RAGs LLMs on Custom Data using Langchain

In this project, I've implemented LLMs on custom data, using the power of Langchain and RAG.

Getting Started

i. Add you openai api ket in constants.py file.
ii. You need to add supported files to docs folder, then execute the index.py to get those files indexed in vector database.
iii. You do not need to reupload data again and again, you can just add more data to docs and execute index.py to index more and more data.
iv. Then you can execute the main.py and you are good to go.

1-Cloning the Repository

To get started, you can clone this repository to your local machine using the following command:

git clone https://github.com/javaidiqba11/custom-rags-llm-langchain.git

2- Install Required Libraries

To install the requirements, run the command.

pip install -r requirements.txt

3- Indexing

Create a folder named docs, add supported files given below to docs.

Supported Document Types:
PDF (.pdf)
Text (.txt)
Word (.docx)
Excel (.xlsx)
PowerPoint (.pptx)
HTML (.html)
Markdown (.md)
EPUB (.epub)
Image (.jpg, .jpeg, .png)
CSV (.csv)
JSON (.json)
XML (.xml)

Run the command:

python index.py

4- Retrieval and Generating

To start the ChatBot, run the command

python main.py

5- Running ChatBot

Running the ChatBot using the above commands. It is working with the same previous command...

python main.py

Note:

Feel free to explore the code, open issues, and make pull requests. I appreciate your support and look forward to collaborating with you!