Qdrant Reviews and details

Questions & Answers

1. Why should a person choose Qdrant over its competitors?

   Advanced Features, Performance, Scalability, Developer Experience, and Resources Saving.

2. What makes Qdrant unique?

   Highest performance https://qdrant.tech/benchmarks/, scalability and ease of use.

3. Which are the primary technologies used for building your product?

   Qdrant is written completely in Rust. SDKs available for all popular languages Python, Go, Rust, Java, .NET, etc.

Social recommendations and mentions

• Hindi-Language AI Chatbot for Enterprises Using Qdrant, MLFlow, and LangChain

  Great. Now that we have the embeddings, we need to store them in a vector database. We will be using Qdrant for this purpose. Qdrant is an open-source vector database that allows you to store and query high-dimensional vectors. The easiest way to get started with the Qdrant database is using the docker. - Source: dev.to / 3 days ago

• Boost Your Code's Efficiency: Introducing Semantic Cache with Qdrant

  I took Qdrant for this project. The reason was that Qdrant stands for high-performance vector search, the best choice against use cases like finding similar function calls based on semantic similarity. Qdrant is not only powerful but also scalable to support a variety of advanced search features that are greatly useful to nuanced caching mechanisms like ours. - Source: dev.to / 10 days ago

• Ask HN: Has Anyone Trained a personal LLM using their personal notes?

  I'm currently looking to implement locally, using QDrant [1] for instance. I'm just playing around, but it makes sense to have a runnable example for our users at work too :) [2]. [1]. https://qdrant.tech/. - Source: Hacker News / about 1 month ago

• Open-source Rust-based RAG

  There are much better known examples, such as https://qdrant.tech/ and https://github.com/lancedb/lancedb. - Source: Hacker News / about 2 months ago

• Perform Image-Driven Reverse Image Search on E-Commerce Sites with ImageBind and Qdrant

  Initialize the Qdrant Client with in-memory storage. The collection name will be “imagebind_data” and we will be using cosine distance. - Source: dev.to / 2 months ago

• 7 Vector Databases Every Developer Should Know!

  Qdrant is an open-source vector search engine optimized for performance and flexibility. It supports both exact and approximate nearest neighbor search, providing a balance between accuracy and speed for various AI and ML applications. - Source: dev.to / 3 months ago

• Step-by-Step Guide to Building LLM Applications with Ruby (Using Langchain and Qdrant)

  Qdrant serves as a vector database, optimized for handling high-dimensional data typically found in AI and ML applications. It's designed for efficient storage and retrieval of vectors, making it an ideal solution for managing the data produced and consumed by AI models like Mistral 7B. In our setup, Qdrant handles the storage of vectors generated by the language model, facilitating quick and accurate retrievals. - Source: dev.to / 3 months ago

• Qdrant - Using FastEmbed for Rapid Embedding Generation: A Benchmark and Guide

  Qdrant is a modern, open-source vector search engine specifically designed for handling and retrieving high-dimensional data, such as embeddings. It plays a crucial role in various machine learning and data analytics applications, particularly those involving similarity searches in large datasets. Understanding Qdrant's capabilities and architecture is key to leveraging its full potential. - Source: dev.to / 4 months ago

• Exploring GPTs: ChatGPT in a trench coat?

  This is undocumented (frustrating) but it looks like it's chunking them, running embeddings on the chunks and storing the results in a https://qdrant.tech/ vector database. We know it's Qdrant because an error message leaked that detail: https://twitter.com/altryne/status/1721989500291989585. - Source: Hacker News / 6 months ago

• I've changed my mind about Code Interpretor

  As an open-source and self-hosted solution, developers can deploy their own version of the plugin and register it with ChatGPT. The plugin leverages OpenAI embeddings and allows developers to choose a vector database (Milvus, Pinecone, Qdrant, Redis, Weaviate or Zilliz) for indexing and searching documents. Information sources can be synchronized with the database using webhooks. Source: 10 months ago

• How do I implement FAISS when using Pinecone?

  There are plenty of other vector DBs, for example, Qdrant. Qdrant Cloud has a generous free tier if you want to use SaaS, but since it's Open Source, you can also run it locally. Source: 11 months ago

• Open source vector databases?

  There are plenty of options, but I'd suggest Qdrant on Docker: https://qdrant.tech/. Source: 11 months ago

• Ask HN: Who is hiring? (June 2023)

  Qdrant https://qdrant.tech | Berlin / Remote (worldwide). - Source: Hacker News / 11 months ago

• Building a Vector Database with Rust to Make Use of Vector Embeddings

  Welcome in the vector search space. I started at Qdrant last month and we are also open source and fully written in Rust. Here's to learning from each other to bring the whole space forward. Source: 11 months ago

• Ask ATP ... using LLMs!

  It's mostly Rust with a sprinkling of python for some of the ML stuff. The transcription is done via whisper (https://github.com/ggerganov/whisper.cpp) and the search is handle via standard lexical search (https://github.com/quickwit-oss/tantivy) combined with a vector database (https://qdrant.tech) to find relevant pieces of content. Source: 12 months ago

• Looking for advice on an Enterprise Vector Database to serve as long-term chatbot memory.

  How about Qdrant? It has an on-premise mode, so you don't need to send the data to any external service. Source: 12 months ago

• Looking for a comprehensive debutant guide to use ChromaDB with detailed documentation

  Have you checked Qdrant? It's Open Source and is also integrated with Lanchain and offers local mode, even without a server. However, if you want to scale things up, you can move to on-premise or cloud without changing anything in your client code. Source: 12 months ago

• Get rewarded for Rust Open Source contribution. 💰🦀

  Qdrant OSS team now has a new approach to picking the right candidates to join the #Rust team. Previously we followed the well-known coding task approach, but since we are an #opensource company, we now let candidates contribute first. Not a new idea, right? But we have a pretty fair deal. If there is an open Rust developer position and you are interested in applying for it, pick a task on our GitHub issue... Source: about 1 year ago

• Top 10 Best Vector Databases & Libraries

  Qdrant (5.8k ⭐) → A vector similarity search engine and vector database. It provides a production-ready service with a convenient API to store, search, and manage points - vectors with an additional payload. Qdrant is tailored to extended filtering support. It makes it useful for all sorts of neural-network or semantic-based matching, faceted search, and other applications. - Source: dev.to / about 1 year ago

• Alternatives to Pinecone? (Vector databases) [D]

  I would describe Qdrant as an beautifully simple vector database. Definitely worth a try, it has an forever-free tier as well. Source: about 1 year ago

• Vector Databases as Memory for your AI Agents

  There are some more players in the industry. If you want a local mode, on-premise deployment and cloud available, Qdrant offers everything at once and is integrated with the modern stack, like LangChain. Source: about 1 year ago