Retrieval Augmented Generation#

What is Retrieval-Augmented Generation (RAG)? (6 min)

Watch the walkthrough above, then dive into the notebook for step-by-step practice.

You will learn to implement Retrieval Augmented Generation (RAG) to enhance language models’ responses by incorporating relevant context, covering:

  • LLM Context Limitations: Understanding the constraints of context windows in large language models.
  • Retrieval Augmented Generation: The technique of retrieving and using relevant documents to enhance model responses.
  • Embeddings: How to convert text into numerical representations that are used for similarity calculations.
  • Similarity Search: Finding the most relevant documents by calculating cosine similarity between embeddings.
  • OpenAI API Integration: Using the OpenAI API to generate responses based on the most relevant documents.
  • Tourist Recommendation Bot: Building a bot that recommends tourist attractions based on user interests using embeddings.
  • Next Steps for Implementation: Insights into scaling the solution with a vector database, re-rankers, and improved prompts for better accuracy and efficiency.

Here are the links used in the video:

Backward Vector Databases Function Calling Forward