🔑Custom chatbots can provide personalized responses based on a custom knowledge base.
💡Semantic search can retrieve relevant information from a large database based on user queries.
🎯Transforming user queries into embeddings allows for efficient similarity search.
📊Chunking large documents improves indexing and search performance.
💻The app code and related notebooks are available on GitHub for customization.
How does the custom chatbot work?
—The chatbot uses a custom knowledge base created by feeding podcast transcripts. It retrieves relevant information based on user queries and uses semantic search and language models to provide personalized responses.
What is semantic search?
—Semantic search is a technique that retrieves information based on the meaning and context of the user's query. It allows the chatbot to find relevant chunks of text from the podcast transcripts.
How are user queries transformed into embeddings?
—User queries are passed through an embedding model that converts them into numerical representations. These embeddings are then used for efficient similarity search in the custom knowledge base.
What is the benefit of chunking large documents?
—Chunking large documents improves indexing and search performance by breaking them into smaller, more manageable pieces. This allows for faster retrieval of relevant information.
Where can I find the code and notebooks?
—The code and related notebooks are available on GitHub. You can customize them for your own use and build your own AI chatbot using podcast transcripts.
00:01The video demonstrates how to create an AI chatbot using podcast transcripts.
02:30Custom chatbots can provide personalized responses based on a custom knowledge base.
04:45Semantic search allows the chatbot to retrieve relevant information from the podcast transcripts based on user queries.
06:15User queries are transformed into embeddings to enable efficient similarity search.
07:30Chunking large documents improves indexing and search performance.
08:45The code and related notebooks are available on GitHub for customization.
