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Qdrant RAG Demo 🎯

A comprehensive demonstration of SuperOptiX's RAG capabilities using Qdrant vector database for high-performance similarity search and intelligent knowledge retrieval.

Overview

This demo showcases how to integrate Qdrant - a blazingly fast vector database - with SuperOptiX's RAG system to create intelligent agents capable of retrieving and synthesizing information from large knowledge bases with exceptional performance.

This demo demonstrates:

⚡ Qdrant Vector Database Integration - Lightning-fast connection to Qdrant for high-performance vector storage

🎯 Precision Similarity Search - Intelligent retrieval using Qdrant's optimized similarity algorithms

📊 Collection Management - Efficient document storage, indexing, and retrieval workflows

🚀 Ultra-fast Query Processing - Exceptional response times with Qdrant's performance optimizations

🔄 RAG Pipeline Integration - Complete retrieval-augmented generation workflow

Prerequisites

1. Install SuperOptiX

pip install superoptix

2. Install Qdrant Dependencies

pip install qdrant-client

3. Set Up Qdrant Server

# Using Docker (recommended)
docker run -d \
  --name qdrant \
  -p 6333:6333 \
  -p 6334:6334 \
  -v $(pwd)/qdrant_storage:/qdrant/storage \
  qdrant/qdrant:latest

4. Install and Serve Model

# Install a model (if not already installed)
super model install llama3.1:8b

# Start Ollama server (if using Ollama backend)
ollama serve

Quick Start

1. Pull the Demo Agent

super agent pull rag_qdrant_demo

2. Compile the Agent

super agent compile rag_qdrant_demo

3. Run the Demo

super agent run rag_qdrant_demo --goal "What are the key features of Qdrant and how does it work with SuperOptiX?"

Key Configuration Points:

🔧 Vector Database Setup - Configured to connect to Qdrant at http://localhost:6333

📊 Collection Management - Uses superoptix_knowledge collection for document storage

🎯 Embedding Model - Leverages sentence-transformers/all-MiniLM-L6-v2 for vector generation

⚙️ Search Parameters - Optimized with top_k: 5 and similarity_threshold: 0.7

Playbook Configuration

The demo uses a specialized playbook with Qdrant-specific configurations:

rag:
  enabled: true
  retriever_type: qdrant
  config:
    top_k: 5
    chunk_size: 512
    chunk_overlap: 50
    similarity_threshold: 0.7
  vector_store:
    embedding_model: sentence-transformers/all-MiniLM-L6-v2
    url: http://localhost:6333
    collection_name: superoptix_knowledge

Customization

Modify Qdrant Connection

vector_store:
  url: http://your-qdrant-server:6333
  collection_name: YourCustomCollection
  api_key: your-api-key  # If using authentication

Adjust Search Parameters

config:
  top_k: 10  # Retrieve more documents
  similarity_threshold: 0.8  # Higher similarity threshold
  chunk_size: 1024  # Larger chunks

Configure Collection Settings

vector_store:
  collection_config:
    vectors:
      size: 384  # Vector dimension
      distance: Cosine  # Distance metric
    optimizers_config:
      memmap_threshold: 20000

Troubleshooting

Connection Issues

  • Error: "Connection refused"
  • Solution: Ensure Qdrant server is running on port 6333
  • Check: curl http://localhost:6333/collections

Collection Issues

  • Error: "Collection not found"
  • Solution: Create collection manually or check collection name
  • Check: curl http://localhost:6333/collections/superoptix_knowledge

Performance Issues

  • Slow queries: Optimize collection settings or reduce top_k
  • Memory issues: Adjust chunk_size and chunk_overlap

Use Cases

🏭 Industrial Applications - High-throughput document processing and retrieval

🎮 Gaming & Entertainment - Real-time content recommendation systems

🏥 Healthcare - Medical document analysis and patient information retrieval

💰 Financial Services - Risk assessment and regulatory compliance document search

Ready to experience lightning-fast vector search with Qdrant? ⚡

Start with this demo to understand how Qdrant's performance optimizations can supercharge your AI applications with blazingly fast knowledge retrieval and similarity search capabilities.