Using Sentence Transformers in RAG

Retrieval-Augmented Generation (RAG) combines the power of large language models with external knowledge sources to improve the relevance and accuracy of generated content. Using Sentence Transformers within a RAG framework is an effective approach for embedding and retrieving relevant documents or passages to condition the generation process.

What Are Sentence Transformers?

Sentence Transformers are models designed to convert sentences, paragraphs, or documents into dense vector embeddings in a semantic space. These embeddings capture the meaning of the text, allowing similarity comparisons through metrics like cosine similarity. This is especially useful in retrieval tasks where you want to find documents or passages semantically closest to a query.

Role of Sentence Transformers in RAG

In RAG, you need to retrieve relevant documents or snippets to feed as context to a language model for generation. Sentence Transformers play the role of the retriever by:

• Encoding all documents/passages in your knowledge base into fixed embeddings.

• Encoding the user query or prompt into an embedding.

• Computing similarity scores between the query embedding and the document embeddings.

• Selecting top-k most relevant documents to provide context.

This method enables semantic retrieval, which goes beyond simple keyword matching to find truly relevant information.

Implementing Sentence Transformers in RAG

1. Preprocessing and Indexing:

   • Split your knowledge base into manageable chunks (e.g., paragraphs).

   • Use a Sentence Transformer model (e.g., all-mpnet-base-v2, all-MiniLM-L6-v2) to encode each chunk into an embedding vector.

   • Store these embeddings in a vector database or an efficient search index (such as FAISS, Annoy, or ElasticSearch with vector support).

2. Query Encoding and Retrieval:

   • When a user query arrives, encode the query with the same Sentence Transformer model.

   • Perform a similarity search in the vector index to retrieve the top relevant chunks.

3. Contextual Generation:

   • Concatenate the retrieved chunks as context.

   • Pass this enriched context along with the original query to a generative language model (e.g., GPT, BERT-based generative models) to produce an informed response.

Advantages of Using Sentence Transformers in RAG

• Semantic Understanding: Retrieves documents based on meaning, not just keywords.

• Efficiency: Vector databases optimized for similarity search make retrieval fast.

• Flexibility: Can handle large knowledge bases without exhaustive text matching.

• Improved Generation Quality: Provides the generative model with highly relevant context, reducing hallucinations.

Practical Tips

• Use batching to encode documents for faster processing.

• Fine-tune Sentence Transformers on your domain-specific data for better embedding quality.

• Limit the number and length of retrieved documents to fit within the generative model’s input limits.

• Combine with reranking methods if necessary to improve retrieval precision.

Example Workflow

• Embed 10,000 documents using all-MiniLM-L6-v2.

• Store embeddings in FAISS index.

• Receive query: “How does photosynthesis work?”

• Encode query and retrieve top 5 documents with highest cosine similarity.

• Concatenate these documents with the query.

• Pass combined text to GPT for generating a detailed answer.

Integrating Sentence Transformers within RAG frameworks enhances retrieval accuracy and the relevance of generated content, creating more powerful and informative AI applications.