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กำลังแสดงโพสต์ที่มีป้ายกำกับ GenAI

8 Top MCP Server Tools to Boost Your Development Workflow to Future

Top MCP Server Tool for yout Development Workflow 1. Claude Agent SDK  Languages supported Python TypeScript Production deployments with Claude models, security-conscious applications requiring explicit tool allowlists Link:  https://claude.ai/ 2. OpenAI Agents SDK Languages supported Python TypeScript Agent handoffs and delegation patterns, responsive UIs with streaming tool calls, lightweight composable agents Link:  https://openai.com/ 3. Microsoft Agent Framework is an open-source development kit for building AI agents and multi-agent workflows for .NET and Python. It brings together and extends ideas from Semantic Kernel and AutoGen projects, combining their strengths while adding new capabilities. Built by the same teams, it is the unified foundation for building AI agents going forward Link:  https://azure.microsoft.com/en-us/solutions/ai/ 4. Google Agent Development Kit is a flexible and modular framework for developing and deploying AI agents. W...

What is and How to Bulid the Milvus is an open-source vector database built for GenAI applications,

What is Milvus? Milvus is an open-source vector database built for GenAI applications. Install with pip, perform high-speed searches, and scale to tens of billions of vectors with minimal performance loss. Milvus is an open-source vector database that suits AI applications of every size from running a demo chatbot in Jupyter notebook to building web-scale search that serves billions of users. In this guide, we will walk you through how to set up Milvus locally within minutes and use the Python client library to generate, store and search vectors. Concept of Milvus Milvus is an open-source, cloud-native vector database designed for high-performance similarity search on massive vector datasets. Built on top of popular vector search libraries including Faiss, HNSW, DiskANN, and SCANN, it empowers AI applications and unstructured data retrieval scenarios. Before proceeding, familiarize yourself with the basic principles of embedding retrieval.