Create and Configure Knowledge Base

Objectives

After completing the environment and data preparation, the next step is to set up the core component of the RAG architecture. In this section, we will initialize the Knowledge Base, acting as an intelligent intermediary mechanism that connects unstructured data sources with the reasoning capabilities of foundation models.

We will accomplish 3 key technical objectives:

  1. Establish an Automated Pipeline: Configure the Knowledge Base to automate the entire RAG data processing workflow (including extraction, text chunking, and vector creation) to eliminate manual processing tasks.
  2. Initialize Vector Store: Deploy a collection on Amazon OpenSearch Serverless to store semantic vectors, serving accurate and efficient information retrieval.
  3. Data Synchronization (Data Ingestion): Perform the initial data ingestion process, converting static documents from S3 into searchable vectors within the system.

Key Components

During this configuration process, we will interact with and connect the following services:

  • Knowledge Bases for Amazon Bedrock: A managed service acting as the orchestrator of data flow, connecting information sources, and executing semantic queries.
  • Amazon Titan Embeddings G1 - Text v2: A specialized model for converting text data into numerical vectors (Embeddings) with high accuracy and multi-language support.
  • Amazon OpenSearch Serverless: A fully managed vector database responsible for storage and executing similarity search algorithms (k-NN).

Implementation Steps

  1. Initialize Knowledge Base
  2. Check Vector Store and Sync Data