🧠

Model

Serverless Rag Demo

by Aws Samples gh-model--aws-samples--serverless-rag-demo

Nexus Index

0.0 Top 18%

P: Popularity 0

F: Freshness 0

C: Completeness 0

U: Utility 0

Tech Context

Vital Performance

0 DL / 30D

0.0%

Widespread AI adoption is being driven by generative AI models that can generate human-like content. However, these foundation models are trained on general data making it less effective for domain specific tasks. There lies the importance of Retrieval Augmented Generation (RAG). RAG allows augmenting prompts with relevant external data for better domain-specific outputs. With RAG, documents and queries are converted to embeddings, compared to find relevant context, and that context is append...

Source →

Tiny - Params

- Context

0 Downloads

Model Information Summary
Entity Passport
Registry ID	gh-model--aws-samples--serverless-rag-demo
Provider	github

📜

Cite this model

Academic & Research Attribution

BibTeX

@misc{gh_model__aws_samples__serverless_rag_demo,
  author = {Aws Samples},
  title = {Serverless Rag Demo Model},
  year = {2026},
  howpublished = {\url{https://github.com/aws-samples/serverless-rag-demo}},
  note = {Accessed via Free2AITools Knowledge Fortress}
}

APA Style

Aws Samples. (2026). Serverless Rag Demo [Model]. Free2AITools. https://github.com/aws-samples/serverless-rag-demo

🔬Technical Deep Dive

Full Specifications [+]

Quick Commands

🐙 Git Clone

git clone https://github.com/aws-samples/serverless-rag-demo

⚖️ Nexus Index V16.5

Methodology Index Protocol

0.0

TOP 18% SYSTEM IMPACT

Popularity (P) 0

Freshness (F) 0

Completeness (C) 0

Utility (U) 0

💬 Index Insight

The Free2AITools Nexus Index for Serverless Rag Demo aggregates Popularity (P:0), Freshness (F:0), and Completeness (C:0). The Utility score (U:0) represents deployment readiness and ecosystem adoption.

Free2AITools Nexus Index

Verification Authority

HuggingFace API GitHub Metadata Arxiv Citation DB System Audit

Unbiased Data Node Refresh: VFS Live

---

🚀 What's Next?

📊

Find Training Datasets

Discover datasets compatible with this model

📈

Compare Benchmarks

See how this model ranks on standard tests

⚡

Deployment Guide

Understand deployment options

Technical Deep Dive

Scalable RAG solutions/Agentic Workflows with Amazon Bedrock and Amazon Opensearch serverless service

Overview

Amazon Opensearch Serverless(AOSS) offers vector engine to store embeddings for faster similarity searches. The vector engine provides a simple, scalable, and high-performing similarity search capability in Amazon OpenSearch Serverless that makes it easy for you to build generative artificial intelligence (AI) applications without having to manage the underlying vector database infrastructure.

[!NOTE] This repository offers a production ready easily deployable Generative AI solution with the below features:

Document chat

Multi-Agent collaboration with Strands sdk

Sentiment Analysis

PII Redaction

OCR

[!IMPORTANT] The Older UI is maintained in the v0.0.1(Old-UI) branch.

Demos

Doc Chat/Doc Management (Multi-lingual)

output

Multi-Agent Demo

PII Redaction

OCR

ocr

Sentiment Analysis

Latest project updates

* 10-Jun-2025 Claude 4 support. Ensure you have RPM/TPM quotas to try out Claude-4 * 28-May-2025 Multi-Agent Orchestration now through Strands SDK * 08-Nov-2024 Supports Claude-3.5 Haiku for RAG/OCR/PII Identification/Sentiment Analysis * 29-Oct-2024 Supports Claude-3.5 Sonnet V2/Opus for RAG/OCR/PII Identification/Sentiment Analysis * 1-Sept-204 Document Aware chunking strategy, to answer questions comparing several documents. For example: What did I say in Doc 1 that I contradict in Doc 7 ?

Prerequisites

Prerequisites

Familiarity with below Services

Architecture

architecture

Deploying the Solution to your AWS account with AWS Cloudshell

Section 1: Create an Admin User to deploy this stack

Section 1 - Create an IAM user with Administrator permissions (OPTIONAL: If you're already an Admin role, you may skip this step)

Search for the service IAM on the AWS Console and go the IAM Dashboard and click on “Roles“ tab under ”Access Management” and Click on “Create Role”
Select AWS Account and click “Next“
Under permissions select Administrator access
Give the role a name and create the role
You can now assume this role and proceed to deploy the stack. Click on Switch-Role
Switch role
Proceed to Section 2

Section 2 - Deploy the RAG based Solution (Total deployment time 40 minutes)

Section 2 - Deploy this RAG based Solution (The below commands should be executed in the region of deployment)

Switch to Admin role. Search for Cloudshell service on the AWS Console and follow the steps below to clone the github repository
Git Clone the serverless-rag-demo repository from aws-samples
text
```
 git clone https://github.com/aws-samples/serverless-rag-demo.git
```
Go to the directory where we have the downloaded files.
text
```
  cd serverless-rag-demo
```
Fire the bash script that creates the RAG based solution. Pass the environment and region for deployment. environment can be dev,qa,sandbox. Look at Prerequisites to deploy to the correct region.
text
```
  sh creator.sh
```
Press Enter to proceed with deployment of the stack or ctrl+c to exit
The UI is hosted on AppRunner the link to AppRunner could be found in CloudShell once the script execution is complete, or you could also go to the AppRunner service on the AWS Console and obtain the https url. The UI is authenticated through Amazon Cognito hence the very first time you would have to sign-up and then sign-in to login to the application
On Amazon Bedrock page enable access to the below models

(ADVANCED) Using an existing Bedrock Knowledge base

[!IMPORTANT] You could query your existing Knowledge base created on Amazon Bedrock provided it uses Amazon Opensearch Serverless service.

Steps

Get the Collection ARN and the embedding model used by your Knowledge base on Bedrock
Head to Amazon Opensearch Serverless and search by ARN to fetch Opensearch Endpoint
Modify the configurations of your bedrock_rag_query_* lambda function. Set the below a. IS_BEDROCK_KB = yes
b. OPENSEARCH_VECTOR_ENDPOINT = <> c. EMBED_MODEL_ID = <>. Find the base model Id from here (https://docs.aws.amazon.com/bedrock/latest/userguide/model-ids.html) d. VECTOR_INDEX_NAME = <> e. BEDROCK_KB_EMBEDDING_KEY = <>
Get the ARN of the Lambda role
Head to Amazon Opensearch on the AWS Console and click on Data Access Policies. Search for the Data Access Policy attached to your Bedrock KB and click on the Edit button
In the principal section add the ARN of your Lambda role and hit save
Now try Document Chat on the UI, it should query from your Amazon Bedrock Knowledge base.

[!IMPORTANT] We do not support indexing to an existing Knowledge base. That can be done through the Amazon Bedrock Console.

🚀 Quick Start

bash

git clone https://github.com/aws-samples/serverless-rag-demo.git

📝 Limitations & Considerations

• Benchmark scores may vary based on evaluation methodology and hardware configuration.
• VRAM requirements are estimates; actual usage depends on quantization and batch size.
• FNI scores are relative rankings and may change as new models are added.
• Source: Unknown

🐙 Data Source: GitHub ↗

🔄 Daily sync (03:00 UTC)

AI Summary: Based on GitHub metadata. Not a recommendation.

📊 FNI Methodology 📚 Knowledge Baseℹ️ Verify with original source

🛡️ Model Transparency Report

Verified data manifest for traceability and transparency.

100% Data Disclosure Active

🆔 Identity & Source

id: gh-model--aws-samples--serverless-rag-demo
source: github
author: Aws Samples
tags: opensearchragopensearchserverlessanthropicclaudehaikusonnetstrandsstrands-agentspython

⚙️ Technical Specs

architecture: null
params billions: null
context length: null
pipeline tag: feature-extraction

📊 Engagement & Metrics

likes: 0
downloads: 0

Free2AITools Constitutional Data Pipeline: Curated disclosure mode active. (V15.x Standard)

Welcome to Free2AI Tools!

Smart Search

FNI Score

You're All Set!