Knowledge Retrieval Service for Energy Sector Documents on Vertex AI

Added Jun 2025 3 design docs

Energy companies manage some of the densest documentation in industry: grid compliance filings, environmental impact assessments, turbine maintenance manuals, and regulatory correspondence spanning decades. Engineers and analysts lose hours locating provisions inside these archives, and institutional knowledge walks out the door with every retirement. A retrieval system that answers plain-language questions from this corpus, hosted on infrastructure the company already trusts, addresses both problems at once. The intern builds this knowledge retrieval service as a cloud-based application on Google Cloud's Vertex AI. Documents are processed and embedded into a vector index, and a Python pipeline built with LangChain performs retrieval-augmented generation: fetching the passages relevant to a query and composing answers through OpenAI and Vertex AI models with citations back to the source documents. The intern implements the document processing workflow, the retrieval configuration tuned for long technical documents, and the query API through which analysts interact with the system, all deployed and running on GCP services. The project demonstrates cloud-native AI engineering: building RAG on a managed ML platform, handling industrial document collections, and delivering grounded question answering with the citation discipline that regulated sectors require.

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