couchbase-examples/vector-search-cookbook
Oct 2025 – Oct 2025
1 Month active
Languages Used
Jupyter NotebookMarkdownPython
Technical Skills
AI Agent MemoryAI IntegrationCouchbaseCrewAIDatabase ManagementEmbeddings
Viraj Agarwal
PROFILE
Viraj Agarwal
Viraj Agarwal developed the GSI Vector Search Tutorial Series in the couchbase-examples/vector-search-cookbook repository, delivering end-to-end workflows for vector search using Couchbase Global Secondary Indexes. He designed hands-on tutorials that integrate Hugging Face embeddings, CrewAI, and Jina AI to demonstrate semantic search, retrieval-augmented generation, and memory architectures. Using Python and Jupyter Notebooks, Viraj focused on reproducibility and practical onboarding, providing production-ready examples that document best practices for GSI-powered vector search. His work addressed the need for clear, cross-technology integration patterns, enabling developers to adopt advanced vector search capabilities and accelerating customer value through accessible, well-documented solutions.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
4Total
Bugs
0
Commits
4
Features
1
Lines of code
15,724
Activity Months1
Your Network
41 people
Work History
October 2025
4 Commits • 1 Features
Oct 1, 2025Monthly summary for 2025-10: Key features delivered include the GSI Vector Search Tutorial Series in couchbase-examples/vector-search-cookbook. This set of tutorials demonstrates end-to-end vector search workflows using Couchbase Global Secondary Index (GSI), including semantic search with Hugging Face embeddings and GSI caching; RAG workflows with CrewAI leveraging GSI; RAG workflows with Jina AI integrated with GSI; and CrewAI-driven short-term memory powered by GSI-accelerated indexes. Major bugs fixed: none reported this period. Overall impact: enabled rapid adoption of vector search capabilities, improved developer onboarding, and provided reproducible, production-ready examples to accelerate customer value. Technologies/skills demonstrated: Couchbase GSI, vector search, Hugging Face embeddings, CrewAI, Jina AI, RAG, caching, and memory architectures; distributed integration patterns. Repo: couchbase-examples/vector-search-cookbook. Commits included four tutorial additions with hashes: 0a6d4cc99f446c0c8d47cdd683dc1b390580a0ef (DA-933: add huggingface gsi tutorial (#5)); de58013a67859550dac8ba99e636a84ce2411db5 (DA-1067: Add gsi tutorial for CrewAI (#6)); 498500dee542d857714116767a15eba24fb54c1d (DA-1070: Add gsi tutorial for jina (#8)); 96fac41b65fd9a534eec56027a806204c8092587 (DA-1068: Add gsi tutorial for crewai short term mem (#7)).
4 Commits • 1 Features
Oct 1, 2025Monthly summary for 2025-10: Key features delivered include the GSI Vector Search Tutorial Series in couchbase-examples/vector-search-cookbook. This set of tutorials demonstrates end-to-end vector search workflows using Couchbase Global Secondary Index (GSI), including semantic search with Hugging Face embeddings and GSI caching; RAG workflows with CrewAI leveraging GSI; RAG workflows with Jina AI integrated with GSI; and CrewAI-driven short-term memory powered by GSI-accelerated indexes. Major bugs fixed: none reported this period. Overall impact: enabled rapid adoption of vector search capabilities, improved developer onboarding, and provided reproducible, production-ready examples to accelerate customer value. Technologies/skills demonstrated: Couchbase GSI, vector search, Hugging Face embeddings, CrewAI, Jina AI, RAG, caching, and memory architectures; distributed integration patterns. Repo: couchbase-examples/vector-search-cookbook. Commits included four tutorial additions with hashes: 0a6d4cc99f446c0c8d47cdd683dc1b390580a0ef (DA-933: add huggingface gsi tutorial (#5)); de58013a67859550dac8ba99e636a84ce2411db5 (DA-1067: Add gsi tutorial for CrewAI (#6)); 498500dee542d857714116767a15eba24fb54c1d (DA-1070: Add gsi tutorial for jina (#8)); 96fac41b65fd9a534eec56027a806204c8092587 (DA-1068: Add gsi tutorial for crewai short term mem (#7)).
October 2025
Activity
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Quality Metrics
Correctness100.0%
Maintainability95.0%
Architecture100.0%
Performance100.0%
AI Usage40.0%
Skills & Technologies
Programming Languages
Jupyter NotebookMarkdownPython
Technical Skills
AI Agent MemoryAI IntegrationCouchbaseCrewAIDatabase ManagementEmbeddingsFull Stack DevelopmentGSIHugging FaceJina AIJupyter NotebookJupyter NotebooksLLM IntegrationLangChainOpenAI Embeddings
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