Developed and enhanced the couchbase-examples/vector-search-cookbook repository over two months, focusing on retrieval-augmented generation (RAG) pipelines and semantic search capabilities. Delivered end-to-end tutorials integrating Couchbase with Azure OpenAI, Cohere, Claude, and Deepseek, demonstrating both full-text search and GSI-based indexing strategies for scalable, flexible search. Refactored existing code to support alternative indexing paths, improved caching for performance, and standardized notebook outputs for clarity. Leveraged Python, LangChain, and AWS Bedrock to implement robust backend solutions, emphasizing maintainability and developer experience. The work enabled users to build, configure, and optimize RAG workflows with multiple LLM providers and vector database integrations.
Delivery of end-to-end RAG tutorials across the Couchbase vector-search-cookbook, featuring Azure OpenAI, Cohere, Claude, and Deepseek integrations, plus semantic search refinements with Bedrock. Focused on business value: quick-built retrieval-augmented pipelines, scalable search with GSI, faster performance through caching, and robust data handling. Notebook polish and logging improvements to improve developer experience and reduce noise in production runs.
Delivery of end-to-end RAG tutorials across the Couchbase vector-search-cookbook, featuring Azure OpenAI, Cohere, Claude, and Deepseek integrations, plus semantic search refinements with Bedrock. Focused on business value: quick-built retrieval-augmented pipelines, scalable search with GSI, faster performance through caching, and robust data handling. Notebook polish and logging improvements to improve developer experience and reduce noise in production runs.
Month: 2025-08. The Vector Search Cookbook project delivered a Global Secondary Index (GSI) semantic search capability for the AWS Bedrock example, adding GSI-based indexing for semantic search and providing a clear GSI path as an alternative to the existing FTS approach. This included updates to tutorials and routing to differentiate FTS vs GSI for RAG with Couchbase and Bedrock. No major bugs fixed this month. Overall, the work increases search flexibility, performance, and maintainability, enabling customers to choose indexing strategies that fit data scale and performance needs. Technologies demonstrated include AWS Bedrock, GSI, semantic search, RAG, Couchbase, and FTS-based refactoring.
Month: 2025-08. The Vector Search Cookbook project delivered a Global Secondary Index (GSI) semantic search capability for the AWS Bedrock example, adding GSI-based indexing for semantic search and providing a clear GSI path as an alternative to the existing FTS approach. This included updates to tutorials and routing to differentiate FTS vs GSI for RAG with Couchbase and Bedrock. No major bugs fixed this month. Overall, the work increases search flexibility, performance, and maintainability, enabling customers to choose indexing strategies that fit data scale and performance needs. Technologies demonstrated include AWS Bedrock, GSI, semantic search, RAG, Couchbase, and FTS-based refactoring.
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