Uhlolojikelele
I-Contextual AI yakha izinhlelo ze-end-to-end retrieval-augmented generation (RAG) zamabhizinisi, ezisungulwe abacwaningi abaqamba igama elithi RAG. Ibalulekile ngoba ibhekana nengxenye enzima kakhulu yebhizinisi i-AI: ukunikeza amamodeli olimi izimpendulo ezinembile, ezisekelwe kumadokhumenti ayimfihlo enkampani.
I-Contextual AI Enterprise RAG iqondwa kangcono kumongo wesu, ukufinyelela kwemodeli, izinqumo zeplathifomu, nobudlelwano be-ecosystem.
I-Deep Dive
I-Contextual AI yasungulwa ngo-2023 nguDouwe Kiela kanye no-Amanpreet Singh, ababhali abaphambili bephepha lokuqala le-RAG lika-2020 elivela ku-Facebook AI Research. Kunokuba ithengise i-chatbot, inkampani inikeza inkundla ye-RAG ephethwe lapho yonke ingxenye - ukukhishwa, ukubuyisa, ukuhlelwa kabusha, kanye nezinyathelo zokukhiqiza - ishunwa ndawonye njengesistimu eyodwa kunokuba iboshwe. Imodeli yabo yolimi olusekelwe phansi (i-GLM) iqeqeshelwe ukuphendula kuphela ezindimeni ezibuyisiwe kanye nokusho ukuthi ayazi lapho ubufakazi bungekho, okunciphisa imibono engekho emikhakheni elawulwayo njengezezimali, umthetho, nobunjiniyela. Iphuzu liwukuthi amamodeli angekho eshalofini athungelwe kusizindalwazi se-vector enza ngaphansi kokusebenza kwephayiphi eyakhelwe inhloso, ethuthukiswe ngokuhlanganyela kuzisekelo zolwazi lwebhizinisi langempela.
I-Technical Insight
I-RAG Yakudala ishumeka amadokhumenti kuma-vector, ibuyisa izingxenye eziseduze embuzweni, futhi iwahlohle ekwazisweni. I-Contextual AI ilungiselela lonke uchungechunge: isihlungi sedokhumenti esilondoloza amathebula nesakhiwo, indlela yokubuyisa ingxube, imodeli yokuhlela kabusha ehlela kabusha amakhandidethi ngokuhambisana, kanye nomshini okhiqiza amandla osekelwe phansi ohlawuliswa izimangalo ezingasekelwe. Ukushuna lezi zigaba ngokuhlanganyela - esikhundleni sokuphatha ngasinye njengengxenye ehlukile yomthengisi - yilokho okuphakamisa ukunemba kudatha yebhizinisi eminyene, ehlelekile.
I-Mastering Contextual AI Enterprise RAG
I-Contextual AI yakha izinhlelo ze-end-to-end retrieval-augmented generation (RAG) zamabhizinisi, ezisungulwe abacwaningi abaqamba igama elithi RAG. Ibalulekile ngoba ibhekana nengxenye enzima kakhulu yebhizinisi i-AI: ukunikeza amamodeli olimi izimpendulo ezinembile, ezisekelwe kumadokhumenti ayimfihlo enkampani. I-Contextual AI Enterprise RAG iqondwa kangcono kumongo wesu, ukufinyelela kwemodeli, izinqumo zeplathifomu, nobudlelwano be-ecosystem. Ukuze wakhe ukuqonda okujulile, phatha i-Contextual AI Enterprise RAG njengemodeli yokusebenza, hhayi isici esisodwa: chaza imiphumela efiselekayo, ucacise ukucabanga, futhi uhlukanise lokho uhlelo olungakwenza ngokwethembeka kulokho okusadinga ukwahlulela kochwepheshe.
Empeleni, amaqembu aqinile asebenzisa i-Contextual AI Enterprise RAG ahlola isu lomthengisi, ukwethembeka kwemephu yomgwaqo, kanye nobungozi bokukhiya ngaphambi kokuzibophezela. Babhala imibandela yempumelelo ecacile, ukuhlola okuqhathaniswa nedatha engokoqobo nokugeleza komsebenzi, futhi baphindaphinde ngokusekelwe kumaphethini okuhluleka aqashiwe esikhundleni sokuwina kwebhentshimakhi yesikhathi esisodwa. Yilapho ukuqonda kwethiyori kuguquka kube amandla ahlala njalo kuwo wonke umkhiqizo, inqubomgomo, kanye nokusebenza.
Imephu yemigwaqo yabathengisi ithonya izici ithimba lakho elingazakha ngokulandelayo. Ngesikhathi esifanayo, izimemezelo Zokuqalisa zingase zidlule ukuzinza ekusebenzeni kwangempela kokukhiqiza. Indlela eqine kakhulu iwukuhlanganisa isivinini sokuhlola nesiyalo sokuphatha: qhuba abashayeli bezindiza, bamba ubufakazi, ushicilele amalogi ezinqumo, futhi ubuyekeze izivikelo ngokuqhubekayo njengoba imodeli yokuziphatha, okulindelwe ngabasebenzisi, kanye nezimfuneko zokulawula zishintsha.
I-Strategic Impact
Imephu yemigwaqo yabathengisi ithonya izici ithimba lakho elingazakha ngokulandelayo.
Imephu yemigwaqo yabathengisi ithonya izici ithimba lakho elingazakha ngokulandelayo. Ekusetshenzisweni kwekhwalithi ephezulu, lokhu kuhunyushwa emithethweni yokusebenza elinganisekayo, imingcele yobunikazi, nemikhuba yokubuyekeza ephindelelayo ukuze amaqembu akwazi ukukala ukuzethemba esikhundleni sokukala ukungaqondakali.
Imigomo yezohwebo nezinketho zokuthunyelwa zithinta izindleko zesikhathi eside nobungozi.
Imigomo yezohwebo nezinketho zokuthunyelwa zithinta izindleko zesikhathi eside nobungozi. Ekusetshenzisweni kwekhwalithi ephezulu, lokhu kuhunyushwa emithethweni yokusebenza elinganisekayo, imingcele yobunikazi, nemikhuba yokubuyekeza ephindelelayo ukuze amaqembu akwazi ukukala ukuzethemba esikhundleni sokukala ukungaqondakali.
Izinxephezelo zenkampani zibumba okuzenzakalelayo kwemikhiqizo, ukuma kokuphepha, nokuvuleleka.
Izinxephezelo zenkampani zibumba okuzenzakalelayo kwemikhiqizo, ukuma kokuphepha, nokuvuleleka. Ekusetshenzisweni kwekhwalithi ephezulu, lokhu kuhunyushwa emithethweni yokusebenza elinganisekayo, imingcele yobunikazi, nemikhuba yokubuyekeza ephindelelayo ukuze amaqembu akwazi ukukala ukuzethemba esikhundleni sokukala ukungaqondakali.
Ukuqaliswa Komhlaba Wangempela
Abahlaziyi bebhange babuza izinkulungwane zemibiko yocwaningo lwangaphakathi kanye nokufakwa kwemali etholwayo futhi bathole izimpendulo ngezingcaphuno eziqondile ekhasini lomthombo.
Inkampani yonjiniyela isesha amashumi eminyaka ezincwadi zemishini kanye namalogi okulungisa ukuze ihlole amaphutha omshini ngaphandle kokufunda wonke ama-PDF.
Ithimba lomshwalense lihlola amagama enqubomgomo kumakhulukhulu ezinhlobonhlobo zenkontileka ukuze liqinisekise ukuthi isimangalo esithile siyakhava.
Inkampani eyenza imithi ibheka izinqubo zokuhlola zomtholampilo ezifanele kanye nokuhanjiswa okulawulwayo kuyilapho igcina idatha ngaphakathi kwendawo yayo.
Amaphethini Okusebenzisa
I-Contextual AI Enterprise RAG isebenza
Abahlaziyi bebhange babuza izinkulungwane zemibiko yocwaningo lwangaphakathi kanye nokufakwa kwemali etholwayo futhi bathole izimpendulo ngezingcaphuno eziqondile ekhasini lomthombo.
Abahlaziyi bebhange babuza izinkulungwane zemibiko yocwaningo lwangaphakathi kanye nokufakwa kwemali etholwayo futhi bathole izimpendulo ezinengcaphuno eqondile ekhasini lomthombo Amathimba ngokuvamile athola imiphumela engcono uma echaza izilinganiso zekhwalithi ngaphambili, egcina indlela yokukhuphuka komuntu ezimweni ezibucayi, futhi alandelela kokubili izinzuzo zokukhiqiza nezindleko zamaphutha ngokuhamba kwesikhathi.
I-Contextual AI Enterprise RAG isebenza
Inkampani yonjiniyela isesha amashumi eminyaka ezincwadi zemishini kanye namalogi okulungisa ukuze ihlole amaphutha omshini ngaphandle kokufunda wonke ama-PDF.
Inkampani yobunjiniyela isesha amashumi eminyaka ezincwadi zemishini kanye namalogi okulungisa ukuze kuhlonzwe amaphutha emishini ngaphandle kokufunda wonke Amaqembu e-PDF ngokuvamile athola imiphumela engcono lapho echaza izinga eliphezulu ngaphambili, egcina indlela yokukhuphuka yomuntu yamacala abucayi, futhi alandelele kokubili izinzuzo zokukhiqiza nezindleko zamaphutha ngokuhamba kwesikhathi.
I-Contextual AI Enterprise RAG isebenza
Ithimba lomshwalense lihlola amagama enqubomgomo kumakhulukhulu ezinhlobonhlobo zenkontileka ukuze liqinisekise ukuthi isimangalo esithile siyakhava.
Ithimba lomshwalense lihlola amagama enqubomgomo kumakhulukhulu ezinhlobonhlobo zenkontileka ukuze liqinisekise ukuthi isimangalo esithile siyambozwa yini Amaqembu ngokuvamile athola imiphumela engcono uma echaza ikhwalithi ephezulu ngaphambili, egcina indlela yokukhuphuka kwabantu yamacala abucayi, futhi alandelele kokubili izinzuzo zokukhiqiza nezindleko zamaphutha ngokuhamba kwesikhathi.
I-Contextual AI Enterprise RAG isebenza
Inkampani eyenza imithi ibheka izinqubo zokuhlola zomtholampilo ezifanele kanye nokuhanjiswa okulawulwayo kuyilapho igcina idatha ngaphakathi kwendawo yayo.
Inkampani eyenza imithi ibheka izinqubo zokuhlolwa komtholampilo ezifanele kanye nokuhanjiswa okulawulwayo kuyilapho igcina idatha ngaphakathi kwendawo yayo Amathimba ngokuvamile athola imiphumela engcono lapho echaza izinga eliphezulu ngaphambili, egcina indlela yokukhuphuka komuntu ezimweni ezibucayi, futhi elandelela kokubili izinzuzo zokukhiqiza nezindleko zamaphutha ngokuhamba kwesikhathi.
Izingozi & Guardrails
Izimemezelo zokwethula zingase zeqe ukuzinza ekugelezeni komsebenzi wangempela wokukhiqiza.
Izintengo ze-API noma izinguquko zenqubomgomo zingaphula ukucabanga ngobusuku obubodwa.
Ukuncika komthengisi oyedwa kukhulisa izindleko zokukhiya nokufuduka.
Ukuqalisa Umhlahlandlela
Linganisa abahlinzeki usebenzisa eyakho imisebenzi namasethi edatha.
Linganisa abahlinzeki usebenzisa eyakho imisebenzi namasethi edatha. Phatha isinyathelo ngasinye njengesango lobufakazi: uma imibandela ingafinyelelwa, misa ukukhishwa, vala igebe, bese unweba ukusetshenziswa.
Buyekeza ubumfihlo, ukuphepha, nemibandela yomthetho ngaphambi kokuhlanganiswa.
Buyekeza ubumfihlo, ukuphepha, nemibandela yomthetho ngaphambi kokuhlanganiswa. Phatha isinyathelo ngasinye njengesango lobufakazi: uma imibandela ingafinyelelwa, misa ukukhishwa, vala igebe, bese unweba ukusetshenziswa.
Gcina uhlelo lokubuyela emuva kuwo wonke amamodeli noma abathengisi.
Gcina uhlelo lokubuyela emuva kuwo wonke amamodeli noma abathengisi. Phatha isinyathelo ngasinye njengesango lobufakazi: uma imibandela ingafinyelelwa, misa ukukhishwa, vala igebe, bese unweba ukusetshenziswa.
Gada amanothi okukhululwa ukuze izinguquko zemephu yomgwaqo zingamangazi amaqembu.
Gada amanothi okukhululwa ukuze izinguquko zemephu yomgwaqo zingamangazi amaqembu. Phatha isinyathelo ngasinye njengesango lobufakazi: uma imibandela ingafinyelelwa, misa ukukhishwa, vala igebe, bese unweba ukusetshenziswa.