Optimizing Hybrid Search for RAG Using BGE-Reranker in Python

Vector search alone often fails when you need exact keyword matches or specific terminology, which is why I've shifted my entire RAG pipeline to a hybrid approach (BM25 + Vector) paired with a BGE-Reranker. The "magic" isn't in the retrieval—it's in the reranking stage. Without a reranker, you're basically gambling that the top-k results from your hybrid merge are actually relevant, but hybrid search often pulls in "noisy" documents that share keywords but lack semantic meaning.

I'm using FlagEmbedding for the BGE-Reranker because it's lightweight enough to run on a mid-range GPU but punches way above its weight in NDCG scores.

The workflow I've found most stable is:
1. Retrieve top 50 candidates using a hybrid search (Reciprocal Rank Fusion).
2. Pass those 50 candidates through the BGE-Reranker.
3. Slice the top 5 truly relevant documents for the LLM context window.

Here is the implementation pattern I use to handle the reranking logic:

from FlagEmbedding import FlagReranker

# Use BGE-Reranker-v2-m3 for multi-lingual support
reranker = FlagReranker('BAAI/bge-reranker-v2-m3', use_fp16=True) 

def refine_results(query, hybrid_docs):
    # hybrid_docs is a list of strings retrieved from Vector + BM25
    # We pair the query with each doc for the cross-encoder
    pairs = [[query, doc] for doc in hybrid_docs]
    
    # Get relevance scores
    scores = reranker.predict(pairs)
    
    # Sort documents by score in descending order
    sorted_results = [doc for _, doc in sorted(zip(scores, hybrid_docs), key=lambda x: x[0], reverse=True)]
    
    return sorted_results[:5] # Only keep the gold nuggets

One major gotcha: don't feed too many documents into the reranker. Cross-encoders are computationally expensive compared to bi-encoders (vector search). If you try to rerank 200 documents per query, your latency will spike and your UX will tank. Stick to a top-50 or top-100 window.

To squeeze more performance out of this, I've tuned a few specific configurations:

Max Token Limits: BGE-Reranker has a specific sequence length. If your documents are long, the reranker might truncate the most important part of the text. I now chunk my documents into 300-token segments with a 50-token overlap to ensure the "answer" isn't split across chunks.

Score Thresholding: Instead of just taking the top 5, I implement a hard score cutoff. If the top reranked document has a score below a certain threshold (e.g., 0.1), I trigger a "no relevant information found" response instead of letting the LLM hallucinate based on irrelevant context.

FP16 Quantization: Setting use_fp16=True is a non-negotiable for me. It cuts the VRAM usage nearly in half with almost zero detectable loss in ranking accuracy.

The productivity gain is massive. My "hallucination rate" dropped significantly because the LLM is no longer being distracted by documents that matched a keyword but were contextually useless. It turns the RAG system from a "maybe it works" tool into something I actually trust for production data.