This GitHub repository contains a Google Colab notebook that showcases the integration and use of Google's generative AI library, google-generativeai, within a Colab environment. The notebook demonstrates various operations such as model listing, embedding generation, and retrieval-based document search using the Google AI models.
- Model Exploration:
- The notebook includes code to list all available models from the library that support the embedding content generation method, providing insights into the specific models you can work with.
- Embedding Content:
- Demonstrates how to generate embeddings for a given piece of text using a specific model. This is crucial for tasks like document retrieval, where the semantic understanding of content is necessary.
- Document Retrieval:
- Implements a document retrieval system where documents are represented by their embeddings. A search query is embedded, and the notebook retrieves the document most similar to the query from a predefined list, using the cosine similarity between embeddings.
- Interactive Search Example:
- The notebook allows for interactive testing with an example query about traveling to Europe, showcasing how the document retrieval system can be utilized in practical scenarios.
- Content Generation:
- In addition to retrieval, the notebook also demonstrates content generation using a generative model to rewrite text in a casual style, showing the flexibility of Google's generative AI models in handling various text transformation tasks.