Build RAG API Service
Build a complete RAG API service with retrieval pipeline, context assembly, LLM integration, and streaming responses.
Lab Overview
🛠Lab from the AI Platform Engineering Bootcamp. Used in Week 3. Bootcamp landing page: https://academy.tekanaid.com/bootcamps/ai-platform-engineering-bootcamp Parent course(s):
- Week 3: RAG Architectures and Vector Databases (slug: rag-architectures-103)
🟡 Beta bootcamp lab. Hands-on instructions, check scripts, and solve scripts are in place. Lab is part of the running Platform Assistant capstone arc that grows across all 8 weeks of the bootcamp.
This hands-on lab teaches you to build a production-ready RAG API service.
You'll learn to:
- Build a retrieval pipeline that finds relevant documents
- Assemble context with source attribution
- Integrate LLM for answer generation
- Implement streaming responses for better UX
- Package the RAG service for Kubernetes (manifest authoring + local test)
This lab creates the core RAG service for your Platform Assistant.
What You'll Learn
Build a retrieval pipeline using ChromaDB vector database
Assemble context from retrieved documents with source attribution
Integrate Ollama LLM for answer generation with streaming
Create a FastAPI application with RAG endpoints
Prerequisites
document-ingestion-pipeline
llm-api-integration
Technologies Covered
Part of a Course
This lab is part of the RAG Architectures and Vector Databases course
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