GDP-ADMIN/gen-ai-examples
Jun 2025 – Jul 2025
2 Months active
Languages Used
PythonDockerfile
Technical Skills
API IntegrationAgent DevelopmentCode RefactoringGenerative AILangChainMulti-Agent Systems
RavindraGDP
PROFILE
Ravindragdp
Putu R. Wiguna developed and maintained features for the GDP-ADMIN/gen-ai-examples repository, focusing on generative AI and data retrieval workflows. He built a LangChain-based multi-agent example in Python, demonstrating coordinated task delegation between specialized weather and math agents, with dynamic tool creation and a central coordinator agent. He also updated API integrations to maintain compatibility with evolving LangChain library changes, ensuring ongoing reliability. In addition, Putu addressed a bug in historical data retrieval from arXiv MCP by refining Dockerfile configurations and Python scripts, enabling robust access to older research papers. His work reflects depth in agent development and code refactoring.
Overall Statistics
Feature vs Bugs
33%Features
Repository Contributions
3Total
Bugs
2
Commits
3
Features
1
Lines of code
782
Activity Months2
Your Network
31 people
Work History
July 2025
1 Commits
Jul 1, 2025July 2025 (2025-07) monthly summary for GDP-ADMIN/gen-ai-examples: Delivered a critical reliability improvement to historical data retrieval from arXiv MCP. Implemented bug fix to fetch older research papers by updating the Dockerfile to clone a temporary repository and adjusting arxiv-mcp-server command arguments; updated the example Python script's date range to reflect a more relevant historical period. This change ensures broader coverage of literature and improves data completeness for researchers and downstream analytics. The work is captured in commit 5af065e59401c5b3f7d8982f794e5596f422a7d6 with message "[Arxiv MCP] Feature and Fix: Fix capability of fetching old paper (#77)". Impact: users now retrieve older papers reliably, enhancing historical analysis, reproducibility, and decision support. Technologies/skills: Dockerfile, arxiv-mcp-server, Python scripting, version control, and arXiv MCP integration.
1 Commits
Jul 1, 2025July 2025 (2025-07) monthly summary for GDP-ADMIN/gen-ai-examples: Delivered a critical reliability improvement to historical data retrieval from arXiv MCP. Implemented bug fix to fetch older research papers by updating the Dockerfile to clone a temporary repository and adjusting arxiv-mcp-server command arguments; updated the example Python script's date range to reflect a more relevant historical period. This change ensures broader coverage of literature and improves data completeness for researchers and downstream analytics. The work is captured in commit 5af065e59401c5b3f7d8982f794e5596f422a7d6 with message "[Arxiv MCP] Feature and Fix: Fix capability of fetching old paper (#77)". Impact: users now retrieve older papers reliably, enhancing historical analysis, reproducibility, and decision support. Technologies/skills: Dockerfile, arxiv-mcp-server, Python scripting, version control, and arXiv MCP integration.
July 2025
June 2025
2 Commits • 1 Features
Jun 1, 2025June 2025 monthly summary for GDP-ADMIN/gen-ai-examples. Delivered a LangChain Multi-Agent Example demonstrating a coordinated multi-agent workflow with weather and math specialized agents and a coordinating agent proving dynamic tool creation and task delegation for simple queries. Also implemented an API compatibility update to align LangChain examples with the latest library changes (model keyword for ChatOpenAI), strengthening maintainability and demo reliability across aip-agent-quickstart.
June 2025
2 Commits • 1 Features
Jun 1, 2025June 2025 monthly summary for GDP-ADMIN/gen-ai-examples. Delivered a LangChain Multi-Agent Example demonstrating a coordinated multi-agent workflow with weather and math specialized agents and a coordinating agent proving dynamic tool creation and task delegation for simple queries. Also implemented an API compatibility update to align LangChain examples with the latest library changes (model keyword for ChatOpenAI), strengthening maintainability and demo reliability across aip-agent-quickstart.
Activity
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Quality Metrics
Correctness93.4%
Maintainability93.4%
Architecture93.4%
Performance86.6%
AI Usage46.6%
Skills & Technologies
Programming Languages
DockerfilePython
Technical Skills
API IntegrationAgent DevelopmentCode RefactoringDockerGenerative AILangChainMulti-Agent SystemsPython
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