Getting Started with Gemini Pro API on Google AI Studio
TLDRGoogle has made Gemini Pro Models accessible to the public for free. This video tutorial demonstrates how to utilize Gemini Pro's text and vision models via Python SDK. It covers the pricing structure, which is free for up to 60 queries per minute, and how to integrate the models with tools like L chain and Lama index for building rag pipelines. The video also explains how to set up a development environment in Google AI Studio, use the API key, and explore various features like safety settings and text generation. Additionally, it highlights the potential of the embedding model and the multimodal capabilities of Gemini Pro Vision.
Takeaways
- 🚀 Google has opened API access to Gemini Pro Models, which are now available for public use with a free tier.
- 💰 The API pricing is designed to support app development, with up to 60 queries per minute being free for everyone.
- 📈 Gemini Pro is a multimodal model, offering both vision and text capabilities for developers to integrate into their projects.
- 🔍 Google's pricing for Gemini Pro is significantly lower compared to GPT 3.5, making it a cost-effective option for developers.
- 🔒 If you pay for the API usage, Google will not use your input or output data to train or improve their products.
- 🛠️ Google AI Studio, formerly known as Maker Suite, allows users to test Gemini Pro models with an easy-to-use interface.
- 📊 The script provides a detailed guide on how to use the Python SDK to integrate Gemini Pro into custom applications.
- 🔄 Gemini Pro supports streaming responses, allowing for text generation in chunks for a better user experience.
- 🌐 The API key is essential for using Gemini Pro models outside of Google AI Studio and must be securely stored and used in code.
- 🖼️ Gemini Pro Vision can process images and generate text based on visual content, opening up possibilities for multimodal applications.
Q & A
What is the significance of Google opening API access to Gemini Pro Models?
-The significance lies in the fact that Gemini Pro Models are powerful AI models, and making them accessible via an API allows the public to utilize these models for various applications without any cost for up to 60 queries per minute, fostering innovation and development of new applications.
What are the capabilities of Gemini Pro in comparison to GPT 3.5 turbo?
-Gemini Pro is a multimodal model capable of both text and vision tasks. It offers a lower price point compared to GPT 3.5 turbo, especially when considering the input and output tokens. Additionally, Gemini Pro provides the ability to process images, which is a unique feature not typically found in text-based models like GPT 3.5.
How does Google plan to use the data from Gemini Pro API queries?
-Google intends to use the input data provided by users and the output from the model to improve their products and services. This data helps in refining the model's performance and expanding its capabilities.
What safety settings can users define for the Gemini Pro model?
-Users can define safety settings across four harmful categories: harassment, hate speech, sexually explicit content, and dangerous content. They can adjust the model's responses by setting different thresholds for each category, giving developers more control over the content generated.
How can developers use the Gemini Pro API within their own applications?
-Developers can use the Gemini Pro API within their applications by creating an API key specific to their project. They then integrate this key into their codebase, allowing them to utilize the text and vision capabilities of Gemini Pro in their applications.
What is the process for testing Gemini Pro models within Google AI Studio?
-Within Google AI Studio, users can experiment with Gemini Pro models by selecting the text or vision model and providing inputs. The platform allows for testing and viewing outputs without needing to integrate the API into a project. It's a sandbox environment similar to the OpenAI playground.
How does the Gemini Pro model handle multiple drafts of text?
-Similar to some other models like Bard, Gemini Pro can generate multiple drafts of text, referred to as candidates. It presents these candidates to the developer, who can then choose which response is most appropriate to display to the end user.
What is the role of the embedding model in the Google generative AI package?
-The embedding model within the Google generative AI package is used for text embedding tasks. It can be utilized for document retrieval, semantic similarity analysis, classification, and clustering. The model generates a vector with 768 dimensions, providing a comprehensive representation of the text.
How can the Gemini Pro Vision model be integrated into a multimodal RAG pipeline?
-The Gemini Pro Vision model, with its ability to understand and generate responses based on images, can be integrated into a multimodal RAG (Retrieval-Augmented Generation) pipeline. This allows for the creation of applications that can process both text and image inputs, enhancing the capabilities of the pipeline and providing richer, more contextual outputs.
What is the pricing structure for using the Gemini Pro API beyond the free tier?
-Beyond the free tier, which allows up to 60 queries per minute, users are required to pay a fee for using the Gemini Pro API. The exact pricing is not detailed in the script, but it is mentioned that it is competitive and an order of magnitude lower than the pricing for GPT 3.5.
How does Gemini Pro ensure the safety and appropriateness of its generated content?
-Gemini Pro incorporates safety settings that allow developers to define thresholds for harmful content. The model also provides feedback on the probability of the generated content falling into categories like harassment, hate speech, sexually explicit content, and dangerous content, helping to ensure that the content is appropriate and safe.
Outlines
🚀 Google's Gemini Pro API Launch and Pricing
This paragraph introduces Google's new Gemini Pro Models, which have been made accessible to the public through an API. It highlights the availability of a free tier for testing and outlines the pricing structure, which is designed to be affordable for developers with less than 60 queries per minute. The paragraph emphasizes the value of Gemini Pro as the second best model from Google and its capabilities in both vision and text processing. It also mentions the integration with tools like L chain and Lama index, allowing for the creation of rag pipelines on top of Gemini Pro. The pricing is compared favorably to GPT 3.5, with the potential for image processing that is not available with other models. The paragraph concludes by noting that if users opt for paid usage, Google will not use their data to improve its products.
💻 Getting Started with Google AI Studio and API Key
This section guides users on how to get started with the Gemini Pro API, explaining that the API is available within the Google AI Studio, formerly known as Maker Suite. It details the process of experimenting with the models in the studio, which includes a text model and a vision model capable of understanding images. The paragraph also covers the need for an API key to use the models in personal projects and provides a brief tutorial on how to create and use this key. Additionally, it touches on the safety settings that Google has implemented, giving developers control over the type of content their users can see.
📝 Using the API in Google Colab and Text Generation
The paragraph focuses on the practical steps to set up a development environment in Google Colab, including how to access and use the API key. It explains the process of generating text responses from the Gemini Pro model and how to stream these responses. The paragraph also discusses the use of the chat model, detailing how to initiate a chat and send messages to receive responses from the model. It emphasizes the ease of API implementation and the additional properties of the response object, such as prompt feedback and multiple candidate responses.
🗣️ Chat Model and Embedding Model Exploration
This part delves into the use of the Gemini Pro model as a chat model, explaining how to create a chat history and utilize the send message function to simulate a conversation. It also explores the embedding model released by Google, which can be used for various applications such as document detection, clustering, and question-answering within rag pipelines. The paragraph provides an overview of the different tasks for which the embedding model can compute embeddings and discusses the potential for future updates to the model.
🖼️ Working with the Gemini Pro Vision Model
The final paragraph discusses the capabilities of the Gemini Pro Vision model, demonstrating how it can process both image inputs and text prompts. It provides an example of how the model can generate a response based on an image of food and a text prompt, showcasing its potential for creating engaging content. The paragraph also mentions the model's ability to be integrated into a multimodal rag pipeline and encourages users to explore the documentation and prompt gallery provided by Google for further insights.
Mindmap
Keywords
💡Gemini Pro API
💡Google AI Studio
💡Vision Model
💡Text Model
💡Python SDK
💡Pricing
💡Safety Settings
💡API Key
💡Multimodal RAG Pipelines
💡Embedding Model
💡Streaming Responses
Highlights
Google has opened API access to their Gemini Pro Models to the public for free testing.
Gemini Pro is the second best model from Google and is a multimodal model.
The API is free for everyone if they make less than 60 queries per minute.
Google will use the input and output data from the API to improve their products.
Gemini Pro has integrations with tools like L chain and Lama index for building rag pipelines.
The pricing for Gemini Pro is significantly lower compared to GPT 3.5.
Gemini Pro Vision model can process images in addition to text.
Google AI Studio, formerly known as Maker Suite, allows testing of Gemini Pro models.
The safety settings for the model can be defined by the user, including levels for harassment, hate speech, sexually explicit content, and dangerous content.
By paying for Gemini Pro API usage, Google will not use your input or output data for training or improving their products.
The Google generative AI package can be installed in a Google Collab notebook for development purposes.
The API key can be set as a secret in the Google Collab notebook for secure access.
The Gemini Pro model can generate text responses and supports streaming of responses.
The chat model function of Gemini Pro can be used for conversational AI applications.
The embedding model provided by Google can be used for various applications like document retrieval, semantic similarity, and clustering.
The Gemini Pro Vision model can understand and generate responses based on images.
The Vision model can also take text prompts along with images to generate more contextually relevant responses.
Google has provided a prompt gallery with examples of how to interact with the Gemini Pro models.