لماذا تدفع 300$ شهرياً مقابل RAG "خامل"؟
بناء أنظمة الـ RAG لا يعني بالضرورة فواتير باهظة. تعلم كيف تشحن Serverless RAG Pipeline على AWS بمواصفات تقنية مذهلة:
التكلفة: تنخفض من مئات الدولارات إلى 2$ فقط.
الأداء: Scale to Zero (لا تدفع إلا مقابل ما تستخدمه). الخصوصية: بياناتك بالكامل داخل حسابك في AWS.
الشمولية: معالجة النصوص، الصور، الفيديو، والصوت.
استخدم مشروع RAGStack-Lambda لتوفير تكاليف البنية التحتية دون التنازل عن القوة.
الدليل العملي هنا:
freecodecamp.org/news/how-to…#الذكاء_الاصطناعي
A new AI review! finic-ai/rag-stack ⭐3.2/5.0
RAGstack aims to be an “in-a-box” private ChatGPT-style application you can host in your own VPC/VNet, pairing an open-source LLM (GPT4All locally; Falcon/Llama 2 on GPU clusters) with a vecto...
gitrated.com/finic-ai/rag-st…
🚀 Just released RAGStack-Lambda. Full serverless RAG pipeline on AWS.
• Zero idle costs (Lambda/DynamoDB) • Multimodal Embeddings (Amazon Nova) • Native MCP Support • No control plane, 100% in your account
Repo: github.com/HatmanStack/RAGSt…#AWS#Serverless#RAG#OpenSource
For Knowledge Retrieval: Use LlamaIndex or RAGStack to connect your LLM (GPT, Gemini, Llama 3) to your private data (Docs, Notion, PDFs).
The single biggest unlock for custom, non-hallucinated LLM answers.
For Complex Reasoning: Use LangChain/AutoGen for agentic workflows.
For Knowledge Retrieval: Use LlamaIndex or RAGStack to connect your LLM (GPT, Gemini, Llama 3) to your private data (Docs, Notion, PDFs).
The single biggest unlock for custom, non-hallucinated LLM answers.
I don’t think this is necessary to make it work—you would need only need the knowledge graph to be a RAGstack and a way to tell the LLM where on the knowledge stack to start a given conversation/lesson.
I look forward to the day that LLMs have memory and progression. Instead of training LLMs to memorize ever bigger corpora, they should be designed to develop themes across dialogue, across days, across lifetimes. Such a shift would change how we interact with AI beyond treating them as a database query or a clever autocomplete.
🌟 Meet Ashok Vishwakarma 🌟
GDE in Web & AI | Founder & CTO @ Impulsive Web
At #DevFestGoa2025, he’ll show how to make AI think smarter by adding a reasoning layer to your RAG stack with Neo4j 🤖⚡
📅 October 11 ,2025
📍 Panjim Convention Center
#Neo4j#AI#RAGStack#GDGGoa
✨ A huge thank you to the team at #RiseUpSummit for hosting an incredible event and giving our CEO, Dr. Amr Awadallah, the stage to speak on the future of enterprise AI.
In his keynote, Amr unpacked the real-world challenges enterprises face in adopting advanced RAG (Retrieval-Augmented Generation) platforms — from managing hallucinations and securing data, to avoiding fragmented DIY implementations that can’t scale. The message was clear: GenAI can’t succeed in the enterprise without trust, precision, and a unified approach.
We’re energized by the conversations that followed and excited to continue helping organizations move from experimentation to real impact.
#Vectara#GenAI#EnterpriseAI#RAGstack#TrustworthyAI#AIInfrastructure#AmrAwadallah#AIWithoutHallucination
The AI stack in 2025 isn’t a playground.
It’s the backend.
→ Retrieval as your brain
→ Agents as your teammates
→ Observability as your safety net
Ship fast, monitor deeply, and scale with eyes open.
More here: @zeroxaitales#LLMOps#AItools#ragstack#agentframeworks
Layer 1: Context Retrieval
LLMs without context are hallucination machines.
This is where RAG (retrieval-augmented generation) shines.
Your AI is only as smart as what you feed it.
#RAGstack#vectorDB