RAG-Chatbot - GitLite

KO | EN

gitlite — search

#python #react #typescript #llm #golang #rust #linux #macos #cli #kubernetes #vue #awesome-list #openai #open-source

RAG-Chatbot

(★ 169)

RAG enabled Chatbots using LangChain and Databutton

File Explorer

Download Latest Version (.zip)

.gitignore
app.py
brain.py
compare medium.gif
LICENSE
README.md
requirements.txt
thumbnail.webp

# Installation Guide

1. Get the code

git clone https://github.com/avrabyt/RAG-Chatbot

Downloads the entire project code from GitHub to your computer.

cd RAG-Chatbot

Moves into the project folder you just downloaded.

2. Python

Easy Recommended

Prerequisites

Git Needed to download the project code from GitHub.
Python 3 On Windows, be sure to check 'Add Python to PATH' during installation.

pip install -r requirements.txt

Installs the Python libraries listed in requirements.txt (or similar).

python app.py

Runs the Python script (or module).

✅ If it runs without errors and prints output in the terminal, it worked.

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# Retrieval Augmented Generation (RAG) for chatbots
RAG enabled Chatbots using [LangChain](https://www.langchain.com) and [Databutton](https://databutton.com/login?utm_source=github&utm_medium=avra&utm_article=rag)
![](https://github.com/avrabyt/RAG-Chatbot/blob/main/thumbnail.webp)

- For the front-end : `app.py`
- PDF parsing and indexing : `brain.py`
- API keys are maintained over databutton secret management
- Indexed are stored over session state

Oversimplified explanation : (**Retrieval**) Fetch the top N similar contexts via similarity search from the indexed PDF files -> concatanate those to the prompt (**Prompt Augumentation**) -> Pass it to the LLM -> which further generates response (**Generation**) like any LLM does. **More in the blog!**

**Blog Post - [Here](https://medium.com/databutton/why-your-next-ai-product-needs-rag-implemented-in-it-9ee22f9770c8)**

**YouTube video - [Here](https://youtu.be/Yh1GEWqgkt0)**

To get started quickly, you can use the “Chat with PDF” [template](https://databutton.com/new?templateId=pt-x2Rh7dEYwIuCxXaR) within Databutton 🚀

> Alternatively, you can use [Streamlit](https://streamlit.io) to build and deploy! In that case few changes such as `st.secrets` needs to be implemented!

# Similar projects

#### [Building a Simple Chatbot using ChatGPTAPI & Databutton with memory 🧠](https://github.com/avrabyt/MemoryBot)

>Memory implementation can also be an interesting feature in this current RAG enabled Chatbot.

Repo - [MemoryBot](https://github.com/avrabyt/MemoryBot)

The live demo app is hosted over [here](https://next.databutton.com/v/lgzxq112/Memory_Bot)

Blog - [here](https://medium.com/@avra42/how-to-build-a-chatbot-with-chatgpt-api-and-a-conversational-memory-in-python-8d856cda4542)

Video - [here](https://youtu.be/cHjlperESbg)

#### [PDF Chatbot with Memory](https://github.com/avrabyt/PersonalMemoryBot)
> Similar to Chat with PDF approach, with enabled memory.

Demo App - [here](https://next.databutton.com/v/lgzxq112/Personalised_Memory_Bot)

Video - [here](https://youtu.be/daMNGGPJkEE)

Blog - [here](https://medium.com/@avra42/how-to-build-a-personalized-pdf-chat-bot-with-conversational-memory-965280c160f8)

![](https://github.com/avrabyt/RAG-Chatbot/blob/main/compare%20medium.gif)