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GenAI / LLM · Intermediate

Hybrid keyword + vector search

Combine lexical matching and semantic retrieval to build a search layer that performs better than either approach on its own.

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Project blueprint

What you'll build

Keyword search is precise when the query contains the right words. Vector search is useful when the user describes an idea differently from the source. This project combines both signals into a retrieval layer built for real queries.

You will inspect ranked results, tune the blend, and create a retrieval interface ready to feed a generation step.

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Included build referenceBuild reference — BM25, vector search, and ranked fusion
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Project overview

Understand the build here. Learn and access the assets in the LMS.

This page covers what the project does, what you need, and how the build comes together. The LMS contains the complete lessons, source code, circuit diagrams, and deployment materials.

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Before you start

Build requirements

01Basic Python familiarity
02A local vector store or database
03A small indexed document collection
From setup to working system

Build sequence

Follow the steps in order, verify each checkpoint, and finish with a working project you can explain and extend.

01

Build lexical search

Index documents with keyword relevance scoring and inspect the result ranking.

02

Add semantic retrieval

Generate embeddings and retrieve documents by vector similarity.

03

Fuse the result sets

Combine both ranking signals into a single retrieval response.

04

Evaluate realistic queries

Compare retrieval quality across exact, semantic, and mixed queries.

Build the next checkpoint

Stop collecting tutorials. Ship a working build.

Use this project as a concrete piece of your embedded, IoT, or edge AI portfolio.

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