To generate embeddings for vector search, you first need to convert your data (text, images, etc.) into numerical vectors using a machine learning model. Embeddings capture semantic meaning in a high-dimensional space, allowing similar items to be grouped closer together. Start by selecting a model trained for your specific use case—for example, BERT or RoBERTa for text, or ResNet or CLIP for images. These models are designed to map inputs into dense vector representations. Pre-trained models are often effective because they’ve learned general patterns from large datasets, but you can also fine-tune them on your data if needed. Input data must be preprocessed to match the model’s requirements, such as tokenizing text or resizing images.
Next, process your data through the model to generate embeddings. For text, split sentences into tokens (words or subwords) and feed them into the model. Many frameworks, like Hugging Face’s Transformers or TensorFlow, provide APIs to simplify this step. For example, using the sentence-transformers library, you can generate sentence embeddings with just a few lines of code. For images, use a convolutional neural network (CNN) like ResNet to extract features from the pixel data. The model’s output layer (or a hidden layer) provides the embedding vector. These vectors are typically normalized (e.g., using L2 normalization) to ensure distances in the vector space correspond to semantic similarity. Store the embeddings in a database optimized for vector search, such as FAISS, Annoy, or a dedicated vector database like Pinecone.
Finally, use the stored embeddings for search by comparing vectors. Vector databases use algorithms like approximate nearest neighbors (ANN) to efficiently find similar items. For example, to search for text documents similar to a query, generate an embedding for the query and compute its cosine similarity with stored embeddings. Tools like FAISS allow indexing embeddings for fast retrieval, even with millions of vectors. If your data changes over time, periodically regenerate embeddings to keep the search results relevant. For custom use cases, consider training your own embedding model using frameworks like PyTorch or TensorFlow, but start with pre-trained models to save time. Always validate embedding quality by testing search results against expected outcomes.