Tax Code Assistant with Qdrant, Mistral.ai & OpenAI

This workflow builds a sophisticated Retrieval-Augmented Generation (RAG) system tailored for querying complex documents like government tax codes. It starts by downloading a ZIP file from a government website, extracting pages as chapters, and parsing them into sections using data manipulation expressions. Each section is embedded and upserted into a Qdrant vector store with rich metadata (source, chapter, section numbers), preserving context that basic chunking often loses.

The AI Agent leverages a custom workflow tool to query Qdrant via its API, enabling advanced filtering and scoped searches. For precise responses, it uses Qdrant's scroll API to retrieve full extracts based on metadata, turning the vector store into a hybrid key-value system. Powered by Mistral.ai and OpenAI, the agent delivers accurate, context-aware answers to queries like tax rules on specific commerce types.

Benefits include superior retrieval accuracy, reduced hallucinations, and scalability for large documents. Use cases span legal assistants, compliance tools, policy chatbots, or any domain needing structured document Q&A, such as HR policies or technical manuals. Ideal for businesses automating knowledge access without losing hierarchical context.