Compact RAG for Raspberry Pi

Plugin that lets you use LM Studio to ask questions about your documents including audio and video files.

AniSearchModel leverages Sentence-BERT (SBERT) models to generate embeddings for synopses, enabling the calculation of semantic similarities between descriptions. This allows users to find the most similar anime or manga based on a given description.

Interactive PDF Chat Assistant built with Streamlit and LangChain. Upload PDFs and ask questions, receiving AI-powered answers based on document content, leveraging Groq, Hugging Face embeddings, and FAISS.

Extract ESCO skills from texts such as job descriptions or CVs

💡 All-in-one open-source embeddings database for semantic search, LLM orchestration and language model workflows

Interact with your PDF documents effortlessly

Private chat with local GPT with document, images, video, etc. 100% private, Apache 2.0. Supports oLLaMa, Mixtral, llama.cpp, and more. Demo: https://gpt.h2o.ai/ https://gpt-docs.h2o.ai/

local-first semantic code search engine

Axis Tour: Word Tour Determines the Order of Axes in ICA-transformed Embeddings

A Retrieval-Augmented Generation (RAG) app for chatting with content from uploaded PDFs. Built using Streamlit (frontend), FAISS (vector store), Langchain (conversation chains), and local models for word embeddings. Hugging Face API powers the LLM, supporting natural language queries to retrieve relevant PDF information.

This application is built in four stages using infrastructure as code with CDK with Python to deploy. In the first stage, an Amazon Aurora PostgreSQL vector database is set up. In the second stage, the Knowledge Base for Amazon Bedrock is created using the established database. The third stage involves creating an Amazon

AI-dataframe to enrich, transform and analyze data from cloud storages for ML training and LLM apps

The Memory layer for your AI apps

Local file search using embedding techniques

Text2Text: Crosslingual NLP/G toolkit

Fast, Accurate, Lightweight Python library to make State of the Art Embedding

Load embeddings and featurize your sentences.

Test ChromaDB