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Retrieval Augmented Generation in Go – Eli Bendersky’s Website by todsacerdoti

I’ve been reading more and more about LLM-based applications recently, itching to build something useful as a learning experience. In this post, I want to share a Retrieval Augmented Generation (RAG) system I’ve built in 100% Go and some insights I learned along the way. Some limitations of current LLMs Let’s take OpenAI’s API as

ByHackTechNovember 10, 20230Comments

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Retrieval Augmented Generation (Rag) on Audio Data with LangChain by SleekEagle

Retrieval Augmented Generation (RAG) is a method to augment the relevance and transparency of Large Language Model (LLM) responses. In this approach, the LLM query retrieves relevant documents from a database and passes these into the LLM as additional context. RAG therefore helps improve the relevancy of responses by including pertinent information in the context

ByHackTechSeptember 26, 20230Comments

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Show HN: Dead Simple API for Retrieval Augmented Generation by vignesh_warar

EazyRAGQ&A with your data made easy: our API overcomes intricate vector DB challenges, enabling accurate answer generation and seamless citations.Add Contentcurl –location ‘https://api.eazyrag.com/add’ –data ‘{‘contents’: [ { ‘content’:’Full document or texts..’} … ]}’Questioncurl –location ‘https://api.eazyrag.com/answer?query=what is EazyRag?’Use CasesQ&A integration across chatbots, docs, and more with EazyRAGDocument Q&AIndex and search many documents to generate grounded answers

ByHackTechSeptember 16, 20230Comments

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