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

RAG enabled Chatbots using LangChain and Databutton
https://github.com/avrabyt/RAG-Chatbot

chatbot databutton langchain langchain-python rag rag-implementation

Last synced: about 1 month ago
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RAG enabled Chatbots using LangChain and Databutton

• Host: GitHub
• URL: https://github.com/avrabyt/RAG-Chatbot
• Owner: avrabyt
• License: mit
• Created: 2023-10-26T12:22:54.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2023-11-06T10:48:59.000Z (about 1 year ago)
• Last Synced: 2024-12-01T07:53:56.246Z (about 1 month ago)
• Topics: chatbot, databutton, langchain, langchain-python, rag, rag-implementation
• Language: Python
• Homepage:
• Size: 15.6 MB
• Stars: 133
• Watchers: 4
• Forks: 54
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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• jimsghstars - avrabyt/RAG-Chatbot - RAG enabled Chatbots using LangChain and Databutton (Python)

README

        
# 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)