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Agentic RAG

Build a chatbot that answers questions from your knowledge base

Build a conversational AI chatbot grounded in your actual data. Unlike generic chatbots that hallucinate, this RAG-powered chatbot retrieves relevant context from your knowledge base before every response — delivering accurate, cited answers in a natural conversation flow.

Stack

EigenForge Agent ForgeVector databaseEmbedding modelLLM for conversationWebSocket or streaming API

Implementation

  1. 1

    Prepare your knowledge base

    Ingest your docs, FAQs, and support articles into a vector store. Use smart chunking strategies that preserve context across document sections.

  2. 2

    Design the conversation agent

    Create an agent with retrieval tools and conversation memory. Define the system prompt with your brand voice, response format, and escalation rules.

  3. 3

    Add conversation memory

    Configure short-term memory for multi-turn conversations. The agent maintains context across messages and references earlier parts of the conversation.

  4. 4

    Implement streaming responses

    Set up streaming output so users see responses as they're generated. Configure typing indicators and partial response handling.

  5. 5

    Test and deploy

    Run evaluation suites against common questions. Deploy with an embeddable widget or API endpoint for your application.

What You Get

  • Chatbot answers grounded in your actual documentation
  • Multi-turn conversation with context retention
  • Real-time streaming responses for natural UX
  • Automatic escalation to humans for out-of-scope questions

Related Blueprints

Build an Agentic RAG Pipeline

AI Knowledge Base with Agentic RAG

Document Q&A with Retrieval Augmented Generation

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