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Build a self-hosted RAG chatbot with dual-agent LLM pipeline and failover

By PulseAugur Editorial · [2 sources] ·

This article details the construction of a self-hosted Retrieval-Augmented Generation (RAG) chatbot designed to provide accurate answers grounded in a controlled knowledge base. The system employs a dual-agent pipeline for distinct reasoning steps: one agent selects tools and arguments for retrieval, and a second agent refines the retrieved information into a final, grounded answer. A key feature is the automatic failover mechanism, which seamlessly switches to a backup LLM if the primary model is unavailable or rate-limited, ensuring query continuity. AI

IMPACT Provides a technical blueprint for building more robust and reliable RAG chatbots with automatic LLM failover.

RANK_REASON The article describes the technical implementation of a chatbot system, not a new product release or frontier research.

Read on Medium — MCP tag →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Build a self-hosted RAG chatbot with dual-agent LLM pipeline and failover

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0 / 100
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Newsworthiness bucket
Tool
The article describes the technical implementation of a chatbot system, not a new product release or frontier research.
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2 independent sources
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Topics
product, infra
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High
Clearly on-topic for AI-industry coverage.
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115 days old
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Full methodology in our editorial standards.

COVERAGE [2]

  1. Medium — MCP tag TIER_1 English(EN) · Musa Peker ·

    Building a Resilient Multi-Agent Text-to-SQL Chatbot with MCP, LangGraph & Chainlit

    <div class="medium-feed-item"><p class="medium-feed-image"><a href="https://medium.com/@msapeker/building-a-resilient-multi-agent-text-to-sql-chatbot-with-mcp-langgraph-chainlit-f5a5bf593329?source=rss------mcp-5"><img src="https://cdn-images-1.medium.com/max/1700/1*TGHT-3Mv85Pv3…

  2. dev.to — LLM tag TIER_1 English(EN) · PAWAN YADAV (AI Engineer) ·

    Building a Self-Hosted RAG Chatbot with a Dual-Agent LLM Pipeline (and Automatic LLM Failover)

    <p>Over the past few weeks I built a Retrieval-Augmented Generation (RAG) chatbot from the ground up — one that answers strictly from a knowledge base I control, supports role-based access for regular users vs. admins, and never makes things up. In this post I want to walk throug…

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