deeplake

Intelligent sales assistant built using Deep Lake, Whisper, LangChain, and GPT 3.5/4

Chatbot assistant enabling GitHub repository interaction using LLMs with Retrieval Augmented Generation

Examples for quickly getting started using Deep Lake! https://activeloop.ai/

Using langchain, deeplake and openai to create a Q&A on the Mojo lang programming manual

This is a CLI app using LangChain and Activeloop vector DB to index and chat with the Chainstack docs

Intelligent Document Reranking RAG System

Unlock the potential of AI-driven solutions and delve into the world of Large Language Models. Explore cutting-edge concepts, real-world applications, and best practices to build powerful systems with these state-of-the-art models.

DrakeLLM is developed to help students to solve the issue of making notes from videos, books and others. Utilising RAG, Drake helps in making quick notes along with a Q&A bot. Books, YouTube tutorials or Videos, Drake supports all your means.

Artificial Intelligence LLM experiments and quick intros using Ollama, GPT4All, OpenAI and HuggingFace models with LangChain and DeepLake vector store

An example Airflow Pipeline with Deeplake for Machine Learning

A Statistical Research GPT that integrates DeepLake and Eurostat API

This Python code retrieves an article from a provided URL, extracts its title and text, and then utilizes the ChatOpenAI library (assuming access) to generate a bulleted summary using the GPT-4 model.

ActiveLoop's Course on LangChain and Vector Databases in Production

LLMs are deep learning models with billions of parameters that excel at a wide range of natural language processing tasks. They can perform tasks like translation, sentiment analysis, and chatbot conversations without being specifically trained for them

This repository enables users to interact with their documents using a chat interface

This Python script downloads a YouTube video, transcribes its audio content, and generates a summary of the transcription using language modeling techniques.

A list of examples for different use of LangChain

RAG-based question answering chatbot using Deep Lake, LangChain, and GPT-3.5. This project builds a chatbot that retrieves relevant information from text datasets using vector embeddings and generates context-aware answers via GPT-3.5 Turbo. Includes a Streamlit app for a user-friendly interface.

RAG voice chatbot: Whisper speech-to-text, GPT + Deep Lake, ElevenLabs text-to-speech. Streamlit UI.