So while looking around for things for this weeks issues I revisited jina ai to see whats new in the repo. Jina AI, which was a search centered AI library has put so many great tools out there now for the wave of AI tools we can and should be using and building with. So I am focusing this issue on Jina AI in particular
This is linked with the playlist for you to check out other videos from this conference.
Jina-serve is a framework for building and deploying AI services that communicate via gRPC, HTTP and WebSockets. Scale your services from local development to production while focusing on your core logic.
Native support for all major ML frameworks and data types
High-performance service design with scaling, streaming, and dynamic batching
LLM serving with streaming output
Built-in Docker integration and Executor Hub
One-click deployment to Jina AI Cloud
Enterprise-ready with Kubernetes and Docker Compose support
💻: Python
⭐ 21.1k 👀 213 🍴 2.2k 🚧 23
CLIP-as-service is a low-latency high-scalability service for embedding images and text. It can be easily integrated as a microservice into neural search solutions.
⚡ Fast: Serve CLIP models with TensorRT, ONNX runtime and PyTorch w/o JIT with 800QPS[*]. Non-blocking duplex streaming on requests and responses, designed for large data and long-running tasks.
🫐 Elastic: Horizontally scale up and down multiple CLIP models on single GPU, with automatic load balancing.
🐥 Easy-to-use: No learning curve, minimalist design on client and server. Intuitive and consistent API for image and sentence embedding.
👒 Modern: Async client support. Easily switch between gRPC, HTTP, WebSocket protocols with TLS and compression.
🍱 Integration: Smooth integration with neural search ecosystem including Jina and DocArray. Build cross-modal and multi-modal solutions in no time.
💻: Python
⭐ 12.4k 👀 221 🍴 2.1k 🚧 302
DiscoArt is an elegant way of creating compelling Disco Diffusion[*] artworks for generative artists, AI enthusiasts and hard-core developers. DiscoArt has a modern & professional API with a beautiful codebase, ensuring high usability and maintainability. It introduces handy features such as result recovery and persistence, gRPC/HTTP serving w/o TLS, post-analysis, easing the integration to larger cross-modal or multi-modal applications
💻: Python
⭐ 3.8k 👀 34 🍴 250 🚧 31
Your LLMs deserve better input.
Reader does two things:
Read: It converts any URL to an LLM-friendly input with
https://r.jina.ai/https://your.url. Get improved output for your agent and RAG systems at no cost.Search: It searches the web for a given query with
https://s.jina.ai/your+query. This allows your LLMs to access the latest world knowledge from the web.
💻: TypeScript
⭐ 6.8k 👀 40 🍴 540 🚧 54
Learn more about them on https://jina.ai/
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