Author of the rpcx microservice framework, original book author, and chair of Baidu's Go CMC committee.
This article explains how function (tool) calling lets large language models like GPT or Gemini invoke external APIs, walks through defining tools with LangChain, and demonstrates a complete Python example that fetches real‑time weather data and returns a natural‑language answer.
This article explains LangChain's two core abstractions—LLM for simple text completion and ChatModel for multi‑turn conversational AI—detailing their input/output formats, practical code examples, and why ChatModel is generally preferred for modern dialogue applications.
Learn how to serialize LangChain components—including prompts, chains, and agents—using JSON and YAML, enabling reproducibility, collaboration, persistence, and decoupling, with step‑by‑step code examples for dumping objects to files and loading them back into executable LLM pipelines.
LangChain’s callback system lets developers hook into every stage of an LLM chain— from chain start/end to token generation—using built‑in handlers like StdOutCallbackHandler or custom handlers derived from BaseCallbackHandler, with examples showing constructor‑level and request‑level attachment, plus a custom handler implementation.
This article explains how LangChain’s RunnableParallel component enables true parallel execution of independent sub‑tasks, walks through concrete Python examples, compares serial versus parallel runtimes, and outlines when and why to apply this pattern for faster, more capable LLM applications.
This guide walks you through installing the LangChain library, adding model‑specific packages, verifying the setup with a Python script, configuring API keys via environment variables or a .env file, and preparing to use OpenAI‑compatible models such as DeepSeek or Qwen.
This repository offers a comprehensive, step‑by‑step LangChain tutorial series that walks developers through installation, the LangChain Expression Language, streaming, parallel execution, callbacks, serialization, model customization, prompt templates, memory, multimodal support, and advanced tools like LangGraph and LangSmith, enabling the creation of sophisticated AI applications.
Go 1.25 introduces Trace Flight Recorder, a lightweight circular‑buffer tracing tool that lets developers capture recent execution data in production with minimal overhead, and the article walks through its concepts, configuration, code demos, analysis workflow, and practical use cases.
The article analyzes the long‑standing issues of Go's original encoding/json package, explains the design and API of the experimental encoding/json/v2 and jsontext packages, and shows how the new implementation improves correctness, flexibility, and performance while preserving compatibility.
Go 1.25 introduces a container‑aware default for GOMAXPROCS that aligns the runtime’s parallelism with Kubernetes CPU limits, eliminating unnecessary throttling, improving tail‑latency, and making Go applications more production‑ready out of the box.
