Building a Local Retrieval Augmented Generation (RAG) Pipeline from Scratch

TLDRLearn how to build and run a local RAG pipeline using Python, PyTorch, and Transformers. Retrieve relevant information, augment the prompt, and generate outputs using an LLM.

Key insights

🔍Retrieval: Retrieve relevant information based on query

🤖Augmentation: Augment the input prompt with retrieved information

💡Generation: Pass the augmented prompt to an LLM for generative outputs

🚀Prevent hallucinations by incorporating factual information

🙌Improve generation outputs by leveraging private or domain-specific knowledge


What is RAG?

RAG stands for Retrieval Augmented Generation, where relevant information is retrieved, augmented to the prompt, and passed to an LLM for generative outputs.

Why is augmentation important in RAG?

Augmentation incorporates retrieved information into the input prompt, allowing the LLM to generate more accurate and contextually relevant outputs.

How does RAG prevent hallucinations?

RAG prevents hallucinations by incorporating factual information from relevant sources, ensuring that the generative outputs are grounded in real-world knowledge.

Can RAG leverage private or domain-specific knowledge?

Yes, RAG can leverage private or domain-specific knowledge by retrieving and augmenting information that may not be available in public sources.

What are the benefits of building a local RAG pipeline?

Building a local RAG pipeline allows you to have more control over the retrieval, augmentation, and generation process, as well as the ability to incorporate private or domain-specific knowledge.

Timestamped Summary

00:00Introduction to building a local RAG pipeline from scratch

02:00Overview of the retrieval augmented generation process in RAG

08:00Importance of augmentation in improving generation outputs

12:00Preventing hallucinations by incorporating factual information

16:00Leveraging private or domain-specific knowledge in RAG