📂 Agentic RAG Workflow - Intelligent Knowledge Base

🧠 Agentic RAG Workflow

An advanced AI Agent system that understands user intent and retrieves contextually relevant information from custom databases using Vector Search.

📐 System Architecture

graph TD
  User((User Query)) --> Hook[n8n Webhook]
  Hook --> Embed[Embedding]
  Embed --> Search{Vector Search}
  Search -- Query --> PDB[(Pinecone/MongoDB)]
  PDB -- Context --> AI[AI Model: Groq]
  AI --> Result[Synthesized Answer]
  Result --> Response[Send Response]

  style Search fill:#f9f,stroke:#333
  style PDB fill:#27272e,stroke:#fff,color:#fff

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🚀 Key Features

Intent Recognition: Understands exactly what the user is asking before searching.

Vector Search: High-speed retrieval of relevant data chunks from Pinecone.

Low Latency: Optimized with Groq for near-instant AI responses.

🛠️ Tech Stack

• Orchestration:

• Vector DB:

• LPU Engine:

• NoSQL: