Get Started with Qdrant Vector Database: Build your First RAG (Part 1)
16.7 هزار بار بازدید -
10 ماه پیش
-
In this tutorial, I'll guide
In this tutorial, I'll guide you through the exciting world of vector databases and show you how to harness the power of Qdrant for advanced and high-performant vector similarity search.
🔍 What is Qdrant?
Qdrant is more than just a vector database; it's your ticket to the future of Gen AI applications. With its powerful and convenient API, you can seamlessly store, search, and manage vectors with additional payload, making it perfect for various applications like neural network matching, semantic-based searches, and faceted searches.
🦀 Built for Speed and Reliability
Qdrant is crafted in Rust 🦀, ensuring exceptional speed and reliability even under high loads. It transforms your embeddings or neural network encoders into full-fledged applications for matching, searching, recommending, and much more!
🛠️ Setting up Qdrant
I'll walk you through setting up Qdrant using Docker, so you can hit the ground running with your Gen AI projects.
📚 BGE Embeddings from Huggingface
To supercharge your vector database, we're leveraging the "BGE Embeddings" model by BAAI on Huggingface, widely recognized as one of the best open-source embedding models. Get ready to unlock the full potential of your data.
🪄 Langchain Orchestration
Learn how to use Langchain as an orchestrator to streamline your workflow and make your AI projects more efficient and powerful than ever before.
If you find this tutorial helpful, don't forget to hit that thumbs-up button, leave a comment with your questions or feedback, and subscribe to the channel for more exciting Gen AI and tech tutorials.
GitHub Repo: https://github.com/AIAnytime/Build-yo...
BGE Embedding Model: https://huggingface.co/BAAI/bge-large-en
#generativeai #ai #python
🔍 What is Qdrant?
Qdrant is more than just a vector database; it's your ticket to the future of Gen AI applications. With its powerful and convenient API, you can seamlessly store, search, and manage vectors with additional payload, making it perfect for various applications like neural network matching, semantic-based searches, and faceted searches.
🦀 Built for Speed and Reliability
Qdrant is crafted in Rust 🦀, ensuring exceptional speed and reliability even under high loads. It transforms your embeddings or neural network encoders into full-fledged applications for matching, searching, recommending, and much more!
🛠️ Setting up Qdrant
I'll walk you through setting up Qdrant using Docker, so you can hit the ground running with your Gen AI projects.
📚 BGE Embeddings from Huggingface
To supercharge your vector database, we're leveraging the "BGE Embeddings" model by BAAI on Huggingface, widely recognized as one of the best open-source embedding models. Get ready to unlock the full potential of your data.
🪄 Langchain Orchestration
Learn how to use Langchain as an orchestrator to streamline your workflow and make your AI projects more efficient and powerful than ever before.
If you find this tutorial helpful, don't forget to hit that thumbs-up button, leave a comment with your questions or feedback, and subscribe to the channel for more exciting Gen AI and tech tutorials.
GitHub Repo: https://github.com/AIAnytime/Build-yo...
BGE Embedding Model: https://huggingface.co/BAAI/bge-large-en
#generativeai #ai #python
10 ماه پیش
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