Multimodal RAG: AI Search & Recommender Systems with GPT-4

learn where the source code lives in the course by clicking resources, then right-clicking the lecture to access the course source code link and download the code or slides.

Set up your development environment with Python, a code editor such as VSCode, and an OpenAI API key to follow along, noting possible minimal costs.

Explore a deep dive into rag and multi-modal rag, covering embeddings, chunking, indexing, vector stores, retrieval, and augmented generation with LLMs for contextual data.

RAG enables practical applications such as question answering, search enhancement, document summarization, and intelligent tutoring by grounding responses in retrieved sources and improving factuality without retraining.

Explore multimodal rag by creating text and image embeddings, fusing them into a unified embedding space for cross-modal retrieval, and generating responses with a multimodal language model.

Explore how a multimodal rag system integrates search by fusing text and image embeddings into a multi-modal embedding, enabling retrieval and a final generated answer from a large language model.

Explore how a multimodal search engine combines text and image data to deliver richer, cross-modal results, enabling text- or image-based queries, personalized recommendations, and more accurate product search.

Explore building a multimodal search system by creating a chroma vector database and image embeddings using open clip, loading images, and creating a multimodal collection for text and image data.

Finish the multimodal search system by querying a chroma vector database with text queries, retrieving distances, metadata, documents, and uris, and plotting the matching images of tiger, lion, and foods.

Explore how multimodal recommender systems personalize results by combining text, images, and nutrition facts into a multi fusion embedding space, enabling tailored recommendations and faster search.

Fetch a dataset from Hugging Face and load the flowers 102 dataset, then inspect the train set with 8189 rows for a multi-modal rag recommender.

Create a data set folder and save 500 images to a local directory, naming each as flower with incremental ids, and provide a URI helper to display them.

Set up chroma db, load a dataset of 500 images, generate open clip embeddings, and save them into the vector database as a new collection with IDs and URIs.

Test and refine the multimodal recommender by building vector-database queries against the flower collection, retrieving images with URIs and distances, and validating results with matplotlib.

Set up the retrieval augmented generation flow for multimodal search by retrieving images, encoding them in base64, and querying a vision-enabled GPT-4 via LangChain to generate outputs.

Assemble and test a multimodal rag recommender system by querying a database, creating image embeddings in a vector space with Chroma DB, and using a vision-enabled LLM to generate results.

Build and run a Streamlit ui for a multimodal rag recommender, wiring data from Hugging Face, Chroma DB, and a vision model to generate bouquet recommendations.

Develop a multimodal recommender system with retrieval augmented generation and multimodal search, using vector databases like Chrome IMDb, guided by link chain and GPT-4 vision.