What is Cross Encoder and Bi Encoder in RAG

 In the context of Retrieval-Augmented Generation (RAG), cross encoders and bi-encoders are two different methods for encoding query-document pairs to evaluate relevance. They represent two different approaches for measuring similarity between a query and potential documents during retrieval.

1. Bi-Encoder:

What it is: In a bi-encoder architecture, both the query and the documents are encoded independently into vector representations, typically using the same model. These vectors are then compared (e.g., using cosine similarity) to determine relevance.

How it works: The bi-encoder first encodes the query and the document separately into their respective embeddings. The similarity between the query and the document is computed after both have been encoded, without direct interaction between the two during encoding.

Pros:

Efficient retrieval: Since both queries and documents are independently encoded, you can pre-compute and store document embeddings in a vector database, making it fast and scalable for large datasets.

Scalability: Works well for large-scale retrieval tasks where embeddings of many documents are compared to a query.

Cons:

Lower precision: Since the query and document are encoded separately, there is no interaction between them during encoding, which might result in lower relevance compared to a cross-encoder.

Loss of interaction: The model cannot leverage interactions between query and document words, which might miss nuanced relevance.

Use case: Ideal for tasks requiring fast retrieval over large document sets, where embeddings can be precomputed and stored for quick lookup.

2. Cross-Encoder:

What it is: In a cross-encoder architecture, the query and the document are encoded together in a single pass through the model. This allows for cross-attention between the query and document, making the similarity judgment more precise.

How it works: The query and document are concatenated and passed through a model (like a transformer). The model processes them jointly, allowing direct interaction between the two. The model then outputs a relevance score based on the joint encoding.

Pros:

Higher precision: Since the query and document are encoded together, the model can take into account word-level interactions between them, leading to more accurate relevance judgments.

Better understanding of context: By processing both query and document together, the cross-encoder can capture subtle relationships and semantic nuances between the two.

Cons:

Slow for large-scale retrieval: Since every query-document pair needs to be encoded together, it's computationally expensive for large-scale retrieval tasks.

No pre-computation: Unlike bi-encoders, you can't pre-compute the document embeddings, which limits scalability.

Use case: Best suited for re-ranking a small set of documents retrieved by a bi-encoder or other methods. It is typically used in a two-step process where a bi-encoder retrieves a broad set of documents, and a cross-encoder refines the ranking.

Comparison in RAG:

In RAG (Retrieval-Augmented Generation), typically, a bi-encoder is used to perform initial retrieval from a large corpus (due to its efficiency and scalability), followed by a cross-encoder to re-rank or refine the results for better accuracy, especially when precision is critical.

Bi-Encoder: Used for fast, scalable retrieval.

Cross-Encoder: Used for accurate re-ranking of a small subset of documents retrieved by the bi-encoder.

Example Workflow in RAG:

Bi-Encoder Retrieval: The system first uses a bi-encoder to retrieve a broad set of candidate documents that are relevant to the user's query by comparing the query's embedding with precomputed document embeddings.

Cross-Encoder Re-Ranking: Once a smaller subset of relevant documents is retrieved, the system can apply a cross-encoder to re-rank the documents by jointly encoding the query and each document and generating a more precise relevance score.

Both methods have their place in RAG-based systems: bi-encoders handle large-scale retrieval, while cross-encoders improve precision for re-ranking and final selection.