With this project, we’re diving into the world of AI and building a chat with PDF app using Python. We’ll harness the power of Lang chain, OpenAI, and Streamlit to create something truly mind-blowing. Get ready to upload a PDF, ask any question, and receive a straight-up answer. It’s like magic, but with code! Let’s dive in and see this in action. 🚀📚
Introduction 🚀
In this tutorial, we will learn how to build a chat with PDF app using Python, Lang chain, OpenAI, and Streamlit libraries. These libraries are essential for creating AI and Alm applications, and in this video, we will walk through the process of building this special project.
Installing Required Libraries
To begin, we need to install several libraries using pip, including:
- Python
- Pi PDF
- Streamlit
- Lang chain
- OpenAI
- And more
Once these libraries are installed, we will create a new folder and a Python virtual environment to start writing the code.
Importing Libraries and Setting Environment Variables
Now, we will import the necessary libraries and set the environment variables. This includes importing modules from LangChain, initializing OpenAI API key, and setting LangChain verbosity attribute to false.
| Library | Module |
|---|---|
| LangChain | text splitter, embeddings, faiss, callbacks |
| OpenAI | question answering, Language models |
Processing the PDF Text
Next, we will define a function to process the text from the PDF. This function will involve splitting the text into chunks using Lang chain and converting the chunks into embeddings to form a knowledge base. We will also create a main function to read the PDF file and input a query from the user.
Defining LLM Instance Using OpenAI
To facilitate the interaction with OpenAI, we will define an LLM (Large Language Model) instance using the OpenAI API key. This will allow us to invoke the chain and monitor the cost incurred for every API call. Once the response is obtained, it will be displayed on the screen using Streamlit.
Running the Application
After setting up the required components, we can run the application using Streamlit server and test the chat with PDF app. By uploading a PDF file and asking a question, the app will retrieve information from the PDF and display the answer.
Conclusion
In conclusion, this tutorial has demonstrated how to build a chat with PDF app using Lang chain, OpenAI, and Streamlit libraries in Python. With less than 100 lines of code, we were able to create a powerful application for retrieving information from PDF files. If you enjoyed this tutorial, be sure to like, share, and subscribe for more content.
Key Takeaways
- Python libraries such as LangChain, OpenAI, and Streamlit are essential for building AI and Alm applications.
- Processing PDF text and creating a knowledge base allows for efficient retrieval of information from PDF files.
- Building a chat with PDF app in Python requires minimal code thanks to powerful libraries available.
FAQ
Q: Can this application be used with any type of PDF file?
A: Yes, the app is designed to work with any type of PDF file, allowing users to ask questions and retrieve information from the document.
Q: Are there any limitations to the usage of OpenAI API for this application?
A: While the OpenAI API offers powerful features, it’s important to monitor the costs incurred for every API call to ensure efficient usage.
If you want to learn something new or have suggestions for future tutorials, feel free to comment. Thank you for watching! Keep coding and exploring new possibilities. 🌟
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