Retrieval-Augmented Generation (RAG)
Last updated
Last updated
Retrieval-Augmented Generation (RAG) is fan AI framework that combines retrieval-based systems with the generative capabilities of large language models. This allows systems to provide more accurate, contextually relevant responses by integrating external knowledge from external sources. When a query is made, the RAG system extracts relevant information from a big dataset or knowledge base, which is then utilized to inform and guide the response generation process.
Let's break down the step-by-step process of how RAG works, focusing on each stage from data collection to the final generation of responses:
The first step in setting up a RAG system is gathering the data that will be used for the knowledge base. This data serves as the foundation for the system to generate responses. Depending on the application, the data can come from various sources:
For a customer support chatbot, you might gather information from user manuals, product specifications, FAQs, and troubleshooting guides.
For a medical AI application, the data could include research papers, clinical guidelines, and medical records.
The data needs to be comprehensive and structured, allowing the system to retrieve relevant information when required. Ensuring that the data is up-to-date and accurate is key to the success of the RAG system.
Once the data is collected, it must be processed before it can be used in the RAG system. This is where data chunking comes into play. Chunking refers to the process of breaking down large datasets, documents, or knowledge bases into smaller, more manageable pieces (or "chunks").
Why chunking is important:
Efficiency: Processing the entire dataset at once is computationally expensive and inefficient. By breaking it into smaller chunks, the system can more quickly retrieve relevant information.
Output Relevance: When data is chunked, each piece can be more precisely matched to a user query. For instance, a 100-page user manual might be divided into sections based on topics, and when a user asks a specific question, only the most relevant section is retrieved.
Once the data has been chunked, it needs to be transformed into a format that is suitable for machine processing. This is done through document embeddings. Embeddings are numerical representations (Vectors) of text that capture the semantic meaning of the content. These embeddings are produced by models like BERT, or other pre-trained neural networks and stored in a vector database (represented as a vector space).
Why is this Needed:
Semantic understanding: Embeddings allow the system to understand the meaning of the text, rather than just matching individual words. The system can recognize that "password reset" and "resetting your password" are similar, even if they use different words.
Efficient matching: Embeddings allow for fast comparison of chunks, as similar pieces of text (in terms of meaning) are represented by vectors that are close to each other in the embedding space.
When a user submits a query to the system, it needs to be processed in the same way as the document chunks. The query is first transformed into an embedding using the same model that was used for the chunks embedding. This ensures that the system can compare the query’s meaning against the stored embeddings to find the most relevant chunks of text.
How retrieval works:
Vector search: The system performs a similarity search, finding the most similar chunks of text using algorithms such as cosine similarity or k-nearest neighbors (KNN). These methods quantify how similar two vectors are in the high-dimensional space.
Contextual relevance: The chunks that are returned are those most relevant to the user’s query, meaning they contain information that directly answers the query.
In the generation stage, the retrieved chunks of text, along with the user query, are passed to a language model for generating the final response. The language model processes the input and generates a coherent and contextually accurate response based on the retrieved chunks of information. The final output is the response generated by the language model, which is then presented to the user.
The RAG framework offers several advantages over traditional methods of information retrieval and generation:
Increased Relevance: By retrieving specific chunks of data that directly relate to the users query, RAG can generate responses that are highly relevant and accurate.
Contextual Awareness: RAG ensures that the generative model can respond based on real-world, external data, making it more accurate and informed.
Fights Hallucinations: RAG helps reduce hallucinations (instances where the model generates incorrect or made-up information) by using responses in actual, retrieved data, rather than relying solely on the model's internal knowledge.
