Qdrant.md

1. Qdrant-Sematic-cache

2. Private RAG Information Extraction Engine

3. Production ready Secure and Powerful AI Implementations with Azure Services

4. LlamaIndex Agents and Qdrant’s Hybrid Search

5. Building a Traceable RAG System with Qdrant and Langtrace: A Step-by-Step Guide

6. Qdrant Internals: Immutable Data Structures

7. Data-Driven RAG Evaluation: Testing Qdrant Apps with Relari AI

8. Agentic RAG With LangGraph and Qdrant *** 9. Multimodal RAG with ColPali, crewAIInc & Qdrant

   9. Vibe Coding RAG with our MCP server
   10. Qdrant RAG with HoneyHive Tracing

Relari

8. Qdrant-Pulumi

9. Revolutionizing RAG by Integrating Vision Models for Enhanced Document Processing

10. LLM Tracing Implementation to Analyze and Visualize LLM at Scale

11. Build a GraphRAG Agent with Neo4j and Qdrant

12. Creating and Deploying Memory-Efficient Medical Agents Using Agno, Qdrant, MongoDB & LiteLLM

This project implements two domain‐specific agents — medical and legal — that split short-term conversational state (stored in MongoDB) from long-term semantic knowledge (indexed in Qdrant) under Agno’s orchestration. The intention is to validate a lightweight, memory-efficient architecture — powered by LiteLLM across multiple model providers — that can deliver real-time, context-aware support without the overhead of reloading large history embeddings, while maintaining full auditability and compliance in clinical and legal workflows.

Use Case:

• A Real-life Use-case With Codes: Build an AI-powered Research Assistant With Llama3, LlamaIndex, Qdrant, and FastAPI
• Qdrant-memory-langchain
• Better Search Results Through Intelligent Chunking and Metadata Integration
• RAG Stack using SambaNova
• Visualizing Chunking Impacts in Agentic RAG with Agno, Qdrant, RAGAS and LlamaIndex
• Twelve-Labs-Content-Recommendation-.git
• A Multi-Agent System on GCP Integrated with Slack and Trello
• GraphRAG-Qdrant
• ColiPali-Qdrant The King of Multi-Modal RAG: ColPali