To use all-MiniLM-L12-v2 for embeddings, the practical workflow is: load the model in your embedding runtime, encode text into vectors in batches, and store those vectors for similarity search. In Python, teams commonly use a sentence embedding framework that wraps tokenization, pooling, and batching. Your inputs should be sentences or short paragraphs; for longer documents, split them into chunks before encoding. The output will be one embedding vector per input string, and you typically apply L2 normalization if you plan to use cosine similarity. Once you have this, you can compute semantic similarity directly (cosine distance) or persist embeddings for retrieval.
In production, you usually separate offline and online embedding. Offline: run a batch job that reads documents, cleans and chunks them, generates embeddings, and writes results with metadata. Online: embed each incoming user query and retrieve the nearest neighbors. The details that make this reliable are not the model call itself, but everything around it: consistent preprocessing, stable chunking rules, and a clear schema for storing vectors and metadata. For example, store doc_id, chunk_id, title, section, lang, version, and updated_at. These fields let you filter retrieval and provide better UX (show the source title/section in results). Also keep an eye on input length: if you feed very long text, you may silently truncate and degrade embedding quality.
For scalable retrieval, store embeddings in a vector database such as Milvus or Zilliz Cloud. The typical flow is: encode(chunks) → insert(vectors, metadata) and then encode(query) → search(topK, filter). This gives you fast ANN search and metadata filtering, which is often the difference between “semantic search demo” and “semantic search product.” If you want to improve quality without changing models, experiment with chunking strategies, metadata filters, and top-k size, and evaluate with a small labeled query set. all-MiniLM-L12-v2 is easy to use for embeddings; the real skill is using those embeddings consistently inside a retrieval system.
For more information, click here: https://zilliz.com/ai-models/all-minilm-l12-v2