Use LLMs for building real-world apps
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Updated
Mar 10, 2024 - HTML
Use LLMs for building real-world apps
Bedrock Knowledge Base and Agents for Retrieval Augmented Generation (RAG)
"Enhancing LLM Factual Accuracy with RAG to Counter Hallucinations: A Case Study on Domain-Specific Queries in Private Knowledge-Bases" by Jiarui Li and Ye Yuan and Zehua Zhang
Medical RAG QA App using Meditron 7B LLM, Qdrant Vector Database, and PubMedBERT Embedding Model.
'Talk to your slide deck' (Multimodal RAG) using foundation models (FMs) hosted on Amazon Bedrock and Amazon SageMaker
A proof-of-concept for a RAG to query the scikit-learn documentation
Saarthi is a Flask-based voice assistant and chatbot. It converts speech to text, processes user input with OpenAI's ChatGPT API, and generates responses. With Twilio integration, users can call and interact with Saarthi. Customize the conversation flow and contribute to this open-source project. Let's chat with Saarthi!
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.
Implementing Retrieval-Augmented Generation (RAG) with constructed Knowledge Graph
A bit of JavaScript to fix your text rag for prepositions, small words, or emphasis, adapted to the French language
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.
An Art-Deco bot that utilizes RAG. Benchmarking of RAG vs. LLMs on QA and timing
Ever thought of talking to your Email Inbox, like talking to a Real-human 😲. Well, you can do it completely on Device!! 🔥🔥🔥 No privacy issues. I used Chroma with Docker, Mistral-7B-Instruct, and Ollama.
List of experiments on Gen AI ecosystem
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