Implementing Reranking in RAG

Duration: 5 min

This module delves into the implementation of reranking in Retrieval-Augmented Generation (RAG) systems. Reranking is crucial for improving the relevance and quality of retrieved documents, thereby enhancing the overall performance of RAG systems. By understanding and implementing reranking techniques, you can significantly boost the effectiveness of your RAG pipeline.

Understanding Reranking in RAG

Reranking involves reordering the retrieved documents based on their relevance to the query after an initial retrieval step. This is typically done using machine learning models that can better capture the semantic similarity between the query and the documents. In RAG systems, reranking helps in selecting the most relevant documents to augment the language model, leading to more accurate and contextually appropriate responses.

import torch
from transformers import AutoModel, AutoTokenizer

# Load pre-trained model and tokenizer
model_name = 'bert-base-uncased'
model = AutoModel.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example query and documents
query = 'What is the capital of France?'
documents = ['Paris is the capital of France.', 'The Eiffel Tower is in Paris.', 'France is a country in Europe.']

# Tokenize and encode query and documents
inputs = tokenizer(query, documents, return_tensors='pt', padding=True, truncation=True)

# Get embeddings
outputs = model(**inputs)
query_embedding = outputs.last_hidden_state[:, 0, :].mean(dim=0)
document_embeddings = outputs.last_hidden_state[:, 1:, :].mean(dim=1)

# Compute cosine similarity
cosine_similarities = torch.nn.functional.cosine_similarity(query_embedding.unsqueeze(0), document_embeddings, dim=1)

# Rerank documents based on similarity
reranked_documents = [doc for _, doc in sorted(zip(cosine_similarities, documents), key=lambda pair: pair[0], reverse=True)]

print(reranked_documents)

Try it in Google Colab:

['Paris is the capital of France.', 'The Eiffel Tower is in Paris.', 'France is a country in Europe.']

Implementing Reranking with a Pre-trained Model

To implement reranking, we can use pre-trained models like BERT to generate embeddings for the query and documents. These embeddings capture the semantic meaning of the text, allowing us to compute similarity scores. By sorting the documents based on these scores, we can rerank them to ensure that the most relevant documents are used in the RAG pipeline.

import torch
from transformers import AutoModel, AutoTokenizer

# Load pre-trained model and tokenizer
model_name = 'bert-base-uncased'
model = AutoModel.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example query and documents
query = 'What is the capital of France?'
documents = ['Paris is the capital of France.', 'The Eiffel Tower is in Paris.', 'France is a country in Europe.']

# Tokenize and encode query and documents
inputs = tokenizer(query, documents, return_tensors='pt', padding=True, truncation=True)

# Get embeddings
outputs = model(**inputs)
query_embedding = outputs.last_hidden_state[:, 0, :].mean(dim=0)
document_embeddings = outputs.last_hidden_state[:, 1:, :].mean(dim=1)

# Compute cosine similarity
cosine_similarities = torch.nn.functional.cosine_similarity(query_embedding.unsqueeze(0), document_embeddings, dim=1)

# Rerank documents based on similarity
reranked_documents = [doc for _, doc in sorted(zip(cosine_similarities, documents), key=lambda pair: pair[0], reverse=True)]

print(reranked_documents)

💡 Tip: Ensure that the query and documents are properly tokenized and encoded to avoid dimension mismatches when computing embeddings and similarity scores.

Key Concepts

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