How to Load Image Embeddings into Pinecone

Vector databases, such as Pinecone, are crucial for applications involving Large Language Models (LLMs) and Large Multimodal Models (LMMs) because they store text and image embeddings for processes like Retrieval Augmented Generation (RAG). This guide outlines how to use Roboflow Inference, a scalable tool for running vision models, to calculate and load image embeddings into Pinecone, with a focus on using the CLIP model for creating these embeddings. Once embeddings are calculated, they are stored in Pinecone, a vector database that supports various models, including those from OpenAI and Hugging Face, and can be queried using SDKs in languages like Python. The guide demonstrates setting up a Pinecone database, calculating CLIP embeddings using Roboflow Inference, and running a search query to retrieve images based on text embeddings, showcasing the efficacy of semantic search within vector databases.