Building a Vector Search Engine with ChromaDB | CodeSignal Learn

intermediate

Building a Vector Search Engine with ChromaDB

Natural Language Processing

4 courses

57 practices

8 hours

This path introduces ChromaDB as the backbone for building vector-based search—covering embedding generation, semantic retrieval, and scalable optimization. Learn to create fast, intelligent search systems from the ground up.

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Earn a shareable

Certificate of Achievement

Verified skills you'll gain

INTERMEDIATE

Text Data Collection and Preparation

DEVELOPING

Feature Engineering and Text Representation

INTERMEDIATE

Machine Learning Modeling for NLP

INTERMEDIATE

NLP Model Evaluation and Optimization

Tools you'll use

ChromaDB

Python

Trusted by learners working at top companies

Course 1

Understanding Embeddings and Vector Representations

4 lessons

12 practices

This course introduces vector embeddings, why they are useful for search, and how to generate them using different models like OpenAI and Hugging Face.

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Course 2

Storing, Indexing, and Managing Vector Data with ChromaDB

5 lessons

21 practices

This course focuses entirely on ChromaDB, a lightweight open-source vector database. It covers setting up, storing embeddings, searching efficiently, handling indexing, and managing large-scale vector data.

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Course 3

Implementing Semantic Search with ChromaDB

4 lessons

13 practices

This course focuses on advanced semantic search techniques in ChromaDB, including multi-query expansion, hybrid retrieval (combining different search strategies), reranking techniques, and improving search relevance beyond basic vector similarity.

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Course 4

Optimizing and Scaling ChromaDB for Vector Search

3 lessons

11 practices

This course focuses on scaling ChromaDB for large-scale deployments, improving search efficiency, reducing retrieval latency, and parallelizing queries for real-time performance.

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