Running Llama 2 and other Open-Source LLMs on CPU Inference Locally for Document Q&A

Document Q&A chatbot

This Repositry is an experiment with an agent that searches documents and asks questions repeatedly in response to the main question. It automatically determines the optimal answer from the current documents or recognizes when there is no answer.

🐋 DeepSeek-R1: Retrieval-Augmented Generation for Document Q&A 📄

An LLM-powered Slack bot built with Langchain.

AI agent API (Python/FastAPI) to upload documents (PDF/TXT) and answer questions using RAG with Azure OpenAI and LangChain.

A Document QA chatbot using LangChain, Pinecone for vector storage, and Amazon Bedrock (mistral.mixtral-8x7b-instruct for LLM and titan-embed-text for embeddings). Built with a Streamlit frontend for document uploads and contextual question answering.

Sistema RAG (Retrieval Augmented Generation) para asistencia de documentación técnica en español utilizando LangChain, OpenAI Y Streamlit para la interfaz visual

Successfully developed a Multi-Domain AI Personal Assistant using LangChain, OpenAI, and Streamlit. The application seamlessly integrates multiple specialized capabilities, including document-based question answering (QA), Python code execution, debugging, explanation and optimization, web search, latest news retrieval, and currency conversion.

Cross-document QA leveraging LLM and taxonomy

This repository acts as an archive of the owners experience working with large language models neatly presented within jupyter notebooks

An intelligent document assistant powered by Open-Source Large Language Models