RAGFlow is a leading open-source Retrieval-Augmented Generation (RAG) engine that fuses cutting-edge RAG with Agent capabilities to create a superior context layer for LLMs
Fix: disable thinking to avoid potential infinite loops in Qwen3.5/Qwen3.6 models (#15101)
### What problem does this PR solve? This PR fixes the issue where Qwen3.5/Qwen3.6 series models may spend excessive time on simple document-parsing tasks, such as Auto Metadata extraction, keyword extraction, question generation, and image description when using the MinerU parser. For these tasks, Qwen3.5/Qwen3.6 models may perform unnecessary reasoning by default, which can lead to very long response times, high token consumption, and, in some cases, potential infinite output loops. Since Qwen3.5/Qwen3.6 multimodal models are instantiated as `CvModel` when configured as `image2text`, the existing `enable_thinking=False` logic in `chat_model.py` does not apply to them. This PR adds the corresponding handling for the CV/image-to-text model path as well. This helps reduce unnecessary thinking time, avoid potential infinite loops, and improve parsing efficiency without noticeably affecting output quality for these simple extraction and image-description tasks. Fixes #15083.
A
Aeovy committed
600590cd18c4b1f9015eb96e09e8ff74024a475a
Parent: 5b02fe4
Committed by GitHub <noreply@github.com>
on 6/2/2026, 5:21:35 AM