RAG-Based Text Style Transfer System: Enabling AI to Rewrite Content in Different Styles

This article introduces the open-source project stylized-RAG-pipeline, which integrates Retrieval-Augmented Generation (RAG) technology into text style transfer tasks. It uses hybrid retrieval (BM25 and Chroma) to obtain external contextual knowledge and combines large language models to achieve stylized rewriting while preserving semantics, addressing issues like scarce training data and insufficient style control in traditional methods.

Text style transfer aims to transform text expression while preserving semantics, but traditional methods rely on large amounts of parallel corpora or pre-trained style embeddings, facing challenges like scarce training data and insufficient style control. Developer Aditya Utpat's stylized-RAG-pipeline project innovatively introduces RAG technology, which combines real-time retrieval of external knowledge with large model generation to produce more accurate and rich stylized text.

Text style transfer is essentially the transformation of expression while preserving semantics. For example, rewriting "Machine learning is changing industries worldwide" into a cooking recipe style: "Take a large amount of data, mix and stir with algorithms, let the machine learn the patterns within, until it is ready to change industries worldwide." Common scenarios include converting technical documents to popular science, formal text to colloquial language, and factual descriptions to literary creations.

RAG technology guides generation by retrieving relevant context from external knowledge bases. This project uses a hybrid strategy of BM25 lexical retrieval (good at keyword matching) and Chroma semantic vector retrieval (captures semantic similarity), with dual-track parallel processing to improve recall and relevance, helping the model generate more authentic target-style text.

System workflow: Obtain web page HTML → Clean and extract plain text with BeautifulSoup → Split into document chunks using sliding window (1000 characters + 100 overlap) → Build BM25 retriever and Chroma vector storage → Hybrid retrieval with deduplication and fusion → Submit formatted prompts to Mistral-7B-Instruct-v0.3 model for generation. Vector embedding uses sentence-transformers/all-mpnet-base-v2, and BM25 is implemented based on the rank_bm25 library.

Application scenarios: Education (adapting academic concepts to different age groups), content creation (generating multi-style versions), business communication (customized expression of technical solutions). Future improvements: Add Streamlit/Gradio interactive interface, local persistent vector database, support custom document upload, weighted balance of retrieval results, expand more styles (formal, poetic, humorous, etc.).

The stylized-RAG-pipeline project demonstrates the potential of RAG technology in creative generation tasks, breaking through the dependence of traditional methods on training data, achieving flexible and controllable style transfer, and providing reference for other generation tasks that require external knowledge enhancement. It is an excellent learning case for developers who want to understand RAG construction and explore the boundaries of large models.