Hands On AI Build a RAG Model from Scratch with Open Source

Hands On AI Build a RAG Model from Scratch with Open Source
     Get Bonus Downloads Here.url -


     01 - Introduction to RAG models.mp4 -


     01 - Introduction to RAG models.srt -


     01 - Running your LLM from open source.mp4 -


     01 - Running your LLM from open source.srt -


     02 - Collecting data to generate our corpus.mp4 -


     02 - Collecting data to generate our corpus.srt -


     03 - What are vector embeddings, and how are they generated.mp4 -


     03 - What are vector embeddings, and how are they generated.srt -


     04 - Setting up a database and retrieving vectors and files.mp4 -


     04 - Setting up a database and retrieving vectors and files.srt -


     05 - Vectorizing a query and finding relevant text.mp4 -


     05 - Vectorizing a query and finding relevant text.srt -


     06 - Prompt engineering and packaging pieces together.mp4 -


     06 - Prompt engineering and packaging pieces together.srt -


     01 - Setting up a dev container.mp4 -


     01 - Setting up a dev container.srt -


     02 - Setting up environment and installing Ollama.mp4 -


     02 - Setting up environment and installing Ollama.srt -


     03 - Creating a model file.mp4 -


     03 - Creating a model file.srt -


     04 - Running Ollama programmatically through Python.mp4 -


     04 - Running Ollama programmatically through Python.srt -


     05 - Generating the corpus.mp4 -


     05 - Generating the corpus.srt -


     06 - Extract text from different local file formats with Docling.mp4 -


     06 - Extract text from different local file formats with Docling.srt -


     01 - Vector embeddings and their implementation.mp4 -


     01 - Vector embeddings and their implementation.srt -


     02 - Setting up your Postgres vector database.mp4 -


     02 - Setting up your Postgres vector database.srt -


     03 - Setting up a simple database schema.mp4 -


     03 - Setting up a simple database schema.srt -


     04 - Uploading vectors, text, and filenames to the database.mp4 -


     04 - Uploading vectors, text, and filenames to the database.srt -


     05 - Retrieving content from your database.mp4 -


     05 - Retrieving content from your database.srt -


     01 - Overview of the RAG pipeline.mp4 -


     01 - Overview of the RAG pipeline.srt -


     02 - Preparing context, part 1.mp4 -


     02 - Preparing context, part 1.srt -


     03 - Preparing context, part 2.mp4 -


     03 - Preparing context, part 2.srt -


     04 - Prompt engineering.mp4 -


     04 - Prompt engineering.srt -


     05 - Putting it all together to generate a working RAG model.mp4 -


     05 - Putting it all together to generate a working RAG model.srt -


     01 - What's next.mp4 -


     01 - What's next.srt -


     Bonus Resources.txt -

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