Learn LangChain in 7 Easy Steps
In this tutorial, I will teach you LangChain as efficiently as possible by breaking down the framework into seven key components you need to understand to start developing more advanced LLM applications.
AI Engineer & Architect. I designed the personalization architecture for the world's largest jewelry company and automated claims handling for the leading insurance company in the Nordics
In this tutorial, I will teach you LangChain as efficiently as possible by breaking down the framework into seven key components you need to understand to start developing more advanced LLM applications.
In this tutorial, we will build high-performance real-time Retrieval Augmented Generation (RAG) chains using Llama 3, GroqCloud, LangChain, and Redis.
In this tutorial, we'll explore how to generate insights from BigQuery using Llama 3 and Langchain. The focus will be on handling errors gracefully and feeding them back into the chain for iterative improvement.
This tutorial will explore building dashboards using Large Language Models (LLMs) and LangChain. We will use LangChain chains to extract insights from BigQuery by first generating SQL and then pushing the generated SQL to BigQuery as views, forming the basis for interactive dashboards.
In this tutorial, we'll explore how to build knowledge bases for advanced SQL generation using LangChain. We'll leverage LangChain chains to extract insights from BigQuery by generating and executing SQL queries. Redis will store the SQL query examples needed for few-shot-prompting.
In this tutorial, we'll connect LLMs to Google Analytics 4 data using LangChain. This allows us to build chat interfaces to marketing analytics data, speeding up the process of generating actionable insights.