I've built a basic RAG pipeline, but I'm getting a lot of irrelevant context in the top-k results, which confuses the LLM. I heard LlamaIndex has built-in "Post-processors" for re-ranking. Which re-ranker (like Cohere or SentenceTransformers) provides the best balance between latency and accuracy for a customer support bot?
3 answers
I personally prefer the FixedRecencyPostprocessor if your data is time-sensitive. It ensures the LLM sees the most recent information first, which is vital for support docs.
Re-ranking is the single most effective way to "level up" a RAG system. In my projects throughout 2023, I found that the CohereRerank module in LlamaIndex offered the best precision, especially for nuanced queries where simple vector similarity fails. However, if you are worried about external API costs or latency, the LLMRerank or a local SentenceTransformerRerank (like BGE-Reranker) is a solid alternative. The trick is to retrieve a larger number of nodes initially (e.g., k=20) and then let the re-ranker narrow it down to the top 3 high-quality chunks for the LLM.
Have you tried implementing "Hybrid Search" before moving straight to a re-ranker? Sometimes combining BM25 keyword matching with vector search solves the relevancy issue without the added latency of a second model pass.
Timothy, while hybrid search helps with keyword matching, re-rankers are specifically designed to understand the semantic "fit" of a chunk to a question. For customer support, where users ask questions in weird ways, a re-ranker is almost mandatory. LlamaIndex makes it easy to stack both: use hybrid search to get the candidates, then use a re-ranker to polish the list for the final answer.
That's a very niche but important tip, Michelle! Combining recency with a semantic re-ranker covers both accuracy and relevance. It's the ultimate setup for dynamic help desks.