Fast Retrieval, Smart Reranking: Building an Efficient RAG Pipeline with Bi and Cross-Encoders
Retrieval-Augmented Generation (RAG) has become a popular framework for enhancing the relevance and depth of AI responses. By retrieving documents to inform the model’s answers, RAG combines language generation with external knowledge retrieval, improving accuracy and reducing “hallucinations.”
The efficiency of a RAG pipeline, however, hinges on optimizing two critical processes: retrieval and reranking. In this blog, we explore how using Bi-Encoders for fast retrieval, paired with Cross-Encoders for precise reranking, can create a powerful and efficient RAG system capable of delivering high-quality results with reduced latency.
2. Understanding Bi and Cross-Encoders in RAG Pipelines
Bi-Encoders
Bi-Encoders are an essential component in the first stage of retrieval within a RAG pipeline. These models work by encoding queries and documents independently, transforming them into vector representations in a shared embedding space. During retrieval, we measure the similarity between the query and document embeddings, typically using metrics like cosine similarity.
Bi-Encoders are fast because the document embeddings can be precomputed and stored in an index. Once a query arrives, it only needs to be encoded and matched to the closest document embeddings, making Bi-Encoders ideal for quickly filtering down a large document pool to a top-k selection. However, they can lack fine-grained relevance because they do not jointly consider the query and document when encoding.
Cross-Encoders
Cross-Encoders, on the other hand, process both the query and the document simultaneously, allowing them to evaluate the compatibility between the two inputs with greater nuance. Unlike Bi-Encoders, Cross-Encoders produce a score by directly analyzing the relationship between a query and document, making them highly effective at accurately reranking documents.
Although Cross-Encoders provide superior accuracy, their computation is expensive because they evaluate the query-document pair together, so they are best applied only to a limited set of candidates. This makes them the ideal choice for reranking the top-k results obtained from the Bi-Encoder stage.
3. Why Combine Bi and Cross-Encoders?
Using both encoders in tandem leverages their strengths: Bi-Encoders excel at fast retrieval, while Cross-Encoders deliver precise reranking. The two-step approach balances efficiency and accuracy, a necessity for building scalable RAG systems where computational resources are often limited.
In this pipeline, the Bi-Encoder serves as a first-pass filter, swiftly retrieving the most relevant documents from a vast repository. The Cross-Encoder then refines this selection, reranking the top-k candidates with greater accuracy to ensure the final outputs are of the highest quality. This combination ultimately optimizes the RAG process, providing relevant results without overburdening computational resources.
4. Building an Efficient RAG Pipeline with Bi and Cross-Encoders
Let’s walk through building a RAG pipeline that incorporates both Bi and Cross-Encoders.
Data Preprocessing
Before retrieval, it’s essential to preprocess your data. Tokenize and clean the documents, then create vector embeddings for each document using a pre-trained Bi-Encoder model (such as BERT or Sentence-BERT). Store these embeddings in an efficient vector index, such as Facebook AI Similarity Search (FAISS), which supports fast similarity searches across large datasets.
Initial Retrieval with Bi-Encoders
- Encode the Query: When a query is received, encode it using the same Bi-Encoder used for the documents.
- Retrieve Candidates: Calculate the similarity score between the query and each document embedding in the index, typically using cosine similarity or dot product.
- Select Top-k Candidates: Retrieve the top-k documents that best match the query based on their similarity scores.
This step yields a shortlist of documents quickly, setting up the pipeline for more precise reranking with a Cross-Encoder.
Smart Reranking with Cross-Encoders
- Pair Encoding: Pass the query along with each of the top-k document candidates into a Cross-Encoder model, which evaluates their joint compatibility.
- Compute Scores: For each query-document pair, the Cross-Encoder outputs a relevance score. These scores are more accurate as the model considers both inputs together.
- Rank the Candidates: Sort the documents by their relevance scores from the Cross-Encoder to finalize the list of most relevant results.
By reranking the Bi-Encoder’s top-k results, the Cross-Encoder improves retrieval quality, ensuring only the most contextually relevant documents are used in the RAG response generation.
5. Implementation Details and Code Snippet
Tech Stack
To implement this RAG pipeline, you can use:
- Hugging Face Transformers for pre-trained Bi and Cross-Encoder models,
- FAISS for building a fast document index,
- PyTorch or TensorFlow for deep learning operations.
Sample Code
Below is a simplified code snippet illustrating the use of Bi and Cross-Encoders in a RAG pipeline:
pythonCopy codefrom transformers import AutoTokenizer, AutoModel
from sklearn.metrics.pairwise import cosine_similarity
import faiss
import torch# Load Bi-Encoder and Cross-Encoder models
bi_encoder_model = AutoModel.from_pretrained("sentence-transformers/all-MiniLM-L6-v2")
cross_encoder_model = AutoModel.from_pretrained("cross-encoder/ms-marco-MiniLM-L-12-v2")
tokenizer = AutoTokenizer.from_pretrained("sentence-transformers/all-MiniLM-L6-v2")# Precompute document embeddings with Bi-Encoder
documents = ["Document 1 text...", "Document 2 text...", ...]
doc_embeddings = bi_encoder_model.encode(documents)# Initialize FAISS index
faiss_index = faiss.IndexFlatL2(doc_embeddings.shape[1])
faiss_index.add(doc_embeddings)# Encode query and retrieve top-k candidates
query = "Your query here"
query_embedding = bi_encoder_model.encode([query])
_, top_k_indices = faiss_index.search(query_embedding, k=10)
top_k_docs = [documents[i] for i in top_k_indices[0]]# Rerank using Cross-Encoder
reranked_docs = sorted(top_k_docs, key=lambda doc: cross_encoder_model(query, doc))
Optimization Tips
- Precompute document embeddings with Bi-Encoders and store them for future use.
- Use FAISS or other optimized indexes for large datasets to accelerate retrieval.
- For smaller-scale applications, hybrid models can reduce redundancy.
6. Performance Considerations and Trade-offs
Balancing latency and accuracy is crucial in RAG systems. Bi-Encoders are fast and effective for initial retrieval, but the quality of results can sometimes suffer. Cross-Encoders offer better accuracy at the cost of higher computation.
Choosing the right k-value for Bi-Encoder retrieval is essential; a small k may exclude relevant documents, while a large k can slow down reranking. Test different values based on your dataset size and latency requirements.
7. Real-World Use Cases
Enterprise Search
RAG pipelines with Bi and Cross-Encoders can enhance document retrieval accuracy, which is essential for enterprise-level knowledge bases and search engines where high relevance is critical.
Chatbots and Virtual Assistants
Using this setup, chatbots can retrieve and rank relevant answers with minimal delay, improving user satisfaction by providing quick and accurate responses.
Recommendation Systems
In e-commerce or content platforms, this approach can prioritize relevant recommendations based on a user’s query and past interactions, leading to a personalized experience.
8. Conclusion and Key Takeaways
A combination of Bi and Cross-Encoders offers a powerful way to build an efficient, accurate RAG pipeline. The Bi-Encoder provides speed for initial retrieval, while the Cross-Encoder ensures relevance by reranking top candidates. By balancing these strengths, developers can create scalable RAG systems that meet both performance and quality requirements.
