Pavan Belagatti’s Post

Learn how to use #LangChain to develop end-to-end #AI apps!

Learn how to use LangChain..

Learn how to use #LangChain to develop end-to-end #AI apps! In the realm of AI application development, LangChain serves as one of the solid foundational frameworks, enabling developers to create intelligent applications by seamlessly connecting language models with various data sources and computational tools. Developers begin by using LangChain's core components: creating prompts that guide AI interactions, selecting appropriate language models, implementing memory mechanisms for context retention, and designing agents capable of complex reasoning and task completion. As the application complexity grows, LangGraph steps in to provide advanced workflow orchestration, allowing developers to build sophisticated AI systems with state management, conditional logic, and intricate process flows. LangGraph enables the creation of multi-step, dynamic AI workflows that can make decisions, loop through processes, and handle complex interaction patterns. Throughout the development and deployment process, LangSmith acts as a critical observability layer, providing comprehensive logging, monitoring, and evaluation tools. Developers can trace each step of their AI application, conduct performance analysis, run A/B tests, and gain deep insights into the application's behavior. This integrated ecosystem—LangChain for building, LangGraph for orchestrating, and LangSmith for observing—creates a powerful, end-to-end solution for developing intelligent, reliable, and sophisticated AI applications. Here is my complete guide on LangChain for beginners: https://lnkd.in/grWUZXDR Here is my step-by-step guide on using LangGraph: https://lnkd.in/gKaEjEvk Here is my another guide on building RAG applications using LangChain: https://lnkd.in/gsz_cXfv

Learn how to use LangChain to build end-to-end AI applications! In the realm of AI application development, LangChain serves as one of the solid foundational frameworks, enabling developers to create intelligent applications by seamlessly connecting language models with various data sources and computational tools. Developers begin by using LangChain's core components: creating prompts that guide AI interactions, selecting appropriate language models, implementing memory mechanisms for context retention, and designing agents capable of complex reasoning and task completion. As the application complexity grows, LangGraph steps in to provide advanced workflow orchestration, allowing developers to build sophisticated AI systems with state management, conditional logic, and intricate process flows. LangGraph enables the creation of multi-step, dynamic AI workflows that can make decisions, loop through processes, and handle complex interaction patterns. Throughout the development and deployment process, LangSmith acts as a critical observability layer, providing comprehensive logging, monitoring, and evaluation tools. Developers can trace each step of their AI application, conduct performance analysis, run A/B tests, and gain deep insights into the application's behavior. This integrated ecosystem—LangChain for building, LangGraph for orchestrating, and LangSmith for observing—creates a powerful, end-to-end solution for developing intelligent, reliable, and sophisticated AI applications. Here is my complete guide on LangChain for beginners: https://lnkd.in/grWUZXDR Here is my step-by-step guide on using LangGraph: https://lnkd.in/gKaEjEvk Here is my another guide on building RAG applications using LangChain: https://lnkd.in/gsz_cXfv You can learn about LangSmith here: https://lnkd.in/gakewC-E

Learn how to use LangChain to build end-to-end AI applications! In the realm of AI application development, LangChain serves as one of the solid foundational frameworks, enabling developers to create intelligent applications by seamlessly connecting language models with various data sources and computational tools. Developers begin by using LangChain's core components: creating prompts that guide AI interactions, selecting appropriate language models, implementing memory mechanisms for context retention, and designing agents capable of complex reasoning and task completion. As the application complexity grows, LangGraph steps in to provide advanced workflow orchestration, allowing developers to build sophisticated AI systems with state management, conditional logic, and intricate process flows. LangGraph enables the creation of multi-step, dynamic AI workflows that can make decisions, loop through processes, and handle complex interaction patterns. Throughout the development and deployment process, LangSmith acts as a critical observability layer, providing comprehensive logging, monitoring, and evaluation tools. Developers can trace each step of their AI application, conduct performance analysis, run A/B tests, and gain deep insights into the application's behavior. This integrated ecosystem—LangChain for building, LangGraph for orchestrating, and LangSmith for observing—creates a powerful, end-to-end solution for developing intelligent, reliable, and sophisticated AI applications. Here is my complete guide on LangChain for beginners: https://lnkd.in/grWUZXDR Here is my step-by-step guide on using LangGraph: https://lnkd.in/gKaEjEvk Here is my another guide on building RAG applications using LangChain: https://lnkd.in/gsz_cXfv You can learn about LangSmith here: https://lnkd.in/gakewC-E

The best set of frameworks and tools I have used for far. Especially as someone working with JavaScript most of the time.

Struggling to Scale AI Innovations in your business? It's all about systems and focus. As web devs, we know systems need to work together seamlessly without creating bottlenecks, feedback loops or bugs. 🎯 Think about which uses of AI work for your business and which are creating confusion. Any extra experiments can wait, failed pilots and pieces that don't fit should fall away now. 🧩 Take what works: how does it fit together? Is there a step missing or a data entry task that could be automated to improve this workflow? ♻ Reuse code to speed up development. We build websites that work as business tools: automate your workflows such as delivery labels, invoicing and more. #webdev #websiteagency #systemsthinking https://lnkd.in/eSFyjqZ8

I'm doing some Research👩🎓: Has AI reduced manual work for your team? I'm still curious about how this all looks in other businesses. It's 18 months (ish) since ChatGPT launched, and AI is still a buzzword, but who's using it really? Almost every tool we use for SEO has an AI component that we are beginning to work with across the team, and it's been a lesson in systems thinking for me. For example: ✨AI data analysis✨ such as Semrush's Personal Keyword Difficulty scores. This is so much more helpful than generative AI text. PKD compares the keywords your domain already ranks for against the keyword research you're doing to give an index of how likely you are to rank. We don't have to do this manually anymore, saves us so much time! Tell me in the comments: - What AI tools are you using, and for what processes? - What industry you work in?

𝐖𝐡𝐲 𝐋𝐚𝐧𝐠𝐂𝐡𝐚𝐢𝐧 𝐢𝐬 𝐚 𝐆𝐚𝐦𝐞-𝐂𝐡𝐚𝐧𝐠𝐞𝐫 𝐢𝐧 𝐀𝐈 𝐚𝐧𝐝 𝐋𝐋𝐌 𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬? In today’s rapidly evolving tech landscape, Generative AI is redefining how we build intelligent systems, and one standout tool leading this revolution is LangChain. 𝐖𝐡𝐚𝐭 𝐢𝐬 𝐋𝐚𝐧𝐠𝐂𝐡𝐚𝐢𝐧? LangChain is an innovative framework designed for developers to build applications powered by large language models (LLMs). It simplifies how we interact with, customize, and combine the capabilities of LLMs for solving real-world problems. 𝐓𝐡𝐞 𝐂𝐨𝐫𝐞 𝐂𝐨𝐧𝐜𝐞𝐩𝐭𝐬 ➤ Prompt Management: Streamline your prompts for consistency and reusability. ➤ Chains: Combine LLMs with other tools (e.g., APIs, databases) for end-to-end workflows. ➤ Memory: Create conversational applications with context retention. ➤ Agents: Enable dynamic and goal-oriented task execution. 𝐖𝐡𝐲 𝐒𝐡𝐨𝐮𝐥𝐝 𝐘𝐨𝐮 𝐋𝐞𝐚𝐫𝐧 𝐋𝐚𝐧𝐠𝐂𝐡𝐚𝐢𝐧? ➥ Ease of Development: Quickly prototype and deploy AI-driven apps. ➥ Versatility: Perfect for chatbots, content generation, automation, and more. ➥ Market Demand: A must-have skill as AI integration becomes mainstream. 𝐊𝐞𝐲 𝐓𝐨𝐨𝐥𝐬 𝐘𝐨𝐮'𝐥𝐥 𝐄𝐱𝐩𝐥𝐨𝐫𝐞: ➾ OpenAI GPT Models ➾ Pinecone (for vector search) ➾ LangSmith (for debugging and observability) 𝐆𝐞𝐭 𝐒𝐭𝐚𝐫𝐭𝐞𝐝 𝐓𝐨𝐝𝐚𝐲! ➤ Start with the LangChain Documentation. ➤ Practice simple chains and gradually integrate APIs and memory. ➤ Explore projects built with LangChain to expand your understanding. 𝐈𝐧 𝐒𝐮𝐦: LangChain bridges the gap between the immense potential of LLMs and practical application development. By mastering LangChain, you equip yourself with a future-proof skill to create powerful, intelligent solutions. 🚀 𝐓𝐡𝐞 𝐟𝐮𝐭𝐮𝐫𝐞 𝐢𝐬 𝐠𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐯𝐞—embrace it with LangChain! If you found this insightful, share it with others in your network. Let’s learn and grow together! Do you want to integrate AI into your business applications or automate your business with AI? Let’s connect... #LangChain #AIIntegration #GenerativeAI

Interesting read! Just came across an article on Writer’s latest LLM model, Palmyra X 004, reshaping the future of enterprise workflows with its powerful AI capabilities. This model is set to revolutionize how businesses operate by automating tasks, integrating systems, and accelerating decision-making processes. Key Takeaways: 🔑 Palmyra X 004 is Writer’s most advanced LLM, offering cutting-edge tool calling capabilities, a vast 128k context window, and outperforming major models in tool usage and execution. 📈 State-of-the-art reasoning techniques deliver industry-leading performance. 💻 Handles text, audio, images, and video across 30+ languages with multimodal support. How It Works: 🛠️ Interacts with external systems to automate workflows. 📊 Integrates data with real-time retrieval augmented generation. 👨💻 From writing to deploying code, it handles complex development tasks. 🔄 Generates structured outputs for simplified system integration. Why It’s a Game-Changer: 🔥 Dramatically reduces manual task time. 🤖 Performs real-world tasks like system updates and workflow triggers. 💸 Delivers high performance at a fraction of the cost compared to other models. 🏆 #1 on Berkeley’s Tool Calling Leaderboard and top-ranked on Stanford’s HELM benchmarks. What’s Next: 🚀 Stay tuned for the structured output feature, making AI integration into workflows even easier. 🤝 Expect AI-driven automation across various departments. 🔄 Continuous updates will push the boundaries of AI integration in enterprise tools and services. #ai #enterpriseai #llms #toolcalling #workflowautomation #aistudio #palmyra