In the previous two parts, we built a strong foundation of LangGraph fundamentals—nodes, edges, message states, conditional routing, reducers, summarization loops, and graph orchestration.
Street Learner
05 Feb 2025
In Part-1 of this LangGraph Blog Series, we understood the foundation of LangGraph — Graph structure, Nodes, Edges, Conditional Routing, State system, and Graph Execution. Now in Part-2, we upgrade our knowledge and turn LangGraph into a real conversation system.
Street Learner
02 Feb 2025
Modern AI workflows need more than just a prompt and a model call. Real applications require memory, state transitions, branching logic, routing decisions, and orchestration of multiple AI models. This is where LangGraph enters the scene.
Street Learner
30 Jan 2025
In this tutorial, we’ll build a Retrieval-Augmented Generation (RAG) chatbot for a customer support knowledge base. This bot will be able to answer queries using company manuals, FAQs, and guides. We will go from document ingestion → splitting → embeddings → vectorstore → retrieval → generation, step by step.
Street Learner
28 Jan 2025
Large Language Models (LLMs) like GPT-4 are powerful, but they suffer from three fundamental limitations: They do not know your private or latest data – an LLM cannot answer questions about your PDFs, internal documents, or databases unless that information is explicitly provided at runtime. They hallucinate – when an LLM is unsure, it may confidently generate incorrect information. They lack traceability – answers are not grounded in verifiable sources. Retrieval-Augmented Generation (RAG) is the architectural pattern designed to solve these problems
Street Learner
27 Jan 2025