Build RAG Knowledge Base with Models
Ingest company documents into an embedding-backed RAG store for accurate, cited answers.
30–45 minsIntermediateAgenticHub
Verifiable Outcome
A vectorized knowledge repository ready to be queried with semantic chunking and source citations.
Prerequisites
- 3–5 domain documents in PDF, DOCX, or Markdown format
- Running Embedding model service in your workspace
- A target agent for binding verification
01
Create Knowledge Base & Chunking Strategy
Configure chunk sizes and token overlaps matching your document layout.
02
Upload Documents & Monitor Vectorization
Batch upload source files and track document indexing progress.
03
Test Retrieval Recall in Playground
Run semantic search queries, evaluate similarity scores, and adjust Top-K thresholds.
04
Bind to Agent & Verify End-to-End Chat
Attach the knowledge base to your agent and verify that responses include precise citations.
Once verified, proceed to the next milestone.
Next Steps & Deep Dive
Connect your hands-on achievements back to platform documentation for production best practices.
Read RAG Optimization Guide