Hands-On AI - Build a RAG Model from Scratch with Open Source
- Category Other
- Type Tutorials
- Language English
- Total size 364.3 MB
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Infohash : DE37A6F3C2CDEED7B258028DF4810D3F4A8FD760
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.
Files:
[ WebToolTip.com ] Hands-On AI - Build a RAG Model from Scratch with Open Source- Get Bonus Downloads Here.url (0.2 KB) ~Get Your Files Here ! 01 - Introduction
- 01 - Introduction to RAG models.mp4 (2.5 MB)
- 01 - Introduction to RAG models.srt (1.3 KB)
- 01 - Running your LLM from open source.mp4 (3.1 MB)
- 01 - Running your LLM from open source.srt (4.3 KB)
- 02 - Collecting data to generate our corpus.mp4 (2.7 MB)
- 02 - Collecting data to generate our corpus.srt (3.4 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 - Setting up a database and retrieving vectors and files.mp4 (5.0 MB)
- 04 - Setting up a database and retrieving vectors and files.srt (4.8 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 - Prompt engineering and packaging pieces together.mp4 (4.6 MB)
- 06 - Prompt engineering and packaging pieces together.srt (5.2 KB)
- 01 - Setting up a dev container.mp4 (15.7 MB)
- 01 - Setting up a dev container.srt (11.3 KB)
- 02 - Setting up environment and installing Ollama.mp4 (11.7 MB)
- 02 - Setting up environment and installing Ollama.srt (7.6 KB)
- 03 - Creating a model file.mp4 (18.3 MB)
- 03 - Creating a model file.srt (11.4 KB)
- 04 - Running Ollama programmatically through Python.mp4 (20.9 MB)
- 04 - Running Ollama programmatically through Python.srt (9.4 KB)
- 05 - Generating the corpus.mp4 (33.3 MB)
- 05 - Generating the corpus.srt (14.0 KB)
- 06 - Extract text from different local file formats with Docling.mp4 (15.0 MB)
- 06 - Extract text from different local file formats with Docling.srt (6.1 KB)
- 01 - Vector embeddings and their implementation.mp4 (5.7 MB)
- 01 - Vector embeddings and their implementation.srt (7.0 KB)
- 02 - Setting up your Postgres vector database.mp4 (24.1 MB)
- 02 - Setting up your Postgres vector database.srt (11.0 KB)
- 03 - Setting up a simple database schema.mp4 (23.6 MB)
- 03 - Setting up a simple database schema.srt (9.6 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 - Retrieving content from your database.mp4 (17.4 MB)
- 05 - Retrieving content from your database.srt (10.0 KB)
- 01 - Overview of the RAG pipeline.mp4 (6.3 MB)
- 01 - Overview of the RAG pipeline.srt (4.1 KB)
- 02 - Preparing context, part 1.mp4 (19.1 MB)
- 02 - Preparing context, part 1.srt (9.0 KB)
- 03 - Preparing context, part 2.mp4 (25.9 MB)
- 03 - Preparing context, part 2.srt (11.0 KB)
- 04 - Prompt engineering.mp4 (20.4 MB)
- 04 - Prompt engineering.srt (9.6 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)
- 01 - What's next.mp4 (2.6 MB)
- 01 - What's next.srt (1.6 KB)
- Bonus Resources.txt (0.1 KB)
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