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@hurrainfatima034: ✌️🥰#foryou #foryoupage #foryoupage❤️❤️

Hurrain fatima
Hurrain fatima
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Region: PK
Saturday 15 August 2026 16:10:56 GMT
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Comments

abrargamingyt
Abrar Gaming YT :
آپ کو کہیں دیکھا ہے میں نے 🤔
2026-08-16 17:20:43
0
nasirkhan36363
Nasir chandia :
very nice 🙂
2026-08-15 16:14:41
0
sanam7994
Zindagi 💔 :
ji samjh gai beautiful girl🥰
2026-08-15 19:39:43
0
muhammadtariq2580
muhammadtariq2580 :
nice
2026-08-16 02:51:31
0
sana.choudhry10
Golgapa :
mashallah
2026-08-15 16:40:11
0
waheed_sahu
Waheed_ sahu302☠️ :
🥰🥰🥰
2026-08-28 10:13:57
0
waheed_sahu
Waheed_ sahu302☠️ :
❤️❤️❤️
2026-08-28 10:13:56
0
m.waqas.waqas58
M Waqas Waqas :
❤️❤️❤️
2026-08-22 16:45:35
0
m.waqas.waqas58
M Waqas Waqas :
😊😊😊
2026-08-22 16:45:32
0
rashid.bhatti6803
Rashid Bhatti :
💋💋💋
2026-08-19 21:09:44
0
rehman7189
rehman :
🥰🥰🥰
2026-08-16 16:53:24
0
zeeshaan747
▄︻┻═┳一꧁༺AWAN༻꧂ :
🥰🥰🥰🥰🥰😋
2026-08-16 10:45:47
0
tanveershah370
TANVEER SHAH🥰 :
🥰🥰🥰
2026-08-16 08:54:12
0
khizar.abbas5428
Khizar Abbas :
🥰🥰🥰
2026-08-16 07:47:53
0
hassanraza.5121
Hassanraza 512 :
❤️❤️❤️
2026-08-16 04:52:24
0
rehmanbhi007
RAنA8093 :
🥰🥰🥰
2026-08-16 03:17:51
0
mahartahirabbas38
Tahir Haral :
👌👌👌
2026-08-16 02:02:16
0
nimraislive7
نمرہ خان 😎 :
😁😁😁
2026-08-15 20:28:46
0
userayfw8b6jgn
Meer Sahab :
💖💖💖
2026-08-15 19:54:22
0
userayfw8b6jgn
Meer Sahab :
💞💞💞
2026-08-15 19:54:20
0
aliimranaliimran69
aliimranaliimran69 :
🥰🥰🥰
2026-08-15 19:02:49
0
malikawan6002580
Malik Shafique Awan :
👍👍👍
2026-08-15 18:30:14
0
kamisiaal12
kamran Khan :
🥰🥰🥰
2026-08-15 17:54:58
0
numanali8902
NOMI BABU 🚩 :
💞💞💞
2026-08-15 17:47:12
0
saifullah4755
ghunio :
🥰🥰🥰
2026-08-15 16:14:51
0
To see more videos from user @hurrainfatima034, please go to the Tikwm homepage.

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When someone says “RAG over millions of PDFs” they’re not asking about AI… they’re asking about search + systems. Here’s what that actually looks like 👇 I’d break it into 5 parts: ingestion, embeddings, retrieval, generation, monitoring 1️⃣ Ingestion is offline, not request-time At scale, this must be async • Stream documents from storage (S3/GCS) • OCR only when needed • Clean + normalize text • Chunk intelligently (not randomly) • Attach rich metadata (doc, page, section, etc.) None of this should ever touch your user request path 2️⃣ Embeddings + indexing built for scale You don’t embed on demand • Batch embedding jobs (GPU or queued) • Distributed ANN indexes (Milvus, Qdrant, Vespa, Elastic) • Sharding + HNSW / IVF / PQ • Store metadata alongside vectors Key insight: metadata filtering is your first gate vector search is the fallback 3️⃣ Retrieval + generation (tight path) Your request path should stay minimal: query → metadata filter → cache → ANN search → rerank → LLM • Most queries never even hit vector search • Many don’t hit the DB at all (cache wins) • Rerank a small set only • Send 5–10 chunks max to the LLM More context ≠ better results More chunks usually hurt 4️⃣ Caching is everything This is what controls cost + latency • Query → answer cache (FAQs, repeats) • Query → retrieval cache (top chunks) • Data/index cache (hot vectors, parsed docs) Real path looks like: query → cache → (miss) retrieve + LLM → write back 5️⃣ Monitoring closes the loop Without this, your system silently degrades • Retrieval quality (recall@k) • Answer quality (feedback loops) • Latency + cache hit rates • Re-embed + re-shard as data evolves BOTTOM LINE: RAG at scale is NOT an LLM problem It’s a search + caching architecture problem Most people are building demos Real AI engineers are building systems Link in bio for the full breakdown + a group with live expert-led calls and systems for you to get hired ASAP
When someone says “RAG over millions of PDFs” they’re not asking about AI… they’re asking about search + systems. Here’s what that actually looks like 👇 I’d break it into 5 parts: ingestion, embeddings, retrieval, generation, monitoring 1️⃣ Ingestion is offline, not request-time At scale, this must be async • Stream documents from storage (S3/GCS) • OCR only when needed • Clean + normalize text • Chunk intelligently (not randomly) • Attach rich metadata (doc, page, section, etc.) None of this should ever touch your user request path 2️⃣ Embeddings + indexing built for scale You don’t embed on demand • Batch embedding jobs (GPU or queued) • Distributed ANN indexes (Milvus, Qdrant, Vespa, Elastic) • Sharding + HNSW / IVF / PQ • Store metadata alongside vectors Key insight: metadata filtering is your first gate vector search is the fallback 3️⃣ Retrieval + generation (tight path) Your request path should stay minimal: query → metadata filter → cache → ANN search → rerank → LLM • Most queries never even hit vector search • Many don’t hit the DB at all (cache wins) • Rerank a small set only • Send 5–10 chunks max to the LLM More context ≠ better results More chunks usually hurt 4️⃣ Caching is everything This is what controls cost + latency • Query → answer cache (FAQs, repeats) • Query → retrieval cache (top chunks) • Data/index cache (hot vectors, parsed docs) Real path looks like: query → cache → (miss) retrieve + LLM → write back 5️⃣ Monitoring closes the loop Without this, your system silently degrades • Retrieval quality (recall@k) • Answer quality (feedback loops) • Latency + cache hit rates • Re-embed + re-shard as data evolves BOTTOM LINE: RAG at scale is NOT an LLM problem It’s a search + caching architecture problem Most people are building demos Real AI engineers are building systems Link in bio for the full breakdown + a group with live expert-led calls and systems for you to get hired ASAP


#foryou #everyonehighlightsfollowers #fypシ #everyone @✨ D A N I 🔥ابڑو @SAJJAD BAKHRANI ⛎ @Sajjad_Baloch_78 @W a d e r i 😎
#foryou #everyonehighlightsfollowers #fypシ #everyone @✨ D A N I 🔥ابڑو @SAJJAD BAKHRANI ⛎ @Sajjad_Baloch_78 @W a d e r i 😎
Waligaa Kibirka iska ilaali🫂 . #viraltiktok #sheekhalmisyaxye #viralvideos #wacdi #foryoupage
Waligaa Kibirka iska ilaali🫂 . #viraltiktok #sheekhalmisyaxye #viralvideos #wacdi #foryoupage

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