How RAG Works in Detail
Retrieval-Augmented Generation (RAG) combines the strengths of two AI models: a retrieval model and a generative model. This synergy allows the system to pull in relevant external information when generating responses, making the output both more informative and contextually relevant. Here’s a deeper dive into the mechanics:
Step 1: Retrieval
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Indexing: Before retrieval can occur, the dataset or knowledge base is pre-processed into an index. This index is a structured format that allows for efficient searching and matching of query terms with documents containing them. Tools like Elasticsearch or FAISS are often used for this purpose.
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Query Processing: When a query or prompt comes in, the retrieval model processes it into a similar vector representation as the documents in the index. This often involves using embeddings from models like BERT or GPT.
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Document Retrieval: The model then searches the index for document vectors closest to the query vector, typically using cosine similarity as a measure of closeness.
Step 2: Generation
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Input Combination: The retrieved documents are combined with the original query to serve as the extended input for the generative model.
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Response Generation: This combined input is fed into a generative model, like a variant of GPT (Generative Pre-trained Transformer), which generates a response based on both the query and the information from the retrieved documents.
Integrating the Components
The magic of RAG lies in how these components—the retrieval and the generation—are integrated. During training, both components learn to work together: the retrieval model learns to fetch the most useful documents for the generation task at hand, while the generative model learns to utilize the retrieved information effectively.
Additional References and Resources
For those looking to implement RAG or dive deeper into its mechanics, the following resources are invaluable:
- The original RAG paper by Lewis et al., Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks, provides a foundational understanding.
- Hugging Face offers an implementation of RAG, detailed in their blog post Introducing Retrieval-Augmented Generation, complete with code examples and usage instructions.
- For an in-depth look at vector search and document indexing, the Elasticsearch documentation (Elasticsearch Guide) and the FAISS GitHub repository (FAISS by Facebook Research) are excellent starting points.
- Elasticsearch What is retrieval augmented generation (RAG)
- With AWS OpenSearch Service’s vector database capabilities, you can implement semantic search, Retrieval Augmented Generation (RAG) with LLMs, recommendation engines, and search rich media