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Alvin Rachmat

Machine Learning Engineer / Data Scientist

Indonesia

Alvin has 5+ years of experience in data & AI. He has worked in startups (Chatbot & Retrieval Engine, Crypto AI), IT research labs, & corporate environments (Financial Sector), giving him a broad perspective on data team operations across various environments & fields. His stack spans from Data Science (PyTorch, Scikit-learn, MongoDB, SQL), LLM frameworks (LangChain, LlamaIndex, LangGraph) to daily cloud operations and analytical dashboards. Although experienced with diverse tools, he believes that technology stacks are merely instruments to solve real problems. He now works at Artefact, one of the world's leading data science consulting firms.

Skills

AI AgentsLLMGenerative AICloud (GCP, Azure)DockerFinancial Data SciencePandasScikit LearnFastAPINodeJSLangChainLangGraphHuggingFaceElasticSearchMilvusChromaDBStreamlitAutogenFaceswap

Contact

alvinracht@gmail.comLinkedInGitHub
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Building Hybrid Search with ElasticSearch & Milvus
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August 28, 2025

Building Hybrid Search with ElasticSearch & Milvus

AI/MLSearchVector DB

In today's AI-driven world, building effective search systems requires combining the best of traditional keyword search with modern vector similarity search. This comprehensive guide explores how to implement a hybrid search system using ElasticSearch, Milvus, and ChromaDB.

Understanding Hybrid Search

Hybrid search combines lexical search (keyword matching) with semantic search (vector similarity) to provide more accurate and contextually relevant results. This approach is particularly powerful for AI applications where understanding user intent is crucial. Basically, the principle is:

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