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  • Introduction
  • Quickstart
    • Starter guide
    • Core Concepts
      • Stack Type
      • Data Loader
      • Inputs/Outputs
      • Text Splitters
      • Embedding Model
      • Vector Store
      • Large Language Model
      • Memory
      • Chain
    • Testing Stack
    • Deployment
    • Knowledge Base
    • Organization and Teams
    • Secret Keys
    • Logs
  • Components
    • Inputs
    • Outputs
    • Document Loaders
    • Prompts
    • Text Splitters
    • Embeddings
    • Vector Store
    • Retrievers
    • Multi Modals
    • Agents
    • Large Language Models
    • Memories
    • Chains
    • Output Parsers
  • Customization
    • Writing Custom Components in GenAI Stack
    • Build your own custom component
    • Define parameters used for required components
  • Usecases
    • Simple QA using Open Source Large Language Models
    • Multilingual Indic Language Translation
    • Document Search and Chat
    • Chat with Multiple Documents
  • Terminologies
    • RAG - Retrieval Augmented Generation
    • Hybrid Search - Ensemble Retriever
  • REST APIs
    • GenAI Stack REST APIs
    • Chat API Reference
    • Text Generation API Reference
    • Rate Limiting and Sleep Mode
  • Troubleshooting
    • How to verify what is loaded and chunked from the loader?
  • Acknowledgements
    • Special Mentions
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  1. Quickstart

Core Concepts

Creating a Generative AI-LLM application pipeline can seem daunting without prior familiarity with Langchain and RAG. However, our quickstart guide is designed to make the process remarkably straightforward. In this tutorial, we'll walk you through the steps of building a pipeline flow in a simplified manner, ensuring that even those without prior knowledge of Langchain and RAG.

Note:

  • Each component features input and output connectors represented by circular icons. Hovering over these icons reveals compatible components, simplifying the process of constructing a seamless flow within the system.

  • Moreover, each component encompasses a range of parameters that must be provided by the user, including API keys, input file/URL, and more. These values can be modified by clicking on the edit icon (⋯). Users have the flexibility to enable or disable parameters based on specific requirements.

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Last updated 1 year ago

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