langchain-openai-chainlit

Chat with your documents (pdf, csv, text) using Openai model, LangChain and Chainlit.
In these examples, we’re going to build an chatbot QA app. We’ll learn how to:

• Upload a document
• Create vector embeddings from a file
• Create a chatbot app with the ability to display sources used to generate an answer

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Chat with your documents 🚀

• OpenAI model as Large Language model
• LangChain as a Framework for LLM
• Chainlit for deploying.

System Requirements

You must have Python 3.11 or later installed. Earlier versions of python may not compile.

When using python 3.10, got the following error message. Need to use python 3.11.

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Steps to Replicate

1. Fork this repository and create a codespace in GitHub as I showed you in the youtube video OR Clone it locally.

git clone https://github.com/sudarshan-koirala/langchain-openai-chainlit.git
cd langchain-openai-chainlit

2. Rename example.env to .env with cp example.env .envand input the OpenAI API key as follows. Get OpenAI API key from this URL. You need to create an account in OpenAI webiste if you haven't already.

   OPENAI_API_KEY=your_openai_api_key
   

3. Create a virtualenv and activate it

   python3 -m venv .venv && source .venv/bin/activate
   

   If you have python 3.11, then the above command is fine. But, if you have python version less than 3.11. Using conda is easier. First make sure that you have conda installed. Then run the following command.

   conda create -n .venv python=3.11 -y && source activate .venv
   

4. Run the following command in the terminal to install necessary python packages:

   pip install -r requirements.txt
   

5. Run the following command in your terminal to start the chat UI:

   chainlit run pdf_qa.py -w
   chainlit run txt_qa.py -w
   chainlit run pdf_txt_qa.py -w
   chainlit run csv_qa.py -w
   

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Disclaimer

This is test project and is presented in my youtube video to learn new stuffs using the openly available resources (models, libraries, framework,etc). It is not meant to be used in production as it's not production ready. You can modify the code and use for your usecases ✌️