Uploaded April 2026 | Updated September 2026, 51 minutes ago
Legal research is brutally complex by design. Finding a specific clause in thousands of contracts requires extreme precision - you can't just retrieve semantically similar text from 2022 when the user asked about 2024 agreements.
Traditionally, building a production-ready legal assistant takes months. You have to orchestrate retrievers, manage conversation state, write complex query logic. It's a massive undertaking.
But when our finance team asked us to build them something to navigate internal contracts, we used the 𝗤𝘂𝗲𝗿𝘆 𝗔𝗴𝗲𝗻𝘁 to build a production-ready application in 36 hours.
Then with 𝗪𝗲𝗮𝘃𝗶𝗮𝘁𝗲 𝗔𝗴𝗲𝗻𝘁 𝗦𝗸𝗶𝗹𝗹𝘀, we made it possible to build the entire thing with a single prompt in Claude Code or Cursor.
In the video, I walk through building it from scratch. The coding agent handles everything:
• Downloads and processes the CUAD legal contract dataset
• Creates three collections with proper schema (Commercial, Corporate & IP, Operational)
• Embeds PDFs with ColQwen multivector model + Muvera compression
• Sets up the Query Agent with ask mode
• Builds the FastAPI backend with async client and dependency injection
• Creates the Next.js frontend with streaming and source display
The whole build takes roughly 12 minutes and about 20k tokens on Sonnet 4.6.
Blog post with full details: weaviate.io/blog/legal-rag-app?utm_source=youtube&utm_medium=vs_video&utm_campaign=paralegal&utm_content=680839433
00:00 - Intro
00:30 - What's hard about legal RAG?
01:30 - The Query Agent
02:18 - Overview: App Architecture
03:30 - Tutorial: Setup
03:58 - The Prompt
04:49 - Tutorial: The Process
05:15 - Tutorial: The Results
05:40 - Conclusion
▬▬▬▬▬▬▬▬▬▬▬▬ CONNECT WITH US ▬▬▬▬▬▬▬▬▬▬▬▬
- Visit weaviate.io
- Star us on GitHub github.com/weaviate/weaviate
- Stay updated and subscribe to our newsletter: newsletter.weaviate.io
- Try out Weaviate Cloud for free here: https://console.weaviate.cloud/
Got a question?
- Forum: forum.weaviate.io
Connect with us on
- Twitter: twitter.com/weaviate_io
- LinkedIn: linkedin.com/company/weaviate-io
Legal research is brutally complex by design. Finding a specific clause in thousands of contracts requires extreme precision - you can't just retrieve semantically similar text from 2022 when the user asked about 2024 agreements.
Traditionally, building a production-ready legal assistant takes months. You have to orchestrate retrievers, manage conversation state, write complex query logic. It's a massive undertaking.
But when our finance team asked us to build them something to navigate internal contracts, we used the 𝗤𝘂𝗲𝗿𝘆 𝗔𝗴𝗲𝗻𝘁 to build a production-ready application in 36 hours.
Then with 𝗪𝗲𝗮𝘃𝗶𝗮𝘁𝗲 𝗔𝗴𝗲𝗻𝘁 𝗦𝗸𝗶𝗹𝗹𝘀, we made it possible to build the entire thing with a single prompt in Claude Code or Cursor.
In the video, I walk through building it from scratch. The coding agent handles everything:
• Downloads and processes the CUAD legal contract dataset
• Creates three collections with proper schema (Commercial, Corporate & IP, Operational)
• Embeds PDFs with ColQwen multivector model + Muvera compression
• Sets up the Query Agent with ask mode
• Builds the FastAPI backend with async client and dependency injection
• Creates the Next.js frontend with streaming and source display
The whole build takes roughly 12 minutes and about 20k tokens on Sonnet 4.6.
Blog post with full details: weaviate.io/blog/legal-rag-app?utm_source=youtube&utm_medium=vs_video&utm_campaign=paralegal&utm_content=680839433
00:00 - Intro
00:30 - What's hard about legal RAG?
01:30 - The Query Agent
02:18 - Overview: App Architecture
03:30 - Tutorial: Setup
03:58 - The Prompt
04:49 - Tutorial: The Process
05:15 - Tutorial: The Results
05:40 - Conclusion
▬▬▬▬▬▬▬▬▬▬▬▬ CONNECT WITH US ▬▬▬▬▬▬▬▬▬▬▬▬
- Visit weaviate.io
- Star us on GitHub github.com/weaviate/weaviate
- Stay updated and subscribe to our newsletter: newsletter.weaviate.io
- Try out Weaviate Cloud for free here: https://console.weaviate.cloud/
Got a question?
- Forum: forum.weaviate.io
Connect with us on
- Twitter: twitter.com/weaviate_io
- LinkedIn: linkedin.com/company/weaviate-io
