Focused on AI programming, sharing ready-to-use development efficiency solutions.
The article explains LangGraph’s checkpoint system that lets agents retain dialogue context, detailing the InMemorySaver for development and PostgresSaver for production, how to use checkpointer.put/get, thread_id for session isolation, manual state manipulation, and time‑travel replay, with full Python examples.
This tutorial explains the ReAct (Reason‑Act‑Observe) loop in LangGraph, shows how to control execution branches with conditional edges, provides a full hand‑written agent example, demonstrates the convenience of the prebuilt create_react_agent, and covers multi‑turn dialogue, streaming output, and loop‑count limits.
This article walks through LangGraph's three‑step tool‑calling workflow, shows how to define tools with the @tool decorator, demonstrates executing calls via ToolNode, provides a complete agent example, explains bind_tools, shares best‑practice guidelines, streaming execution, and a concise recap.
This article explains LangGraph’s two state‑update modes—overwrite and merge—shows how to use Annotated with custom reducers such as operator.add or add_messages, demonstrates when reducers run, and provides full Python examples, including persistence with checkpointers and custom merge functions.
This article explains how LangGraph’s graph‑based StateGraph lets you model agent workflows visually, contrasting it with LangChain’s high‑level API, detailing the three core components, showing complete Python examples, and highlighting benefits such as easier debugging, extensibility, and checkpoint support.
This article presents a complete LangChain architecture diagram, quick‑reference tables for core modules, a side‑by‑side comparison with LlamaIndex and Haystack, practical interview Q&A covering advantages, RAG optimization, Agent vs Chain differences, token‑cost reduction, and a seven‑day recap with advanced learning paths.
This article explains why LLMs need memory, compares BufferMemory and ConversationSummaryBufferMemory, demonstrates how to build memory‑aware chains with LangChain Expression Language (LCEL), and shows practical examples including translation, RAG, and callbacks for monitoring chain execution.
This article explains how LangChain agents use a ReAct (Reasoning‑Acting) loop to decide when and which tools to invoke, shows built‑in and custom tools via the @tool decorator, compares ReAct with GPT‑4 native tool calling, and demonstrates a research‑assistant workflow that cuts report drafting time from hours to minutes.
This tutorial walks through the complete RAG pipeline—indexing documents into a vector store, retrieving relevant chunks with similarity, Top‑K, and MMR methods, and generating answers using LCEL chains, culminating in a multi‑turn internal knowledge‑base chatbot that cuts query latency below 100 ms.
Because LLMs have limited context windows, the article shows how to feed a 50‑page PDF to an AI by loading the document, splitting it into manageable chunks, converting each chunk into embeddings, storing them in a vector database, and then retrieving the most relevant passages for answering questions.
