#REST API

Explore LocalAI, a free and open-source REST API providing compatibility with major AI specifications for on-premise deployment. Operates effectively on consumer hardware without requiring GPUs, suitable for running language models, and generating images and audio. Developed by Ettore Di Giacinto, it supports quick deployment via scripts or Docker, and offers a model gallery and community support on Discord. Keep informed about its advancements in decentralized inferencing and federated modes.

Explore a REST API crafted for high-throughput, low-latency text embedding services. Easily deploy models from HuggingFace using fast inference backends such as Torch, Optimum, and CTranslate2. Infinity enables multi-modal orchestration, allowing diverse model deployment and management. Built with FastAPI, it complies with OpenAI's API specifications, ensuring straightforward implementation. Benefit from dynamic batching and GPU acceleration with NVIDIA CUDA, AMD ROCM, etc. Learn integration with serverless platforms for optimal scalability and performance.

Discover Azure OpenAI Service to develop advanced AI solutions with robust language models. Engage in prompt engineering and content creation through hands-on workshops. Develop AI applications via practical projects, such as natural language queries and custom datasets. Targeted at power users, software engineers, and data scientists, this workshop provides clear, step-by-step guidance and real-world scenarios. Contribute to the open-source community with strong support from Microsoft.

Xendit Go SDK offers a straightforward approach to integrating the Xendit REST API in Go applications without complication. Install the package, use your API key for authorization, and access detailed API documentation. It facilitates management of invoices, payments, and refunds, among other services. Improve development processes with clear examples and solid support for connecting to Xendit's financial services effortlessly.

The Anthropic TypeScript SDK facilitates server-side interaction with the Anthropic REST API using TypeScript and JavaScript. It supports streaming responses via Server Sent Events, configurable requests, and robust error handling with retry mechanisms. Users can evaluate token usage, handle message batches, and access experimental features like tool use and the AWS Bedrock API. Configuration settings include proxy support, request timeouts, and customization of headers. The library adheres to semantic versioning and is compatible with Node.js, Deno, Bun, Cloudflare Workers, among other platforms, ensuring stable and backward-compatible development.

RestAI provides an AI as a Service platform, allowing creation and consumption of AI projects through a REST API. It supports various agent types and includes features like robust authentication, automatic VRAM management, and integration of public and local LLMs. The platform includes tools for visual projects with Stable Diffusion and LLaVA, and a router to guide inquiries to appropriate projects. Accompanied by a documented API, a user-friendly frontend, and Docker-based orchestration, RestAI offers an efficient setup for effective AI service deployment.

The workshop instructs on developing a secure chat interface for document interaction utilizing OpenAI’s GPT models and retrieval augmented generation (RAG). Features include interactive chat, third-party login, secure document management, and a dynamic REST API with row-level security. It includes detailed guidance for setting up a Supabase project, either locally or in the cloud, optimizing the frontend for file uploads, and is complemented by a comprehensive YouTube tutorial.

The OpenAI Python library enables interaction with the OpenAI REST API for Python 3.7+ applications, supporting both synchronous and asynchronous client modes. Features include streaming response support, and utilities for polling and bulk uploads. Installable via PyPI, it includes documentation and tools for issues like retries and timeouts, providing an efficient solution for AI model integration and fine-tuning tasks.

The Kotlin Compiler Server is a versatile REST server designed for compiling and executing Kotlin code. Compatible with multiple environments such as Spring Boot, Docker, AWS Lambda, and Kotless, it empowers the Kotlin Playground library and provides comprehensive API documentation through Swagger. Users can manage dependencies via build scripts and configure security policies effectively. Features include advanced logging, CORS configuration, and Gradle Build Scans for detailed insights, ensuring seamless integration and usage.

This open-source project features a high-performance RAG pipeline suitable for diverse AI applications. It supports a wide range of document formats and vector databases, offering a robust REST API that is customizable with different encoding models. The built-in code interpreter enhances computational Q&A capabilities, with session management via unique IDs. It suggests starting with the free Cloud API for ease of access. Installation is straightforward: clone the repository, set up a virtual environment, and launch with Uvicorn. It's compatible with multiple encoders and vector databases, ensuring flexibility across AI solutions.

The OpenAI Node library provides streamlined interaction with the OpenAI REST API via TypeScript and JavaScript, derived from OpenAPI specification with Stainless. It includes features such as streaming responses, automated tool invocation, and TypeScript request and response definitions. The library facilitates bulk file uploads, robust error management, and easy auto-pagination. It is also equipped with retry strategies, adjustable timeouts, and middleware support for monitoring, enabling integration with Microsoft Azure OpenAI, which broadens its application range.

VecTextSearch leverages OpenAI's language models to convert text into vectors, facilitating efficient similarity searches in the Weaviate database. Built with Golang, it provides a user-friendly REST API for locating related texts, making it useful for content recommendation, plagiarism detection, and intelligent Q&A systems. It improves similarity search capabilities, offering sophisticated solutions for finding similar articles and enhancing content recommendations.

Learn how the Allegro REST API enables efficient external software integration with Allegro services. Navigate GitHub for issue submissions and suggestions, access in-depth documentation, and subscribe to newsletters for the latest updates. Engage in a collaborative developer community within the Allegro API forum, fostering best practices and productive discussions.

LangServe provides seamless deployment of runnables as REST APIs, integrated with FastAPI and Pydantic for precise data validation. It offers efficient endpoints for invoking, batching, and streaming with high concurrency. The platform includes auto-inferred schemas, detailed API documentation, and tracing to LangSmith. With JavaScript client support and a comprehensive SDK, LangServe facilitates robust API management and interactive testing, featuring various deployment examples to optimize development workflows.

Superlinked serves as a framework and REST API server, facilitating enhanced vector search relevance by embedding metadata with data. It acts as an intermediary between data, vector databases, and backend services, allowing for the creation of custom embedding models using pre-trained encoders. The platform supports both structured and unstructured data, enabling natural language queries and custom models. Suitable for use in semantic search, recommendation systems, and analytics, it is easily deployable in production and compatible with popular vector databases such as Redis and MongoDB.

This open-source backend, developed in Go, features an embedded SQLite database with real-time subscriptions and a REST-like API. It offers file and user management, an admin dashboard, and official SDKs in JavaScript and Dart. Aimed at developers integrating JavaScript or building with the Go framework, it is ideal for custom app logic. While actively developed with backward compatibility anticipated by version 1.0, it provides opportunities for community contribution and integration, spurring continuous improvements.

Elixir is a Python-based tool for efficient indexing of C and C++ project releases, such as the Linux kernel. It uses Git for source code storage and Berkeley DB for cross-reference data, ensuring minimal system impact. A straightforward architecture enables rapid query responses, and a REST API enhances functionality. Installation requires Python 3.8+, Git 1.9+, and specified libraries. The modular design supports easy indexing and project updates, with Docker image support enhancing development efficiency.