GenAI Data and Knowledge Layer
Design the data layer for LLMs: embeddings, vector databases, knowledge graphs and production pipelines.
This document covers the design, development and maintenance of data pipelines to efficiently feed your Large Language Models with your proprietary data.
At its most fundamental level, an embedding is a numerical translation. It is the process of taking something from our world — a word, a sentence, a concept — and converting it into a language that machines truly understand: a mathematical representation. Concretely, this becomes a list of numbers, a vector. Think of it as a dense, unique fingerprint where the pattern of numbers encapsulates the meaning and context of the original text.
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What's inside
6 sections- 1 Table of Contents
- 2 Embeddings and Vector Store Fundamentals
- 3 Vector Database Architecture and Operations
- 4 Knowledge Graphs and Structured Knowledge
- 5 Production Pipelines, Advanced Preprocessing and Synthetic Data
- 6 Summary
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