Domain-specific RAG system for construction documentation with hybrid search, streaming responses, and context-aware chat history management — built on Flask and LangGraph.
Construction teams were spending excessive time manually searching through hundreds of pages of technical documents, compliance reports, and contracts — with no intelligent system to surface precise answers quickly.
Built a LangGraph-orchestrated RAG pipeline on Flask with hybrid retrieval (dense embeddings + BM25), token-streaming for real-time responses, and a context summarization layer to manage long multi-turn conversations without losing coherence.
Developed a domain-specific Retrieval-Augmented Generation (RAG) system tailored for the construction industry, addressing the challenge of extracting precise, contextual answers from large volumes of unstructured technical documentation including blueprint specs, safety manuals, compliance reports, and project contracts.
The platform features a dual-interface architecture: an Admin portal where authorized users upload construction PDFs and documents which are then parsed, chunked, and stored as vector embeddings — and a User-facing query interface where natural language questions are answered by retrieving the most semantically relevant content through a hybrid search strategy combining dense vector similarity with sparse keyword matching.
Built on Flask with LangGraph orchestrating the RAG pipeline, the system delivers token-by-token streaming responses for a fluid conversational experience. A robust chat history management layer handles context summarization and tokenization to maintain coherent multi-turn conversations without exceeding context window limits.
Tangible Impact
Delivered a fully functional knowledge assistant that allows site engineers and project managers to query the entire document base in natural language, with accurate streamed responses and persistent conversational context across sessions.
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