In RAG, we take a list of documents/chunks of documents and encode these textual documents into a numerical representation called vector embeddings, where a single vector embedding represents a single chunk of document and stores them in a database called vector store.
21 Apr 2024
6 Jun 2024 · The most basic is to chunk text into fixed sizes. This works for fairly homogenous datasets that use content of similar formats and sizes, like ...
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29 Jan 2024 · Text splitting, or chunking, is usually the first in a RAG (Retrieval Augmented Generation) workflow. It simply means transforming long text ...
Sliding window chunking is an text processing technique where text is divided into overlapping chunks using a predefined window size and a specific step size.
First things first, we have Character Chunking. This strategy divides the text into chunks based on a fixed number of characters. Its simplicity makes it a ...
23 Feb 2024 · Chunking involves breaking down texts into smaller, manageable pieces called "chunks." Each chunk becomes a unit of information that is ...
4 Jul 2024 · So, just for hard limits of the models, preparing documents for RAG requires splitting text into smaller 'chunks'. This is called 'chunking', ...
15 May 2024 · We explored various facets of chunking strategies within Retrieval-Augmented Generation (RAG) systems in this guide.
9 Aug 2023 · I'm trying to make an LLM powered RAG application without LangChain that can answer questions about a document (pdf) and I want to know some ...
16 Jun 2024 · This technique involves breaking down a large corpus of text into smaller, more manageable segments. Such segmentation is key to enhancing the ...