Create a tax law assistant using Qdrant, .AI, and OpenAI
Workflow Description
An intelligent tax law assistant combining Qdrant's vector search capabilities with OpenAI's language processing to deliver accurate, context-aware answers to tax inquiries. Users ask questions about tax regulations, compliance, and planning through a conversational interface backed by a comprehensive legal knowledge base.
How it works
- 1.Load and segment tax documents and regulations into manageable chunks
- 2.Convert text segments into semantic embeddings using AI models
- 3.Store embeddings in Qdrant for fast similarity-based retrieval
- 4.Receive user questions through the chat interface and process them
- 5.Search Qdrant for relevant legal documents matching the query
- 6.Generate detailed answers using OpenAI based on retrieved context
Use cases
- Providing tax compliance guidance for businesses and individuals on deductions and filing requirements
- Answering complex tax law questions with direct references to source materials and regulations
Requirements
- Updated and authoritative tax law documents and regulatory materials
- Active OpenAI API account with access to advanced language models
- Deployed and properly configured Qdrant vector database instance
Service Value
Ideal as a smart automation service combining integrations and AI to produce ready-to-use results.
Apps Used
Details
How to Use
- 1.Click "Download Template"
- 2.Open your n8n dashboard
- 3.Go to Workflows > Import from File
- 4.Select downloaded file and configure credentials
Nodes Used (38)
When clicking ‘Test workflow’
Manual Trigger
Embeddings Mistral Cloud
Embeddings Mistral Cloud
Default Data Loader
Document Default Data Loader
Recursive Character Text Splitter
Text Splitter Recursive Character Text Splitter
Get Tax Code Zip File
HTTP Request
Extract Zip Files
Compression
Files as Items
Split Out
Extract PDF Contents
Extract From File
Extract From Chapter
Set
Map To Sections
Set
Execute Workflow Trigger
Execute Workflow Trigger
Get Mistral Embeddings
HTTP Request
Content Chunking @ 50k Chars
Set
Split Out Chunks
Split Out
For Each Section...
Split In Batches
Sections To List
Split Out
Only Valid Sections
Filter
Use Qdrant Search API1
HTTP Request
Use Qdrant Scroll API
HTTP Request
Get Search Response
Set
Sticky Note
Sticky Note
Sticky Note1
Sticky Note
Sticky Note2
Sticky Note
Qdrant Vector Store
Vector Store Qdrant
Sticky Note3
Sticky Note
Sticky Note4
Sticky Note
AI Agent
Agent
Window Buffer Memory
Memory Buffer Window
When chat message received
Chat Trigger
Window Buffer Memory1
Memory Buffer Window
OpenAI Chat Model
OpenAI
1sec
Wait
Ask Tool
Tool Workflow
Search Tool
Tool Workflow
Switch
Switch
Get Ask Response
Set
Sticky Note5
Sticky Note
Sticky Note6
Sticky Note