Kakao Open-Sources Two Korean-Optimized AI Search Embedding Models on Hugging Face
Kakao Open-Sources Two AI Search Models Optimized for Korean
On October 29, Kakao open-sourced two embedding models on Hugging Face, Kanana-2-3b-embedding (3 billion parameters) and Kanana-2-1.3b-embedding (1.3 billion parameters), targeting personalized AI agents and RAG scenarios. Kakao said that in internal evaluations, the two models outperformed llama-nemotron-embed-1b-v2 and Qwen3-Embedding-4B of comparable scale in Korean retrieval performance, with comparable English performance and higher inference efficiency, while memory usage and inference speed improved by more than tenfold compared with the previous-generation Kanana embedding models. The models use MRL to let developers adjust embedding vector size, and are released under the Kanana Open License, which permits commercial use.
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Kakao released two open-source embedding models on Oct. 29 for use in developing personalized AI agents. (Image: Kakao)
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Kakao has released two open-source embedding models designed for developing personalized artificial intelligence (AI) agents. The models feature improved Korean-language search performance and smaller model sizes to enhance operational efficiency.
Kakao said on Oct. 29 that it had released two Kanana-2-embedding models on Hugging Face, a global AI model-sharing platform. The release expands access to its proprietary AI technology following the company's publication of four small language models (SLMs) in July.
The newly released models are Kanana-2-3b-embedding, with 3 billion parameters, and Kanana-2-1.3b-embedding, with 1.3 billion parameters.
Embedding is a technology that converts the meaning of sentences into numerical representations to retrieve relevant information. It can identify semantically similar information even when exact words do not match, making it useful for personalized AI agents and retrieval-augmented generation (RAG) systems.
For example, even if a user cannot remember the name of a restaurant mentioned in a previous conversation, the system can retrieve relevant information from a query such as "the quiet restaurant we talked about before." Kakao plans to use the technology as a core foundation for personalized AI agents that connect users' past conversations with relevant information.
According to Kakao, internal evaluations showed that both models delivered stronger Korean-language search performance than similarly sized global open models, including llama-nemotron-embed-1b-v2 and Qwen3-Embedding-4B. English-language performance was comparable, while inference efficiency was also higher than that of Qwen3-Embedding.
Search performance also improved compared with Kakao's previous Kanana embedding models. The company said it confirmed efficiency gains of several dozen times or more in memory usage and inference speed.
Kakao used its proprietary small language model, Kanana-2-SLM, to develop the new models. It first developed the 3 billion-parameter model by applying language understanding capabilities to retrieval tasks, then built the 1.3 billion-parameter model using knowledge distillation, a technique that transfers learning from a larger model to a smaller one.
The company also incorporated technology that allows developers to balance search performance and resource consumption according to their service environments. Using Matryoshka Representation Learning (MRL), developers can adjust the size of embedding vectors to optimize storage requirements and processing speed.
Both models are released under the Kanana Open License, which permits commercial use subject to its licensing terms. Companies and developers in South Korea and overseas can use the models to build semantic search services and personalized AI agents.
Kakao plans to expand research into embedding training and knowledge distillation using larger language models.