Overview
Built a production-grade hybrid intelligence system that lets non-technical users interact with both structured inventory data and unstructured corporate policy documents using plain natural language. The system intelligently routes each query to the right data source — SQL for analytical questions, RAG for policy lookups — and handles hybrid queries that need both.
GitHub: saideepa05/Enterprise_inventory
What I Built
- Hybrid query orchestration engine — a smart routing layer built with LangChain LCEL that detects query intent and dispatches to either the SQL module, the RAG pipeline, or both simultaneously for hybrid queries
- SQL intelligence module — natural language interface over a 50,000+ record diamond inventory SQLite database, supporting complex analytical queries like “What is the average price of Ideal cut diamonds?”
- Document intelligence (RAG pipeline) — FAISS vector store with semantic search over corporate policy documents (grading policies, return procedures, approval workflows), enabling queries like “What is the restocking fee for high-end returns?”
- ETL & data pipeline — automated data ingestion from CSV, database initialization scripts, and document chunking/embedding pipeline from raw text to indexed vector store
- Streamlit dashboard — professional UI with glassmorphic design, unifying both data sources in a single responsive interface accessible to non-technical stakeholders
Tech Stack
Python · LangChain (LCEL) · OpenAI API · FAISS · SQLite · Streamlit · Pandas · Embedding Models
Impact
Unified two fundamentally different data sources — a relational inventory database and unstructured policy documents — behind a single natural language interface. Non-technical users can now run queries that previously required a developer or data analyst, including hybrid questions that cross both structured and unstructured data in a single response.