GoogleGenAI.jl

Overview

A Julia wrapper to the Google generative AI API. For API functionality, see reference documentation.

Installation

From source:

julia> using Pkg; Pkg.add(url="https://github.com/tylerjthomas9/GoogleGenAI.jl/")

julia> ]  # enters the pkg interface
Pkg> add https://github.com/tylerjthomas9/GoogleGenAI.jl/

Quick Start

1. Create a secret API key in Google AI Studio.
2. Set the GOOGLE_API_KEY environment variable.

Generate Content

using GoogleGenAI

secret_key = ENV["GOOGLE_API_KEY"]
model = "gemini-1.5-flash-latest"
prompt = "Hello"
response = generate_content(secret_key, model, prompt)
println(response.text)

outputs

"Hello! 👋  How can I help you today? 😊"

api_kwargs = (max_output_tokens=50,)
response = generate_content(secret_key, model, prompt; api_kwargs)
println(response.text)

outputs

"Hello! 👋  How can I help you today? 😊"

using GoogleGenAI

secret_key = ENV["GOOGLE_API_KEY"]
model = "gemini-1.5-flash-latest"
prompt = "What is this image?"
image_path = "test/example.jpg"
response = generate_content(secret_key, model, prompt, image_path)
println(response.text)

outputs

"The logo for the Julia programming language."

Multi-turn conversations

using GoogleGenAI

provider = GoogleProvider(api_key=ENV["GOOGLE_API_KEY"])
api_kwargs = (max_output_tokens=50,)
model = "gemini-1.5-flash-latest"
conversation = [
    Dict(:role => "user", :parts => [Dict(:text => "When was Julia 1.0 released?")])
]

response = generate_content(provider, model, conversation)
push!(conversation, Dict(:role => "model", :parts => [Dict(:text => response.text)]))
println("Model: ", response.text) 

push!(conversation, Dict(:role => "user", :parts => [Dict(:text => "Who created the language?")]))
response = generate_content(provider, model, conversation; api_kwargs)
println("Model: ", response.text)

outputs

"Model: Julia 1.0 was released on **August 8, 2018**."

"Model: Julia was created by a team of developers at MIT, led by **Jeff Bezanson, Stefan Karpinski, Viral B. Shah, and Alan Edelman**."

Count Tokens

using GoogleGenAI
model = "gemini-1.5-flash-latest"
n_tokens = count_tokens(ENV["GOOGLE_API_KEY"], model, "The Julia programming language")
println(n_tokens)

outputs

4

Create Embeddings

using GoogleGenAI
embeddings = embed_content(ENV["GOOGLE_API_KEY"], "embedding-001", "Hello")
println(size(embeddings.values))

outputs

(768,)

using GoogleGenAI
embeddings = embed_content(ENV["GOOGLE_API_KEY"], "embedding-001", ["Hello", "world"])
println(embeddings.response_status)
println(size(embeddings.values[1]))
println(size(embeddings.values[2]))

outputs

200
(768,)
(768,)

List Models

using GoogleGenAI
models = list_models(ENV["GOOGLE_API_KEY"])
for m in models
    if "generateContent" in m[:supported_generation_methods]
        println(m[:name])
    end
end

outputs

gemini-1.0-pro-latest
gemini-1.0-pro
gemini-pro
gemini-1.0-pro-001
gemini-1.5-pro-latest
gemini-1.5-pro-001
gemini-1.5-pro
gemini-1.5-pro-exp-0801
gemini-1.5-pro-exp-0827
gemini-1.5-flash-latest
gemini-1.5-flash-001
gemini-1.5-flash-001-tuning
gemini-1.5-flash
gemini-1.5-flash-exp-0827
gemini-1.5-flash-8b-exp-0827

Safety Settings

More information about the safety settings can be found here.

using GoogleGenAI
secret_key = ENV["GOOGLE_API_KEY"]
safety_settings = [
    Dict("category" => "HARM_CATEGORY_HATE_SPEECH", "threshold" => "HARM_BLOCK_THRESHOLD_UNSPECIFIED"),
    Dict("category" => "HARM_CATEGORY_SEXUALLY_EXPLICIT", "threshold" => "BLOCK_ONLY_HIGH"),
    Dict("category" => "HARM_CATEGORY_HARASSMENT", "threshold" => "BLOCK_MEDIUM_AND_ABOVE"),
    Dict("category" => "HARM_CATEGORY_DANGEROUS_CONTENT", "threshold" => "BLOCK_LOW_AND_ABOVE")
]
model = "gemini-1.5-flash-latest"
prompt = "Hello"
api_kwargs = (safety_settings=safety_settings,)
response = generate_content(secret_key, model, prompt; api_kwargs)