# aws-samples/amazon-bedrock-samples

This repository contains examples for customers to get started using the Amazon Bedrock Service. This contains examples for all available foundational models

Repository: https://github.com/aws-samples/amazon-bedrock-samples
Canonical: https://ross.abutalabs.com/products/amazon-bedrock-samples
Homepage: https://aws.amazon.com/bedrock/
Language: Jupyter Notebook
License: MIT-0
License Family: permissive
Topics: amazon-bedrock, bedrock, embeddings, generative-ai, rag, amazon-titan, knowledge-base, langchain
Last push: 2026-08-21T11:19:54+00:00

## Health v2 (maintenance only)
Score: 73/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 98, release rhythm 35, longevity 82
- inputs: {"age_days": 1155, "days_push": 12, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1496, forks 723 (observed 2026-08-28T04:04:53.139316+00:00)

## What it is
A collection of Jupyter notebook examples and guides for getting started with Amazon Bedrock, AWS's managed generative AI service. It covers foundation model invocation, prompt engineering, agents, RAG, embeddings, multimodal use cases, evaluation, and productionization patterns.

## Use cases
- learn how to use amazon bedrock
- build a rag application with bedrock knowledge bases
- examples of calling claude or titan models on aws
- get started with generative ai agents on bedrock
- learn prompt engineering techniques for bedrock models
- use embeddings models on amazon bedrock
- evaluate and monitor bedrock model outputs
- move a bedrock proof of concept to production

## When to choose
- you are building on Amazon Bedrock specifically and want official, maintained sample code
- you want guided notebooks covering the full Bedrock feature set from basics to production
- you need reference implementations of RAG, agents, or embeddings with Bedrock models

## When to avoid
- you use a different LLM provider or want provider-agnostic examples
- you need a production-ready library or framework rather than example notebooks
- you cannot access AWS or do not have Bedrock model access enabled

## Facets
- artifact type: learning-resource
- maturity: active
- function: rag, prompt-engineering, agent-framework, llm-inference
- domain: large-language-models, artificial-intelligence, cloud-computing, tutorials
- platform: python, cloud
- tags: amazon-bedrock, aws, jupyter-notebooks, generative-ai, sample-code, foundation-models, knowledge-bases, langchain, retrieval-augmented-generation, ai-agents, jupyter

## Member repositories
- aws-samples/amazon-bedrock-samples (main) score 73

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:53.139316+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T04:33:12.147423+00:00, confidence not recorded.
  - readme: https://github.com/aws-samples/amazon-bedrock-samples (fetched 2026-08-28T04:04:53.139316+00:00, sha 1131ad18fdad)
  - homepage: https://aws.amazon.com/bedrock/ (fetched 2026-08-29T11:38:36.680933+00:00, sha 8d66ae96e54f)
  - site_page: https://aws.amazon.com/getting-started?nc2=h_dsc_aa_gs (fetched 2026-08-29T11:38:36.692387+00:00, sha f35fd0664950)
  - site_page: https://aws.amazon.com/about-aws/global-infrastructure?nc2=h_dsc_aa_gi (fetched 2026-08-29T11:38:36.694088+00:00, sha 79a446e5fbd1)
  - site_page: https://aws.amazon.com/products/developer-tools/agent-toolkit-for-aws?nc2=h_dsc_ex_s5 (fetched 2026-08-29T11:38:36.690248+00:00, sha 3cc21b596f92)
  - site_page: https://aws.amazon.com/pricing?nc2=h_pr_hub (fetched 2026-08-29T11:38:36.695907+00:00, sha e2abd01d7981)
  - site_page: https://aws.amazon.com/savingsplans?nc2=h_pr_sp (fetched 2026-08-29T11:38:36.697596+00:00, sha 9ff3c7ad93e0)
  - site_page: https://aws.amazon.com/s3/pricing?nc2=h_pr_s3 (fetched 2026-08-29T11:38:36.699265+00:00, sha 97bbf99c3793)
  - site_page: https://aws.amazon.com/bedrock/pricing?nc2=h_pr_br (fetched 2026-08-29T11:38:36.702470+00:00, sha 4ccc538a3696)
  - site_page: https://aws.amazon.com/rds/pricing?nc2=h_pr_rds (fetched 2026-08-29T11:38:36.704336+00:00, sha 538280f30c8a)
- Data as of 2026-08-30T08:39:29.467469+00:00.