụjụ ndị gbasara dataset. Ha abụọ na-eri nri ahaziri ahazi na ụdị ọgbọ.
Mmetụta atụmatụ
Ọsọ na ọnụ ọgụgụ
Usoro ọrụ asụsụ nwere ike ịga ngwa ngwa n'achụghị nkwụsi ike.
Nweta na iru
Ọ na-agbasawanye ohere n'ofe asụsụ na ụdị nzikọrịta ozi.
Mkpebi doro anya
Ndị otu nwere ike itinyekwu oge na ikpe ebe akpaaka na-ejikwa nkwughachi.
Ọdịnihu nke eserese ihe ọmụma GraphRAG
Na-atụ anya ka GraphRAG jikọta ya na ọdụ data eserese ihe onwunwe, mmụta ontology akpaka, yana mmelite eserese ka ihe ọmụma wee dị ọhụrụ na-enweghị ndezigharị zuru ezu. Sistemụ ngwakọ na-ejikọta myirịta vector na ntụgharị eserese na-aghọ ọkọlọtọ, na ọkpọkọ ndị na-arụ ọrụ ga-ekwe ka ụdị jụọ eserese ahụ ugboro ugboro. Ka mma mmịpụta na-akawanye mma, GraphRAG kwesịrị ime ọtụtụ hop, azịza a ga-akọwa - nwere ụzọ ihe nwere ike ịchọpụta - bara uru maka ntọala ihe ọmụma ụlọ ọrụ, akwụkwọ sayensị, na nyocha nyocha.
Mmejuputa n'ezie n'ụwa
Onye nyocha jụrụ 'Kedu isiokwu jikọtara akụkọ 10,000 ndị a?' na GraphRAG na-aza site na maapụ-belata karịa nchịkọta obodo.
Otu ndị na-emepụta ọgwụ na-ejikọta mkpụrụ ndụ ihe nketa, ọgwụ na ọrịa n'ofe akwụkwọ iji kwalite mmekọrịta multi-hop nke nyocha vector ga-atụfu.
Ngwa nrubeisi na-achọpụta ka azụmahịa si ejikọta ụlọ ọrụ site na ndị na-emekọrịta ihe iji gosipụta mmekọrịta dị ize ndụ zoro ezo.
Ọbá akwụkwọ GraphRAG mepere emepe Microsoft na-egosi otu ụlọ ọrụ na obodo Leiden maka ajụjụ mpaghara na nke zuru ụwa ọnụ.
Ihe ize ndụ & okporo ụzọ nche
Eziokwu ndị e chepụtara echepụta nwere ike jiri nwayọ tinye akụkọ, nkwado nkwado, ma ọ bụ nsonaazụ nyocha.
Mmetụta ngwa ngwa nwere ike ịmepụta nsonaazụ na-ekwekọghị ekwekọ n'ofe arịrịọ ndị yiri ya.
Enwere ike ikpughe data ederede nwere mmetụta ma ọ bụrụ na njikwa ohere adịghị ike.
Map mmejuputa
Kọwaa usoro mmepụta, ụda, na ụkpụrụ ịdịmma tupu ibugharị.
Weghachite nzaghachi site na isi mmalite ntụkwasị obi mgbe ọ bụla izi ezi dị mkpa.
Debe ebe nleba anya mmadụ maka mpụta dị elu.
Sochie ụkpụrụ ọdịda ma na-azụghachi mkpali ma ọ bụ usoro ọrụ mgbe niile.
Nọgide na-eme nchọpụta
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Ntuziaka na-esote
Njikwa Ọmụma AI
Ajụjụ a na-ajụkarị
What is GraphRAG Knowledge Graphs?
GraphRAG na-akwalite ọgbọ eweghachite-agbakwunyere site n'ịmepụta eserese ihe ọmụma nke ụlọ ọrụ na mmekọrịta site na nchịkọta akwụkwọ, wee weghachite ihe owuwu ahụ kama ịchịkọta ederede dịpụrụ adịpụ. Ọ dị mkpa n'ihi na ọ na-aza ajụjụ sara mbara, njikọ-ntụpọ nke ọchụchọ vector dị larịị enweghị ike.
Kedu isi ihe GraphRAG na-ewu nke na-eme ka ọ dị iche na RAG ọkọlọtọ?
GraphRAG na-ewepụta ihe dị iche iche na mmekọrịta dị n'etiti ha ka ọ bụrụ eserese ọmụma, kama ịdabere naanị na mpempe akwụkwọ dịpụrụ adịpụ.
Kedu ụdị ajụjụ GraphRAG na-edozi karịa ọchụchọ vector dị larịị?
GraphRAG kacha mma na ajụjụ zuru oke, ọtụtụ hop nke ihe akaebe gbasasịrị wee jikọta ya site na ụlọ ọrụ etiti.
Kedu algọridim nke GraphRAG na-ejikarị achịkọta ihe ndị metụtara n'ime obodo?
GraphRAG na-emetụta ụzọ nchọpụta obodo dị ka Leiden na ọnụ ọnụ ndị metụtara otu, wee chịkọta obodo ọ bụla.
N'oge ntinye aha, kedu ihe ụdị asụsụ ahụ na-ewepụta site na mkpirisi akwụkwọ?
Otu LLM na-agụ iberibe ma wepụta ihe arụrụ arụ, njiri mara na mmekọrịta nke jikọtara ọnụ na eserese ahụ.
Kedu ihe dị iche n'etiti ọchụchọ 'mpaghara' na 'ụwa' na GraphRAG?
Ọchịchọ mpaghara na-agafe ndị agbata obi nke ụlọ ọrụ dakọtara ajụjụ, ebe ọchụchọ zuru ụwa ọnụ na-achịkọta nchịkọta obodo iji zaa ajụjụ setịpụ data.