ChatGPT

The ChatGPT model has been trained on a vast amount of text data, including conversations and other types of human-generated text, which allows it to generate text that is similar in style and content to human conversation. ChatGPT can be used to generate responses to questions, code, make suggestions, or provide information in a conversational manner, and it is able to do so in a way that is often indistinguishable from human-generated text. The initial model has been trained using Reinforcement Learning from Human Feedback (RLHF), using methods similar to InstructGPT, but with slight differences in the data collection setup. The model is trained using supervised fine-tuning, where human AI trainers provided conversations in which they played both sides—the user and an AI assistant. The trainers would have had access to model-written suggestions to help them compose their responses.

General
Relese date	November 30, 2022
Author	OpenAI
API Documentation	ChatGPT API
Type	Autoregressive, Transformer, Language model

Start building with ChatGPT

GPT-3 have a rich ecosystem of libraries and resources to help you get started. We have collected the best GPT-3 libraries and resources to help you get started to build with GPT-3 today. To see what others are building with GPT-3, check out the community built GPT-3 Use Cases and Applications.

👉 Discover more ChatGPT Tutorials on lablab.build

ChatGPT Links

All important links about ChatGPT in one place

ChatGPT Website Try ChatGPT for yourself. Chat with ChatGPT in your browser.
ChatGPT Blog Post Read the blog post about ChatGPT. Learn more about the model and how it works.
ChatGPT Frequently Asked Questions Read the frequently asked questions about ChatGPT.

ChatGPT Boilerplates

Boilerplates to help you get started" id="boilerplates

GPT-3.5-Turbo Chatbot with cost management A Python-based boilerplate for using OpenAI's GPT-3.5-turbo and GPT-4 model to create a chatbot. This chatbot utilizes the OpenAI API and Tiktoken library to manage tokens and costs.
JS ChatGPT Clone with DALL.E Image Generation A ChatGPT clone using OpenAI's API, DALL-E image generation and Firebase. Built with Node.js, Express, and React.

ChatGPT API libraries and connectors

The ChatGPT API endpoint provides a convenient way to incorporate advanced language understanding into your applications.

ChatGPT API ChatGPT API reference
OpenAI Node.js API library for ChatGPT The OpenAI Node.js library provides convenient access to the OpenAI API from Node.js applications.
OpenAI Python Library for ChatGPT The OpenAI Python library provides convenient access to the OpenAI API from applications written in the Python language

SourceCourt Exact Lite

SourceCourt Exact Lite is a bounded, evidence-first research assistant built for AMD AI League Match 4. It answers source-grounded questions while refusing to invent certainty when the available evidence does not support an exact answer. The system retrieves and parses bounded HTTP/HTTPS sources, interprets a user’s question, identifies relevant evidence, ranks candidate passages and links, and extracts an exact answer only when the evidence is sufficient. When the answer is ambiguous, unsupported, or unverifiable, SourceCourt refuses rather than fabricating a result. The project was hardened against unsafe retrieval behavior. Its transport layer rejects private and loopback network targets, protects against SSRF and DNS rebinding, preserves TLS hostname verification, limits redirects, constrains response size and timing, and avoids unsafe proxy behavior. It also includes bounded resource use, deterministic error handling, citation checks, and explicit ambiguity/refusal behavior. SourceCourt Exact Lite is packaged as a real command-line product. It can be installed as a Python package, run locally, or executed from a container image. The public release includes reproducible setup instructions, a deterministic demo, and a security-focused test suite. The release is provider-neutral at the model-adapter boundary, keeping retrieval, ranking, evidence extraction, citation, refusal, and safety logic independent from any single model provider while allowing the official Match 4 runtime or grader to supply the required model configuration. Container: `ghcr.io/null007void/sourcecourt-exact-lite:1.1.2` Digest: `sha256:39cd2d31e6fb95219da190d12a5620868e68293ac6ad88f8659142b744e4feec` SourceCourt Exact Lite returns the exact answer when the source supports it, shows the supporting evidence, and refuses when the result cannot be verified.

AEGIS

AEGIS – Short Version AEGIS is a low-cost autonomous ground rover for border and field patrolling, target navigation, surveillance, and operation in GNSS-denied environments. Built on ROS 2 and Nav2, it supports GPS-based target navigation and multi-waypoint autonomous patrol missions. When GPS becomes unreliable, AEGIS switches to local navigation using odometry, inertial sensors, visual odometry, and pre-built maps, enabling continued patrol without satellite positioning. Its camera-based perception system uses YOLOv8 to detect drone-like aerial targets, people, and predefined human activities, generating operator alerts for suspicious events. It does not identify individuals or infer intent. The rover uses a rechargeable battery with solar-tracking support for longer field endurance. A modular, regulated RF counter-UAS research module is included for authorized defence/testing environments, focusing on GNSS-degradation detection rather than operational jamming. AEGIS is intended as a safe, modular technology demonstrator for perimeter security, disaster response, infrastructure monitoring, and search-and-rescue missions, reducing human exposure in hazardous areas..

Connected Industrial Failure Intelligence

Connected Industrial Failure Intelligence is a machine-learning-based predictive maintenance system designed to help identify potential equipment failures before they occur. It uses NASA's C-MAPSS FD001 simulated turbofan engine dataset to estimate Remaining Useful Life (RUL), assess failure risk, and provide interpretable sensor-trend evidence through an interactive dashboard. The system combines a Random Forest regression baseline with an LSTM-based sequence model to estimate engine degradation. It evaluates predictions using metrics such as Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE), while a configurable threshold converts predicted RUL into risk categories. The Streamlit dashboard presents engine health information, predicted RUL, sensor trends, and transparent, rule-based maintenance recommendations. Illustrative maintenance examples are explicitly identified as synthetic rather than real industrial records. The project emphasizes reproducible training, engine-level data splitting, artifact integrity checks, automated testing, and honest evaluation of model performance. Its goal is to demonstrate how machine learning can support predictive maintenance workflows and provide a foundation for future validation on real industrial data and AMD GPU infrastructure. The current implementation uses simulated benchmark data; real-world deployment would require validation on actual equipment telemetry. AMD GPU execution has not yet been verified.

Keepmine-AI

KeepMine AI is an AI-powered conversation memory and information retrieval platform designed to help users turn overwhelming chat histories into searchable, useful knowledge. Important information is often buried in long conversations, making it difficult to remember payment details, find customer requests, track orders, retrieve delivery information, or recall previous agreements. KeepMine AI addresses this problem by helping users access relevant information from their conversations quickly and efficiently. The platform aims to transform everyday messaging into a practical knowledge base, reducing the time spent scrolling through old messages and minimizing the risk of missing important details. By combining artificial intelligence with conversational search, KeepMine AI makes it easier to find what matters when it matters. Our vision is to make digital conversations more organized, searchable, and valuable for individuals, entrepreneurs, and businesses that rely on messaging for communication and daily operations.

CareBridge: Safe Discharge Q&A with Nurse Handoff

After surgery, patients go home with instructions they often forget or misread, and nurses repeat the same answers. A general chatbot can sound confident and be wrong, which is the worst outcome in healthcare. CareBridge is built around one idea: know when not to answer. A patient chats in English, Telugu or Hindi. Every message is first screened for red flags (chest pain, heavy bleeding, overdose, self-harm) using rules in each language plus an AI classifier. Red flags are never answered; they go to a nurse as urgent. Otherwise the system retrieves passages from that patient's own discharge document only, writes a plain-language answer with citations, and scores confidence from retrieval strength, a grounding check, agreement across sampled answers and, for Telugu and Hindi, translation consistency. Below a calibrated threshold, no answer is shown; the case goes to a nurse with the question, the passages found and the reason. CareBridge never diagnoses or changes medication. On {{N_TEST}} held-out questions, the confidence gate cut wrong answers from {{A}}% to {{B}}% while still answering {{C}}% of questions. Red-flag recall: {{EN}}% English, {{TE}}% Telugu, {{HI}}% Hindi. {{MODEL}} runs on {{PLATFORM}} at {{TPS}} tokens/second. Limits: synthetic documents, a small test set, a demonstration only and not a clinical tool.