This lab is currently in Beta, content may be updated as we refine the material
LABINTERMEDIATE

Document Ingestion Pipeline

Build a document ingestion pipeline for Markdown and PDF documents with chunking strategies, metadata extraction, and vector storage.

60 minutes
ai-infrastructure/rag
Document Ingestion Pipeline - Platform Engineering Hands-On Lab Icon

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 complete document ingestion pipeline.

You'll learn to:

  • Create document loaders for Markdown and text files
  • Implement recursive and markdown-aware chunking strategies
  • Extract metadata from document structure
  • Embed and store chunks in Chroma vector database

This lab connects to the vector database you deployed previously.

What You'll Learn

Build document loaders that read and normalize Markdown and plain-text source files

Apply recursive and markdown-aware chunking strategies to balance context and retrieval precision

Extract structural metadata from document headers and frontmatter for filtered retrieval

Generate embeddings and store document chunks in a Chroma vector database

Query the populated vector store to verify ingestion quality and retrieval accuracy

Prerequisites

vector-database-deployment

chunking-strategies-lesson

Technologies Covered

document-processingchunkingembeddingschromapythonrag

Part of a Course

This lab is part of the RAG Architectures and Vector Databases course

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