Python

Table of Contents Introduction Understanding Agentic Workflows: Core Concepts Setting Up Your Development Environment Building Your First Agent: The ReAct Pattern Tool Integration and Function Calling Memory Systems for Stateful Agents Multi-Agent Orchestration Patterns Error Handling and Reliability Patterns Observability and Debugging Agentic Systems Production Deployment Strategies Advanced Patterns: Graph-Based Workflows Security and Safety Considerations Performance Optimization Techniques Conclusion Top 10 Resources Introduction Agentic workflows represent the next evolution in AI application development. Unlike traditional request-response systems, agents autonomously plan, execute, and adapt their actions to achieve complex goals. In 2026, the landscape has matured significantly—LLM providers offer robust function calling, frameworks have standardized on proven patterns, and production deployments are increasingly common. ...

NumPy, short for Numerical Python, is the foundational library for scientific computing in Python, providing efficient multidimensional arrays and a vast collection of mathematical functions.[1][2][5] This comprehensive guide takes you from absolute beginner to advanced NumPy hero, complete with code examples, practical tips, and curated resource links. Whether you’re a data scientist, machine learning engineer, or just starting with Python, mastering NumPy will supercharge your numerical workflows. Let’s dive in! ...

Python LLM applications often start small: a FastAPI route, a call to an LLM provider, some prompt engineering, and you’re done. Then traffic grows, latencies spike, and your CPUs sit mostly idle while users wait seconds—or tens of seconds—for responses. What went wrong? One of the most common and least understood culprits is thread pool starvation. This article explains what thread pool starvation is, why it’s especially dangerous in Python LLM apps, how to detect it, and concrete patterns to avoid or fix it. ...

As an expert AI infrastructure and LLM engineer, I’ll guide you from zero Redis knowledge to production-ready LLM applications. Redis supercharges LLMs by providing sub-millisecond caching, vector similarity search, session memory, and real-time streaming—solving the core bottlenecks of cost, latency, and scalability in AI apps.[1][2] This comprehensive tutorial covers why Redis excels for LLMs, practical Python implementations with redis-py and Redis OM, integration patterns for RAG/CAG/LMCache, best practices, pitfalls, and production deployment strategies. ...

BM25 (Best Matching 25) is a probabilistic ranking function that powers modern search engines by scoring document relevance based on query terms, term frequency saturation, inverse document frequency, and document length normalization. As an information retrieval engineer, you’ll use BM25 for precise lexical matching in applications like Elasticsearch, Azure Search, and custom retrievers—outperforming TF-IDF while complementing semantic embeddings in hybrid systems.[1][3][4] This zero-to-hero tutorial takes you from basics to production-ready implementation, pitfalls, tuning, and strategic decisions on when to choose BM25 over vectors or hybrids. ...