Retrieval Augmented Generation (RAG)

Unlock the full potential of your AI with RAG powered by Qdrant. Dive into a new era of intelligent applications that understand and interact with unprecedented accuracy and depth.

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Highest RPS

Qdrant leads with top requests-per-second, outperforming alternative vector databases in various datasets by up to 4x.

Fast Retrieval

Qdrant achieves the lowest latency, ensuring quicker response times in data retrieval: 3ms response for 1M Open AI embeddings.

Multi-Vector Support

Integrate the strengths of multiple vectors per document, such as title and body, to create search experiences your customers admire.

Built-in Compression

Significantly reduce memory usage, improve search performance and save up to 30x cost for high-dimensional vectors with Quantization.

Qdrant integrates with all leading LLM providers and frameworks

Cohere

Integrate Qdrant with Cohere's co.embed API and Python SDK.

Gemini

Connect Qdrant with Google's Gemini Embedding Model API seamlessly.

OpenAI

Easily integrate OpenAI embeddings with Qdrant using the official Python SDK.

Aleph Alpha

Integrate Qdrant with Aleph Alpha's multimodal, multilingual embeddings.

Jina AI

Easily integrate Qdrant with Jina AI's embeddings API.

AWS Bedrock

Utilize AWS Bedrock's embedding models with Qdrant seamlessly.

LangChain

Qdrant seamlessly integrates with LangChain for LLM development.

LlamaIndex

Qdrant integrates with LlamaIndex for efficient data indexing in LLMs.

RAG Evaluation

Retrieval Augmented Generation (RAG) harnesses large language models to enhance content generation by effectively leveraging existing information. By amalgamating specific details from various sources, RAG facilitates accurate and relevant query results, making it invaluable across domains such as medical, finance, and academia for content creation, Q&A applications, and information synthesis.

However, evaluating RAG systems is essential to refine and optimize their performance, ensuring alignment with user expectations and validating their functionality.

We work with the best in the industry on RAG evaluation:

Learn how to get started with Qdrant for your RAG use case

Question and Answer System with LlamaIndex

Combine Qdrant and LlamaIndex to create a self-updating Q&A system.

Jupyter Notebook

Retrieval Augmented Generation with OpenAI and Qdrant

Basic RAG pipeline with Qdrant and OpenAI SDKs.

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See how Dust is using Qdrant for RAG

Dust provides companies with the core platform to execute on their GenAI bet for their teams by deploying LLMs across the organization and providing context aware AI assistants through RAG.

Read Case Study