MediChat: An AI-powered medical chatbot using the Llama-2-7B-Chat model for precise clinical responses. Integrates Chroma DB and all-MiniLM-L6-v2 embeddings trained on medical literature, including texts like Clinical Emergency Medicine and Gale Encyclopedia. Accurate, fast, and reliable for healthcare queries.

To make LLM faster we need faster retrieval system. Here comes Embedding Quantization. Embedding quantization is great technique to save cost on Vector DB, significantly faster retrieval while preserving retrieval performance.

👽SFSeeker - an AI assistant with a semantic engine/question writing tutor in Sci-Fi Stack Exchange service

A RAG to deal with multiple documents simultaneously using Weaviate, Mistral-7B-v0.1 and Langchain.

Streamlit chatbot app that can answer questions based on a pdf. see the readme for more information

The Llama-2-GGML-CSV-Chatbot is a conversational tool leveraging the powerful Llama-2 7B language model. It facilitates multi-turn interactions based on uploaded CSV data, allowing users to engage in seamless conversations.

Llama2-Medical-Chatbot is a medical chatbot that uses the Llama-2-7B-Chat-GGML model and the pdf The Gale Encyclopedia of Medicine, Volume 1, 2nd Edition. It is still under development, but it has the potential to be a valuable tool for patients, healthcare professionals, and researchers.

Implementing Vector Database on CoNaLa dataset to retrieve program snippets relevant to user queries. This is a very simple simulation of a Vector Database.