Elasticsearch (ES) is a distributed RESTful search engine optimized for large-scale and long-text search scenarios. Recent research on text-to-Query has explored using large language models (LLMs) to convert user query intent to executable code, making it an increasingly popular research topic. To our knowledge, we are the first to introduce the novel semantic parsing task text-to-ES. To bridge the gap between LLM and ES, in detail, we leverage LLMs and employ domain experts to generate ES query bodies, which are Domain-Specific Language (DSL), along with the corresponding post-processing code to support multi-index ES queries. Consequently, we propose the text-to-ES benchmark that consists of two datasets: Large Elasticsearch Dataset (LED), containing 26,207 text-ES pairs derived from a 224.9GB schema-free database, and ElasticSearch (BirdES)with 10,926 pairs sourced from the Bird dataset on a 33.4GB schema-fixed database. Compared with fourteen advanced LLMs and six code-based LLMs, the model we trained outperformed DeepSeek-R1 by 15.64% on the LED dataset, setting a new state-of-the-art, and achieved 78% of DeepSeek-R1’s performance on the BirdES dataset. Additionally, we provide in-depth experimental analyses and suggest future research directions for this task. Our datasets are available at https://huggingface.co/datasets/Barry1915/Text-to-ES.

Medical visual question answering (MedVQA) requires the model to answer natural language questions based on the given medical images. Recently, large vision-language models (LVLMs) have emerged as the dominant paradigm for this task, enabling open-ended predictions and effectively integrating textual and visual information. However, due to the lack of domain-specific medical knowledge, LVLMs often suffer from hallucination issues, leading to inaccurate answers and flawed chain-of-thought (CoT) reasoning. This limits both the accuracy and interpretability of their responses. Hence, we propose MedKCoT, a framework that integrates a multi-modal medical knowledge graph (MMKG) to mitigate LVLM hallucinations during CoT generation and introduces a novel supervision strategy to train an MMKG retriever without requiring manual annotations. Firstly, we define an LVLM preference score as the supervision signal to train an MMKG retriever. Secondly, the retriever searches entity knowledge related to the medical question-answering pair, which assists the LVLM in generating high-quality CoTs. Finally, the obtained CoTs and original question-answering pairs are used to fine-tune a lightweight VLM as the MedVQA model. We evaluate MedKCoT on two public MedVQA datasets Slake and VQA-RAD. Experimental results show that MedKCoT achieves improvements of 2.93 % and 2.58 % over previous methods. The source code is available at https://github.com/EnjoyFailure/MedKCoT.