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Comprehensive RAG 시스템 Tools for Every Need

Get access to RAG 시스템 solutions that address multiple requirements. One-stop resources for streamlined workflows.

RAG 시스템

  • LLM-Powered RAG System

    An open-source framework enabling retrieval-augmented generation chat agents by combining LLMs with vector databases and customizable pipelines.
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    What is LLM-Powered RAG System?
    LLM-Powered RAG System is a developer-focused framework for building retrieval-augmented generation (RAG) pipelines. It provides modules for embedding document collections, indexing via FAISS, Pinecone, or Weaviate, and retrieving relevant context at runtime. The system uses LangChain wrappers to orchestrate LLM calls, supports prompt templates, streaming responses, and multi-vector store adapters. It simplifies end-to-end RAG deployment for knowledge bases, allowing customization at each stage—from embedding model configuration to prompt design and result post-processing.

  • Controllable RAG Agent

    An open-source Python framework to build Retrieval-Augmented Generation agents with customizable control over retrieval and response generation.
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    What is Controllable RAG Agent?
    The Controllable RAG Agent framework provides a modular approach to building Retrieval-Augmented Generation systems. It allows you to configure and chain retrieval components, memory modules, and generation strategies. Developers can plug in different LLMs, vector databases, and policy controllers to adjust how documents are fetched and processed before generation. Built on Python, it includes utilities for indexing, querying, conversation history tracking, and action-based control flows, making it ideal for chatbots, knowledge assistants, and research tools.
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