An easy to use Neural Search Engine. Index latent vectors along with JSON metadata and do efficient k-NN search.

Weaviate vector database – examples

MiniPilot is a GenAI-assisted chatbot backed by Redis. Chat with your documents

Bedrock Knowledge Base and Agents for Retrieval Augmented Generation (RAG)

Sentence Transformers API: An OpenAI compatible embedding API server

Search on your images via text or image to image search. Uses OpenAI CLIP embedding and LanceDB

An Art-Deco ChatBot that utilizes RAG using performative Pulsejet vector database. Benchmarking of RAG vs. LLMs on QA and timing

LangChain Documentation Helper

The goal is to evaluate CVs based on the O-1A visa qualification criteria

Use LLMs to analyze wine reviews

Trained chat-gpt 3.5 turbo model on 1000+ FAQs for students by vectorizing data using Pinecone DB. Used Langchain API & Reddit API for embedding & querying data, hosted w/ AWS Elastic Beanstalk.

example portfolio for chatbots made with streamlit, u need to use your OpenAI API key to start a chat

This application aims to provide users with a convenient way to interact with Langchain documentation through a chat interface powered by advanced Generative AI technologies

HACKTOBERFEST '23 Open Source Contribution to Weaviate: Implemented python version of Multi-Modal Search using Weaviate

RAG based conversational sales agent chatbot with Gradio frontend that can answer queries about BMW Mini cars and provide suitable recommendations based on personal info.

💬🤖 Build a better chatbot 🤖💬

🚀 Revolutionize your data interaction with a cutting-edge chatbot built on Retrieval-Augmented Generation (RAG) and OpenAI’s GPT-4. Upload documents, create custom knowledge bases, and get precise, contextual answers. Ideal for research, business operations, customer support, and more!

This Python application creates a simple document assistant using Streamlit, pinecone (vector store) and a language model (openai) for generating responses to user queries.

This is a "Question and Answer" application built using IBM watsonx.ai flows engine. The project leverages a vector database to enhance the Large Language Model's (LLM) context awareness with a set of documents, specifically watsonxdocs.