weaviate/recipes
Nov 2024 – Dec 2024
2 Months active
Languages Used
Jupyter NotebookPythonMarkdown
Technical Skills
Data EngineeringData ScienceDocument ProcessingGPU ComputingJupyter NotebooksMachine Learning
m-newhauser
PROFILE
M-newhauser
Mary Newhauser developed and documented end-to-end Retrieval Augmented Generation (RAG) workflows for the weaviate/recipes repository, focusing on AI-enabled document processing and vector search. She built Jupyter notebooks demonstrating PDF parsing with Docling, embedding generation using ModernBERT, and integration with Weaviate for storage and retrieval. Her work included step-by-step guidance for installation, environment setup, and GPU configuration, as well as improvements to onboarding and documentation clarity. By addressing both feature delivery and bug fixes, Mary enabled rapid experimentation with machine learning and natural language processing, lowering barriers for teams to prototype, extend, and evaluate document retrieval solutions using Python.
Overall Statistics
Feature vs Bugs
67%Features
Repository Contributions
5Total
Bugs
1
Commits
5
Features
2
Lines of code
1,991
Activity Months2
Work History
December 2024
3 Commits • 1 Features
Dec 1, 2024December 2024 monthly summary for weaviate/recipes: Delivered an end-to-end ModernBERT embeddings with Weaviate integration recipe, including data loading, embedding generation, indexing into Weaviate, and sample queries. Also fixed Colab link path to ensure Colab notebooks are accessible for the ModernBERT embeddings recipe. Commits included: ab768bbfe383050c3fa21685cfef1e4ffdaae84d, 4356b7d6ef3f133e2a8136865e890b47ba4ba89b, and 274503e53a48a6ff08dc92237322f992d6ce6d97. Impact: lowers onboarding friction, enables rapid experimentation with vector search, and improves documentation clarity and discoverability. Technologies/skills demonstrated: Python, Colab workflows, ModernBERT embeddings, Weaviate vector search, data loading and indexing, documentation practices.
3 Commits • 1 Features
Dec 1, 2024December 2024 monthly summary for weaviate/recipes: Delivered an end-to-end ModernBERT embeddings with Weaviate integration recipe, including data loading, embedding generation, indexing into Weaviate, and sample queries. Also fixed Colab link path to ensure Colab notebooks are accessible for the ModernBERT embeddings recipe. Commits included: ab768bbfe383050c3fa21685cfef1e4ffdaae84d, 4356b7d6ef3f133e2a8136865e890b47ba4ba89b, and 274503e53a48a6ff08dc92237322f992d6ce6d97. Impact: lowers onboarding friction, enables rapid experimentation with vector search, and improves documentation clarity and discoverability. Technologies/skills demonstrated: Python, Colab workflows, ModernBERT embeddings, Weaviate vector search, data loading and indexing, documentation practices.
December 2024
November 2024
2 Commits • 1 Features
Nov 1, 2024November 2024 monthly summary for weaviate/recipes repository focusing on feature delivery and documentation improvements that enable rapid experimentation with RAG over PDFs. No major bugs fixed this period; emphasis was on end-to-end demonstration and environment readiness that unlocks business value through faster AI-enabled document workflows.
November 2024
2 Commits • 1 Features
Nov 1, 2024November 2024 monthly summary for weaviate/recipes repository focusing on feature delivery and documentation improvements that enable rapid experimentation with RAG over PDFs. No major bugs fixed this period; emphasis was on end-to-end demonstration and environment readiness that unlocks business value through faster AI-enabled document workflows.
Activity
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Quality Metrics
Correctness92.0%
Maintainability92.0%
Architecture92.0%
Performance84.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
Jupyter NotebookMarkdownPython
Technical Skills
Data EngineeringData ScienceDocument ProcessingDocumentationGPU ComputingHugging Face DatasetsJupyter NotebooksMachine LearningNatural Language ProcessingPythonRAG (Retrieval Augmented Generation)Sentence TransformersVector DatabasesWeaviate
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