Offered By: IBMSkillsNetwork
RAG: Vector Database to Store Document Embeddings
Explore vector databases such as Chroma DB and Facebook AI Similarity Search (FAISS) in this guided project. You'll learn how to convert documents into vector embeddings, store them effectively, and perform similarity searches to retrieve relevant information. This project is ideal for anyone interested in understanding how to integrate machine learning techniques with database solutions for tasks such as recommendation systems and information retrieval.
Continue readingGuided Project
Artificial Intelligence
At a Glance
Explore vector databases such as Chroma DB and Facebook AI Similarity Search (FAISS) in this guided project. You'll learn how to convert documents into vector embeddings, store them effectively, and perform similarity searches to retrieve relevant information. This project is ideal for anyone interested in understanding how to integrate machine learning techniques with database solutions for tasks such as recommendation systems and information retrieval.
What you'll learn
After completing the project, you will be able to:
- Prepare and preprocess documents for embeddings.
- Generate embeddings using watsonx.ai's embedding model.
- Store these embeddings in Chroma DB and FAISS.
- Perform similarity searches to retrieve relevant documents based on new inquiries.
What you'll need
- A basic understanding of Python programming.
- Familiarity with machine learning concepts.
- Access to a modern web browser such as Chrome, Edge, Firefox, Internet Explorer, or Safari for the best experience with the IBM Skills Network Labs environment, which comes with essential tools pre-installed.
Estimated Effort
30 Minutes
Level
Intermediate
Skills You Will Learn
Artificial Intelligence, Information Retrieval, NLP, Python, Vector Database, Vector Embeddings
Language
English
Course Code
GPXX0U2ZEN