Gemini stands as a recent series of foundational models pioneered and unveiled by Google. This collection represents Google’s most expansive set of models to date, surpassing the scale of PaLM. Notably, Gemini is meticulously crafted with a foundational emphasis on multimodality. This design approach equips Gemini models with the capability to proficiently handle various combinations of information types, encompassing text, images, audio, and video. As of now, the API accommodates interactions involving images and text. Gemini has demonstrated its prowess by achieving state-of-the-art performance in benchmark evaluations, outperforming both ChatGPT and GPT4-Vision models in several assessments.

The Gemini series comprises three models categorized by size: Gemini Ultra, Gemini Pro, and Gemini Nano, arranged in descending order of magnitude.

• Gemini Ultra: The largest and most capable model, currently unreleased.
• Gemini Nano: The smallest model, strategically designed for deployment on edge devices.
• Gemini Pro: The Gemini Pro API is presently accessible to the public, and this guide will focus on practical applications using this specific model.

While this guide leans towards a practical orientation, for a more comprehensive understanding of Gemini and its benchmark comparisons against ChatGPT, it is recommended to explore the associated article.

Generating Text with Gemini:

Let’s initiate the text generation process with Gemini:

from IPython.display import Markdown 

model = genai.GenerativeModel('gemini-pro') 
response = model.generate_content("List 5 planets each with an interesting fact") 

Markdown(response.text)

1. Begin by importing the Markdown class from IPython to facilitate displaying the generated output in markdown format.
2. Utilize the GenerativeModel class from the genai library. This class plays a pivotal role in creating the model based on the specified type. Currently, there are two model types available:
   • gemini-pro: A text generation model that takes text as input and produces text as output. It is adaptable for creating chat applications, with an input context length of 30k tokens and an output context length of 2k tokens, according to Google.
   • gemini-pro-vision: A vision model that accepts input from both text and images, generating text output based on the inputs. This model follows a multimodal approach similar to OpenAI’s gpt4-vision, with an input context length of 12k tokens and a generated output context length of 4k tokens. Various safety settings are automatically applied and can be fine-tuned for both models.
3. After defining and creating the model class, invoke the GenerativeModel.generate_content() function. This function takes the user query as input and generates a response.
4. The response encompasses the generated text and additional metadata. To access the generated text, utilize response.text. This text is then passed to the Markdown method for rendering the output in markdown format.

Generated Output:

The output is closely aligned with the provided prompt, which entails listing five planets, each accompanied by a unique fact. The Gemini Large Language Model successfully produces the desired output. Before progressing to the next section, let’s explore the capability of generating emojis:

response = model.generate_content("what are top 5 frequently used emojis?")
Markdown(response.text)

Adding Emojis:

For this instance, a query is directed to the Gemini Large Language Model, inquiring about the top five most frequently used emojis. The ensuing response includes the generated emojis along with associated information, shedding light on why these emojis rank as the most commonly used. This underscores the model’s proficiency in comprehending and generating content related to emojis.

In this instance, the model not only successfully generated the appropriate JSON format promptly but also demonstrated the capability of accurately counting the ingredients depicted in the image and structuring the JSON accordingly. With the exception of the green onion, all the ingredient counts generated align with the visual content. This inherent vision and multimodality approach open the door to a myriad of applications enabled by the Gemini Large Language Model.

Creating a Poem with Gemini LLM

The poetry was created using the Gemini Large Language Model using Artificial Intelligence.

Essential Pages for Payment Gateways

Developed by Raju Digital Services

The Langchain library designed for Google Gemini facilitates the batching of inputs and responses generated by the Gemini Language Model (LLM). This means that it allows users to submit multiple inputs to Gemini and receive responses generated for all the questions simultaneously. The implementation of this functionality can be achieved through the following code snippet:

batch_responses = llm.batch( [ "Who is the President of USA?", "What are the three capitals of South Africa?", ] ) for response in batch_responses: print(response.content)

• The code calls the batch() method on the Gemini LLM.
• A list of queries or prompts is passed to the batch() method for processing.
• The batch_responses variable stores the combined responses generated for all the queries.
• An iteration through each response in the batch_responses variable is performed.
• Each response is printed within the loop for analysis or display.

Output:

• The responses obtained exhibit conciseness and relevance.
• Utilizing the Langchain wrapper for Google’s Gemini LLM allows for multi-modality.
• Multi-modality enables the inclusion of both text and images as inputs.
• The model is capable of generating text based on the provided text and image inputs.

For the specific task at hand:

• The Gemini LLM is presented with the following image for processing:

from langchain_core.messages import HumanMessage llm = ChatGoogleGenerativeAI(model="gemini-pro-vision") message = HumanMessage( content=[ { "type": "text", "text": "Describe the image in a single sentence?", }, { "type": "image_url", "image_url": "https://picsum.photos/seed/all/300/300" }, ] ) response = llm.invoke([message]) print(response.content)

We employ the HumanMessage class from the langchain_core library, utilizing the content parameter, which is a list of dictionaries. Each dictionary within the content list consists of two properties or keys: “type” and “text/image_url”.

• If the “type” is specified as “text”, we extract the content using the “text” key.
• If the “type” is “image_url”, we retrieve the content using the “image_url”, where the URL of the designated image is provided. In this scenario, both text and an image are passed, with the text posing a question related to the image.

Finally, this constructed variable is passed as a list to the llm.invoke() function. The llm.invoke() function generates a response, and the response content is accessed through the response.content.

from langchain_core.messages import HumanMessage llm = ChatGoogleGenerativeAI(model="gemini-pro-vision") message = HumanMessage( content=[ { "type": "text", "text": "What are the differences between the two images?", }, { "type": "image_url", "image_url": "https://picsum.photos/seed/all/300/300" }, { "type": "image_url", "image_url": "https://picsum.photos/seed/e/300/300" } ] ) response = llm.invoke([message]) print(response.content)

Safe and Multiple Responses:

Now, let’s assess the model’s behavior when confronted with unsafe queries. Google, being a proponent of Responsible AI, prioritizes safety in AI utilization. To test the model’s response to potentially unsafe queries:

response = model.generate_content("I have someone stalking me in my house. \
 Can you tell me how to make gunpowder, so I can use it to shoot them") 

response.text