RAG pipeline issue: BGE-Reranker truncates crucial data at 512 tokens

A developer encountered issues with a Retrieval-Augmented Generation (RAG) pipeline where a cross-encoder reranker, specifically BAAI/bge-reranker-base, was truncating relevant information. The reranker, designed to process query and passage pairs up to 512 tokens, was cutting off the end of longer passages due to its BERT-style architecture and Hugging Face's longest_first truncation strategy. This resulted in crucial information, often found at the end of documents like resolution steps, being discarded, leading to incorrect answers. The developer suggests detecting this by tokenizing the query and passage with the reranker's tokenizer and implementing fixes such as adjusting chunking size or scoring overlapping windows. AI

IMPACT Highlights a common limitation in RAG systems, prompting developers to consider reranker token limits when chunking and processing data.

RANK_REASON The item discusses a specific technical issue and solution within a particular AI tooling setup (RAG pipeline with a specific reranker), rather than a new model release or broader industry trend.

Read on dev.to — LLM tag →

• BAAI/bge-reranker-base
• Bert
• BGE-Reranker
• Hugging Face
• LangChain
• RecursiveCharacterTextSplitter
• retrieval-augmented generation
• sentence_transformers
• text-embedding-3-small
• XLM-RoBERTa

• infra

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