'Talk to your slide deck' (Multimodal RAG) using foundation models (FMs) hosted on Amazon Bedrock and Amazon SageMaker
-
Updated
Jul 2, 2024 - HTML
'Talk to your slide deck' (Multimodal RAG) using foundation models (FMs) hosted on Amazon Bedrock and Amazon SageMaker
CodeRAG-Bench: Can Retrieval Augment Code Generation?
Open source implementation of Sova - RAG-based Web search engine using power of LLMs. Using Langchain, Ollama, HuggingFace Embeddings and scraping google search results.
This is a RAG based chatbot in which semantic cache and guardrails have been incorporated.
AI-based search engine done right
Source code for the Gilded Age Gourmet, a cooking chat app based on the Boston Cooking-School Cook Book.
This is a RAG implementation using Open Source stack. BioMistral 7B has been used to build this app along with PubMedBert as an embedding model, Qdrant as a self hosted Vector DB, and Langchain & Llama CPP as an orchestration frameworks.
This example repository illustrates the usage of LLMs with Quarkus by using the quarkus-langchain4j extension to build integrations with ChatGPT or Hugging Face. The code dives into simple conversations, retrieval augmented generation (RAG) and building agents.
Web application to help develop scraping techniques to create context in LLM prompts.
An Art-Deco bot that utilizes RAG. Benchmarking of RAG vs. LLMs on QA and timing
Source code for the Gilded Age Gourmet, a cooking chat app based on the Boston Cooking-School Cook Book
Retrieval-Augmented Generation using Azure OpenAI
a sophisticated recommendation system for CNC machines, leveraging state-of-the-art AI technology, and integrating the capabilities of Google's Gemini 1.0 API
Just training on langchain to improve RAG skills
Advancing the next generation of Retrieval Augmented Generation (RAG): A dynamic exploration of RAG technology's evolving landscape. This repository is the go-to resource for state-of-the-art developments, conceptual advancements, and the future trajectory of AI-driven information retrieval and generation.
Build an efficient Python-based Retrieval-Augmented Generation (RAG) system for contextual query answering over personal data, all with natural language using ChatGoogleGenerativeAI (gemini-pro).
💬🤖 Build a better chatbot 🤖💬
This application aims to provide users with a convenient way to interact with Langchain documentation through a chat interface powered by advanced Generative AI technologies
Add a description, image, and links to the retrieval-augmented-generation topic page so that developers can more easily learn about it.
To associate your repository with the retrieval-augmented-generation topic, visit your repo's landing page and select "manage topics."