I just published Harnessing GPT-4o’s Vision for Advanced Search: Building Image Embeddings with Qdrant link.medium.com/An7HhcIaBJb OpenAI, Qdrant , LlamaIndex 🦙

GPT-4o has an earlier knowledge cutoff compared to GPT-4-turbo.

That's not an issue, though.

Just add Qdrant to supply it with real-time knowlege! We put together a super quick tutorial that uses the new GPT-4o model.

You can build a chatbot in a few minutes if you follow…

FastEmbed now supports GPU inference! 🚀

Generate embeddings faster than ever. Check it out: 🔗qdrant.to/fastembed

(Image embeddings are coming soon. 😉)

📢 We've integrated Qdrant vector database into the 🐫 CAMEL-AI framework! This enhancement enables advanced vector similarity search capabilities, crucial for next-generation AI optimizations. 🚀

Kudos to Tianqi Xu for implementing this powerful feature! 🤝 Explore…

We've just completed testing Intel's newest 5th-generation Xeon processor with Qdrant and observed major improvements in how quickly and efficiently it handles searches. 🔍

Here's what we found:

✔ Query speeds on machines with 32 cores are now 1.38x faster.
✔ Latency for…

The ultimate guide to building agents in TypeScript is here!

This open-source repo takes you step-by-step through
⭐️ Building a basic agent
⭐️ Using a remote LLM like OpenAI
⭐️ Using a local LLM like Mixtral (via ollama)
⭐️ Giving it access to your data via a RAG query engine…

We're happy to welcome Jishan Bhattacharya and Celine Hoang as our QSoC 2024 interns! 🚀

Jishan will be improving our Web UI with a WASM-based dimension reduction visualization, while Celine will be advancing our model support with ONNX Cross Encoders in Python.

We couldn't be…

Today, at 1PM ET, we're going live with seaplane.io and Vultr to construct two globally deployed pipelines essential for any RAG application.

Join us here: buff.ly/4bNIesj

🎯Happy to share that Qdrant vector database has been integrated into #OPEA to support RAG! Thanks to the contributor Anush from @qdrant_ngine. Wei Li Huma Abidi Melissa Evers Arun Gupta and Ke. Go, #OPEA !

Speed up your RAG projects with semantic caching!

Join Daniel Romero for an in-depth exploration of how semantic caching works and how it can minimize latency and boost your app performance. 🚀

Watch the video: buff.ly/3UtjE8Z

Let's build a 100% local RAG app with ⌘R, bge embeddings, MiniLM reranking, Qdrant, and LlamaIndex 🦙

Take a look at Akshay 🚀's excellent thread below 👇

Let's RAG with Spring AI and Ollama!

This article, written by ManthaPavanKumar, goes beyond introductory tutorials. It equips you to build a robust Java-based RAG system for tackling complex document analysis.

It leverages Spring AI to organize documents, stores critical data…