This is a simple interface for chroma - it takes in documents, embeds them into a DB and allows you to query over them using GPT 3.5

A Retrieval-Augmented Generation (RAG) chatbot built with LangChain, Ollama, and CHROMA to answer questions from a custom university knowledge base. Features a simple Gradio interface for interactive Q&A.

A simple, interactive Gradio dashboard that recommends books based on meaning and mood. Each book’s description is embedded with OpenAI models, stored in a Chroma vector DB, and served through an easy web interface.

A PDF-based Q&A chatbot that lets users ask questions and get accurate answers grounded only in the document content. Built with LangChain, Chroma, and OpenAI, it provides fast, reliable, and context-aware responses through a simple Streamlit interface.

RAG with LM Studio est un projet de chatbot local basé sur vos propres documents. Il combine une base vectorielle (Chroma) pour la recherche sémantique et LM Studio pour la génération de réponses, avec une interface Streamlit simple type ChatGPT. Tout fonctionne en local (Windows/macOS, GPU CUDA ou MPS).

Expense Assistant is an AI-powered personal finance tool that helps users analyze and query their spending data using natural language. It uses local embeddings with SentenceTransformers and a vector database (Chroma) to semantically search through transactions, then provides detailed summaries and expense insights via a simple Flask web interface.

A Flask-based Q&A web app utilizing Langchain and OpenAI’s GPT-3.5. It loads, splits, and embeds documents using Chroma, enabling users to query the content through a simple API or web interface. Responses are generated by a retriever model to provide relevant answers based on the provided text data.