Yannis Moudere

Unlocking the Secrets of RAG Pipelines: How Retrieval Window and Document Count Shape Performance https://ift.tt/KrUkhL6 Retrieval Window in RAG Retrieval-Augmented Generation (RAG) is a method that boosts the performance of language models by incorporating external knowledge into the text generation process. This method comprises two main components: Retrieval: Obtaining relevant documents or passages from an external knowledge base. Generation: Creating coherent and informative text using the retrieved information. The retrieval window refers to the span of context (measured in tokens or characters) considered during the retrieval process. This window defines the amount of previous content used to formulate the query for fetching relevant information. Why Is the Retrieval Window Important in a RAG Pipeline? This retrieval window is critical for various reasons. Relevance of the Retrieved Information: A well-calibrated retrieval window ensures that the information obtained from external sources is extremely relevant to the current context. If the window is too tiny, important context may be overlooked, resulting in less relevant retrievals. Conversely, if the window is too big, it may include irrelevant or extraneous information, reducing the usefulness of the retrieved content Efficiency and Performance Tuning the retrieval_window_size is important to prevent decreased RAG pipeline efficiency. The size at which it is generalized must be chosen carefully, or else passing a too large window might lead to increased computation and retrieval time, slowing down the process of generation. Balancing the size of your window is critical for timely access while maintaining information relevance. Context Preservation The retrieval window helps in keeping the coherence and context of the conversation or text generation which is part of a document. By taking a certain amount of past content into account, retrieval can bring you information that adheres well with the live narrative and thus keeps up a good flow in your dialogues as is. Mitigation of Hallucinations Hallucination: a phenomenon in which language models generate information that looks sensible but is actually incorrect or irrelevant. By using a clearly delineated retrieval window, the RAG model can ground what it generates in verifiable and useful external knowledge which should make hallucination less likely. Evaluation Metrics There are two types of evaluation measures for IR systems: online and offline metrics. Online metrics are those captured during actual use of the IR system while it is online. Examples of such metrics are user interactions: the user clicked on a show recommended to them by Netflix; a link in an email advertisement was clicked — that is, click-through rate (CTR). There are many online metrics, but they all relate to some form of user interaction. Offline metrics are those that measure levels in isolated environments before establishing a new...