| # Hands-On AI: Building and Deploying LLM-Powered Apps | |
| This is the repository for the LinkedIn Learning course `Hands-On AI: Building and Deploying LLM-Powered Apps`. The full course is available from [LinkedIn Learning][lil-course-url]. | |
| _See the readme file in the main branch for updated instructions and information._ | |
| ## Lab3: Enabling Load PDF to Chainlit App | |
| Building on top of the current simplified version of ChatGPT using Chainlit, we now going to add loading PDF capabilities into the application. | |
| In this lab, we will utilize the build in PDF loading and parsing connectors inside Langchain, load the PDF, and chunk the PDFs into individual pieces with their associated metadata. | |
| ## Exercises | |
| We will build on top of our existing chainlit app code in `app/app.py` in the `app` folder. As in our previous app, we added some template code and instructions in `app/app.py` | |
| 1. Please go through the exercises in `app/app.py`. | |
| 2. Please lanuch the application by running the following command on the Terminal: | |
| ```bash | |
| chainlit run app/app.py -w | |
| ``` | |
| ## Solution | |
| Please see `app/app.py`. | |
| Alternatively, to launch the application, please run the following command on the Terminal: | |
| ```bash | |
| chainlit run app/app.py -w | |
| ``` | |
| ## References | |
| - [Langchain PDF Loaders](https://python.langchain.com/docs/modules/data_connection/document_loaders/pdf) | |
| - [Langchain Text Splitters](https://python.langchain.com/docs/modules/data_connection/document_transformers/#text-splitters) | |
| - [Chainlit Ask File Message](https://docs.chainlit.io/api-reference/ask/ask-for-file) | |