Batteries included SDK for RAG development.
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Updated
Aug 29, 2024 - TypeScript
Batteries included SDK for RAG development.
x0-GPT is an advanced AI-powered tool that enables you to interact seamlessly with any website or document (including PDFs) using natural language. Whether you're looking to extract specific data, automate tasks, or gain insights, x0-GPT makes it possible with ease. Best of all, it's free and accessible to everyone.
AnythingLLM Embed widget submodule for the main AnythingLLM application
End-to-end deployment of a scalable RAG chatbot utilizing LangChain for retrieval-based QnA. The project leverages robust CI/CD practices integrating MLFlow with emphasizes on cost analysis.
This project implements a Retrieval-Augmented Generation (RAG) model that uses a directory containing text files as documents for information retrieval and generation. The model combines retrieval and generation capabilities to answer questions based on the provided documents.
This web application allows users to interact with a chatbot that can answer questions about any website. Simply enter the URL of the site you’re interested in, and the chatbot will provide insights and answer questions related to the content of that site.
A chatbot to chat with content of any website with RAG implemention.
Hybrid Search RAG Pipeline integrating BM25 and vector search techniques using LangChain
Use RAG with Langchain to chat with your data and display the retrieved source(s)
Chatbot leveraging RAG for Financial Document (10Q, 10K) Summarization
Building a PDF Chatbot with Google Gemma 7B LLM using Groq API and Chainlit
A Retrieval-Augmented Generation (RAG) Chatbot built on top of ChatGPT. Just upload your document and ask DocuMate anything about it.
A chatbot assistant that provides tailored products guidance to customers, using custom RAG from custom data.
An AI-driven solution for optimizing patient scheduling and resource allocation in healthcare facilities. This project features a predictive analytics dashboard powered by Prophet for forecasting patient inflows, real-time staffing recommendations, and an interactive chatbot using LangChain and Neo4j for data-driven insights.
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