Everyone's talking about 'agentic AI' but few show you how to build it.
TL;DR · AI Summary
This video walks through how to build production-grade agentic AI systems using Weaviate's Query Agent and multivector embeddings, especially suitable for legal research.
Key Takeaways
- Weaviate's Query Agent achieves fast retrieval while preserving document layout
- Agentic search mode not only performs keyword matching but also improves accurac
- The video demonstrates the complete implementation process from dataset setup to
Outline
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Introduces the concept of agentic AI and its application potential in legal research.
Details key technical components such as Query Agent and multivector embeddings.
Shows the complete development process from dataset configuration to front-end integration.
Explains how to use ColModernVBERT for efficient document encoding.
Describes the working principles of two search modes: Search Mode and Ask Mode.
Mindmap
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查看大纲文本（无障碍 / 无 JS 友好）
- 代理AI系统构建
- 技术组件
- Query Agent
- 多向量嵌入
- 实现步骤
- 数据集设置
- 前端集成
Highlights
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Instead of OCR and text chunking, the system uses ColModernVBERT with Muvera compression to encode each PDF page directly as visual tokens.
The Query Agent doesn't just do keyword matching. It inspects your schema, constructs structured queries with filters, reranks results for precision, and synthesizes grounded answers with source citat
Victoria walks through the complete implementation: dataset setup, PDF ingestion, collection configuration, Query Agent integration, and frontend interface.
This video walks through the complete architecture: Query Agent reasoning, multivector embeddings, specialized collections, and streaming responses with sources. This is how agentic RAG actually works in https://t.co/fGwpUacyul" / X
Everyone's talking about 'agentic AI' but few show you how to build it. This video walks through the complete architecture: Query Agent reasoning, multivector embeddings, specialized collections, and streaming responses with sources. This is how agentic RAG actually works in production. Legal research demands precision and security and searching across 𝘵𝘰𝘯𝘴 of documents, making it a perfect use case for RAG. But traditionally, building a production-ready legal assistant means months of development time orchestrating retrievers, managing state, and writing complex query logic. In this video walkthrough,
shows you how to build the entire application from scratch using 𝗪𝗲𝗮𝘃𝗶𝗮𝘁𝗲'𝘀 𝗤𝘂𝗲𝗿𝘆 𝗔𝗴𝗲𝗻𝘁 and the CUAD legal contract dataset. This isn't just a simple retrieve-then-generate pipeline. Instead, it implements: 𝗠𝘂𝗹𝘁𝗶𝘃𝗲𝗰𝘁𝗼𝗿 𝗣𝗗𝗙 𝗘𝗺𝗯𝗲𝗱𝗱𝗶𝗻𝗴 Instead of OCR and text chunking, the system uses ColModernVBERT with Muvera compression to encode each PDF page directly as visual tokens. This preserves layout, tables, and formatting while keeping retrieval fast. 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗦𝗲𝗮𝗿𝗰𝗵 The Query Agent doesn't just do keyword matching. It inspects your schema, constructs structured queries with filters, reranks results for precision, and synthesizes grounded answers with source citations. It operates in two modes: - 𝗦𝗲𝗮𝗿𝗰𝗵 𝗠𝗼𝗱𝗲: Retrieves and reranks relevant contract sections for review - 𝗔𝘀𝗸 𝗠𝗼𝗱𝗲: Generates direct answers grounded in retrieved context - streaming responses with cited sources and a full NextJS frontend. Victoria walks through the complete implementation: dataset setup, PDF ingestion, collection configuration, Query Agent integration, and frontend interface. Everything you need to build your own version. Watch the video here: youtu.be/skeKcYbPT9g?si