✨ The Definitive Guide to Testing LLM Applications by LangChain ✨ Reviewing LLM app responses can be a time-consuming and daunting process, from defining criteria for style and accuracy, to spotting new regressions. After partnering with hundreds of companies to enhance their GenAI apps, we've put together a comprehensive guide of best practices for testing throughout the development lifecycle. In this guide, you'll learn how to: • Design a self-correcting RAG or code generation application • Build a dataset for your test suite • Test agents and RAG performance ... and much more! 👉 Get the guide here: https://lnkd.in/eX6yA6H4
Über uns
We're on a mission to make it easy to build the LLM apps of tomorrow, today. We build products that enable developers to go from an idea to working code in an afternoon and in the hands of users in days or weeks. We’re humbled to support over 50k companies who choose to build with LangChain. And we built LangSmith to support all stages of the AI engineering lifecycle, to get applications into production faster.
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Aktualisierungen
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Dive into this step-by-step tutorial on creating a powerful agent with LangGraph, LLama3, and Elasticsearch Vector Store. See how to implement advanced RAG techniques, including smart routing, corrections for reliable fallbacks, and self-correction. Read the blog from Elastic: https://lnkd.in/gV8gVdBu
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Cool to see a product launch highlight ☁️LangGraph Cloud so prominently! 🔎How to find all your competitors on ProductHunt Check out the YouTube video below where Nikita walks through the product and how he uses LangGraph Cloud https://lnkd.in/gNJG-GJv
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🕸️GraphRAG Analysis, Part 2: Graph Creation and Retrieval vs Vector Database Retrieval There's been a lot of talk and interest in GraphRAG recently. Jonathan Bennion takes a detailed and systematic look into it. Does it justify the hype? https://lnkd.in/gvtJNuec
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⭐️Community Content 💬 Chat with your data using natural language 📊 DataLine - the simplest and fastest way⚡️ to analyze and visualize your data! Generate and export charts, tables, reports in seconds Fully OSS, uses LangChain and LangGraph https://lnkd.in/gdyS3aZx
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⭐️Community Content ⚡️Serverless AI Chat with RAG using LangChain.js This sample shows how to build a serverless AI chat experience with RAG using LangChain.js, Azure Static Web Apps, Azure Functions, and Azure Cosmos DB https://lnkd.in/gbd-XsTn
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⭐️Community Content ☁️LangGraph Cloud: Build & Deploy an AI Agent We opened up LangGraph in beta a few weeks ago, good to see some community pieces on how to best use it Easiest way to deploy long-running agents that need persistence/human-in-the-loop https://lnkd.in/gwkRJtzy
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⛳️Using Redpanda, OpenAI, and MongoDB to generate vector embeddings on streaming text This isn’t just another post about Retrieval Augmented Generation (RAG) - It focuses more on how to create text embeddings on streaming data. https://lnkd.in/gQQeyxN2
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📸GPT-Instagram A GPT-based autonomous multi-agent AI app using Next.js, LangChain.js, and LangGraph.js to research and recommend Instagram posts based on user queries and personalities https://lnkd.in/gmde9fJU
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🤖Mixture-of-Agents Demo Powered by Groq This Streamlit application showcases the Mixture of Agents architecture proposed by Together AI, powered by Groq LLMs It allows users to interact with a configurable multi-agent system https://lnkd.in/guht9_eX
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