Focused on deep research and development of large-model applications. Authors of "RAG Application Development and Optimization Based on Large Models" and "MCP Principles Unveiled and Development Guide". Primarily B2B, with B2C as a supplement.
This guide walks through deploying Alibaba's open‑source Tongyi‑DeepResearch 30B MoE model locally, configuring FastAPI and A2A interfaces, implementing a ReAct‑style agent with LangGraph, setting up research tools, and testing the full UI‑API‑Agent pipeline via CLI and Streamlit.
This article outlines practical techniques—including prompt engineering, domain fine‑tuning, retrieval‑augmented generation, structured outputs, workflow constraints, model parameter control, behavior rules, risk‑based AI participation, and comprehensive governance—to curb the unpredictability of large language model agents in enterprise settings.
The article explains Google’s Test‑Time Diffusion Deep Research (TTD‑DR) paradigm, which adds iterative draft‑refinement and self‑evolution to AI agents, enabling multi‑step web retrieval, continuous “denoising” of drafts, and superior research reports compared with first‑generation Deep Research systems.
This article walks through MindsDB’s Knowledge Base feature, showing how to map data sources, create vector indexes, perform semantic search, combine multiple sources with SQL joins, automate updates via jobs, and construct powerful RAG pipelines and Data Agents for AI‑driven query answering.
This article walks through installing MindsDB, configuring its unified data access layer, and demonstrates how to query across relational databases, files, and vector stores while injecting AI models—including traditional ML, LLMs, and embedding models—directly into SQL for intelligent data retrieval and analysis.
When integrating generative AI into existing enterprise systems, architects must address reliability, performance, and security by applying patterns such as circuit breakers, retries with exponential backoff, asynchronous processing, caching, request hedging, input/output guards, sandboxes, and security proxies to ensure continuous, fast, and safe AI‑driven functionality.
This article examines how traditional high‑performance message queues and event‑driven architectures can be revitalized for AI agents, tracing the evolution of messaging middleware, highlighting key integration points, and showcasing Apache Pulsar's cloud‑native features that enable reliable, scalable, and intelligent multi‑agent systems.
This article explores why many enterprise AI projects fail to deliver value, analyzes the complexity of real‑world AI use cases, and presents a step‑by‑step demo that combines vector, keyword, numeric, and spatial queries using OceanBase as a unified multimodal data store.
This guide walks you through the fundamentals of OAuth 2.0, explains how the MCP protocol supports it, and provides a complete Python example that integrates Google OAuth 2.0 into an MCP server and client, covering configuration, code implementation, and testing procedures.
This article presents a detailed design and implementation of an LLM‑based intelligent resume matching system that combines semantic vector retrieval, structured rule filtering, multi‑dimensional weighted scoring, and natural‑language interaction to create a fast, quantifiable, and explainable hiring pipeline.
