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Building Applications with Vector Databases

Vector databases use embeddings to capture the meaning of data, gauge the similarity between different pairs of vectors, and navigate large datasets to identify the most similar vectors. In the context of large language models, the primary use of vector databases is retrieval augmented generation (RAG), where text embeddings are stored and retrieved for specific queries.

However, the versatility of vector databases extends beyond RAG and makes it possible to build a wide range of applications quickly with minimal coding.

In this course, you’ll explore the implementation of six applications using vector databases:

1. Semantic Search: Create a search tool that goes beyond keyword matching, focusing on the meaning of content for efficient text-based searches on a user Q/A dataset.

2. RAG: Enhance your LLM applications by incorporating content from sources the model wasn’t trained on, like answering questions using the Wikipedia dataset.

3. Recommender System: Develop a system that combines semantic search and RAG to recommend topics, and demonstrate it with a news article dataset.

4. Hybrid Search: Build an application that finds items using both images and descriptive text, using an eCommerce dataset as an example.

5. Facial Similarity: Create an app to compare facial features, using a database of public figures to determine the likeness between them.

6. Anomaly Detection: Learn how to build an anomaly detection app that identifies unusual patterns in network communication logs.

After taking this course, you’ll be equipped with new ideas for building applications with any vector database.

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What's inside

Syllabus

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The course provides a comprehensive dive into the implementation of vector databases through hands-on projects

Taught by Tim Tully, a recognized expert in the field of vector databases and AI

Develops skills in building applications with vector databases, which is highly relevant in industry

Offers a wide range of applications, from semantic search to facial similarity, demonstrating the versatility of vector databases

May require some prior knowledge of vector databases and natural language processing

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Reviews summary

Building practical applications with vector databases

According to learners, this course offers a highly practical and hands-on approach to leveraging vector databases. Students praise the diverse range of applications, including RAG, semantic search, and recommender systems, highlighting their direct applicability to real-world scenarios. While the course excels at demonstrating how to build with vector databases, some feedback suggests that it assumes prior knowledge of embeddings and LLMs and could benefit from deeper dives into underlying theory and optimization techniques. The overall sentiment is largely positive, particularly for those seeking quick implementation skills.

Well-structured labs and clear explanations aid learning.

"The labs are well-structured and help solidify understanding."

"The instructors explain complex concepts clearly, and the guided projects are fantastic."

"It was easy to follow along, even for someone relatively new to the specific topic but with a general ML background. The labs are well-maintained."

Emphasizes hands-on building of diverse vector database applications.

"This course is incredibly practical and provides hands-on experience building various applications with vector databases."

"The focus on real-world applications is a huge plus. I learned how to implement a hybrid search system which I can directly apply to my work."

"Highly recommend for anyone wanting to get hands-on with vector databases. The practical applications are very well chosen and demonstrate the power of these tools."

Some application sections could benefit from more detailed coverage.

"Some parts felt a bit rushed, especially the anomaly detection section, and I wish there was more depth on optimizing vector search..."

"I felt some sections could have been expanded, especially regarding performance considerations for production systems."

"The code examples are functional, but sometimes the explanations behind the choices were brief."

Focuses on implementation and demos rather than deep theoretical understanding.

"The course has good intentions, but it feels more like a series of demos rather than a deep dive."

"I found myself needing to look up external resources for better understanding the underlying mechanics of vector databases themselves."

"While the applications demonstrated are interesting, the course lacks fundamental theory... It's mostly about running pre-written code."

Requires prior understanding of embeddings and LLMs for optimal learning.

"It assumes a lot of prior knowledge of embeddings and advanced NLP concepts. For true beginners, this course might be challenging."

"I think it's best for those who already have a basic understanding of embeddings and LLMs."

"Prerequisites are important, basic Python and ML knowledge helps."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Building Applications with Vector Databases with these activities:

Review Linear Algebra

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Strengthen your mathematical foundation by reviewing the fundamentals of linear algebra, providing you with a stronger understanding of the concepts behind vector databases.

Browse courses on Linear Algebra

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Review the concepts of vector spaces and subspaces.
Practice operations on matrices and vectors.

Compile a List of Vector Database Resources

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Expand your knowledge by compiling a comprehensive list of vector database resources, including tutorials, documentation, and open-source projects.

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Search for vector database resources online.
Review and evaluate the resources.
Organize the resources into a structured format.

Practice Retrieval Augmented Generation

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Build proficiency in RAG by practicing retrieval techniques, helping you create more effective applications that leverage external knowledge.

Browse courses on Retrieval Augmented Generation (RAG)

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Identify queries and gather relevant documents.
Implement a retrieval algorithm using a vector database.
Evaluate the performance of your retrieval model.

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Practice Semantic Search

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Reinforce your understanding of semantic search by completing practice exercises aimed at testing your ability to create efficient search tools.

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Create a dataset of questions and answers.
Implement a semantic search algorithm using a vector database.
Evaluate the performance of your search tool.

Build an Anomaly Detection App

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Solidify your understanding of anomaly detection by building an app, allowing you to identify unusual patterns and potential threats in network communication logs.

Browse courses on Anomaly Detection

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Collect network communication logs.
Extract features from the logs.
Train an anomaly detection model using a vector database.
Develop a user interface for your app.

Build a RAG Application

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Deepen your understanding of RAG by implementing your own RAG application, enhancing your LLM applications with external knowledge.

Browse courses on Retrieval Augmented Generation (RAG)

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Choose a topic and gather relevant text data.
Train a text embedding model on the data.
Create a RAG index using the trained model.
Develop a user interface for your RAG application.

Build a Facial Similarity App

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Enhance your understanding of computer vision by building a facial similarity app, allowing you to compare facial features and determine the likeness between individuals.

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Gather a dataset of facial images.
Extract facial features using a pre-trained model.
Create a vector database to store the extracted features.
Develop a user interface for your app.

Develop a Hybrid Search System

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Integrate your knowledge of vector databases and text-based search by creating a hybrid search system, enabling users to search for items using both images and descriptive text.

Browse courses on Hybrid Search

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Gather a dataset of images and text descriptions.
Create a feature extraction pipeline for images and text.
Train a model to combine image and text features.
Develop a user interface for your hybrid search system.

Career center

Learners who complete Building Applications with Vector Databases will develop knowledge and skills that may be useful to these careers:

Reading list

We've selected 50 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Building Applications with Vector Databases.

Search Engines

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Provides a vision of the future of semantic search. It is written by Bruce Croft, a leading researcher in the field.

Building Applications with Vector Databases

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Reviews summary

Building practical applications with vector databases

Activities

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