Author of the rpcx microservice framework, original book author, and chair of Baidu's Go CMC committee.
This article explains why LLMs output plain text, introduces LangChain output parsers as the bridge to structured data, details their workflow, reviews built‑in parsers, and walks through a complete Python example that builds a prompt‑model‑parser chain to generate a JSON‑based joke.
The article explains why few-shot prompting benefits from dynamically selecting a small set of relevant examples, introduces LangChain’s ExampleSelector component, compares three selector strategies—LengthBased, SemanticSimilarity, and MaxMarginalRelevance—detailing their algorithms, advantages, drawbacks, and provides step-by-step Python code demonstrations for each.
This guide explains how LangChain structures interactions with chat models using role‑based Message objects—HumanMessage, AIMessage, SystemMessage, ToolMessage, and others—detailing their purposes, code examples, and how they enable multi‑turn AI agent workflows.
This guide explains how to use LangChain's ChatPromptTemplate and MessagesPlaceholder to construct multi‑turn, memory‑aware chatbots, detailing core message templates, dynamic history insertion, and multiple creation methods with concrete Python code examples.
This article explains why prompt templates are essential for LLM interactions, compares LangChain's PromptTemplate and ChatPromptTemplate, and walks through a complete Python example that prepares the environment, builds reusable templates, formats messages, and integrates them into an LCEL chain for chat models.
This article explains why and how to create custom LLM and chat model wrappers in LangChain by inheriting base classes, implementing core methods like _call and _generate, and demonstrates a full example with an echo‑based chat model, including streaming support and testing benefits.
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.
