Deep_Object_Pose VS qdrant

I've been doing some research in this area and there are a few deep learning solutions to this problem. For example, NVIDIA's Deep Object Pose Estimation will estimate the 6DOF pose of a known object. But you'll have to train the network if you want to detect a new object. PoseCNN, which someone else mentioned, does a similar thing. CenterPose is more interesting, as it can estimate then pose of an object from a known category; e.g. sneakers, or laptops, rather that one specific object (as DOPE and PoseCNN do).

For our last event of ArchE Week, the Ohio State Underwater Robotics Team (Website, Instagram) is hosting a workshop tonight on machine learning! The workshop is an interactive walkthrough of using machine learning solutions to make predictions. Some example problems we could be trying to solve are predicting a grade, predicting the weather, and the classic recognize a digit problem. Our team personally uses machine learning to do real-time object detection with YOLO and NVidia DOPE, so we may touch on that as well!

The chatbot and the tool function will be hosted on Langtail but what about the data and its embeddings? I wanted a vector database that is free, easy to setup and use and allows me to have the actual text data stored there too. That led me to choose Qdrant vector database. It has a generous free tier for the managed cloud option and I can store the text data directly in the payload of the embeddings.

Within the building process, in this case, our platform serves as the bridge between Flowise and Qdrant. It provides a unified platform seamlessly integrating both tools by handling all the underlying infrastructure and configuration. Qubinets automates the setup process, from instantiating a cloud environment to syncing Flowise and Qdrant to work together without any manual intervention.

This is called filtering and it is one of the key features of vector databases. Here is how a filtered vector search looks behind the scenes. We'll cover its mechanics in the following section.

At launch we support migrating to Postgres from Pinecone and Qdrant. You can vote for additional providers in the issue tracker and we'll reference that when deciding which vendor to support next.

Nylas Assistant is an AI-powered email assistant built with Laravel, Nylas, OpenAI, and Qdrant. Sync your inbox, parse emails, and store them as OpenAI embeddings in a Qdrant vector database. Interact with an OpenAI agent through a chat-like interface that provides context-aware responses based on your emails. ✉️💡

During the integration of Qdrant with Zerops, we encountered and solved several complex issues. Fortunately, Qdrant offers robust tools to monitor and manage clusters through its well-documented REST and gRPC APIs.

Overview: Qdrant is an advanced vector search engine designed for high-dimensional data processing. It provides a scalable solution for similarity search and machine learning model integration.

Has anyone had experience with qdrant (https://qdrant.tech/) as a vector store data and can speak to how txtai compares?