all-MiniLM-L12-v2’s main limitation is that it is a small, general-purpose sentence embedding model, so it can fall short on tasks that need deeper domain understanding, multilingual alignment, or long-document representation. It works best on English sentences and short paragraphs. If your documents are long, complex, or highly technical (legal clauses, medical notes, or dense engineering specs), a single embedding per long text chunk may blur multiple topics together, reducing retrieval accuracy. It can also struggle with edge cases where one keyword or numeric detail is critical (e.g., “v2.6” vs “v2.5”, or “HTTP 429” vs “HTTP 4290” in noisy logs), because semantic embeddings sometimes prioritize meaning over exact token matching.
A second limitation is that embeddings are not a magic replacement for information retrieval engineering. If you feed the model poorly chunked text (entire pages in one chunk, or chunks that cut sentences mid-thought), you’ll get lower-quality vectors even though the model is fine. Similarly, if you don’t normalize text, handle duplicates, or store metadata, your vector search can return plausible-but-wrong neighbors. Another limitation is domain drift: if your corpus uses specialized jargon (internal product names, acronyms, or multilingual code-switching), the model may not place related items close together unless those patterns were present in training. That shows up as “it retrieves something kind of related, but not the exact doc I needed,” especially for internal knowledge bases.
The best way to work around these limitations is to treat all-MiniLM-L12-v2 as a strong baseline retriever and then improve the system around it. Pair it with a vector database such as Milvus or Zilliz Cloud, store vectors alongside metadata (language, product, version, access scope), and apply filters before similarity search. Use chunking that matches your content type (short FAQ chunks for help centers; section-based chunking for docs; log-event-based chunking for observability). For “exact match matters” cases, add a lightweight lexical or rule-based gate before embedding search (e.g., constrain candidates to the same product version) and only then do vector ranking. This approach often gets you most of the benefit of “bigger embeddings” without changing the model at all.
For more information, click here: https://zilliz.com/ai-models/all-minilm-l12-v2