Perplexity is valued for answers with a source link, but what a business usually needs is that same answer over its own documents: policies, contracts, the support knowledge base. That is assembled from open models: embeddings find the relevant chunks, a reranker orders them by real relevance, and a language model writes the answer. It all runs on your own server, documents are never indexed outside, queries are unlimited, and retrieval can be tuned to your terminology. The catch is that this is not a finished product: a vector store, document parsing, index refresh and a UI are a development project. And the model can still sound confident and be wrong, which is why the link to the source document is always shown.
Sber embeddings built for Russian: according to the developers, among the best on Russian-language search benchmarks. FRIDA is compact, Giga-Embeddings is more powerful.
A model for meaning-based search in about a hundred languages. The core of RAG: the bot finds the right part of a document before answering.
Embeddings and rerankers based on Qwen3, among the best open ones for multilingual search, including Russian. VL versions search images, screenshots and video.
Proven models for semantic search. The multilingual versions work well with Russian and are still a reliable base for RAG.
Rerankers: they take passages found by search and reorder them by how well they actually match the question. v2-m3 is multilingual and lightweight, often paired with bge-m3.
All five are listed as commercially usable (MIT and Apache 2.0); only some BGE Reranker variants fall under the Gemma license. The language model that writes the final answer is chosen separately and its license checked the same way.
An open model can run on your own server: data stays in-house, there is no per-request fee, and the model can be fine-tuned on your documents.
