Langchain

Building a Real-World RAG Project: Customer Support Knowledge Bot

Building a Real-World RAG Project: Customer Support Knowledge Bot

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.

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Street Learner

28 Jan 2025

Retrieval Augmented Generation (RAG): A Deep, End-to-End Guide with LangChain

Retrieval Augmented Generation (RAG): A Deep, End-to-End Guide with LangChain

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

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Street Learner

27 Jan 2025

Building Intelligent Chatbots with LangChain and OpenAI: A Complete Guide

Building Intelligent Chatbots with LangChain and OpenAI: A Complete Guide

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.

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Street Learner

26 Jan 2025

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