I built a powerful Retrieval-Augmented Generation (RAG) pipeline from scratch using Python!
In this video, you'll learn:
What is RAG and why is it powerful? Understand the core concepts behind RAG and how it leverages retrieval and generation techniques.
Setting up the toolbox: Discover the essential Python libraries you'll need to build your RAG pipeline.
Building the retrieval component: Learn how to embed and store your documents, and perform similarity search using cosine similarity.
Crafting the prompt builder: Explore strategies for constructing informative prompts that guide the large language model (LLM).
Integrating the LLM: Learn how to connect your RAG pipeline with an LLM of your choice, like OpenAI API or Llama2.
Putting it all together: Witness the magic unfold as we combine these components into a seamless RAG pipeline.
Testing and exploring: Experiment with different prompts and observe how they influence the generated text.
By the end of this video, you'll be equipped with the knowledge and skills to build your own RAG pipeline and unlock its potential for various NLP applications, from question answering to creative text generation.
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