What models are commonly used to implement HyDE RAG?

HyDE (Hypothetical Document Embedding) RAG enhances retrieval by first generating a hypothetical answer to a query using a large language model, then embedding this hypothetical document to find semantically similar real documents. This process primarily relies on two types of models: generative models for creating the hypothetical document and embedding models for vector representation and retrieval.

1. Models for Hypothetical Document Generation

The core idea of HyDE is to generate a plausible (though not necessarily factual) document that answers the user's query. This task requires powerful generative capabilities, typically provided by Large Language Models (LLMs).

• Generative Pre-trained Transformers (GPT) series (e.g., GPT-3, GPT-3.5, GPT-4): OpenAI's powerful foundational models are frequently used due to their strong text generation capabilities, allowing them to create coherent and contextually relevant hypothetical documents.
• Open-source LLMs (e.g., Llama, Mistral, T5-based models): A variety of open-source LLMs can be fine-tuned or used directly for this purpose, offering flexibility and cost-effectiveness. Models like Llama-2, Mistral, or Flan-T5 are good candidates.
• Domain-specific or fine-tuned LLMs: For specialized domains, smaller LLMs fine-tuned on relevant corpora might be employed to generate more accurate or relevant hypothetical documents within that specific context.

2. Models for Document Embedding and Retrieval

After the hypothetical document is generated, it (along with all documents in the corpus) needs to be converted into numerical vector representations. These embeddings are then used to perform a similarity search to find the most relevant documents. It's crucial that the *same* embedding model is used for both the hypothetical document and the corpus documents.

• Sentence Transformer Models (e.g., all-MiniLM-L6-v2, mpnet-base-v2): These models are highly popular for their ability to generate dense, semantically meaningful sentence embeddings efficiently. They are widely available through libraries like sentence-transformers.
• OpenAI Embedding Models (e.g., text-embedding-ada-002): OpenAI provides dedicated embedding models known for their high quality and performance, often used when integrating with other OpenAI services.
• E5 Embedding Models: These models (e.g., e5-large-v2, e5-mistral-7b-instruct) are known for strong performance across various embedding benchmarks and are often chosen for their effectiveness.
• Cohere Embed Models: Cohere offers powerful embedding models designed for high-quality semantic search.