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Reels en Tendencia

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#Vectordb Reel by @dhruvtechbytes (verified account) - Reasoning-based retrieval is cool. But when scale, latency, and cost matter - Traditional RAG wins. Vector DB + embeddings = battle-tested infra. V
25.3K
DH
@dhruvtechbytes
Reasoning-based retrieval is cool. But when scale, latency, and cost matter — Traditional RAG wins. Vector DB + embeddings = battle-tested infra. Vectorless RAG? Great experiment. Traditional RAG? Production ready. #AI #RAG #systemdesign #tech #engineers [agentic ai, genai, rag, systemdesign, software engineering, vectordb, developers, ai engineer, databases]
#Vectordb Reel by @jeetsoni.dev - Vector DB set up kar liya but RAG abhi bhi galat answers de raha hai? Yeh 4 mistakes fix karo 👇 1. Storing full docs instead of chunks Poora documen
2.2K
JE
@jeetsoni.dev
Vector DB set up kar liya but RAG abhi bhi galat answers de raha hai? Yeh 4 mistakes fix karo 👇 1. Storing full docs instead of chunks Poora document ek saath store karna biggest mistake hai. LLM ko sirf relevant piece chahiye — 500-800 token chunks mein split karo, overlap rakho 10-15%. 2. Wrong embedding model choose karna OpenAI text-embedding-3-small aur text-embedding-ada-002 same nahi hain. Domain-specific content ke liye model match karna zaroori hai — mismatch hoga toh retrieval fail hoga. 3. Similarity threshold set nahi kiya Default similarity score pe mat rehna. 0.75–0.85 ke beech threshold set karo — warna irrelevant chunks bhi context mein aa jaate hain aur LLM confuse ho jaata hai. 4. No metadata filtering Sirf semantic search kaafi nahi. Date, source, doc_type jaise metadata filters lagao — retrieval 40% faster aur accurate ho jaata hai. Save karo — next project mein kaam aayega 🔖 #AIEngineering #RAG #VectorDB #LLMDevelopment #AIIndia MachineLearning PythonDeveloper
#Vectordb Reel by @cactuss.ai (verified account) - Learn about this concept, comment for Github link #rag #vector #artificialintelligence #deeplearning #machinelearning
1.1M
CA
@cactuss.ai
Learn about this concept, comment for Github link #rag #vector #artificialintelligence #deeplearning #machinelearning
#Vectordb Reel by @tek.loggs - What is a Vector Database 🤔 #ai #coding #programming #vectordb #machinelearning
188
TE
@tek.loggs
What is a Vector Database 🤔 #ai #coding #programming #vectordb #machinelearning
#Vectordb Reel by @hemant.on.growth - DP chodo.. Array bhi dynamic tha 🥲🔥 Comment 'ARRAY' aur sheet DM ✅
146.9K
HE
@hemant.on.growth
DP chodo.. Array bhi dynamic tha 🥲🔥 Comment ‘ARRAY’ aur sheet DM ✅
#Vectordb Reel by @sky.evade - RAG vs VectorLess RAG 🔥 #trendingreels #trending #fyp #explorepage #coding
1.9K
SK
@sky.evade
RAG vs VectorLess RAG 🔥 #trendingreels #trending #fyp #explorepage #coding
#Vectordb Reel by @ai.with.shrey - Vector Databases are DEAD? 💀 (Try this instead) Are you still stuck in the "Chunking + Embedding" loop? Most RAG pipelines fail because Vector Searc
3.5K
AI
@ai.with.shrey
Vector Databases are DEAD? 💀 (Try this instead) Are you still stuck in the "Chunking + Embedding" loop? Most RAG pipelines fail because Vector Search doesn't understand context—it only understands similarity. In this video, I’m breaking down Vectorless RAG using PageIndex. No more messy chunking or expensive Vector DBs. We’re moving from Similarity Search to Reasoning-based Retrieval. In this video: Why traditional RAG is failing. How Tree-based navigation works. Improving accuracy by 10x with PageIndex. Check out the repo here: https://github.com/VectifyAI/PageIndex Subscribe for more "No-Fluff" AI engineering. #vectordatabase #softwareengineering #rag #aiagents #llm #vectors
#Vectordb Reel by @tek.loggs - Page Indexing vs Vector Database 🤔 #ai #vectordb #coding #machinelearning #database
912
TE
@tek.loggs
Page Indexing vs Vector Database 🤔 #ai #vectordb #coding #machinelearning #database
#Vectordb Reel by @ds_ai_ketan - Day 16 - 90 Days Journey to Become AI Engineer Vector Databases #RAG #vectordb #llm #genai
17.0K
DS
@ds_ai_ketan
Day 16 - 90 Days Journey to Become AI Engineer Vector Databases #RAG #vectordb #llm #genai
#Vectordb Reel by @innovation.with.parminder - That's right, vector DBs are dead. GraphRAG (Graph Retrieval-Augmented Generation) is an evolution of standard RAG that uses a Knowledge Graph to pro
2.7K
IN
@innovation.with.parminder
That’s right, vector DBs are dead. GraphRAG (Graph Retrieval-Augmented Generation) is an evolution of standard RAG that uses a Knowledge Graph to provide more accurate, structured, and context-aware answers. While traditional RAG relies on searching through flat text chunks using vector similarity, GraphRAG maps the relationships between entities (people, places, concepts) to understand the “big picture” of your data. Why use it? Standard RAG often fails at “global” questions (e.g., “What are the main themes in these 100 documents?”). Because GraphRAG pre-summarizes data into clusters, it can answer these high-level questions much more effectively. [vector database, vector search, embedding storage, high dimensional vectors, similarity search, semantic search, AI retrieval systems, retrieval augmented generation, RAG architecture, embedding models, cosine similarity, nearest neighbor search, ANN search, machine learning infrastructure, LLM memory systems, AI data indexing, vector indexing, Pinecone database, FAISS library, scalable AI systems] #rag #vector #ai #machinelearning #deeplearning
#Vectordb Reel by @thinkcodecrack - Comment code to get optimised code in your DM. Save for futures interview 🙌 Problem: Search in 2D Matrix Topic: Binary Search #dsa #fyp #datastruc
1.8K
TH
@thinkcodecrack
Comment code to get optimised code in your DM. Save for futures interview 🙌 Problem: Search in 2D Matrix Topic: Binary Search #dsa #fyp #datastructures #engineer #algorithms
#Vectordb Reel by @codechai2026 - "One name. Multiple values. That's the power of Arrays 💪"#codechai #programming #grow #tech #btech
1.5K
CO
@codechai2026
“One name. Multiple values. That’s the power of Arrays 💪”#codechai #programming #grow #tech #btech

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