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In the ever-changing tech landscape, integrating AI capabilities into applications has become crucial. The combination of Amazon Bedrock and .NET Aspire provides an efficient way to achieve this. Amazon Bedrock, a fully managed service offering access to various Foundation Models (FMs), paired with .NET 9, serves as a solid base for developing AI-ready applications. .NET Aspire is a cloud-optimized stack designed for creating observable, production-ready distributed applications, equipping .NET developers with a powerful toolkit to seamlessly incorporate AI features.

In this blog post, we’ll delve into how to leverage .NET Aspire in crafting AI-ready applications utilizing Retrieval Augmented Generation (RAG) capabilities powered by Amazon Bedrock.

Understanding RAG-Powered AI Applications

RAG is an architectural pattern that enhances Large Language Models (LLMs) by supplying them with pertinent information sourced externally. Here’s how a standard RAG-enabled AI application operates:

Key Components of a RAG System:

Data Processing Pipeline: Ingests various data types, breaking them into manageable chunks for preprocessing.
Vector Database System: Converts text segments into vector embeddings for similarity searches.
Retrieval Engine: Handles user queries, identifies relevant document chunks, and ranks the results.
Augmented Generation: Merges retrieved information with user queries to produce informed responses.

Advantages of RAG:

Access to current information that extends beyond the LLM’s training cutoff.
Minimizes hallucinations by grounding outputs in factual data.
Facilitates domain specialization without requiring model fine-tuning.
Enhances transparency through proper citation of sources.
Offers cost-effectiveness compared to frequent model retraining.

Solution Overview

In this example, the AnyCompany application utilizes Amazon Bedrock Knowledge Bases to implement the RAG architecture, with Amazon OpenSearch Serverless providing vector database capabilities.

AWS Services Utilized:

Amazon Bedrock Knowledge Base: This service allows developers to connect Foundation Models to proprietary data sources without writing code, managing the entire RAG workflow from data ingestion to prompt augmentation. This managed service simplifies RAG implementation, enabling developers to concentrate on application logic rather than the complexities of the pipeline.
Amazon OpenSearch Serverless: Functions as the vector database that stores embeddings generated by the Knowledge Base.
Amazon S3 Bucket: Stores documents and product data securely.

Solution Components

The sample application comprises several parts:

.NET Aspire AppHost Project: Acts as the orchestrator, defining service connections and required cloud resources.
Knowledge Base Stack: An AWS Cloud Development Kit (CDK) stack that provisions resources for Amazon Bedrock and OpenSearch.
.NET Web API Service: The backend service interfacing with Amazon Bedrock and OpenSearch.

Step 1 – Setting Up the Solution

To begin setting up the solution:

Create a new project using the .NET Aspire Empty App template in Visual Studio, generating .NET Aspire AppHost and ServiceDefaults projects.
Incorporate the Aspire.Hosting.AWS and Amazon.CDK.Lib NuGet packages into the .NET Aspire AppHost project.
Establish a new class file named KnowledgeBaseStack.cs in your AppHost project and insert the provided code.

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