LLM Gemini

Different tasks call for different models, and FlowHunt is committed to giving you the best of AI without the need to sign up for countless subscriptions. Instead, you get dozens of text and image generator models in a single dashboard. The LLM type of components houses text generation models grouped by provider, allowing you to swap AI models on the fly.

Remember that connecting an LLM Component is optional. All components that use an LLM come with ChatGPT-4o as the default. The LLM components allow you to change the model and control model settings.

LLM Gemini Component Settings

Max Tokens

Tokens represent the individual units of text the model processes and generates. Token usage varies with models, and a single token can be anything from words or subwords to a single character. Models are usually priced in millions of tokens.

The max tokens setting limits the total number of tokens that can be processed in a single interaction or request, ensuring the responses are generated within reasonable bounds. The default limit is 4,000 tokens, the optimal size for summarizing documents and several sources to generate an answer.

Temperature

Temperature controls the variability of answers, ranging from 0 to 1.

A temperature of 0.1 will make the responses very to the point but potentially repetitive and deficient.

A high temperature of 1 allows for maximum creativity in answers but creates the risk of irrelevant or even hallucinatory responses.

For example, the recommended temperature for a customer service bot is between 0.2 and 0.5. This level should keep the answers relevant and to the script while allowing for a natural response variation.

Model

This is the model picker. Here, you’ll find all the supported Gemini models from Google. We support all the latest Gemini models:

• Gemini 2.0 Flash Experimental – An advanced, low-latency model designed for agents. It features new capabilities such as native tool use, image creation, and speech generation. See how well the most advanced Google model handled routine tasks in our testing.
• Gemini 1.5 Flash – A lightweight, multimodal model optimized for speed and efficiency, capable of processing audio, images, video, and text inputs, with a context window of up to 1,048,576 tokens. Learn more here.
• Gemini 1.5 Flash-8B – A smaller, faster, and more cost-efficient variant of the 1.5 Flash model, offering similar multimodal capabilities with a 50% lower price and 2x higher rate limits than 1.5 Flash. How good is the output of the weakest model? Find out here.
• Gemini 1.5 Pro – A mid-size multimodal model optimized for a wide range of reasoning tasks, capable of processing large amounts of data, including extended audio and video inputs, with an input token limit of 2,097,152. See output examples.

How To Add The LLM Gemini To Your Flow

You’ll notice that all LLM components only have an output handle. Input doesn’t pass through the component, as it only represents the model, while the actual generation happens in AI Agents and Generators.

The LLM handle is always purple. The LLM input handle is found on any component that uses AI to generate text or process data. You can see the options by clicking the handle:

This allows you to create all sorts of tools. Let’s see the component in action. Here’s a simple AI Agent chatbot Flow that’s using Gemini 2.0 Flash Experimental to generate responses. You can think of it as a basic Gemini chatbot.

This simple Chatbot Flow includes:

• Chat input: Represents the message a user sends in chat.
• Chat history: Ensures the chatbot can remember and factor in past responses.
• Chat output: Represent the chatbot’s final response.
• AI Agent: An autonomous AI agent that generates responses.
• LLM Gemini: The connection to Google’s text generation models.