Tax Code Knowledgebase Assistant in n8n with Qdrant & AI

Turn government tax policy into a smarter, section-aware chatbot in n8n

This n8n automation builds a Tax Code Knowledgebase Assistant that ingests a government tax code document into Qdrant with rich chapter/section metadata—so your AI answers stay grounded in the right parts of the text, not just arbitrary content chunks.

What this workflow does

• Downloads and extracts source content: The tax code policy document is downloaded as a zip file from the government website, then its pages are extracted into separate chapters.
• Splits chapters into sections (context-preserving): Instead of splitting by content length, the workflow parses each chapter and uses data manipulation to divide it into sections, preserving the structure users expect.
• Indexes sections in Qdrant with metadata: Each section is inserted into the Qdrant vector store and tagged with source, chapter number, and section number as metadata for scoped retrieval.
• Retrieves with targeted Qdrant queries: When the AI Agent needs information, the workflow uses a custom workflow tool to query Qdrant—supporting metadata-aware search instead of broad similarity only.
• Outputs structured answers tied to the code: For example, a user asking about a topic like cargo can receive a response referencing the relevant chapter/section (e.g., “Section 11.25…”).

Use cases

• Tax code Q&A assistants for SaaS products serving businesses or compliance teams.
• Automation engineers looking for a better ingestion strategy than naive chunking (chapter/section-aware indexing).
• Scoped legal/policy lookups where answers must reference the correct section of a government document.

Technical details

• n8n nodes/workflow logic: set, wait, filter, switch, split out, sticky note.
• Vector store: Qdrant with metadata fields for source, chapter, and section numbers.
• Retrieval approach: custom workflow tool to perform metadata-aware Qdrant queries.