You generate embeddings using all-MiniLM-L12-v2 by loading the model through a sentence embedding framework and passing text inputs into it to obtain fixed-length numeric vectors. The model is designed for sentences and short paragraphs, so the usual workflow is to normalize text (trim whitespace, remove obvious noise), batch multiple strings together, and encode them in one call. The output is a dense vector for each input string that represents its semantic meaning. These vectors can then be compared using cosine similarity or inner product to measure how similar two pieces of text are.
In a real system, embedding generation is typically split into two phases. The first is offline indexing, where you embed your document corpus in batches. This step can be CPU-based and run as a background job, because all-MiniLM-L12-v2 is lightweight and efficient. The second phase is online querying, where you embed user queries in real time. Because the model is small, query-time embedding latency is usually low enough for interactive search and chat applications. Developers often normalize embeddings (for example, L2 normalization) so similarity scores behave consistently across queries.
Once embeddings are generated, they are usually stored in a vector database so they can be searched efficiently. A vector database such as Milvus or Zilliz Cloud allows you to insert embeddings along with metadata like document IDs, categories, or timestamps, and then retrieve the most similar vectors at query time. This separation of concerns—embedding with all-MiniLM-L12-v2 and retrieval with a vector database—makes systems easier to scale and tune. If results are poor, you can often improve them by adjusting chunking, metadata filters, or index parameters without changing the embedding model itself.
For more information, click here: https://zilliz.com/ai-models/all-minilm-l12-v2