Building and Running a ChatGPT Model Without the OpenAI API

💡 The video discusses how to build a language model like ChatGPT using our own data.

🔧 The process involves using the LangChain framework to preprocess and convert textual data into chunks for the language model.

💻 The coding part involves loading the document, creating a text loader object, and passing the data file path.

📂 The video demonstrates how to load and save files using the 'loader.load' and 'file.save' functions.

🔧 The transcription explains the pre-processing steps for data, including removing backslash characters and wrapping and joining lines.

📝 The video also covers splitting the text into chunks using the 'text.splitter' function.

The code demonstrates passing values into a Lang chain object and splitting a document into chunks.

The code then performs embedding and back to restaurant processes on the chunks using hugging face.

The video also shows how to obtain and use the API key from the hugging face website.

🔑 Initialize the Hugging Face API token and create an embedding instance.

📚 Import and use the five CPU Library to pass the embedding into the backdoor stores.

🔎 Perform a similarity search using the embedding and a query to retrieve relevant content.

🔍 Using similarity search to find results based on page content and links.

❓ Creating a custom question and answering board using Hugging Face models.

🔗 Building applications on top of a large language model.

⚙️ Using the chain dot run method, the input documents and questions are passed to the model for processing.

📄 Running the code in a Jupyter notebook or Google Colab allows for easier execution and avoids repetitive printing functions.

🖥️ The advantage of using Jupyter notebook or Google Colab is the ability to easily run code without the need for an external GPU.

📚 This video demonstrates how to run an AI model without using the OpenAI API in Google Colab.

🔽 The video explains the steps to install the required library and make necessary changes in the Google Colab runtime.

💻 The presenter shows how to run the code, load the model, and make predictions using the installed AI model.