In this project, we will learn how to build a Retrieval Augmented Generation (RAG) system using FastAPI and OpenAI's API. This system will let users upload documents and chat about the content.
You will learn to:
- Harness semantic search in AI applications.
- Build and optimize a RAG system using OpenAI’s GPT models.
- Create text embeddings with the appropriate chunking strategy.
- Design and manage a database using PostgreSQL.
- Design and manage APIs with FastAPI.
- Parse textual data and the PDF data using OCR.
Skills
- Artificial Intelligence
- API Development
- AI Frameworks
Prerequisites
- Experience with Python
- Basic Understanding of Web Development
Technologies
- OpenAI
- FastAPI
- PostgreSQL
Project Description
Large Language Models (LLMs) are compressions of human knowledge found on the internet. As such, they are fantastic for knowledge retrieval tasks. But there is a catch. LLMs hallucinate, which means they “produce false information contrary to the intent of the user and present it as if true and factual”, according to dictionary.com. Reducing hallucinations is a big challenge in NLP. One solution is to use Retrieval Augmented Generation (RAG). RAG consists in using a knowledge base to ground the LLM response and reduce hallucinations. It makes it possible to use LLMs to chat with your documents, or the content of your website, or even the content of a YouTube video.
Throughout the duration of this project, users will acquire a diverse set of skills that are crucial in modern technology and software development fields. These include understanding and implementing concepts in API Design and Management, ensuring robust Data Handling and Parsing techniques for various document formats. Users will gain proficiency in Database Design and Management, learning how to efficiently structure and manipulate data. The project also emphasizes skills in AI Model Integration, teaching users to seamlessly incorporate advanced AI functionalities into applications.
At the end of this project, the learner will be able to build a RAG system from scratch using FastAPI and the OpenAI’s API. The only requirements for this project are to have some experience with python and some basic understanding of web development.
Project Tasks
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Introduction
- Task 0: Getting Started
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Building the API with FastAPI
- Task 1: Create Basic API endpoints
- Task 2: Integrate OpenAI’s API
- Task 3: Write the Testing Script
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Managing and Parsing Documents
- Task 4: Improve Document Upload
- Task 5: Parse Text Documents
- Task 6: Parse PDF Documents with OCR
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Database Integration and Management
- Task 7: Create and Test the Database
- Task 8: Implement Background Tasks
- Task 9: Integrate the database with the FastAPI application
- Task 10: Test the FastAPI Application
