mongodb-developer/GenAI-Showcase
Oct 2025 – Oct 2025
1 Month active
Languages Used
Jupyter NotebookPython
Technical Skills
Data ScienceEmbeddingsMachine LearningNatural Language ProcessingRAG (Retrieval-Augmented Generation)
ajosh0504
PROFILE
Ajosh0504
During October 2025, the developer created an end-to-end embedding model evaluation workflow for the mongodb-developer/GenAI-Showcase repository. They built Jupyter notebooks to assess Gemini and Voyage AI embeddings, implementing setup routines, dataset acquisition, and embedding generation. Their approach included measuring latency and retrieval quality to enable reproducible, data-driven benchmarking for retrieval-augmented generation (RAG) applications. By updating the Voyage AI notebook to match the Gemini evaluation methodology, they consolidated the evaluation framework for cross-model comparison. The work demonstrated depth in Python, data science, and natural language processing, establishing a robust foundation for model selection and performance analysis in embedding-based search.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
702
Activity Months1
Your Network
23 people
Work History
October 2025
1 Commits • 1 Features
Oct 1, 2025October 2025 monthly summary focusing on key accomplishments and business impact. Key feature delivered: Embedding Model Evaluation Notebooks for Gemini and Voyage AI in mongodb-developer/GenAI-Showcase; includes prerequisites setup, dataset acquisition, embeddings generation, and latency/quality measurements for RAG applications to compare performance across embedding models. Major bugs fixed: none reported this month. Overall impact: establishes a reproducible, data-driven evaluation framework that accelerates benchmarking and model selection for embedding-based search. Technologies demonstrated: Python/Jupyter notebooks, dataset handling, embedding pipelines, latency and retrieval-quality metrics, and cross-model evaluation between Gemini and Voyage AI.
1 Commits • 1 Features
Oct 1, 2025October 2025 monthly summary focusing on key accomplishments and business impact. Key feature delivered: Embedding Model Evaluation Notebooks for Gemini and Voyage AI in mongodb-developer/GenAI-Showcase; includes prerequisites setup, dataset acquisition, embeddings generation, and latency/quality measurements for RAG applications to compare performance across embedding models. Major bugs fixed: none reported this month. Overall impact: establishes a reproducible, data-driven evaluation framework that accelerates benchmarking and model selection for embedding-based search. Technologies demonstrated: Python/Jupyter notebooks, dataset handling, embedding pipelines, latency and retrieval-quality metrics, and cross-model evaluation between Gemini and Voyage AI.
October 2025
Activity
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Quality Metrics
Correctness80.0%
Maintainability80.0%
Architecture80.0%
Performance80.0%
AI Usage80.0%
Skills & Technologies
Programming Languages
Jupyter NotebookPython
Technical Skills
Data ScienceEmbeddingsMachine LearningNatural Language ProcessingRAG (Retrieval-Augmented Generation)
Repositories Contributed To
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