Using LLMs and RAG to generate and optimize BigQuery searches
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Updated
Mar 10, 2024 - TypeScript
Using LLMs and RAG to generate and optimize BigQuery searches
A generative AI app that responds to Phasmophobia-related queries.
just attempting to build a retrieval augmented generation package primarily focused on the antropic llm claude AI if everything goes as planned will start working on other LLMS. I'll appreciate the opportunity to talk about this.
AI powered Video-Frame Analysis
RAGを導入した授業プラットフォーム
Demo RAG for prompting pdf files
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Retrieval Augmented Generation Application that utilizes Pinecone Vector Database, LangChain Framework and OpenAI embeddings.
Prototipo di una applicazione web integrante un chatbot con accesso ai tuoi documenti, utilizzando Phi-3 mini, GPT 3.5 Turbo, Gemini 1.5 Pro, PaLM 2 Bison, LangChain4J, Spring e React.
Playground for Langchain + RAG
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A demonstration of RAG that utilizes Next.js, LangChain, and OpenAI. RAG (Retrieval Augmented Generation) is a technique that combines information retrieval and language generation to produce more informed and contextual responses.
Create documents to improve the accuracy of large language models which employ retrieval augmented generation.
Campus 360 is an AI-powered chatbot designed to assist students, faculty, and visitors at the Model Institute of Engineering & Technology (MIET).
Learning NextJs 14 with app router. Messing with proper server/client component architecture, Next API routes, Next streaming, Next cache beta, retrieval augmented generation with OpenAI embeddings API, PineconeDB to store context/query embeddings, and chatbot with OpenAI prompt.
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A RAG (retrieval augmented generation) playground application for LangChain. Supports Google Gemini Pro, OpenAI GPT-3.5-Turbo, and Anthropic Claude 2.1. Built using LangChain, Angular, Tailwind, and NgAnt.
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