THub Technical Documentation
  • Introduction
  • 🔗 LangChain
    • 🕵️ Agents
    • 🗄️Cache
    • ⛓️Chains
    • 🗨️Chat Models
    • 📁Document Loaders
    • 🧬Embeddings
    • 🧠Large Language Models(LLM)
    • 💾Memory
    • 🛡️Moderation
    • 👥Multi Agents
    • 🔀Output Parsers
    • 📝Prompts
    • 📊Record Managers
    • 📑Retrieval-Augmented Generation
    • 🔍Retrivers
    • ✂️Text Splitters
    • 🛠️Tools
    • 🔌Utilities
    • 🗃️Vector Stores
  • 🦙LLama Index
    • 🕵️ Agents
    • 🗨️Chat Models
    • 🧬Embeddings
    • 🚀Engine's
    • 🧪Response Synthesizer
    • 🛠️Tools
    • 🗃️Vector Stores
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Introduction

Welcome to the official THub documentation.

What is THub?

THub streamlines Generative AI Workflows with Low-Code, Drag-and-Drop Automation, and seamless integration for all data types and Large Language Models

Features:

🧩 No Code -Low Code Platform · Develop production ready GenAI apps at scale with no code-low code platform.

🖱️ Drag & Drop Features · Simply drag & drop data loader, LLMs, agents, chains, embedding model etc. to build your custom GenAI app.

🔄 Automated Data Pipeline · Build end to end pipeline for structured, semi-structured & Unstructured data all in one platform.

🧬 Embeddings Unstructured Data · Use industry best embedding model based on specific use case and data types.

🗄️ Vector Database · Use Vector database based on specific requirement from Pinecone, Weaviate, Qdrant etc. to store vector embeddings of unstructured data.

🔗 Langchain & LlamaIndex Framework · Develop Gen AI apps using industry leading development framework.

🤖 Integration with major LLM's · Use best of LLM's based on specific use case ranging from OpenAI, Gemini, Anthropic, Cohere etc.

💰 Pay as You use Model · Pay for what you use to keep your GenAI app cost under control

Next🔗 LangChain

Last updated 10 months ago