Vector Database and Document Retrieval

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prerequisites
intermediate Python • basics of embeddings and similarity • basics of APIs and databases • optional basic Docker
skills learned
Qdrant setup • semantic chunking with LangChain • embedding generation with sentence-transformers • semantic search with metadata filtering • packaging a reusable `retrieve(query, k)` API
Matteus Tanha
1 week · 5-8 hours per week · INTERMEDIATE
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Look inside

Step into the role of an AI engineer building a semantic search system for a compliance team drowning in regulatory text. Working with the EU AI Act, a dense, hundred-page policy document, you’ll create a retrieval pipeline that lets analysts ask plain-language questions and instantly surface the most relevant passages by meaning, not just keyword match. You’ll set up a vector database, chunk the text in ways that preserve legal context, generate and store embeddings, and implement metadata-filtered retrieval. Along the way, you’ll validate chunk quality and package your retrieval logic into reusable functions.

This project is a part of the series Building an Agentic RAG Application.
This project is designed for learning purposes and is not a complete, production-ready application or solution.

prerequisites

This liveProject is for intermediate Python programmers who want to build production-ready Retrieval-Augmented Generation (RAG) systems using vector databases and semantic search.


TOOLS
  • Python (intermediate)
  • Jupyter Notebooks (basics)
  • Command line / Terminal (basics)
TECHNIQUES
  • Machine Learning Fundamentals
  • Data Processing
  • API Usage
  • Database Concepts

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