RAG: Vector Storage & Retrieval
A core component of RAG is the vector store – essentially, a database or index that holds all document embeddings and can quickly retrieve the most similar ones to a given query embedding....
RAG: Embedding Generation for Documents
With a clean set of document chunks, the next phase is to convert each chunk into a vector embedding – a numerical representation that captures the semantic content of the text....
RAG: Preprocessing and Chunking Documents
After curation, we have a collection of documents (or long text files). The next step is to split these documents into smaller chunks suitable for retrieval....
RAG: Data Ingestion and Curation with NVIDIA NeMO + Delta Lake
Building a high-quality RAG system starts with robust data ingestion and curation. We need to gather the enterprise data that will serve as our LLM’s external knowledge source....
Customer Segmentation: Cluster Approach
The clustered RFM model enhances traditional segmentation techniques by scoring customers on Recency, Frequency, and Monetary metrics and consolidating the results into manageable, intuitive categories....
Scaling Data Governance
Effective data governance ensures data remains accurate, accessible, and compliant with evolving regulations. However, scaling governance processes introduces challenges, from integrating diverse data sources to maintaining robust security and privacy across regions....