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Vector Search Manuals
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RAG & GenAI
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Qdrant Internals
Data Exploration
Machine Learning
RAG & GenAI
Practical Examples
Ecosystem
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  • RAG & GenAI

RAG & GenAI

Leverage Qdrant for Retrieval-Augmented Generation (RAG) and build AI Agents

Preview
What is Agentic RAG? Building Agents with Qdrant

Agents are a new paradigm in AI, and they are changing how we build RAG systems. Learn how to build agents with Qdrant and which framework to choose.

Kacper Łukawski

November 22, 2024

Preview
Optimizing RAG Through an Evaluation-Based Methodology

Learn how Qdrant-powered RAG applications can be tested and iteratively improved using LLM evaluation tools like Quotient.

Atita Arora

June 12, 2024

Preview
Semantic Cache: Accelerating AI with Lightning-Fast Data Retrieval

Semantic cache is reshaping AI applications by enabling rapid data retrieval. Discover how its implementation benefits your RAG setup.

Daniel Romero, David Myriel

May 07, 2024

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What is RAG: Understanding Retrieval-Augmented Generation

Explore how RAG enables LLMs to retrieve and utilize relevant external data when generating responses, rather than being limited to their original training data alone.

Sabrina Aquino

March 19, 2024

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Is RAG Dead? The Role of Vector Databases in Vector Search | Qdrant

Uncover the necessity of vector databases for RAG and learn how Qdrant's vector database empowers enterprise AI with unmatched accuracy and cost-effectiveness.

David Myriel

February 27, 2024

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