I was confused for a bit but there is no relation to https://haystack.deepset.ai/. - Source: Hacker News / about 1 month ago

People like to be on the AI bandwagon, but to have good AI models, you need good LLM (large language models). Welcome to Haystack, it's an end-to-end LLM framework that allows you to build applications powered by LLMs, Transformer models, vector search and more. The latest version is a rewrite of the Haystack framework, and includes a new package, powerful pipelines, customisable components, prompt templating, and... - Source: dev.to / about 2 months ago

Haystack can be classified as an end-to-end framework for building applications powered by various NLP technologies, including but not limited to generative AI. While it doesn't directly focus on building generative models from scratch, it provides a robust platform for:. - Source: dev.to / 6 months ago

But if you want an API that you can use to develop your own flow, Haystack from Deepset could be worth a look. Source: 6 months ago

Haystack for production. We cannot afford breaking changes in our production apps. Its stable, documentation is excellent and did I mention its' STABLE!?? Source: 6 months 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 / 18 days 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 / 29 days 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 1 month 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 / about 2 months ago

There are much better known examples, such as https://qdrant.tech/ and https://github.com/lancedb/lancedb. - Source: Hacker News / 3 months ago