Qdrant is a leading open-source high-performance Vector Database written in Rust with extended metadata filtering support and advanced features. It deploys as an API service providing a search for the nearest high-dimensional vectors. With Qdrant, embeddings or neural network encoders can be turned into full-fledged applications. Powering vector similarity search solutions of any scale due to a flexible architecture and low-level optimization. Qdrant is trusted and high-rated by Machine Learning and Data Science teams of top-tier companies worldwide.
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Qdrant's answer:
Advanced Features, Performance, Scalability, Developer Experience, and Resources Saving.
Qdrant's answer:
Highest performance https://qdrant.tech/benchmarks/, scalability and ease of use.
Qdrant's answer:
Qdrant is written completely in Rust. SDKs available for all popular languages Python, Go, Rust, Java, .NET, etc.
My journey with GPT-4 as a novice programmer has been nothing short of remarkable. I used it to write a game, and despite my limited programming knowledge, I was astonished by the results.
It makes coding suggestions, completes my code, and even identifies bugs, which has been a game-changer for me. It feels like having a co-programmer who anticipates my needs and guides me in the right direction.
When I started using ChatGPT in november of 2022, it was very smart. I used it quite a lot to help me rephrase my emails better, to tidy up my writing, to do some simple math that I'm too lazy to do myself, etc. But recently I noticed that it has become less and less helpful, it does things that I specifically asked it NOT to do, it struggles to rephrase my texts the way I want it to, it makes mistakes in basic math and provides all sorts of incorrect information. Now it annoys me way more than it helps me.
ChatGPT is a powerful, open-source language model AI tool, very fastly response query to users.
Based on our record, ChatGPT seems to be a lot more popular than Qdrant. While we know about 815 links to ChatGPT, we've tracked only 40 mentions of Qdrant. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
By following these steps, developers can train ChatGPT on their own data. This will allow to give personalized, accurate, and domain-specific responses. Keep in mind that this process requires technical skills and can take more time than using no-code platforms. - Source: dev.to / 5 days ago
ChatGPT prompt: Act like a senior software developer mentor. Explain to me in the simplest way possible what javascript Symbols are, making very basic examples that DO NOT use "foo" "bar" words. Make small sentences and ask me often if I am able to understand. Thank you. - Source: dev.to / 5 days ago
AI and LLMs such as ChatGPT are amazing at what they do, but they suffer from the lack of "an opposable thumb", to use an analogy. This implies that as stand alone products, they can't really do much, if anything at all. - Source: dev.to / 10 days ago
Prompt Engineering is the art of instructing an LLM such as ChatGPT to do what you want it to do, using nothing but natural language, logic, and reason. The process is probably easily within reach of 80% of the world's population, while less than 0.3% of the world's population knows how to code. - Source: dev.to / 12 days ago
"ChatGPT" in this case means the frontends https://chat.openai.com and https://chatgpt.com, not the API. - Source: Hacker News / 14 days ago
Vector Databases: Qdrant for efficient data storage and retrieval. - Source: dev.to / 7 days ago
AgentCloud uses Qdrant as the vector store to efficiently store and manage large sets of vector embeddings. For a given user query the RAG application fetches relevant documents from vector store by analyzing how similar their vector representation is compared to the query vector. - Source: dev.to / about 1 month ago
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 / about 2 months ago
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 / about 2 months ago
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 / 3 months ago
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