Building blocks for rapid development of GenAI applications

📄 🤖 AI for medical and scientific papers

Vector search demo with the arXiv paper dataset, RedisVL, HuggingFace, OpenAI, Cohere, FastAPI, React, and Redis.

This open source chatbot project lets you create a chatbot that uses your own data to answer questions, thanks to the power of the OpenAI GPT-3.5 model.

An in-memory NoSQL database implemented in Python.

Search through all your personal data efficiently like web search.

COVID-19 Open Research Dataset (CORD-19) Analysis

PostgreSQL-native semantic search engine with multi-modal capabilities. Add AI-powered search to your existing database without separate vector databases, vendor fees, or complex setup. Features text + image search using CLIP embeddings, native SQL joins, and 10-minute Docker deployment.

Chat with your PDFs using AI! This Streamlit app uses RAG, LangChain, FAISS, and OpenAI to let you ask questions and get answers with page and file references.

Chat with your PDFs using AI! This Streamlit app uses RAG, LangChain, FAISS, and OpenAI to let you ask questions and get answers with page and file references.

dead simple document index and search, nothing fancy

SmartRAG is a terminal-based RAG system using LangGraph. It processes queries by retrieving relevant content from markdown or PDFs, then responds using OpenAI GPT. Supports webpage-to-PDF conversion, vector DB search, and modular flow control.

AI-powered hybrid search engine combining keyword, vector, and LLM-based contextual search using RAG with support for AI21, OpenAI or any other LLM.

The extended version of simhash supports fingerprint extraction of documents and images.

Semantic document search system with pgvector and PGAI

Semantik is a self-hosted semantic search engine for your documents.

Given a set of PDFs and the query, the most relevant pdf can be found with the help of TF-IDF. The code has not used any library to implement TF-IDF

Retrieval-Augmented Generation, or RAG, is an innovative approach that enhances the capabilities of pre-trained large language models (LLMs) by integrating them with external data sources. This technique leverages the generative power of LLMs (Large Language Model), and combines it with the precision of specialized data search mechanisms.

A Python-based application that extracts and processes PDF content using a Retrieval-Augmented Generation (RAG) approach. Leverage vector embeddings to enable efficient querying of both text-based and scanned PDFs, and interact with your documents using a large language model.

Semestrální práce z předmětu Information Retrieval