Vector Space Wall

Vector Databases - a database designed to store, retrieve, and manage high-dimensional vectors, often used in machine learning and similarity search tasks. Insights on Vector databases - Selected Qdrant - It seems extremely reliable. I will teach you how to set up and use with LangChain. lnkd.in/dweQDjAC

We've integrated Qdrant into the User Embeddings: Qdrant is an open-source vector similarity search engine and database that offers a production-ready service with an easy-to-use API for storing, searching, and managing points vector with an extra payload.

𝗪𝗵𝘆 have 𝘃𝗲𝗰𝘁𝗼𝗿 𝗗𝗕𝘀 become so 𝗽𝗼𝗽𝘂𝗹𝗮𝗿 & 𝘄𝗵𝘆 are they so 𝗰𝗿𝘂𝗰𝗶𝗮𝗹 for most 𝗠𝗟 𝗮𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 (not only LLMs)? In the world of ML, everything can be represented as an embedding. A vector DB is an intelligent way to use your data embeddings as an index and perform fast and scalable searches between unstructured data points. Using this technique, I used Qdrant to implement RAG for a financial assistant powered by LLMs. But vector DBs go beyond LLMs & RAG. ineering and MLOps.

𝗖𝗵𝗲𝗰𝗸 𝗼𝘂𝘁 𝗮 𝗣𝗼𝘄𝗲𝗿𝗳𝘂𝗹 𝗩𝗲𝗰𝘁𝗼𝗿 𝗦𝗲𝗮𝗿𝗰𝗵 𝗘𝗻𝗴𝗶𝗻𝗲! 🖥️💡 Did you know that vector databases let you create powerful search engines powered by AI, as well as other applications? Qdrant is a vector similarity search engine offering a production-ready service, and a convenient API to store and manage vectors.

Thats fantastic 😎. Recently, I used it for the first time in one of the video I created using LlamaIndex. More to explore into it, hearing good things about Qdrant. Youtube video where it was used -> lnkd.in/ek8PqCEi

Built biomedchem.streamlit.app, a Streamlit app with Qdrant as a vector database to store open source (OpenStax, Rice University) textbooks . Using LangChain and OpenAI's GPT-4 model to produce search results limited to the textbook knowledge. Was very easy to setup the database and even easier to access it through the code! Used the free tier this time to try. Qdrant will be the first option to go with in my next project.

The level of support and expertise provided by the Qdrant developer team regarding my preferred vector database is truly exceptional and unparalleled!

Our experience with semantic search has shown that breaking down complex questions into smaller parts can significantly enhance its effectiveness. By doing so, the AI can better understand the context and nuances of each individual part of the question, resulting in more relevant and accurate search results.

The speed at which Qdrant ships new features that make it easier to build vectorized pipelines is absolutely mind-blowing. 👌🏼 There's absolutely no doubt in my mind that its adoption will only continue to grow in all industries and domains, and to my eyes, Qdrant is the most like-for-like replacement for Elasticsearch and Opensearch that exists today. With the upcoming sparse vector index in v1.7, it's surely going to be the best filterable + hybrid vector search solution in the market in the coming months.

We're delighted to share a significant development from HypeAuditor, an advanced platform for influencer marketing analytics. 🔍 About the Article: The article provides a comprehensive exploration of our innovative Instagram accounts vector search with Qdrant. It highlights how we're leveraging advanced technology to revolutionise the way brands and agencies connect with influencers. 🔗 lnkd.in/eiPd7ccH

🔍 Evaluating different vector databases to strike the ideal balance between usability, feature diversity, and retrieval speed (Qdrant is our favorite so far).

Vector databases are now essential in AI applications for their efficient management of machine learning vector data at scale. This has led to investments of over 350 million USD in this space. 📊 Excited to share my latest Medium article where I explore audio data retrieval and operations on the Qdrant vector database. This comprehensive guide is designed to help you understand the nuances of audio data and the potential of Qdrant vector database. 💡

Talks about #Chatgpt and #LLMs have completely overshadowed another massive game changer if you're building products with #AI: Vector Databases. Let's change that! LLMs typically convert text into high-dimensional vectors known as embeddings. These embeddings capture semantic and syntactic information about the text. Vector databases can store these embeddings efficiently, allowing for quick retrieval and comparison. When the goal is to find text segments that are semantically similar to a query (semantic search), vector databases enable quick search through large volumes of embeddings to find the most relevant matches. At Malt, the team chose Qdrant to implement our neural retriever in production. The result: 5x improvement in performance, albeit a much more sophisticated model! *Disclaimer: i don't have stakes in Qdrant ;)

I would like to share my work on Multimodal Fashion Recommendation prototype RAG pipeline. I used lnkd.in/gCd_Z6BV dataset for the task first I filtered Apparel images and do several filtration and final dataset consists of 500 images with relevant product data. I used 2 collections in Qdrant to store cloth images and product details with relevant metadata. as usual , 1. OpenAI CLIP embeddings used to image embeddings 2. Qdrant FastEmbedEmbedding used for text embeddings 3. LLaVA used for multimodal querying 4. LlamaIndex used for LLM pipeline. It really helps to enhance the performance of image recommendation by providing extra validation. Really enjoyed throughout the whole work.

Medical Code Search and Report Generation Functionality Test With this tool, you can be a medical coder that will perform your job a lot quicker, check the right boxes for the codes and you can immediately generate a report. Technology Stack: 1. Streamlit for easy data display and session state support 2. Qdrant for vector based retrieval 3. LlamaIndex for BM25 lexical search based retrieval 4. Cohere for reranking the initial retrieval results to get a better accuracy closer to the query information. Thanks to: Streamlit, Qdrant, LlamaIndex, Cohere

In the world of AI, OpenAI's GPTs have been a game-changer, especially in simplifying the use of RAG (Retrieval-Augmented generation) systems. Prior to the advent of GPTs, deploying and maintaining RAG systems was a relativelly complex task, reserved for those with deep technical expertise. My latest article sheds light on this transformation. I take you through the journey of building a basic RAG system with Qdrant and then comparing it with a custom GPT. This exploration reveals how GPTs are revolutionizing the way we design systems to deliver tangible business value. lnkd.in/eDfDaDDB

Last Saturday (11/25) I had the opportunity to talk about "Deploying Cloud Base Large Language Models (LLMs) apps in Microsoft Azure" as a Provide visibility of the Vector Search process. That demo was a major success, differentiated from previous talks, and the audience was interested in having a more in depth session in the future. And all that, spending almost nothing on OpenAI gpt-3.5-turbo and text-embedding-ada-002; using Qdrant deployed in Azure #Kubernetes Service (AKS), LangChain, and Streamlit deployed in a Docker, Inc container hosted in Azure Container Registry (ACR). No excuse to not get started building PoCs (prove-of-concept) and MVPs (minimum viable product) with LLMs.

Qdrant + FastEmbed = smooth vector database DX 🚀 FastEmbed is a lightweight Python library designed for generating embeddings, supporting various popular text models. 👉🏽 Quantized model weights 👉🏽 ONNX Runtime, no PyTorch dependency 👉🏽 CPU-first design 👉🏽 Data-parallelism for encoding large datasets You can run it inside simple VMs without GPUs and without querying OpenAI to get embedding. Then, if you use Qdrant as your vector database, it’s dead simple to index documents directly into a collection, thanks to the smooth integration.

Qdrant is an ultra-performant open-source vector database and search engine purpose-built for lightning-fast similarity queries on embedding vectors. It enables the building of customized semantic search and recommendation applications powered by neural networks.