| 01 - Introduction to RAG models.mp4 | 2.5 MB | ||
| 01 - Introduction to RAG models.srt | 1.3 KB | ||
| 01 - Overview of the RAG pipeline.mp4 | 6.3 MB | ||
| 01 - Overview of the RAG pipeline.srt | 4.1 KB | ||
| 01 - Running your LLM from open source.mp4 | 3.1 MB | ||
| 01 - Running your LLM from open source.srt | 4.3 KB | ||
| 01 - Setting up a dev container.mp4 | 15.7 MB | ||
| 01 - Setting up a dev container.srt | 11.3 KB | ||
| 01 - Vector embeddings and their implementation.mp4 | 5.7 MB | ||
| 01 - Vector embeddings and their implementation.srt | 7 KB | ||
| 01 - What's next.mp4 | 2.6 MB | ||
| 01 - What's next.srt | 1.6 KB | ||
| 02 - Collecting data to generate our corpus.mp4 | 2.7 MB | ||
| 02 - Collecting data to generate our corpus.srt | 3.4 KB | ||
| 02 - Preparing context, part 1.mp4 | 19.1 MB | ||
| 02 - Preparing context, part 1.srt | 9 KB | ||
| 02 - Setting up environment and installing Ollama.mp4 | 11.7 MB | ||
| 02 - Setting up environment and installing Ollama.srt | 7.6 KB | ||
| 02 - Setting up your Postgres vector database.mp4 | 24.1 MB | ||
| 02 - Setting up your Postgres vector database.srt | 11 KB | ||
| 03 - Creating a model file.mp4 | 18.3 MB | ||
| 03 - Creating a model file.srt | 11.4 KB | ||
| 03 - Preparing context, part 2.mp4 | 25.9 MB | ||
| 03 - Preparing context, part 2.srt | 11 KB | ||
| 03 - Setting up a simple database schema.mp4 | 23.6 MB | ||
| 03 - Setting up a simple database schema.srt | 9.6 KB | ||
| 03 - What are vector embeddings, and how are they generated.mp4 | 4.8 MB | ||
| 03 - What are vector embeddings, and how are they generated.srt | 5.6 KB | ||
| 04 - Prompt engineering.mp4 | 20.4 MB | ||
| 04 - Prompt engineering.srt | 9.6 KB | ||
| 04 - Running Ollama programmatically through Python.mp4 | 20.9 MB | ||
| 04 - Running Ollama programmatically through Python.srt | 9.4 KB | ||
| 04 - Setting up a database and retrieving vectors and files.mp4 | 5 MB | ||
| 04 - Setting up a database and retrieving vectors and files.srt | 4.8 KB | ||
| 04 - Uploading vectors, text, and filenames to the database.mp4 | 51.2 MB | ||
| 04 - Uploading vectors, text, and filenames to the database.srt | 23.4 KB | ||
| 05 - Generating the corpus.mp4 | 33.3 MB | ||
| 05 - Generating the corpus.srt | 14 KB | ||
| 05 - Putting it all together to generate a working RAG model.mp4 | 25.1 MB | ||
| 05 - Putting it all together to generate a working RAG model.srt | 10.1 KB | ||
| 05 - Retrieving content from your database.mp4 | 17.4 MB | ||
| 05 - Retrieving content from your database.srt | 10 KB | ||
| 05 - Vectorizing a query and finding relevant text.mp4 | 5.3 MB | ||
| 05 - Vectorizing a query and finding relevant text.srt | 4.6 KB | ||
| 06 - Extract text from different local file formats with Docling.mp4 | 15 MB | ||
| 06 - Extract text from different local file formats with Docling.srt | 6.1 KB | ||
| 06 - Prompt engineering and packaging pieces together.mp4 | 4.6 MB | ||
| 06 - Prompt engineering and packaging pieces together.srt | 5.2 KB | ||
| Bonus Resources.txt | 102.4 B | ||
| Get Bonus Downloads Here.url | 204.8 B | ||
| ▲ 50 total files | |||
Hands-On AI: Build a RAG Model from Scratch with Open Source
https://WebToolTip.com
Released 10/2025
With Dr. Alaa Moussawi
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Skill level: Advanced | Genre: eLearning | Language: English + subtitle | Duration: 2h 21m 11s | Size: 364 MB
Dive into Retrieval-Augmented Generation (RAG) models and learn how to build your own efficient, contextually aware AI bots using vector databases and open-source tools.
Course details
In this hands-on course, physicist and educator Dr. Alaa Moussawi guides you toward mastery of Retrieval-Augmented Generation (RAG) models. Learn to construct your own custom AI bots by integrating vector databases with language models for contextually accurate responses. Explore essential concepts such as vector embeddings, query processing, and prompt engineering. Learn how to optimize your resources by running lightweight models efficiently, even on limited hardware. Find out how to generate effective vector embeddings and extract relevant information from diverse data sources. Benefit from the flexibility of open-source software as you adapt the models to specific domains or styles, while customizing the knowledge base of your bot. Keep innovating and pushing boundaries with retrieval-augmented intelligence and join the vibrant open-source community in this exploratory learning adventure.
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