Learn to build real-world AI applications in GO language using LangChain and vector databases like PostgreSQL, Pinecone, and Redis.

Building Generative AI applications with Large Language Models.png

build.tar.gz

POSTGRESQL_CONNECTION_STRING

GOOGLE_AI_API_KEY

PINECONE_API_KEY

PINECONE_INDEX_HOST

REDIS_HOST_PORT

REDIS_PASSWORD

OPENAI_API_KEY

AI21_API_KEY

COHERE_API_KEY

HF_API_KEY

STABILITY_AI_API_KEY

HUGGINGFACEHUB_API_TOKEN

POSTGRES_USERNAME

POSTGRES_PASSWORD

POSTGRES_HOST_PORT

run-app

run-minio-app

This course is designed specifically for Go developers who want to break into the world of generative AI without switching tech stacks. You’ll learn to build real-world AI applications using Go-native tools, including the powerful LangChainGo framework, and integrate with leading vector databases like PostgreSQL (pgvector), Pinecone, and Redis.

You’ll start with the fundamentals of generative AI, prompt engineering, and platforms such as OpenAI, Cohere, and Hugging Face that offer foundation models. Then, dive deep into working with embeddings and vector search and use Go to implement retrieval-augmented generation (RAG) pipelines. You’ll also build complete chat applications that can search and summarize document content—all powered by Go.

This course will help you integrate scalable, high-performance AI features into your Go applications, whether you’re a backend engineer or system developer.

Build AI Applications with LangChainGo

Large language model (LLM) and BERT model

Large language model (LLM) and embedding model

Large language model (LLM) and vector model

Which type of models are used in a RAG solution?

Which type of models are used in a RAG solution?

**(True or False)** A RAG-based solution requires the entire set of documents to be passed to the LLM. 

(True or False) A RAG-based solution requires the entire set of documents to be passed to the LLM.

Which technique is the best fit to improve LLM response quality in applications that have cost constraints?

Which technique is the best fit to improve LLM response quality in applications that have cost constraints?

Which `langchaingo` chain be used for an application that needs the ability to chat with a document?

Which <code>langchaingo</code> chain be used for an application that needs the ability to chat with a document?

Introduction

Getting Started with Foundation Models

Vector Databases

PostgreSQL as a Vector Database

Pinecone Vector Database

Redis as a Vector Database

LangChain Framework Components

Using Vector Stores with LangChain

Build with LangChain

Wrap Up

Final Project

Quiz: Build with LangChain