Mistral Embed 2312
Neural Network
Mistral Embed 2312 — embeddings from Mistral AI: 1024-dimensional vectors for semantic search and RAG systems.
Specifications
- Input
- text
- Output
- vectors (embeddings)
- Context size
- 8 192 tokens
- Vector dimensions
- 1 024
Prompt cost
(per 1M tokens)
How it works Mistral Embed 2312?
Mistral Embed 2312 is a specialized embedding model from Mistral AI, released in late 2023. It converts text into 1024-dimensional vectors suitable for indexing in Qdrant, pgvector, and other vector databases. In BotHub, the model connects via a unified OpenAI-compatible API, so switching between models does not require rewriting the integration. Access is available from Russia without a VPN or foreign card, with payment in rubles based on actual token usage. There are many applications: semantic search for documentation and knowledge bases, a retrieval layer for RAG assistants, support ticket clustering, product card deduplication, and ranking fragments before passing them to a generative model.Frequently asked questions about Mistral Embed 2312
You can use generated results for commercial purposes. You own all rights to the content you create. The only restriction: make sure your prompt does not include copyrighted third-party material. You are responsible for respecting the rights to any input data.
mistral-embed-2312 turns text into vectors, which is the foundation for semantic search, RAG, clustering, deduplication, classification, and recommendations. The 8192-token context allows encoding entire document fragments, not just short phrases. The resulting vectors are convenient to store in Qdrant, pgvector, or another vector storage.
The output is a numerical vector of fixed dimensionality; for Mistral Embed, this is 1024 values, not text. Search quality is determined by how close semantically related fragments are in vector space. The response itself arrives as an array of numbers via the OpenAI-compatible API and goes directly into the vector database.
Step-by-step reasoning is not observed in our data, and for an embedder, this is not a core task: it does not conduct a dialogue but encodes the meaning of text into a vector. Logic on top of the found fragments is built by your application or a separate generative model, which can be connected in the same BotHub window.
In our data, function calling and structured JSON output are not observed: the model returns vectors, not tool arguments. If your scenario requires tool calls, place a generative model alongside it; the unified OpenAI-compatible API of BotHub allows you to switch it without rewriting the integration.
The input of images, audio, and video is not observed in our data: text is provided as input, which is turned into a vector. For multimodal scenarios, choose a suitable model from the catalog; requests will go through the same key and the same endpoint, without separate integration.
We do not have data on supported languages, so we cannot promise accuracy specifically in Russian; it is more reliable to test on your own corpus by comparing the proximity of semantically related fragments. Access to the model from Russia works without a VPN or foreign card, with payment by Russian cards in rubles based on actual usage.