🧠 OpenRAG
OpenRAG — bu biznesning kontentini tozalash va tuzish, uni o'z serverimizda indekslash va tashqi AI tizimlari tomonidan bitta ruxsat berilgan eshik orqali tabiiy tilda so'rov qilish imkonini beruvchi RAG platformasi.
Vektorlar nashr etilmaydi; KIRISH ochiladi.
Bu mahsulotni tanishtirish, va'dalar ro'yxati emas. Quyida, bugun ishlaydigan qismlar va hali yo'l xaritada bo'lgan qismlar alohida belgilangan. Yo'l xaritasi elementlari uchun sanalar berilmaydi; ularning bajarilishi bilan ular "bugun ishlaydi" bo'limiga ko'chib o'tadilar.
Asosiy tamoyil
OpenRAG-ning butun dizayni bitta jumla asosida qurilgan: vektorlar nashr etilmaydi, kirish ochiladi. Tashqariga beriladigan narsa kontentning nusxasi emas, balki undan savol so'rish huquqidir.
Vektor — bu dvigatel, mahsulot emas
Vektor uni yaratgan modelga xosdir; boshqa AI uni shunday ishlatolmaydi. Bu, shuningdek, matnni bir necha baravar oshiradi. Shuning uchun vektorlar hech qachon berilmaydi.
Ochilgan narsa — so'rovga kirish
Savol keladi; javob va unga asoslangan manba o'tish joylari qaytib keladi. To'liq kontent hech qachon nusxalanmaydi, tarqatilmaydi yoki yuklab olinmaydi.
Ruxsat biznesga tegishli
Biznes qaysi kontent tashqi so'rovga javob berish uchun ochilishini hal qiladi; ishtirok etish ixtiyoriy. Ruxsat va rozilik qatlami yo'l xaritada.
Ishlash tamoyili — maqsadli arxitektura
Ushbu oqimning tozalash, tuzish, indekslash va qidirish havolari bugun ishlaydi. O'z-o'zini uy-joylashgan embedding va qayta tartiblash, shuningdek, tashqi dunyoga bitta eshik hali yo'l xaritada; har bir bosqich quyidagilarni ko'rsatadi.
Manbalar to'planadi
xloji saytlari, yuklangan hujjatlar va o'rganilgan veb-sahifalar kontent manbalari hisoblanadi. (WordPress plagini va ommaviy yuklash API yo'l xaritada).
U tozalangan va tuzilgan
Tartibsiz kontent tozalangan, ma'nolli bo'laklarga bo'lingan va RAG ishlatishi mumkin bo'lgan shaklga keltirilgan. Ushbu bosqich bugun ishlaydi.
U indekslangan va qidirilgan
Bo'laklar vektorlarga aylantiriladi va pgvector-ga yoziladi; ular HNSW indeksi va gibrid qidirish (kalit so'z + vektor) orqali topiladi. Ushbu bosqich bugun ishlaydi; qayta tartiblash havolasini qo'shish yo'l xaritada.
U bitta eshik orqali erishiladi
Tashqi AI tizimlari tabiiy tilda bitta ruxsat berilgan eshik orqali so'raydi; javoblar va manba o'tish joylari qaytib keladi, vektorlar emas. Ushbu eshik hali mavjud emas; u yo'l xaritada.
Bugun ishlaydigan qismlarJonli
Quyidagilar bugun platformada ishlamoqda va allaqachon xloji.com-dagi AI vositalari tomonidan ishlatilmoqda.
pgvector + HNSW indeksi
Kontent bo'laklari ma'lumotlar bazasida vektorlar sifatida saqlanadi va kosinus o'xshashligi bo'yicha HNSW indeksi bilan qidiriladi.
Gibrid qidirish
Kalit so'z qidirish va vektor qidirish birgalikda ishlaydi; ikkita natija ro'yxati bitta reytingga birlashtiriladi. Bu aniq atamalar mos kelishini va semantik jihatdan yaqin o'tish joylarini topadi.
Ko'p tenantli izolyatsiya
Har bir biznes, sayt va muallifning o'z RAG-i bor. So'rov faqat uning tenant indeksi bo'yicha ishlaydi; ma'lumotlar tenantlar bo'ylab aralashmaydi.
Hujjatlarni tozalash va RAG tuzilishi
Yuklangan hujjatlar tozalangan, bo'laklarga bo'lingan va RAG uchun tuzilgan. Platformadagi Ma'lumotlarni tuzish vositasi bu quvurga foydalanuvchi interfeysi hisoblanadi.
Veb-sahifalarni o'rganish
Saytning sahifalari o'rganiladi, ularning kontenti chiqariladi va xuddi shu tozalash va indekslash quvuridan o'tkaziladi.
Maxsus AI vositalarida ommaviy RAG oqimi
Siz yaratgan maxsus AI vositasi ommaviy RAG manbaidan oziqlantirilishi mumkin; vosita uning javoblarini shu bilimlar bazasida asoslaydi.
Fayl va media saqlanishi
Fayllar va media ob'ektli saqlanishda saqlanadi — vektor saqlashda emas. RAG yozuvi asl faylga qaytib beradi.
Bizning serverlarimizda embedding va qayta tartiblash
Embedding va qayta tartiblash endi alohida xizmatda bizning serverlarimizda ishlaydi. Matn serverdan chiqmasdan vektorlarga aylantiriladi va nomzod natijalar aniqlikni oshirish uchun mahalliy qayta tartiblovchidan o'tadi.
So'rovni qayta yozish
Noaniq yoki to'liq bo'lmagan savol qidiruv boshlanishidan oldin tartibga solinadi. Foydalanuvchilar "to'g'ri" savolni ifodalashlari shart emas; tizim so'rovni qidiruvga tayyorlaydi.
Tasvir, audio va videodan matn
Tasvirlardan matn chiqarish va audio va videoning transkripsiya qilinishi serverda mahalliy vositalar bilan amalga oshiriladi va natijada hosil bo'lgan matn bir xil tozalash va indekslash quvuriga kiritiladi. Ma'lumotlar serverni tark etmaydi.
Yo'l xaritasida nima borRejalashtirilgan
Quyidagilar hali mavjud emas. Ular rejalashtirilgan va loyihalangan elementlar; bu sahifa ularni allaqachon qurilgani kabi taqdim etmaydi.
Yagona eshik: MCP / API shluzi
Tashqi AI tizimlari tabiiy tilda so'rov qilishi mumkin bo'lgan bitta shluz. Bir marta ulanadigan AI har bir ishtirok etuvchi biznesga bir xil eshik orqali yetib boradi.
Ruxsat va rozilik qatlami
Bizneslar o'z ixtiyorlari bilan qo'shiladi; autentifikatsiya, kalitlarni boshqarish, tezlikni cheklash va suiiste'molga qarshi himoya barcha bu qatlamda joylashgan.
AI ko'rinish qatlami
Kontentni llms.txt, /.well-known/ va schema.org belgilari orqali mashinalar tomonidan topish imkonini berish, shuningdek MCP ro'yxatlarida ro'yxatdan o'tish. Magik avtomatik kashfiyot yo'q; ko'rinish bu tarzda qurilgan.
WordPress plagini
WordPress saytiga OpenRAG-ga kontentini yuborish va AI tizimlari so'rov qilishi mumkin bo'lgan uchburchakni olish imkonini beruvchi plagin.
Biz bu elementlar uchun sanalar bermaymiz. Element tugagandan so'ng u "bugun ishlaydi" ro'yxatiga ko'chib o'tadi va bu sahifa mos ravishda yangilanadi.
🔒 Ma'lumotlar serverni tark etmaydi
OpenRAG-ning maqsadi RAG quvurining har bir bosqichini o'z serverimizda mahalliy, bepul vositalar bilan ishlatishdir: embedding, qayta tartiblash, tasvirlardan matnni chiqarish va audio transkripsiya. RAG-ga kiradigan hech qanday media tashqi xizmatga yuborilmaydi — bu muzokaraga bo'lmaydigan dizayn qoidasi.
Samimiy holat: bu bosqichlarning bir qismi bugun hali tashqi xizmatlar tomonidan bajariladi. Ularni uyga ko'chirish yo'l xaritasidagi birinchi element.
O'sish qanday rejalashtirilgan
Kontent o'sgan sari vektor qidiruvi qimmatlashadi. OpenRAG dizayni boshidan boshlab bu xarajatni uch yo'l bilan cheklaydi.
Bitta katta indeks yo'q
Har bir biznesning o'z kichik indeksi bor va so'rov faqat shu indeks bo'yicha ishlaydi. Umumiy kontent o'ssagina, bitta so'rov skan qiladigan maydon kichik bo'lib qoladi; qidiruv xarajatlari platformaning umumiy hajmi bilan emas, balki bir biznesning hajmi bilan o'sadi.
Vektor o'lchami va aniqligi sozlanishi
Qisqaroq vektor o'lchamlaridan va ixcham raqam formatlaridan foydalanish orqali bir xil kontent sezilarli darajada kamroq joy egallaydi. Ushbu tanlovlar dizaynga boshidan kiritilgan, shunda keyinchalik qimmatga tushadigan migratsiyaga hojat yo'q.
Sovuq qatlam
Uzoq vaqt davomida so'rov qilinmagan biznesning indeksi arzon doimiy saqlashga ko'chiriladi va so'rov kelganda qaytariladi. Shunday qilib, faqat hozirda faol bo'lgan kontent tez xotirada saqlanadi. Ushbu qatlam yo'l xaritasida.
Ko'p so'raladigan savollar
OpenRAG nima?
Bu biznesning kontentini tozalash va tuzish, uni o'z serverimizda indekslash va tashqi AI tizimlari tomonidan tabiiy tilda so'rov qilish imkonini beruvchi RAG platformasi. Bu chat vositasi emas; bu bunday vositalar ostida ishlaydigan bilimlar qatlami.
Siz mening vektorlarimni yoki kontentimni boshqalarga berasizmi?
Yo'q. Asosiy tamoyil: vektorlar nashr etilmaydi, kirish ochiladi. Tashqariga beriladigan narsa kontentning nusxasi emas, balki undan savol so'rish huquqidir; faqat javob bilan birga manba o'tish joylari qaytib keladi. Bundan tashqari, biznes qaysi kontent tashqi so'rovga javob berish uchun ochilishini hal qiladi.
Hozir nima ishlatish mumkin?
Vektor ma'lumotlar bazasi va indeksi, gibrid qidiruv, ko'p tenantli izolyatsiya, hujjatlarni tozalash va RAG tuzilishi, veb-sahifani izlash, moslashtirilgan AI vositalaridagi ochiq RAG oqimi va fayl/media saqlashning barchasi bugun ishlaydi. O'z-o'zidan joylashtirilgan embedding, qayta tartiblash, so'rovlarni qayta yozish, tashqi eshik, ruxsatlar qatlami, ko'rinish qatlami va WordPress plagini hali mavjud emas.
Mening ma'lumotlarim serverdan chiqib ketadimi?
RAG quvuri har bir bosqichi serverdagi mahalliy vositalar bilan ishlasin, hech qanday media RAGga kirganda tashqi xizmatga yuborilmasin, degan maqsad bor. Haqli ravishda aytishimiz mumkin: bu bosqichlarning bir qismi bugun ham tashqi xizmatlar tomonidan bajariladi va ularni ichkariga ko'chirish rejaning birinchi bandi hisoblanadi.
Rejadagi funksiyalar qachon tayyor bo'ladi?
Biz sanalar berishga va'da bermaymiz. Ushbu sahifaning maqsadi aynan shunda: bugun ishlayotgan va rejalashtirilgan narsalarni alohida ko'rsatish. Agar element tugallanganda, u "bugun ishlayotgan qismlar" ro'yxatiga o'tkaziladi.
Mening kontentim AI o'qitish uchun ishlatiladimi yoki sotiladimi?
Yo'q. Bunday foydalanish bugun mavjud emas. Ruxsat berilgan matn korpusi g'oyasi rejaning eng uzoqdagi, huquqiy jihatdan og'ir elementi hisoblanadi; hatto sodir bo'lsa ham, biznesning aniq roziligisiz hech qanday kontent undan foydalanish uchun ishlatilmaydi.
AI portaliga boshqa AI vositalari uchun qayting.
Ko'p so'raladigan savollar
What is OpenRAG?
OpenRAG is a RAG (Retrieval-Augmented Generation) system and a single-door AI gateway running on xloji.com's own server. When a question arrives, it first finds the relevant content, then generates an answer based on this content. It also serves as a central gateway allowing AI tools outside of xloji.com to access xloji.com's information from a single point.
What does RAG mean and how does OpenRAG implement it?
RAG stands for 'Retrieval-Augmented Generation', meaning it searches for information from relevant sources before an AI generates an answer, then creates a response based on that information. OpenRAG runs these two steps, the search and generation steps, together on its own server. Thus, the answers given by OpenRAG are not random, but based on real content belonging to xloji.com.
What does 'single-door AI gateway' mean?
A single-door AI gateway means that different AI requests and integrations are passed through a single central entry point instead of separate systems. OpenRAG takes on this role: both xloji.com's own 'Ask a Question' feature and AI tools connected from outside receive service through the same OpenRAG infrastructure. This central structure allows AI-related traffic to be managed from a single point.
What difference does it make that OpenRAG runs on its own server?
OpenRAG runs on xloji.com's own server without relying on a third-party cloud service. This means that the searched and processed data remains under xloji.com's own control. As a structure running on its own server, OpenRAG performs search and answer generation operations within its own infrastructure without transferring data to external systems.
Who uses OpenRAG?
OpenRAG is used by three different groups: end-users who visit xloji.com and type into the 'Ask a Question' box, systems that query xloji.com's own RAG infrastructure, and external AI tools that access information about xloji.com via OpenRAG. For the end-user, OpenRAG returns an answer based on xloji.com content to the question asked. For external tools, OpenRAG is a context-free gateway used to retrieve information from xloji.com.
How does the 'Ask a Question' box on xloji.com work with OpenRAG?
A question entered into the 'Ask a Question' box on xloji.com is processed by OpenRAG. OpenRAG first searches for content related to the question within xloji.com's own data, then generates an answer based on the content it finds. This ensures that the answer given to the user is supported by real content from xloji.com.
How do external AI tools obtain information via OpenRAG?
OpenRAG serves as a single-gate passage enabling AI tools outside of xloji.com to access information about xloji.com. These tools can connect via OpenRAG and receive self-contained, understandable answers from xloji.com's content. Therefore, the information provided via OpenRAG is designed to be meaningful on its own, without requiring a separate context.
What are the limitations of OpenRAG?
The answers provided by OpenRAG are limited to the content provided to it; OpenRAG cannot produce a definitive answer about information that is not in the system or has not been added. OpenRAG is designed to remain limited to the content it has, rather than fabricating non-existent information. Therefore, it should be remembered that an answer obtained from OpenRAG is limited to the content that xloji.com currently possesses.
Is OpenRAG a chatbot?
OpenRAG is not a standalone chatbot in the classical sense; it is the RAG and gateway infrastructure that powers xloji.com's 'Ask a Question' feature and external tools' access to information. OpenRAG's core function is to find content corresponding to a question and generate an answer based on that content. As such, OpenRAG operates more as the infrastructure layer behind the answers than as an interface that directly chats with the user.
What is the purpose of OpenRAG for xloji.com?
The purpose of OpenRAG is to enable both end-users and external AI tools to access information belonging to xloji.com through a reliable and self-controlled infrastructure. Running on its own server ensures that this access happens without dependence on third-party systems. Thanks to its single gateway structure, OpenRAG combines different access points in a single central system.
OpenRAG haqida batafsil ma'lumot
OpenRAG is a RAG (Retrieval-Augmented Generation) system running on xloji.com's own server, and it is also an AI gateway that passes AI-related requests from a single point. The 'Open' in its name indicates that the system offers an open and accessible structure, while 'RAG' refers to its method of operation, which involves first finding relevant data and then generating an answer based on that data. A question typed into the 'Ask a Question' box on xloji.com is first searched for relevant content by OpenRAG, and then an answer is generated based on this content. The same infrastructure also allows AI tools outside of xloji.com to access information belonging to xloji.com through OpenRAG and use this information in their own answers. This structure enables xloji.com to provide services through a single central infrastructure instead of setting up separate AI solutions for its different products.
The basic logic of the RAG approach is that an AI system, instead of relying solely on the general knowledge it was previously trained on, first retrieves up-to-date and accurate content from a source related to the question and then answers in light of this content. OpenRAG operates by combining these two steps – retrieval and generation – on its own server. This approach aims to reduce the tendency of AI models to sometimes generate non-existent information; because the answer is derived from actually found content, not from the model's own memory. In this way, the answers given by OpenRAG are based on xloji.com's own content, not on random or outdated information. Thus, the source of an answer produced by OpenRAG remains traceable as xloji.com's own data. The server belonging to xloji.com means that the searched and processed data is kept under xloji.com's own control without depending on a third-party cloud service.
The 'single-door AI gateway' side of OpenRAG means that different AI requests and integrations are routed through a single central entry point instead of separate systems. In this way, both xloji.com's own 'Ask a Question' feature and external tools connected via OpenRAG use the same infrastructure and the same basic logic. As a central gateway structure, OpenRAG ensures that AI-related traffic passes through a single point; this increases both the consistency of answers and control over the data as a system running on its own server. This single-point approach also allows maintenance and updates to be done in one place instead of in distributed systems. In this respect, OpenRAG is not only a question-and-answer tool, but also an infrastructure layer that unifies all of xloji.com's AI access points.
OpenRAG is used directly or indirectly by three different user groups: end-users who visit xloji.com and type into the 'Ask a Question' box, systems that query xloji.com's own RAG infrastructure, and external AI tools that access information belonging to xloji.com via OpenRAG. All three user groups receive answers fed from the same OpenRAG infrastructure, the same content pool; therefore, a consistent source of information is reached no matter where questions about xloji.com are asked. The answers given by OpenRAG are limited to the content provided to it; that is, a definitive answer should not be expected from OpenRAG about information that is not in the system or has not been added to its server. This limitation also forms the basis of OpenRAG's reliability: instead of fabricating non-existent information, it aims to produce answers limited to and based on the content it has.