Analytics Vidhya

🦜🔗 Everything you need to build a RAG pipeline with LangChain — in one cheatsheet. If you're building LLM-powered apps, LangChain is your best friend. But knowing where to start can be overwhelming. Here's the full RAG pipeline broken down: 📥 Load → Pull data from PDFs, URLs, directories ✂️ Split → Chunk it with RecursiveCharacterTextSplitter 🗄️ Store → Embed & store in Chroma or Qdrant 🔍 Retrieve → MMR, similarity threshold, parent docs ⚡ Generate → Connect to GPT, Llama, Mistral & more Bonus highlights: 🧠 Memory - Buffer & Knowledge Graph memory for conversational context 🔗 LCEL - Build complex chains with clean, readable syntax 📎 Adding Sources - Return citations alongside answers Whether you're a beginner or shipping production RAG systems, bookmark this. You'll thank yourself later. 💾 Save this post so you never start from scratch again! ♻️ Repost to help your network build better AI apps! #AnalyticsVidhya #Langchaincheatsheet

🚀 Building ML on AWS? Here are the 7 services you need to know. The ML lifecycle isn't just about algorithms it's about having the right infrastructure at every stage. Here's how AWS maps to each phase: 📦 Data Collection → Amazon S3 🔧 Data Preparation → AWS Glue 🔍 Exploratory Analysis → SageMaker Data Wrangler 🧠 Model Training → AWS Deep Learning AMIs ✅ Model Evaluation → Amazon CodeGuru ⚡ Deployment → AWS Lambda Whether you're just starting your ML journey or scaling production workloads, AWS has a purpose-built tool for every step. The best part? These services integrate seamlessly meaning less friction, more focus on what matters: building great models. Moreover, we are offering a Free Course on Getting started with AWS for Data Science: https://lnkd.in/gJR7w4dp ♻️ Repost to help your network level up their ML stack!

If you’re building with embeddings, semantic search, or LLM-powered apps, you can’t ignore vector databases. We just published a simple, visual breakdown of how they actually work Here’s what you’ll learn inside the PDF: • What vectors and embeddings really are (and why similar meanings sit close together in space) • How vector databases store embeddings + metadata • Why nearest neighbor search is the core idea • The role of indexing in scaling to millions and billions of vectors • A clear explanation of LSH, HNSW, and ANNOY • Keyword search vs Vector search vs Hybrid search • Popular open-source and closed-source vector databases If you’ve ever wondered how semantic search, recommendation systems, chatbots, or RAG pipelines retrieve “relevant” results without exact keyword matches, this will connect the dots. And here’s the exciting part 👇 We’re also offering a Free Course on Vector Databases where we go deeper into concepts, implementation, and real-world use cases: https://lnkd.in/gjCPBxdW #AnalyticsVidhya #VectorDatabases

LangGraph is changing how complex AI systems are built. Instead of fragile linear chains, it introduces graph-based workflows where state, branching logic, and multi-step reasoning are first-class citizens. This opens the door to reliable agents, multi-agent coordination, and production-ready LLM systems that can handle real-world complexity. A detailed end-to-end technical guide on LangGraph is now available, covering core concepts, architecture, advanced patterns, memory, deployment strategies, and practical design patterns. It’s designed as a saveable reference for anyone building serious AI applications. If building structured, stateful AI systems is on the roadmap, this guide is worth a deep read. Want a Free Certification Course? Here it is: https://lnkd.in/gY7YbEdv #AnalyticsVidhya #LangGraph #GenAI

One command that unlocks your entire dev toolkit. Meet Claude plugins: Connect AI directly to your design, database, and workflow tools—instantly. What each plugin does: ✅ Figma MCP → Convert designs to production code (no hand-translating) ✅ Playwright MCP → Test complete user flows automatically ✅ Notion MCP → Update documentation without leaving your terminal ✅ GitHub MCP → Detect bugs, create PRs, and manage CI/CD using natural language ✅ Supabase MCP → Query databases and manage schemas with a single sentence The benefits: ✅ Save hours on repetitive tasks ✅ Stay in flow with zero context switching ✅ AI understands and works across your entire tech stack We are offering a Free Certification Course on Claude Code: https://lnkd.in/g9RjQYnA #AnalyticsVidhya #ClaudeCode #ClaudeCodePlugins

Data professionals are doubling down on Docker fundamentals as containerized workflows become standard. We compiled a concise command cheat sheet covering the essentials building, running, inspecting, networking, and cleanup to help streamline day-to-day development. Whether you're scaling pipelines, deploying models, or managing dev environments, this quick reference can make your workflow more predictable and efficient. 🐳⚙️ Moreover, we are offering a Free Docker Course: https://lnkd.in/g5BU-dpN Happy building! 🐳🚀 #AnalyticsVidhya #Docker

🚨 Call for Speakers: DataHack Summit 2026 India’s Most Futuristic AI Conference of the year is back. Apply now: https://lnkd.in/gs5gyB-w Speaker line up: Arun Prakash Asokan Bhaskarjit Sarmah Dipanjan S. Kartik Nighania Kunal Jain Logesh Kumar Umapathi Sandeep Singh Dr. Sayan Ranu #AnalyticsVidhya #DataHackSummit2026 #AIAgents

AI Agents are no longer a future concept. They are already automating workflows, making decisions, and powering real business applications. But here’s the real question: Which framework should you learn to build them? We’ve put together this visual guide covering the most popular AI agent frameworks, including: • LangChain – the most widely used framework with a massive ecosystem • LangGraph – the next evolution for production-grade agent control • CrewAI – role-based multi-agent collaboration • AutoGen – conversational multi-agent systems by Microsoft • LlamaIndex – powerful knowledge-centric agents • Flowise & n8n – visual and no-code agent builders • Agno – lightweight and performance-focused framework Moreover, we are offering a Free Certification Course on AI Agents: https://lnkd.in/gFDy4mVU #AnalyticsVidhya #AIAgents #Framework

Everyone's talking about AI agents. Few understand what actually makes them work. Here are the 4 foundational design patterns: 🔁 Reflection → The agent judges its own output and self-corrects 🛠 Tool Use → The agent acts on the world, not just generates text 🗺 Planning → The agent thinks before it acts, breaking goals into steps 🤝 Multi-Agent → Specialized agents collaborate under an orchestrator These aren't advanced concepts. They're the baseline. If you're building AI products in 2025 and not designing around these patterns, you're leaving reliability and capability on the table. Moreover, we are offering a Free Course on Agentic AI Design Patterns: https://lnkd.in/gB4Kf4vE The instructor for the course is Miguel Otero Pedrido Save this. You'll reference it more than once. #AnalyticsVidhya #AgenticAIDesignPatterns #AgenticAI

Nano Banana! The image model that took the world by storm just got eclipsed by…itself. Yes! Google did it again. After establishing standards by their release of Nano banana, they are back with its high anticipated follow-up: Nano Banana 2 (officially designated as Gemini 3.1 Flash Image). This new model bridges the gap between studio-quality creative control and rapid generation speeds. By merging the quality outputs of the Pro tier with the efficiency of the Flash architecture, Nano Banana 2 aims to make enterprise-grade image generation possible. We’ll talk about its features and test Nano Banana 2’s performance on real-world tasks: https://lnkd.in/giRv7Mvi #AnalyticsVidhya #NanoBanana2 #GenerativeAI