Exploring Gemini's Transformative Embeddings: Quick Experimentation with Python Code

This article delves into Gemini's innovative embedding technology, highlighting its potential for diverse applications. Through practical Python code examples, we will explore how Gemini converts text into numerical fingerprints that computers can understand. These fingerprints unlock powerful capabilities like searching through massive datasets, automatically categorizing documents, and grouping similar texts together, opening doors to exciting new applications in various fields.

1. The Power of Embeddings: At the heart of Gemini's magic lie embeddings, numerical representations of text that unlock various applications. Gemini's embed_content method caters to different tasks through parameters like RETRIEVAL_QUERY for search queries and CLASSIFICATION for text categorization.

Gemini provides the embed_content method for generating embeddings. This method supports different tasks through the task_type parameter, including:

Task Type

Description

RETRIEVAL_QUERY

Specifies the given text is a query in a search/retrieval setting.

RETRIEVAL_DOCUMENT

Specifies the given text is a document in a search/retrieval setting. Using this task type requires a title.

SEMANTIC_SIMILARITY

Specifies the given text will be used for Semantic Textual Similarity (STS).

CLASSIFICATION

Specifies that the embeddings will be used for classification.

CLUSTERING

Specifies that the embeddings will be used for clustering.

2. Gemini's Two-fold Approach: Gemini offers two embedding approaches:

• Task-Oriented: embed_content specializes in specific tasks like retrieval and semantic similarity, catering to specific needs.

The following generates an embedding for a single string for document retrieval:

Note: The retrieval_document task type is the only task that accepts a title. To manage batches of strings, pass a list of strings in content:

• Multimodal Flexibility: Embeddings are currently text-focused, but the underlying glm.Content object hints at future possibilities for handling diverse data types like images and audio

Here is what sets Gemini apart:

• Ease of Use: Simple Python code snippets highlight the power of embeddings, making them accessible for experimentation and exploration.
• Multi-purpose Flexibility: The same embed_content method adapts to different tasks, offering versatility for various applications.
• Scalability: Batch processing capabilities efficiently manage large volumes of text data.
• Foundation for Future Expansion: The multimodal design paves the way for incorporating visuals, audio, and other data modalities in the future.

Conclusion and Future Scope

Gemini's revolutionary embeddings not just enable potent applications but also emphasize responsible AI practices. With a capacity of 1500 requests per minute, it is optimized for generating embeddings for text containing up to 2048 tokens. You can use Gemini Embedding Models with Binary Quantization using Qdrant (vector similarity search engine) - a technique that allows you to reduce the size of the embeddings by thirty-two times without losing the quality of the search results too much. This creates opportunities for ethical advancements and enables individuals to explore state-of-the-art technology, all while adhering to ethical principles. As Gemini evolves, its multimodal capabilities and commitment to safety hold immense promise for the future of AI.

Special Thanks to Anitha Nayar , Padma Murali, Ph.D , Anika Pranavi

References

·       Gemini API Overview

·       How it’s Made: Interacting with Gemini through multimodal prompting

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