aws-samples/amazon-nova-samples
Dec 2024 – May 2025
5 Months active
Languages Used
Jupyter NotebookMarkdownPython
Technical Skills
DocumentationTutorial DevelopmentAPI ConfigurationAgent FrameworksCloud Services (AWS Bedrock)LLM Integration
edfragas
PROFILE
Edfragas
Ed Silva developed advanced AI agent and document processing features for the aws-samples/amazon-nova-samples repository over five months, focusing on practical automation and onboarding. He integrated LiteLLM and the smolagents framework with Amazon Nova models, enabling cross-region inference and agentic tasks using Python and AWS Bedrock. Ed delivered intelligent document processing workflows supporting both PDF and image invoices, expanded test coverage, and improved documentation for user clarity. He also built an agentic academic paper analysis suite with LangGraph, implementing ReAct-based agents for summarization and research extraction. His work demonstrated depth in LLM integration, agent frameworks, and cloud-based data analysis.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
11Total
Bugs
0
Commits
11
Features
6
Lines of code
6,411
Activity Months5
Your Network
1455 people
Work History
May 2025
2 Commits • 1 Features
May 1, 2025Monthly summary for 2025-05: Focused on delivering an agentic academic paper analysis suite that combines LangGraph with Amazon Bedrock. Implemented end-to-end pipeline including project setup, PDF extraction utilities, Bedrock integration, agent state modeling, and a ReAct-based agent. Defined tools for paper summarization, research question extraction, key result identification, and gap analysis, with accompanying setup/usage guidance. This work lays the foundation for scalable literature review automation and future research capabilities.
2 Commits • 1 Features
May 1, 2025Monthly summary for 2025-05: Focused on delivering an agentic academic paper analysis suite that combines LangGraph with Amazon Bedrock. Implemented end-to-end pipeline including project setup, PDF extraction utilities, Bedrock integration, agent state modeling, and a ReAct-based agent. Defined tools for paper summarization, research question extraction, key result identification, and gap analysis, with accompanying setup/usage guidance. This work lays the foundation for scalable literature review automation and future research capabilities.
May 2025
March 2025
2 Commits • 2 Features
Mar 1, 2025March 2025 monthly summary for aws-samples/amazon-nova-samples focused on delivering concrete business value through feature enhancements and data hygiene, with measurable impact on automation coverage and test reliability.
March 2025
2 Commits • 2 Features
Mar 1, 2025March 2025 monthly summary for aws-samples/amazon-nova-samples focused on delivering concrete business value through feature enhancements and data hygiene, with measurable impact on automation coverage and test reliability.
February 2025
5 Commits • 1 Features
Feb 1, 2025February 2025 monthly summary for aws-samples/amazon-nova-samples: Delivered an Intelligent Document Processing (IDP) notebook showcase integrating Nova and Bedrock to demonstrate end-to-end invoice processing. The notebook supports summarization, data extraction via a Nova-configured tool, and data aggregation to derive business insights. Enhanced repository readiness with improved documentation, test assets, and region availability notes, plus sample PDFs and corrected links. No major bugs were reported this month; minor documentation fixes (markdown) were completed.
5 Commits • 1 Features
Feb 1, 2025February 2025 monthly summary for aws-samples/amazon-nova-samples: Delivered an Intelligent Document Processing (IDP) notebook showcase integrating Nova and Bedrock to demonstrate end-to-end invoice processing. The notebook supports summarization, data extraction via a Nova-configured tool, and data aggregation to derive business insights. Enhanced repository readiness with improved documentation, test assets, and region availability notes, plus sample PDFs and corrected links. No major bugs were reported this month; minor documentation fixes (markdown) were completed.
February 2025
January 2025
1 Commits • 1 Features
Jan 1, 2025January 2025: Delivered core integration of LiteLLM with Amazon Nova models and the Hugging Face smolagents framework. Implemented comprehensive setup instructions, router configuration for Nova models, and practical examples including cross-region inference and agent-like tasks such as web searching and custom tool usage. Added a sample codebase with smolagents and litellm to accelerate adoption and enable rapid experimentation. This work establishes a scalable foundation for intelligent agent capabilities across Nova deployments, with documented guidance to support onboarding and future enhancements.
January 2025
1 Commits • 1 Features
Jan 1, 2025January 2025: Delivered core integration of LiteLLM with Amazon Nova models and the Hugging Face smolagents framework. Implemented comprehensive setup instructions, router configuration for Nova models, and practical examples including cross-region inference and agent-like tasks such as web searching and custom tool usage. Added a sample codebase with smolagents and litellm to accelerate adoption and enable rapid experimentation. This work establishes a scalable foundation for intelligent agent capabilities across Nova deployments, with documented guidance to support onboarding and future enhancements.
December 2024
1 Commits • 1 Features
Dec 1, 2024December 2024 monthly summary for aws-samples/amazon-nova-samples: Focused on improving tutorial clarity and onboarding by updating the contextual retrieval tutorial to include Bedrock and Nova models and Llama-index pipelines. Documentation updates to README.md and contextual_retrieval.ipynb enhance user understanding, align with product goals to broaden adoption of the model ecosystem. No major bug fixes recorded this month in this repository. Key commit: 65e1e7264baa17bf9d512a63451dece88a8570a4.
1 Commits • 1 Features
Dec 1, 2024December 2024 monthly summary for aws-samples/amazon-nova-samples: Focused on improving tutorial clarity and onboarding by updating the contextual retrieval tutorial to include Bedrock and Nova models and Llama-index pipelines. Documentation updates to README.md and contextual_retrieval.ipynb enhance user understanding, align with product goals to broaden adoption of the model ecosystem. No major bug fixes recorded this month in this repository. Key commit: 65e1e7264baa17bf9d512a63451dece88a8570a4.
December 2024
Activity
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Quality Metrics
Correctness96.4%
Maintainability95.4%
Architecture96.4%
Performance91.0%
AI Usage47.2%
Skills & Technologies
Programming Languages
Jupyter NotebookMarkdownPython
Technical Skills
AI Agent DevelopmentAI/MLAPI ConfigurationAPI IntegrationAWS BedrockAgent FrameworksAgentic CodeAmazon BedrockAmazon NovaCloud ComputingCloud Services (AWS Bedrock)Data AnalysisDocument ProcessingDocumentationIntelligent Document Processing
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