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
LangChain Expression Language (LCEL) is one of the most powerful and elegant additions to LangChain. It allows developers to build clean, composable, readable, and production-ready AI pipelines using a simple pipe (|) syntax.
Street Learner
26 Jan 2025
In the age of AI, chatbots have evolved from simple rule-based systems to sophisticated conversational agents powered by Large Language Models (LLMs). With OpenAI’s GPT models and the LangChain framework, building chatbots that can handle nuanced conversations, remember context, and output structured responses has never been easier. In this blog, we’ll explore step-by-step how to leverage LangChain and OpenAI to create advanced chatbots.
Street Learner
26 Jan 2025
Text classification is one of the foundational tasks in Natural Language Processing (NLP). Whether it’s detecting emotions in social media posts, categorizing customer reviews, or identifying spam emails, text classification models are indispensable. Among state-of-the-art models, XLNet has emerged as a powerful alternative to traditional BERT and GPT models.
Street Learner
23 Jan 2025
In the field of Natural Language Processing (NLP), question-answering (QA) systems have become one of the most exciting applications of deep learning. BERT (Bidirectional Encoder Representations from Transformers) is a state-of-the-art model designed for understanding context in text, making it ideal for QA tasks.
Street Learner
22 Jan 2025
In recent years, Hugging Face Transformers has emerged as one of the most powerful libraries for Natural Language Processing (NLP) and AI applications. From sentiment analysis to named entity recognition and zero-shot classification, Hugging Face provides pre-trained models that make it easy to perform complex NLP tasks with minimal code.
Street Learner
22 Jan 2025