generative-ai

Gemini AI SDK for .NET and ASP.NET Core enables developers to use Google's state-of-the-art generative AI models to build AI-powered features and applications.

Stars: 69

Visit

The 'Generative AI' repository provides a C# library for interacting with Google's Generative AI models, specifically the Gemini models. It allows users to access and integrate the Gemini API into .NET applications, supporting functionalities such as listing available models, generating content, creating tuned models, working with large files, starting chat sessions, and more. The repository also includes helper classes and enums for Gemini API aspects. Authentication methods include API key, OAuth, and various authentication modes for Google AI and Vertex AI. The package offers features for both Google AI Studio and Google Cloud Vertex AI, with detailed instructions on installation, usage, and troubleshooting.

README:

Gemini AI Client for .NET and ASP.NET Core

Access and integrate the Gemini API into your .NET applications. The packages support both Google AI Studio and Google Cloud Vertex AI.

Name	Package	Status
Client for .NET	Mscc.GenerativeAI
Client for ASP.NET (Core)	Mscc.GenerativeAI.Web
Client for .NET using Google API Client Library	Mscc.GenerativeAI.Google
Client for Microsoft.Extensions.AI and Semantic Kernel	Mscc.GenerativeAI.Microsoft

Read more about Mscc.GenerativeAI.Web and how to add it to your ASP.NET (Core) web applications. Read more about Mscc.GenerativeAI.Google. Read more about Mscc.GenerativeAI.Microsoft and how to use it with Semantic Kernel.

Install the package 🖥️

Install the package Mscc.GenerativeAI from NuGet. You can install the package from the command line using either the command line or the NuGet Package Manager Console. Or you add it directly to your .NET project.

Add the package using the dotnet command line tool in your .NET project folder.

> dotnet add package Mscc.GenerativeAI

Working with Visual Studio use the NuGet Package Manager to install the package Mscc.GenerativeAI.

PM> Install-Package Mscc.GenerativeAI

Alternatively, add the following line to your .csproj file.

  <ItemGroup>
    <PackageReference Include="Mscc.GenerativeAI" Version="2.0.2" />
  </ItemGroup>

You can then add this code to your sources whenever you need to access any Gemini API provided by Google. This package works for Google AI (Google AI Studio) and Google Cloud Vertex AI.

Features (as per Gemini analysis) ✦

The provided code defines a C# library for interacting with Google's Generative AI models, specifically the Gemini models. It provides functionalities to:

List available models: This allows users to see which models are available for use.
Get information about a specific model: This provides details about a specific model, such as its capabilities and limitations.
Generate content: This allows users to send prompts to a model and receive generated text in response.
Generate content stream: This allows users to receive a stream of generated text from a model, which can be useful for real-time applications.
Generate a grounded answer: This allows users to ask questions and receive answers that are grounded in provided context.
Generate embeddings: This allows users to convert text into numerical representations that can be used for tasks like similarity search.
Count tokens: This allows users to estimate the cost of using a model by counting the number of tokens in a prompt or response.
Start a chat session: This allows users to have a back-and-forth conversation with a model.
Create tuned models: This allows users to provide samples for tuning an existing model. Currently, only the text-bison-001 and gemini-1.0-pro-001 models are supported for tuning
File API: This allows users to upload large files and use them with Gemini 1.5.

The package also defines various helper classes and enums to represent different aspects of the Gemini API, such as model names, request parameters, and response data.

Authentication use cases 👥

The package supports the following use cases to authenticate.

API	Authentication	Remarks
Google AI	Authentication with an API key
Google AI	Authentication with OAuth	required for tuned models
Vertex AI	Authentication with Application Default Credentials (ADC)
Vertex AI	Authentication with Credentials by Metadata Server	requires access to a metadata server
Vertex AI	Authentication with OAuth	using Mscc.GenerativeAI.Google
Vertex AI	Authentication with Service Account	using Mscc.GenerativeAI.Google

This applies mainly to the instantiation procedure.

Getting Started 🚀

Use of Gemini API in either Google AI or Vertex AI is almost identical. The major difference is the way to instantiate the model handling your prompt.

Using Environment variables

In the cloud most settings are configured via environment variables (EnvVars). The ease of configuration, their widespread support and the simplicity of environment variables makes them a very interesting option.

Variable Name	Description
GOOGLE_AI_MODEL	The name of the model to use (default is Model.Gemini15Pro)
GOOGLE_API_KEY	The API key generated in Google AI Studio
GOOGLE_PROJECT_ID	Project ID in Google Cloud to access the APIs
GOOGLE_REGION	Region in Google Cloud (default is us-central1)
GOOGLE_ACCESS_TOKEN	The access token required to use models running in Vertex AI
GOOGLE_APPLICATION_CREDENTIALS	Path to the application credentials file.
GOOGLE_WEB_CREDENTIALS	Path to a Web credentials file.

Using any environment variable provides simplified access to a model.

using Mscc.GenerativeAI;

var model = new GenerativeModel();

Choose an API and authentication mode

Google AI with an API key

using Mscc.GenerativeAI;
// Google AI with an API key
var googleAI = new GoogleAI(apiKey: "your API key");
var model = googleAI.GenerativeModel(model: Model.Gemini15Pro);

// Original approach, still valid.
// var model = new GenerativeModel(apiKey: "your API key", model: Model.GeminiPro);

Google AI with OAuth. Use gcloud auth application-default print-access-token to get the access token.

using Mscc.GenerativeAI;
// Google AI with OAuth. Use `gcloud auth application-default print-access-token` to get the access token.
var model = new GenerativeModel(model: Model.Gemini15Pro);
model.AccessToken = accessToken;

Vertex AI with OAuth. Use gcloud auth application-default print-access-token to get the access token.

using Mscc.GenerativeAI;
// Vertex AI with OAuth. Use `gcloud auth application-default print-access-token` to get the access token.
var vertex = new VertexAI(projectId: projectId, region: region);
var model = vertex.GenerativeModel(model: Model.Gemini15Pro);
model.AccessToken = accessToken;

The ConfigurationFixture type in the test project implements multiple options to retrieve sensitive information, i.e. API key or access token.

Using Google AI Gemini API

Working with Google AI in your application requires an API key. Get an API key from Google AI Studio.

using Mscc.GenerativeAI;

var apiKey = "your_api_key";
var prompt = "Write a story about a magic backpack.";

var model = new GenerativeModel(apiKey: apiKey, model: Model.GeminiPro);

var response = await model.GenerateContent(prompt);
Console.WriteLine(response.Text);

Using Vertex AI Gemini API

Use of Vertex AI requires an account on Google Cloud, a project with billing and Vertex AI API enabled.

using Mscc.GenerativeAI;

var projectId = "your_google_project_id"; // the ID of a project, not its name.
var region = "us-central1";     // see documentation for available regions.
var accessToken = "your_access_token";      // use `gcloud auth application-default print-access-token` to get it.
var prompt = "Write a story about a magic backpack.";

var vertex = new VertexAI(projectId: projectId, region: region);
var model = vertex.GenerativeModel(model: Model.Gemini15Pro);
model.AccessToken = accessToken;

var response = await model.GenerateContent(prompt);
Console.WriteLine(response.Text);

More examples 🪄

Supported models are accessible via the Model class. Since release 0.9.0 there is support for the previous PaLM 2 models and their functionalities.

Use system instruction

The model can be injected with a system instruction that applies to all further requests. Following is an example how to instruct the model to respond like a pirate.

var apiKey = "your_api_key";
var systemInstruction = new Content("You are a friendly pirate. Speak like one.");
var prompt = "Good morning! How are you?";
IGenerativeAI genAi = new GoogleAI(apiKey);
var model = genAi.GenerativeModel(Model.Gemini15ProLatest, systemInstruction: systemInstruction);
var request = new GenerateContentRequest(prompt);

var response = await model.GenerateContent(request);
Console.WriteLine(response.Text);

The response might look similar to this:

Ahoy there, matey! I be doin' finer than a freshly swabbed poop deck on this fine mornin', how about yerself?  
Shimmer me timbers, it's good to see a friendly face!  
What brings ye to these here waters?

Use Google Search

To activate Google Search as a tool, set the boolean property UseGoogleSearch to true, like the following example.

var apiKey = "your_api_key";
var prompt = "When is the next total solar eclipse in Mauritius?";
var genAi = new GoogleAI(apiKey);
var model = genAi.GenerativeModel(Model.Gemini20FlashExperimental);
model.UseGoogleSearch = true;

var response = await model.GenerateContent(prompt);
Console.WriteLine(string.Join(Environment.NewLine,
    response.Candidates![0].Content!.Parts
        .Select(x => x.Text)
        .ToArray()));

More details are described in the API documentation on Search as a tool.

Grounding with Google Search

The simplest version is to toggle the boolean property UseGrounding, like so.

var apiKey = "your_api_key";
var prompt = "What is the current Google stock price?";
var genAi = new GoogleAI(apiKey);
var model = genAi.GenerativeModel(Model.Gemini15Pro002);
model.UseGrounding = true;

var response = await model.GenerateContent(prompt);
Console.WriteLine(response.Text);

In case that you would like to have more control over the Google Search retrieval parameters, use the following approach.

var apiKey = "your_api_key";
var prompt = "Who won Wimbledon this year?";
IGenerativeAI genAi = new GoogleAI(apiKey);
var model = genAi.GenerativeModel(Model.Gemini15Pro002,
    tools: [new Tool { GoogleSearchRetrieval = 
        new(DynamicRetrievalConfigMode.ModeUnspecified, 0.06f) }]);

var response = await model.GenerateContent(prompt);
Console.WriteLine(response.Text);

In either case, the returned Candidates item type has an additional property GroundingMetadata which provides the details of the Google Search based grounding

Text-and-image input

using Mscc.GenerativeAI;

var apiKey = "your_api_key";
var prompt = "Parse the time and city from the airport board shown in this image into a list, in Markdown";
var model = new GenerativeModel(apiKey: apiKey, model: Model.GeminiVisionPro);
var request = new GenerateContentRequest(prompt);
await request.AddMedia("https://raw.githubusercontent.com/mscraftsman/generative-ai/refs/heads/main/tests/Mscc.GenerativeAI/payload/timetable.png");

var response = await model.GenerateContent(request);
Console.WriteLine(response.Text);

The part of InlineData is supported by both Google AI and Vertex AI. Whereas the part FileData is restricted to Vertex AI only.

Chat conversations

Gemini enables you to have freeform conversations across multiple turns. You can interact with Gemini Pro using a single-turn prompt and response or chat with it in a multi-turn, continuous conversation, even for code understanding and generation.

using Mscc.GenerativeAI;

var apiKey = "your_api_key";
var model = new GenerativeModel(apiKey: apiKey);    // using default model: gemini-1.5-pro
var chat = model.StartChat();   // optionally pass a previous history in the constructor.

// Instead of discarding you could also use the response and access `response.Text`.
_ = await chat.SendMessage("Hello, fancy brainstorming about IT?");
_ = await chat.SendMessage("In one sentence, explain how a computer works to a young child.");
_ = await chat.SendMessage("Okay, how about a more detailed explanation to a high schooler?");
_ = await chat.SendMessage("Lastly, give a thorough definition for a CS graduate.");

// A chat session keeps every response in its history.
chat.History.ForEach(c => Console.WriteLine($"{c.Role}: {c.Text}"));

// Last request/response pair can be removed from the history.
var latest = chat.Rewind();
Console.WriteLine($"{latest.Sent} - {latest.Received}");

Use Gemini 1.5 with large files

With Gemini 1.5 you can create multimodal prompts supporting large files.

The following example uploads one or more files via File API and the created File URIs are used in the GenerateContent call to generate text.

using Mscc.GenerativeAI;

var apiKey = "your_api_key";
var prompt = "Make a short story from the media resources. The media resources are:";
IGenerativeAI genAi = new GoogleAI(apiKey);
var model = genAi.GenerativeModel(Model.Gemini15Pro);

// Upload your large image(s).
// Instead of discarding you could also use the response and access `response.Text`.
var filePath = Path.Combine(Environment.CurrentDirectory, "verylarge.png");
var displayName = "My very large image";
_ = await model.UploadMedia(filePath, displayName);

// Create the prompt with references to File API resources.
var request = new GenerateContentRequest(prompt);
var files = await model.ListFiles();
foreach (var file in files.Where(x => x.MimeType.StartsWith("image/")))
{
    Console.WriteLine($"File: {file.Name}");
    request.AddMedia(file);
}
var response = await model.GenerateContent(request);
Console.WriteLine(response.Text);

Create a tuned model

The Gemini API lets you tune models on your own data. Since it's your data and your tuned models this needs stricter access controls than API-Keys can provide.

Before you can create a tuned model, you'll need to set up OAuth for your project.

using Mscc.GenerativeAI;

var projectId = "your_google_project_id"; // the ID of a project, not its name.
var accessToken = "your_access_token";      // use `gcloud auth application-default print-access-token` to get it.
var model = new GenerativeModel(apiKey: null, model: Model.Gemini10Pro001)
{
    AccessToken = accessToken, ProjectId = projectId
};
var parameters = new HyperParameters() { BatchSize = 2, LearningRate = 0.001f, EpochCount = 3 };
var dataset = new List<TuningExample>
{    
    new() { TextInput = "1", Output = "2" },
    new() { TextInput = "3", Output = "4" },
    new() { TextInput = "-3", Output = "-2" },
    new() { TextInput = "twenty two", Output = "twenty three" },
    new() { TextInput = "two hundred", Output = "two hundred one" },
    new() { TextInput = "ninety nine", Output = "one hundred" },
    new() { TextInput = "8", Output = "9" },
    new() { TextInput = "-98", Output = "-97" },
    new() { TextInput = "1,000", Output = "1,001" },
    new() { TextInput = "thirteen", Output = "fourteen" },
    new() { TextInput = "seven", Output = "eight" },
};
var request = new CreateTunedModelRequest(Model.Gemini10Pro001, 
    "Simply autogenerated Test model",
    dataset,
    parameters);

var response = await model.CreateTunedModel(request);
Console.WriteLine($"Name: {response.Name}");
Console.WriteLine($"Model: {response.Metadata.TunedModel} (Steps: {response.Metadata.TotalSteps})");

(This is still work in progress but operational. Future release will provide types to simplify the create request.)

Tuned models appear in your Google AI Studio library.

More samples

The folders samples and tests contain more examples.

Sample console application
ASP.NET Core Minimal web application
ASP.NET Core MVP web application (work in progress!)

Troubleshooting ⚡

Sometimes you might have authentication warnings HTTP 403 (Forbidden). Especially while working with OAuth-based authentication. You can fix it by re-authenticating through ADC.

gcloud config set project "$PROJECT_ID"

gcloud auth application-default set-quota-project "$PROJECT_ID"
gcloud auth application-default login

Make sure that the required API have been enabled.

# ENABLE APIs
gcloud services enable aiplatform.googleapis.com

In case of long-running streaming requests it can happen that you get a HttpIOException: The response ended prematurely while waiting for the next frame from the server. (ResponseEnded). The root cause is the .NET runtime and the solution is to upgrade to the latest version of the .NET runtime. In case that you cannot upgrade you might disable dynamic window sizing as a workaround: Either using the environment variable DOTNET_SYSTEM_NET_HTTP_SOCKETSHTTPHANDLER_HTTP2FLOWCONTROL_DISABLEDYNAMICWINDOWSIZING

DOTNET_SYSTEM_NET_HTTP_SOCKETSHTTPHANDLER_HTTP2FLOWCONTROL_DISABLEDYNAMICWINDOWSIZING=true

or setting an AppContext switch:

AppContext.SetSwitch("System.Net.SocketsHttpHandler.Http2FlowControl.DisableDynamicWindowSizing", true);

Several issues regarding this problem have been reported on GitHub:

Using the tests 🧩

The repository contains a number of test cases for Google AI and Vertex AI. You will find them in the tests folder. They are part of the [GenerativeAI solution]. To run the tests, either enter the relevant information into the appsettings.json, create a new appsettings.user.json file with the same JSON structure in the tests folder, or define the following environment variables

GOOGLE_API_KEY
GOOGLE_PROJECT_ID
GOOGLE_REGION
GOOGLE_ACCESS_TOKEN (optional: if absent, gcloud auth application-default print-access-token is executed)

The test cases should provide more insights and use cases on how to use the Mscc.GenerativeAI package in your .NET projects.

Try it out 🤩

The following link opens an instance of the code repository in Google Project IDX.

This lets you work instantly with the code base without having to install anything.

Feedback ✨

For support and feedback kindly create issues at the https://github.com/mscraftsman/generative-ai repository.

License 📜

This project is licensed under the Apache-2.0 License - see the LICENSE file for details.

Citation 📚

If you use Mscc.GenerativeAI in your research project, kindly cite as follows

@misc{Mscc.GenerativeAI,
  author = {Kirstätter, J and MSCraftsman},
  title = {Mscc.GenerativeAI - Gemini AI Client for .NET and ASP.NET Core},
  year = {2024},
  publisher = {GitHub},
  journal = {GitHub repository},
  note = {https://github.com/mscraftsman/generative-ai}
}

Created by Jochen Kirstätter.

For Tasks:

Click tags to check more tools for each tasks

generate content start chat session list available models create tuned models work with large files

For Jobs:

software developer ai engineer web developer data scientist machine learning engineer

Alternative AI tools for generative-ai

Similar Open Source Tools

generative-ai

github

: 69

IntelliNode

IntelliNode is a javascript module that integrates cutting-edge AI models like ChatGPT, LLaMA, WaveNet, Gemini, and Stable diffusion into projects. It offers functions for generating text, speech, and images, as well as semantic search, multi-model evaluation, and chatbot capabilities. The module provides a wrapper layer for low-level model access, a controller layer for unified input handling, and a function layer for abstract functionality tailored to various use cases.

github

: 201

clarifai-python-grpc

This is the official Clarifai gRPC Python client for interacting with their recognition API. Clarifai offers a platform for data scientists, developers, researchers, and enterprises to utilize artificial intelligence for image, video, and text analysis through computer vision and natural language processing. The client allows users to authenticate, predict concepts in images, and access various functionalities provided by the Clarifai API. It follows a versioning scheme that aligns with the backend API updates and includes specific instructions for installation and troubleshooting. Users can explore the Clarifai demo, sign up for an account, and refer to the documentation for detailed information.

github

: 56

LLamaSharp

LLamaSharp is a cross-platform library to run 🦙LLaMA/LLaVA model (and others) on your local device. Based on llama.cpp, inference with LLamaSharp is efficient on both CPU and GPU. With the higher-level APIs and RAG support, it's convenient to deploy LLM (Large Language Model) in your application with LLamaSharp.

github

: 2.8k

hugging-chat-api

Unofficial HuggingChat Python API for creating chatbots, supporting features like image generation, web search, memorizing context, and changing LLMs. Users can log in, chat with the ChatBot, perform web searches, create new conversations, manage conversations, switch models, get conversation info, use assistants, and delete conversations. The API also includes a CLI mode with various commands for interacting with the tool. Users are advised not to use the application for high-stakes decisions or advice and to avoid high-frequency requests to preserve server resources.

github

: 780

Jlama

Jlama is a modern Java inference engine designed for large language models. It supports various model types such as Gemma, Llama, Mistral, GPT-2, BERT, and more. The tool implements features like Flash Attention, Mixture of Experts, and supports different model quantization formats. Built with Java 21 and utilizing the new Vector API for faster inference, Jlama allows users to add LLM inference directly to their Java applications. The tool includes a CLI for running models, a simple UI for chatting with LLMs, and examples for different model types.

github

: 569

PDEBench

PDEBench provides a diverse and comprehensive set of benchmarks for scientific machine learning, including challenging and realistic physical problems. The repository consists of code for generating datasets, uploading and downloading datasets, training and evaluating machine learning models as baselines. It features a wide range of PDEs, realistic and difficult problems, ready-to-use datasets with various conditions and parameters. PDEBench aims for extensibility and invites participation from the SciML community to improve and extend the benchmark.

github

: 793

raglite

RAGLite is a Python toolkit for Retrieval-Augmented Generation (RAG) with PostgreSQL or SQLite. It offers configurable options for choosing LLM providers, database types, and rerankers. The toolkit is fast and permissive, utilizing lightweight dependencies and hardware acceleration. RAGLite provides features like PDF to Markdown conversion, multi-vector chunk embedding, optimal semantic chunking, hybrid search capabilities, adaptive retrieval, and improved output quality. It is extensible with a built-in Model Context Protocol server, customizable ChatGPT-like frontend, document conversion to Markdown, and evaluation tools. Users can configure RAGLite for various tasks like configuring, inserting documents, running RAG pipelines, computing query adapters, evaluating performance, running MCP servers, and serving frontends.

github

: 657

mountain-goap

Mountain GOAP is a generic C# GOAP (Goal Oriented Action Planning) library for creating AI agents in games. It favors composition over inheritance, supports multiple weighted goals, and uses A* pathfinding to plan paths through sequential actions. The library includes concepts like agents, goals, actions, sensors, permutation selectors, cost callbacks, state mutators, state checkers, and a logger. It also features event handling for agent planning and execution. The project structure includes examples, API documentation, and internal classes for planning and execution.

github

: 77

swarms

Swarms provides simple, reliable, and agile tools to create your own Swarm tailored to your specific needs. Currently, Swarms is being used in production by RBC, John Deere, and many AI startups.

github

: 1.2k

GraphRAG-SDK

Build fast and accurate GenAI applications with GraphRAG SDK, a specialized toolkit for building Graph Retrieval-Augmented Generation (GraphRAG) systems. It integrates knowledge graphs, ontology management, and state-of-the-art LLMs to deliver accurate, efficient, and customizable RAG workflows. The SDK simplifies the development process by automating ontology creation, knowledge graph agent creation, and query handling, enabling users to interact and query their knowledge graphs effectively. It supports multi-agent systems and orchestrates agents specialized in different domains. The SDK is optimized for FalkorDB, ensuring high performance and scalability for large-scale applications. By leveraging knowledge graphs, it enables semantic relationships and ontology-driven queries that go beyond standard vector similarity, enhancing retrieval-augmented generation capabilities.

github

: 212

superagent-js

Superagent is an open source framework that enables any developer to integrate production ready AI Assistants into any application in a matter of minutes.

github

: 80

paxml

Pax is a framework to configure and run machine learning experiments on top of Jax.

github

: 448

ResumeFlow

ResumeFlow is an automated system that leverages Large Language Models (LLMs) to streamline the job application process. By integrating LLM technology, the tool aims to automate various stages of job hunting, making it easier for users to apply for jobs. Users can access ResumeFlow as a web tool, install it as a Python package, or download the source code from GitHub. The tool requires Python 3.11.6 or above and an LLM API key from OpenAI or Gemini Pro for usage. ResumeFlow offers functionalities such as generating curated resumes and cover letters based on job URLs and user's master resume data.

github

: 93

instructor-js

Instructor is a Typescript library for structured extraction in Typescript, powered by llms, designed for simplicity, transparency, and control. It stands out for its simplicity, transparency, and user-centric design. Whether you're a seasoned developer or just starting out, you'll find Instructor's approach intuitive and steerable.

github

: 299

oasis

OASIS is a scalable, open-source social media simulator that integrates large language models with rule-based agents to realistically mimic the behavior of up to one million users on platforms like Twitter and Reddit. It facilitates the study of complex social phenomena such as information spread, group polarization, and herd behavior, offering a versatile tool for exploring diverse social dynamics and user interactions in digital environments. With features like scalability, dynamic environments, diverse action spaces, and integrated recommendation systems, OASIS provides a comprehensive platform for simulating social media interactions at a large scale.

github

: 301

For similar tasks

generative-ai

github

: 69

floneum

Floneum is a graph editor that makes it easy to develop your own AI workflows. It uses large language models (LLMs) to run AI models locally, without any external dependencies or even a GPU. This makes it easy to use LLMs with your own data, without worrying about privacy. Floneum also has a plugin system that allows you to improve the performance of LLMs and make them work better for your specific use case. Plugins can be used in any language that supports web assembly, and they can control the output of LLMs with a process similar to JSONformer or guidance.

github

: 1.6k

llm-answer-engine

This repository contains the code and instructions needed to build a sophisticated answer engine that leverages the capabilities of Groq, Mistral AI's Mixtral, Langchain.JS, Brave Search, Serper API, and OpenAI. Designed to efficiently return sources, answers, images, videos, and follow-up questions based on user queries, this project is an ideal starting point for developers interested in natural language processing and search technologies.

github

: 4.5k

discourse-ai

Discourse AI is a plugin for the Discourse forum software that uses artificial intelligence to improve the user experience. It can automatically generate content, moderate posts, and answer questions. This can free up moderators and administrators to focus on other tasks, and it can help to create a more engaging and informative community.

github

: 67

Gemini-API

Gemini-API is a reverse-engineered asynchronous Python wrapper for Google Gemini web app (formerly Bard). It provides features like persistent cookies, ImageFx support, extension support, classified outputs, official flavor, and asynchronous operation. The tool allows users to generate contents from text or images, have conversations across multiple turns, retrieve images in response, generate images with ImageFx, save images to local files, use Gemini extensions, check and switch reply candidates, and control log level.

github

: 160

genai-for-marketing

This repository provides a deployment guide for utilizing Google Cloud's Generative AI tools in marketing scenarios. It includes step-by-step instructions, examples of crafting marketing materials, and supplementary Jupyter notebooks. The demos cover marketing insights, audience analysis, trendspotting, content search, content generation, and workspace integration. Users can access and visualize marketing data, analyze trends, improve search experience, and generate compelling content. The repository structure includes backend APIs, frontend code, sample notebooks, templates, and installation scripts.

github

: 220

generative-ai-dart

The Google Generative AI SDK for Dart enables developers to utilize cutting-edge Large Language Models (LLMs) for creating language applications. It provides access to the Gemini API for generating content using state-of-the-art models. Developers can integrate the SDK into their Dart or Flutter applications to leverage powerful AI capabilities. It is recommended to use the SDK for server-side API calls to ensure the security of API keys and protect against potential key exposure in mobile or web apps.

github

: 462

Dough

Dough is a tool for crafting videos with AI, allowing users to guide video generations with precision using images and example videos. Users can create guidance frames, assemble shots, and animate them by defining parameters and selecting guidance videos. The tool aims to help users make beautiful and unique video creations, providing control over the generation process. Setup instructions are available for Linux and Windows platforms, with detailed steps for installation and running the app.

github

: 395

For similar jobs

sweep

Sweep is an AI junior developer that turns bugs and feature requests into code changes. It automatically handles developer experience improvements like adding type hints and improving test coverage.

github

: 7.1k

teams-ai

The Teams AI Library is a software development kit (SDK) that helps developers create bots that can interact with Teams and Microsoft 365 applications. It is built on top of the Bot Framework SDK and simplifies the process of developing bots that interact with Teams' artificial intelligence capabilities. The SDK is available for JavaScript/TypeScript, .NET, and Python.

github

: 390

ai-guide

This guide is dedicated to Large Language Models (LLMs) that you can run on your home computer. It assumes your PC is a lower-end, non-gaming setup.

github

: 159

classifai

Supercharge WordPress Content Workflows and Engagement with Artificial Intelligence. Tap into leading cloud-based services like OpenAI, Microsoft Azure AI, Google Gemini and IBM Watson to augment your WordPress-powered websites. Publish content faster while improving SEO performance and increasing audience engagement. ClassifAI integrates Artificial Intelligence and Machine Learning technologies to lighten your workload and eliminate tedious tasks, giving you more time to create original content that matters.

github

: 572

chatbot-ui

Chatbot UI is an open-source AI chat app that allows users to create and deploy their own AI chatbots. It is easy to use and can be customized to fit any need. Chatbot UI is perfect for businesses, developers, and anyone who wants to create a chatbot.

github

: 27.7k

BricksLLM

BricksLLM is a cloud native AI gateway written in Go. Currently, it provides native support for OpenAI, Anthropic, Azure OpenAI and vLLM. BricksLLM aims to provide enterprise level infrastructure that can power any LLM production use cases. Here are some use cases for BricksLLM: * Set LLM usage limits for users on different pricing tiers * Track LLM usage on a per user and per organization basis * Block or redact requests containing PIIs * Improve LLM reliability with failovers, retries and caching * Distribute API keys with rate limits and cost limits for internal development/production use cases * Distribute API keys with rate limits and cost limits for students

github

: 953

uAgents

uAgents is a Python library developed by Fetch.ai that allows for the creation of autonomous AI agents. These agents can perform various tasks on a schedule or take action on various events. uAgents are easy to create and manage, and they are connected to a fast-growing network of other uAgents. They are also secure, with cryptographically secured messages and wallets.

github

: 1.2k

griptape

Griptape is a modular Python framework for building AI-powered applications that securely connect to your enterprise data and APIs. It offers developers the ability to maintain control and flexibility at every step. Griptape's core components include Structures (Agents, Pipelines, and Workflows), Tasks, Tools, Memory (Conversation Memory, Task Memory, and Meta Memory), Drivers (Prompt and Embedding Drivers, Vector Store Drivers, Image Generation Drivers, Image Query Drivers, SQL Drivers, Web Scraper Drivers, and Conversation Memory Drivers), Engines (Query Engines, Extraction Engines, Summary Engines, Image Generation Engines, and Image Query Engines), and additional components (Rulesets, Loaders, Artifacts, Chunkers, and Tokenizers). Griptape enables developers to create AI-powered applications with ease and efficiency.

github

: 2.1k