google-gemini/cookbook
Sep 2025 – Sep 2025
1 Month active
Languages Used
MarkdownPython
Technical Skills
API IntegrationData EngineeringInformation RetrievalMachine LearningNatural Language ProcessingVector Databases
Jenny
PROFILE
Jenny
Evgeniya Sukhodolskaya developed an end-to-end legal retrieval pipeline notebook for the google-gemini/cookbook repository, focusing on hybrid search using Gemini embeddings and Qdrant. She configured Qdrant collections to support both dense and sparse vectors, enabling advanced retrieval strategies for legal question-answer datasets. Using Python and leveraging skills in API integration and information retrieval, she embedded and indexed legal data, then evaluated vanilla retrieval, reranking, and hybrid search approaches to assess relevance and performance. The resulting notebook provides a reproducible workflow for engineers and researchers, demonstrating practical applications of machine learning and vector databases in legal-domain search scenarios.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
856
Activity Months1
Your Network
54 people
Work History
September 2025
1 Commits • 1 Features
Sep 1, 2025Monthly summary for 2025-09: Focused on delivering an end-to-end legal retrieval pipeline notebook using Gemini embeddings and Qdrant hybrid search. Implemented a cookbook notebook in google-gemini/cookbook that demonstrates a legal retrieval pipeline with end-to-end steps: setting up a Qdrant collection with dense and sparse vectors, embedding and indexing a legal QA dataset, and evaluating retrieval strategies (vanilla retrieval, reranking, and hybrid search).
1 Commits • 1 Features
Sep 1, 2025Monthly summary for 2025-09: Focused on delivering an end-to-end legal retrieval pipeline notebook using Gemini embeddings and Qdrant hybrid search. Implemented a cookbook notebook in google-gemini/cookbook that demonstrates a legal retrieval pipeline with end-to-end steps: setting up a Qdrant collection with dense and sparse vectors, embedding and indexing a legal QA dataset, and evaluating retrieval strategies (vanilla retrieval, reranking, and hybrid search).
September 2025
Activity
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Quality Metrics
Correctness90.0%
Maintainability80.0%
Architecture90.0%
Performance80.0%
AI Usage90.0%
Skills & Technologies
Programming Languages
MarkdownPython
Technical Skills
API IntegrationData EngineeringInformation RetrievalMachine LearningNatural Language ProcessingVector Databases
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