[ WebToolTip.com ] Hands-On AI - Build a RAG Model from Scratch with Open Source

文件大小

364.50 MB

上传时间

2025-10-24

Hash

de37a6f3c2cdeed7b258028df4810d3f4a8fd760

文件列表

  • Get Bonus Downloads Here.url0.00 MB
  • ~Get Your Files Here !*01 - Introduction2 项
    • 01 - Introduction to RAG models.mp42.50 MB
    • 01 - Introduction to RAG models.srt0.00 MB
  • ~Get Your Files Here !*02 - 1. Conceptual Overview12 项
    • 01 - Running your LLM from open source.mp43.06 MB
    • 01 - Running your LLM from open source.srt0.00 MB
    • 02 - Collecting data to generate our corpus.mp42.72 MB
    • 02 - Collecting data to generate our corpus.srt0.00 MB
    • 03 - What are vector embeddings, and how are they generated.mp44.76 MB
    • 03 - What are vector embeddings, and how are they generated.srt0.01 MB
    • 04 - Setting up a database and retrieving vectors and files.mp44.99 MB
    • 04 - Setting up a database and retrieving vectors and files.srt0.00 MB
    • 05 - Vectorizing a query and finding relevant text.mp45.32 MB
    • 05 - Vectorizing a query and finding relevant text.srt0.00 MB
    • 06 - Prompt engineering and packaging pieces together.mp44.59 MB
    • 06 - Prompt engineering and packaging pieces together.srt0.01 MB
  • ~Get Your Files Here !*03 - 2. Preparing Your LLM and Data12 项
    • 01 - Setting up a dev container.mp415.73 MB
    • 01 - Setting up a dev container.srt0.01 MB
    • 02 - Setting up environment and installing Ollama.mp411.70 MB
    • 02 - Setting up environment and installing Ollama.srt0.01 MB
    • 03 - Creating a model file.mp418.26 MB
    • 03 - Creating a model file.srt0.01 MB
    • 04 - Running Ollama programmatically through Python.mp420.90 MB
    • 04 - Running Ollama programmatically through Python.srt0.01 MB
    • 05 - Generating the corpus.mp433.27 MB
    • 05 - Generating the corpus.srt0.01 MB
    • 06 - Extract text from different local file formats with Docling.mp414.99 MB
    • 06 - Extract text from different local file formats with Docling.srt0.01 MB
  • ~Get Your Files Here !*04 - 3. Setting Up a Database and Retrieving Vectors and Files10 项
    • 01 - Vector embeddings and their implementation.mp45.73 MB
    • 01 - Vector embeddings and their implementation.srt0.01 MB
    • 02 - Setting up your Postgres vector database.mp424.15 MB
    • 02 - Setting up your Postgres vector database.srt0.01 MB
    • 03 - Setting up a simple database schema.mp423.65 MB
    • 03 - Setting up a simple database schema.srt0.01 MB
    • 04 - Uploading vectors, text, and filenames to the database.mp451.18 MB
    • 04 - Uploading vectors, text, and filenames to the database.srt0.02 MB
    • 05 - Retrieving content from your database.mp417.37 MB
    • 05 - Retrieving content from your database.srt0.01 MB
  • ~Get Your Files Here !*05 - 4. Packaging Parts, Pipeline Engineering, and Prompt Engineering10 项
    • 01 - Overview of the RAG pipeline.mp46.29 MB
    • 01 - Overview of the RAG pipeline.srt0.00 MB
    • 02 - Preparing context, part 1.mp419.09 MB
    • 02 - Preparing context, part 1.srt0.01 MB
    • 03 - Preparing context, part 2.mp425.85 MB
    • 03 - Preparing context, part 2.srt0.01 MB
    • 04 - Prompt engineering.mp420.36 MB
    • 04 - Prompt engineering.srt0.01 MB
    • 05 - Putting it all together to generate a working RAG model.mp425.11 MB
    • 05 - Putting it all together to generate a working RAG model.srt0.01 MB
  • ~Get Your Files Here !*06 - Conclusion2 项
    • 01 - What's next.mp42.55 MB
    • 01 - What's next.srt0.00 MB
  • ~Get Your Files Here !1 项
    • Bonus Resources.txt0.00 MB

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