GemAI - Free ChatBot CLI 🤖

GemAI is a command-line interface (CLI) AI ChatBot that uses Google’s Gemini API to generate natural language responses. You can chat with GemAI about anything, from images to documents, and have fun and engaging conversations.

Quick Demo

GemAI.-.Free.ChatBot.CLI.mp4

Installation:

Installing GemAI is simple. For global access, execute one of the commands below:

• Using Node Package Manager (NPM):

npm install -g gemai

• or if you are using (PNPM):

pnpm install -g gemai

Usage:

Logging In:

Before using GemAI, you'll need to log in with your Google Gemini API token. To do this, run the following command:

gemai login [token]

Example:

gemai login Flqw9TUeaSkRfRii3lmgZLid

This command sends a request to the Gemini endpoint for authentication. Upon receiving a successful response, your API key will be verified.

To obtain your free API key, head over to Google Gemini API Key.

If you encounter any issues, try running gemai login <token> again to refresh your credentials.

Viewing Configuration:

To view your current GemAI configuration, simply type:

gemai config

This will display the contents of the gemai.json file stored on your machine.

Updating Configuration:

To update the default values of GemAI, run this command:

gemai config set

By default, when you log in, we have set some default values that are required for the Google Gemini API to work properly, such as:

• maxOutputTokens: 2048
• topK: 40
• topP: 1
• temperature: 0.7
• kwargs: 1

To update these default values, simply run gemai config set. After running this command, you will be prompted to enter your desired values in place of the default values.

Chat with GemAI:

To start chatting with the GemAI chatbot, run this command:

gemai chat

Once you are logged in, you can have a natural language conversation with the chatbot.

Chat with image

You can also chat with an image by running:

gemai vision <image path>

When you provide the path to an image, it will be converted to base64. When you ask a question about the image, GemAI will send a request to the Gemini Vision endpoint with the image data and your question. The response from Gemini will be used to generate a reply.

Example:

gemai vision public/image.jpg

You can ask questions like "What's happening in the image?", "Who are the people in the image?", or "What is the object on the left?".

Chat with Document

You can also chat with a document by running:

gemai read <document path>

You need to provide the path of your document, and optionally specify its file type using the -f flag. Default file type is set to text because it allows for easier splitting and chunking of content.

However, we generally recommend using the text file type for seamless conversations with any document.

Example:

gemai read <document path> -f pdf

GemAI supports five file types: pdf, text, json, csv, and url.

Note: The url file type is included for convenience, allowing you to fetch data from websites that allow bots to scrape their web pages.

GemAI also provides verbose output, displaying the retrieval process of chunks based on your queries.

Example:

gemai read resume.pdf -f pdf -t

Fun Fact: We can't use -v for verbose flag as it is already assigned as version flag.

By default, GemAI use MemoryVectorStore to manage splitted chunks and indexes.

If you want to save the chunks and indexes to your local machine, you can use the -s flag. This will create a directory with a nanoid in /user/.config/configstore/gemai. You can also use the -n flag to give a custom name to the directory.

Example:

gemai read resume.pdf -f pdf -s -n resume-store

If you have already created and saved a vector store on your machine, you can load it by using the -l flag. This flag requires the location/path of the vector store directory.

Example:

gemai read resume.pdf -f pdf -l C:\Users\asus\.config\configstore\gemai\resume-store

Credits:

GemAI utilizes Google's Gemini API for its chatbot capabilities. To obtain your free API key, head over to Google Gemini API Key.