# DjangoPeng/openai-quickstart

A comprehensive guide to understanding and implementing large language models with hands-on examples using LangChain for GenAI applications.

Repository: https://github.com/DjangoPeng/openai-quickstart
Canonical: https://ross.abutalabs.com/products/openai-quickstart
Language: Jupyter Notebook
License: Apache-2.0
License Family: permissive
Last push: 2025-03-08T02:10:34+00:00

## Health v2 (maintenance only)
Score: 33/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 10, release rhythm 35, longevity 81
- inputs: {"age_days": 1143, "days_push": 544, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1773, forks 1163 (observed 2026-08-28T04:05:34.299607+00:00)

## What it is
A comprehensive open-source tutorial and learning guide for developing applications with large language models, built as Jupyter Notebooks. It covers LLM theory (BERT/GPT), OpenAI API development, and hands-on GenAI application building with LangChain.

## Use cases
- learn how large language models like GPT and BERT work
- get started with the OpenAI API and function calling
- build a RAG chatbot with LangChain
- learn LangChain for GenAI application development
- find examples of AutoGPT and machine translation with LLMs
- understand the LLM ecosystem including Hugging Face and ChatGLM
- learn about data privacy and legal compliance for LLM apps

## When to choose
- you want a structured, hands-on tutorial with runnable Jupyter notebooks
- you are new to LLM development and need theory plus practice in one place
- you want to learn LangChain through concrete examples like RAG chatbots

## When to avoid
- you need a production-ready library or framework to ship in your app
- you want a maintained software tool rather than educational material
- you need coverage of non-OpenAI providers beyond what the notebooks include

## Facets
- artifact type: learning-resource
- maturity: active
- function: machine-learning, llm-inference, rag, agent-framework, prompt-engineering, chatbot, nlp
- domain: large-language-models, artificial-intelligence, tutorials
- platform: python, cross-platform, cli
- tags: langchain, openai, jupyter-notebooks, genai, gpt-4, chatglm, hugging-face, hands-on-examples, course-material, retrieval-augmented-generation, ai-agents, natural-language-processing

## Member repositories
- DjangoPeng/openai-quickstart (main) score 33

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:34.299607+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T03:25:24.882560+00:00, confidence not recorded.
  - readme: https://github.com/DjangoPeng/openai-quickstart (fetched 2026-08-28T04:05:34.299607+00:00, sha e5cb6925d799)
- Data as of 2026-08-30T08:39:29.467469+00:00.