[ WebToolTip.com ] Hands-On AI - Build a RAG Model from Scratch with Open Source
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- ~Get Your Files Here !/04 - 3. Setting Up a Database and Retrieving Vectors and Files/04 - Uploading vectors, text, and filenames to the database.mp4 51.2 MB
- ~Get Your Files Here !/03 - 2. Preparing Your LLM and Data/05 - Generating the corpus.mp4 33.3 MB
- ~Get Your Files Here !/05 - 4. Packaging Parts, Pipeline Engineering, and Prompt Engineering/03 - Preparing context, part 2.mp4 25.9 MB
- ~Get Your Files Here !/05 - 4. Packaging Parts, Pipeline Engineering, and Prompt Engineering/05 - Putting it all together to generate a working RAG model.mp4 25.1 MB
- ~Get Your Files Here !/04 - 3. Setting Up a Database and Retrieving Vectors and Files/02 - Setting up your Postgres vector database.mp4 24.1 MB
- ~Get Your Files Here !/04 - 3. Setting Up a Database and Retrieving Vectors and Files/03 - Setting up a simple database schema.mp4 23.6 MB
- ~Get Your Files Here !/03 - 2. Preparing Your LLM and Data/04 - Running Ollama programmatically through Python.mp4 20.9 MB
- ~Get Your Files Here !/05 - 4. Packaging Parts, Pipeline Engineering, and Prompt Engineering/04 - Prompt engineering.mp4 20.4 MB
- ~Get Your Files Here !/05 - 4. Packaging Parts, Pipeline Engineering, and Prompt Engineering/02 - Preparing context, part 1.mp4 19.1 MB
- ~Get Your Files Here !/03 - 2. Preparing Your LLM and Data/03 - Creating a model file.mp4 18.3 MB
- ~Get Your Files Here !/04 - 3. Setting Up a Database and Retrieving Vectors and Files/05 - Retrieving content from your database.mp4 17.4 MB
- ~Get Your Files Here !/03 - 2. Preparing Your LLM and Data/01 - Setting up a dev container.mp4 15.7 MB
- ~Get Your Files Here !/03 - 2. Preparing Your LLM and Data/06 - Extract text from different local file formats with Docling.mp4 15.0 MB
- ~Get Your Files Here !/03 - 2. Preparing Your LLM and Data/02 - Setting up environment and installing Ollama.mp4 11.7 MB
- ~Get Your Files Here !/05 - 4. Packaging Parts, Pipeline Engineering, and Prompt Engineering/01 - Overview of the RAG pipeline.mp4 6.3 MB
- ~Get Your Files Here !/04 - 3. Setting Up a Database and Retrieving Vectors and Files/01 - Vector embeddings and their implementation.mp4 5.7 MB
- ~Get Your Files Here !/02 - 1. Conceptual Overview/05 - Vectorizing a query and finding relevant text.mp4 5.3 MB
- ~Get Your Files Here !/02 - 1. Conceptual Overview/04 - Setting up a database and retrieving vectors and files.mp4 5.0 MB
- ~Get Your Files Here !/02 - 1. Conceptual Overview/03 - What are vector embeddings, and how are they generated.mp4 4.8 MB
- ~Get Your Files Here !/02 - 1. Conceptual Overview/06 - Prompt engineering and packaging pieces together.mp4 4.6 MB
- ~Get Your Files Here !/02 - 1. Conceptual Overview/01 - Running your LLM from open source.mp4 3.1 MB
- ~Get Your Files Here !/02 - 1. Conceptual Overview/02 - Collecting data to generate our corpus.mp4 2.7 MB
- ~Get Your Files Here !/06 - Conclusion/01 - What's next.mp4 2.6 MB
- ~Get Your Files Here !/01 - Introduction/01 - Introduction to RAG models.mp4 2.5 MB
- ~Get Your Files Here !/04 - 3. Setting Up a Database and Retrieving Vectors and Files/04 - Uploading vectors, text, and filenames to the database.srt 23.4 KB
- ~Get Your Files Here !/03 - 2. Preparing Your LLM and Data/05 - Generating the corpus.srt 14.0 KB
- ~Get Your Files Here !/03 - 2. Preparing Your LLM and Data/03 - Creating a model file.srt 11.4 KB
- ~Get Your Files Here !/03 - 2. Preparing Your LLM and Data/01 - Setting up a dev container.srt 11.3 KB
- ~Get Your Files Here !/05 - 4. Packaging Parts, Pipeline Engineering, and Prompt Engineering/03 - Preparing context, part 2.srt 11.0 KB
- ~Get Your Files Here !/04 - 3. Setting Up a Database and Retrieving Vectors and Files/02 - Setting up your Postgres vector database.srt 11.0 KB
- ~Get Your Files Here !/05 - 4. Packaging Parts, Pipeline Engineering, and Prompt Engineering/05 - Putting it all together to generate a working RAG model.srt 10.1 KB
- ~Get Your Files Here !/04 - 3. Setting Up a Database and Retrieving Vectors and Files/05 - Retrieving content from your database.srt 10.0 KB
- ~Get Your Files Here !/05 - 4. Packaging Parts, Pipeline Engineering, and Prompt Engineering/04 - Prompt engineering.srt 9.6 KB
- ~Get Your Files Here !/04 - 3. Setting Up a Database and Retrieving Vectors and Files/03 - Setting up a simple database schema.srt 9.6 KB
- ~Get Your Files Here !/03 - 2. Preparing Your LLM and Data/04 - Running Ollama programmatically through Python.srt 9.4 KB
- ~Get Your Files Here !/05 - 4. Packaging Parts, Pipeline Engineering, and Prompt Engineering/02 - Preparing context, part 1.srt 9.0 KB
- ~Get Your Files Here !/03 - 2. Preparing Your LLM and Data/02 - Setting up environment and installing Ollama.srt 7.6 KB
- ~Get Your Files Here !/04 - 3. Setting Up a Database and Retrieving Vectors and Files/01 - Vector embeddings and their implementation.srt 7.0 KB
- ~Get Your Files Here !/03 - 2. Preparing Your LLM and Data/06 - Extract text from different local file formats with Docling.srt 6.1 KB
- ~Get Your Files Here !/02 - 1. Conceptual Overview/03 - What are vector embeddings, and how are they generated.srt 5.6 KB
- ~Get Your Files Here !/02 - 1. Conceptual Overview/06 - Prompt engineering and packaging pieces together.srt 5.2 KB
- ~Get Your Files Here !/02 - 1. Conceptual Overview/04 - Setting up a database and retrieving vectors and files.srt 4.8 KB
- ~Get Your Files Here !/02 - 1. Conceptual Overview/05 - Vectorizing a query and finding relevant text.srt 4.6 KB
- ~Get Your Files Here !/02 - 1. Conceptual Overview/01 - Running your LLM from open source.srt 4.3 KB
- ~Get Your Files Here !/05 - 4. Packaging Parts, Pipeline Engineering, and Prompt Engineering/01 - Overview of the RAG pipeline.srt 4.1 KB
- ~Get Your Files Here !/02 - 1. Conceptual Overview/02 - Collecting data to generate our corpus.srt 3.4 KB
- ~Get Your Files Here !/06 - Conclusion/01 - What's next.srt 1.6 KB
- ~Get Your Files Here !/01 - Introduction/01 - Introduction to RAG models.srt 1.3 KB
- Get Bonus Downloads Here.url 180 bytes
- ~Get Your Files Here !/Bonus Resources.txt 70 bytes
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