Python RAG Tutorial | AI For PDF using Python
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In this video we will build a python chat with pdf/document script using Google Gemini API, Langchain & ChromaDB. We will take a look at implementing a fully functional retrieval augmented generation (RAG) system in Python. Our Python code will allow us to convert any PDF/Document into vector embeddings and then store them in a vector database such as Chroma DB. Afterwards, we will learn how to use these embeddings coupled with the users query and Google Gemini API to perform retrieval augment generation or RAG for short. The concepts covered will help you implement any chat with document system using Python. Furthermore, you will learn all of the best practices when it comes to working with Python, ChromaDB, Langchain & Gemini API. This is an excellent guide for beginner Python/ML developers, or anyone looking to learn about RAG based chat/document chat using a large language model such as Gemini Pro from Google.
Resources:
Source Code: https://cutt.ly/ceq4tmmC
Lanchain: https://python.langchain.com/docs/get...
ChromaDB: https://www.trychroma.com/
PyPDF: https://pypi.org/project/pypdf/
Sentence Transformers: https://pypi.org/project/sentence-tra...
Google Generative AI: https://pypi.org/project/google-gener...
Gemini API: https://ai.google.dev/
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