Langchain + Qdrant Cloud | Pinecone FREE Alternative (20GB) | Tutorial скачать в хорошем качестве

Langchain + Qdrant Cloud | Pinecone FREE Alternative (20GB) | Tutorial

Do you want an alternative to Pinecone for your Langchain applications? Let's delve into the world of vector databases with Qdrant. If you're interested in harnessing the power of artificial intelligence and language models like ChatGPT from OpenAI, you're in the right place. -------------LINKS 👉 Qdrant: https://qdrant.tech/ 👉 Github: https://github.com/alejandro-ao/qdran... 📕 Colab: https://colab.research.google.com/dri... 💬 Join the Discord Help Server - https://link.alejandro-ao.com/HrFKZn ❤️ Buy me a coffee... or a beer (thanks): https://link.alejandro-ao.com/l83gNq ✉️ Join the mail list: https://link.alejandro-ao.com/AIIguB ------------------------------------- Qdrant is a powerful vector similarity search engine with a user-friendly API that enables you to effortlessly store, search, and manage vectors along with additional payloads. Its expanding features support various applications such as neural network or semantic-based matching, faceted search, and more. In this tutorial, we'll guide you through the process of configuring a free cluster in Qdrant's hosted cloud service. We'll also walk you through deploying a vector database to the cloud and connecting it to an application using Langchain. This combination proves incredibly useful for creating persistent databases for LLM (Language Model) applications. By leveraging the capabilities of Qdrant, Langchain, and Pinecone, you'll gain valuable insights into building robust and efficient systems for natural language processing (NLP) tasks. You'll also get acquainted with other essential tools such as Chroma, Faiss, and OpenAI embeddings, which are vital for working with word embeddings, text embeddings, and more. Whether you're a seasoned developer or just starting your AI journey, this Pinecone tutorial in Python will provide you with the necessary knowledge to kickstart your vector database endeavors. So, join us and unlock the immense potential of Qdrant as we navigate the exciting world of persistent embeddings and language models. Keywords: Qdrant, Pinecone, vector database, Langchain, ChatGPT, OpenAI, language models, LLM, artificial intelligence, LlamaIndex, Chroma, Faiss, persistent database, OpenAI embeddings, persistent embeddings, word embeddings, text embeddings, NLP, natural language processing, Pinecone tutorial Python, Python. -------------------------------------------------------------------------------- ⏰ Time stamps 0:00 Intro 1:53 Create your Free Cluster 4:40 See your Collections 8:22 Create a Qdrant client in Python 12:17 Create a Collection 17:53 Create a vector store 21:38 Add documents to the cloud 30:23 Chat with your documents 35:39 Plug it to an App with GUI