mistral-embed-2312

Embeddings can serve as features for nearest-neighbour or lightweight classifiers, but the model does not classify directly. Requires a downstream classification layer and labelled examples.

The 8192-token window accommodates full abstracts and long passages, so the model can support similarity-based retrieval of papers. It has no generation capability, so synthesis must be handled by a separate model.

No generative output capability, so it cannot hold a conversation. It could support retrieval of knowledge-base articles behind a separate chat model.

Bi-encoder embedding models score query and passage similarity independently and are not cross-encoder rerankers. Stronger results are expected from a dedicated reranking model.

The model returns vector representations and generates no text, so it cannot rewrite queries.

Embedding model with no text generation. It cannot produce summaries.

Embedding model with no text generation. It cannot perform translation.

Usable only as the retrieval stage of a document Q&A pipeline. The model cannot produce answers, so a separate generative model is required for the complete use case.