Retrieval Augmented Generation (RAG) using Azure Cognitive Search
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Updated
Jun 29, 2023 - C#
Retrieval Augmented Generation (RAG) using Azure Cognitive Search
A simple demonstration of how you can implement retrieval augmented generation (RAG) for a book.
Meet Casia, the AI plant assistant 🌱
DocuChat is a locally-hosted application to summarize and chat with your documents. Use the OpenAI API or run models locally for 100% free usage. You can even query multiple documents thanks to SOTA RAG integrations.
CodeChat: Enabling interaction with codebases present in GitHub repositories. Seamlessly explore, query, and discuss code with a powerful RAG pipeline, making coding intuitive and efficient
Homework 3 for the machine learning class at Tsinghua University (fall term 23/24)
RAG application to query multiple docs. Built to query 10K reports of companies.
RAG chatbot that answers questions about SmartCat. Powered by Weaviate and Cohere.
we learn how we can feed the output of vector databases (in our story, we employed ChromaDB) to a Large Language Model to build RAG
Simple implementation of RAG using watsonx.ai, capturing the chat history to keep track of the conversation context and answer follow up questions.
A Gemma based RAG that answers python specific questions
AI chatbot, using LangChain and the 8-bit quantised Falcon-7B LLM. Crafted a conversational agent with Retrieval Augmented Generation (RAG) pipeline.
Exploring LLM RAG applications.
GRAG is a simple python package that provides an easy end-to-end solution for implementing Retrieval Augmented Generation (RAG). The package offers an easy way for running various LLMs locally, Thanks to LlamaCpp and also supports vector stores like Chroma and DeepLake.
Build a RAG app from scratch using Chroma and the ChatGPT API
Document Retrieval Augmented Generation (RAG) Harness
Dive into LangChain, a powerful platform that lets you interact with your data like never before. This guide offers insights on its unique capabilities, helping you tap into your data in conversational ways.
EMNLP'2023: Retrieval-Generation Alignment for End-to-End Task-Oriented Dialogue System
Curate scraped HTML for large language models. Build more robust generative AI applications. Convert HTML to Markdown using Regex, BeautifulSoup4, and filter useless content with Jina Embeddings.
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