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Build an AI Legal Assistant

Build an AI Legal Assistant

AI workflow automation isn’t just about internal task efficiency—it’s about building smart assistants that solve real-world problems. In this use case, we explore how to create an AI legal assistant. 

The tool answers questions about the Texas Tax Code using agentic workflows, vector databases, and AI-powered tools like Qdrant, Mistral.ai, and OpenAI—all orchestrated through n8n, a no-code automation platform.

Use Case AI Legal Assistant Overview: What You’re Building

You’re building an AI-powered chatbot assistant that:

  • Downloads and parses Texas tax code PDFs
  • Splits legal content into searchable sections
  • Embeds each section into a vector database (Qdrant)
  • Lets users ask tax-related questions
  • Returns accurate answers with chapter and section references

Step-by-Step: AI Workflow Automation for Legal Use Case

Step 1: Kick Off the Workflow (Manual Trigger)

This ensures full control when you want to re-index tax code files or update the assistant.

Node: Manual Trigger

Step 2: Download & Extract Tax Code Files

Use an HTTP request node to download a ZIP bundle of official tax code PDFs from the Texas government website. Extract the ZIP contents using a compression node in n8n.

Why This Matters: Legal documents change. This AI Legal Assistant step ensures your assistant always uses the latest version.

Step 3: Read & Parse PDF Content by Chapter/Section

Instead of treating each PDF as a big text blob, you use regex-powered text processing to:

  • Extract individual chapters and sections
  • Assign titles, section numbers, and structured metadata

This is a key differentiator in intelligent AI automation—data is organized in a way AI can understand and retrieve effectively.

AI Workflow Tool Used: Extract From File + Set + Regex parsing logic
Benefit: Clean data, smart structure, precise results.

Step 4: Chunk Content for AI-Friendly Processing

Next, the content is split into manageable “chunks” (~50,000 characters each) to avoid token overflows when generating vector embeddings.

Node: Set + SplitOut


Why: AI models and vector tools have input size limits. This chunking step keeps processing efficient and reliable.

Step 5: Generate Embeddings Using Mistral.ai

Now that your content is structured and chunked, each section is sent to Mistral.ai to generate vector embeddings.

These are numeric representations that capture the meaning of the content—essential for semantic search.

Tool: Mistral Embed API
Result: A smarter, searchable version of your legal content.

Step 6: Store Embeddings in Qdrant Vector Database

With the data embedded, it’s stored in Qdrant, an open-source vector store. Each record includes:

  • The section text
  • Metadata (chapter, title, section number, etc.)
  • Embedding vector for fast search

AI Workflow Component: Qdrant Vector Store
Collection: texas_tax_codes

This makes your entire legal dataset AI-searchable in milliseconds.

Step 7: Add AI Agent as the Legal Assistant

You introduce an AI agent to act as a chatbot assistant.

Step 8: Add Smart Tools – Ask Tool & Search Tool

You equip your AI agent with two specialized tools:

Ask Tool

  • Sends queries to Mistral → retrieves relevant vectors from Qdrant
  • Returns summarized answers + metadata

Search Tool

  • Finds specific sections by chapter/section
  • Uses Qdrant Scroll API for full document recall

Tool Nodes: ToolWorkflow nodes in n8n
Intelligent Routing: Handled by a Switch node that chooses the right tool based on user input

Step 9: Handle User Chat Messages

Using the ChatTrigger and AI AI Legal Assistant Agent, the system now supports:

  • Open-ended questions (e.g., “What’s the tax rule on real estate?”)
  • Direct retrieval (e.g., “Show me Section 1.02”)
  • Multi-turn conversations with memory

  • *Note: For the JSON template, please contact us and provide the blog URL.

Final Step: Test, Iterate, Improve

Like all AI workflow automation tools, success depends on:

  • Monitoring performance
  • Testing user inputs
  • Iteratively fine-tuning your agent’s prompts and logic

Key Capabilities of AI Legal Assistant:

  • Uses OpenAI to generate natural answers
  • Can route tasks to tools like:
    • Ask Tool (semantic Q&A)
    • Search Tool (direct section retrieval)
  • Includes conversational memory for coherent replies

System Prompt Example:

You are a helpful assistant answering user questions on the tax code legislation for the state of Texas. Always cite the chapter and section.

This is AI-powered automation with contextual memory—critical for user satisfaction.

Benefits of This AI-Powered Legal Workflow

It organizes tax data by section, title, and metadata, making it faster and easier to access the right information in seconds—not hours. Best of all, the workflow is scalable and reusable—simply swap in new tax code PDFs or apply it across different jurisdictions for seamless adaptation.

Learn More

Final Thoughts

This is a perfect example of how AI can streamline work processes. You aren’t just automating tasks; you’re creating a clever, adaptable, and useful solution that makes a tangible difference. 

From legal research to content searches and compliance checks, these AI Legal Assistant agentic workflows offer a competitive advantage in modern automation. 

FAQs

How is this different from a traditional search system?

Traditional systems use keyword matching. This one AI Legal Assistant uses semantic search via vector embeddings and AI agents that understand context.

Do I need coding skills?

No! This is built with no-code tools like n8n, so anyone can use it.

Can I use this for other legal domains?

Absolutely. Just change the documents and adjust the prompt. The AI workflow pattern remains the same.

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