Milvus VS quickwit

Legal practitioners often need to find specific cases and clauses across thousands of dense documents. While traditional keyword-based search techniques are useful, they fail to fully capture semantic content of queries and case files. Vector search engines and large language models provide an intriguing alternative. In this talk, I will show you how to build a legal search application using the DSPy framework and the Milvus vector search engine.

Zilliz (zilliz.com) | Hybrid/ONSITE (SF, NYC) | Full-time

I am part of the hiring team for DevRel

NYC - https://boards.greenhouse.io/zilliz/jobs/4307910005

SF - https://boards.greenhouse.io/zilliz/jobs/4317590005

Zilliz is the company behind Milvus (https://github.com/milvus-io/milvus), the most starred vector database on GitHub. Milvus is a distributed vector database that shines in 1B+ vector use cases. Examples include autonomous driving, e-commerce, and drug discovery. (and, of course, RAG)

We are also hiring for other roles that I am not personally involved in the hiring process for such as product managers, software engineers, and recruiters.

Get started with Milvus on GitHub.

Milvus: An open-source vector database designed for AI and ML applications. It excels in handling large-scale vector similarity searches, making it suitable for recommendation systems, image and video retrieval, and natural language processing tasks.

Selecting the right version of open-source Milvus is important to the success of any project leveraging vector search technology. With Milvus offering different versions of its vector database tailored to varying requirements, understanding the significance of selecting the correct version is key for achieving desired outcomes.

Milvus is an open-source vector database designed to handle large-scale similarity search and vector indexing. It supports multiple index types and offers highly efficient search capabilities, making it suitable for a wide range of AI and ML applications, including image and video recognition, natural language processing, and recommendation systems.

Zilliz is hiring! We're looking for REMOTE and/or HYBRID roles in SF

Zilliz is the company behind Milvus (https://github.com/milvus-io/milvus), the most widely adopted vector database. Vector databases are a crucial piece of any technology stack looking to take advantage of unstructured data. Most recently and notably, Retrieval Augmented Generation (RAG). For RAG, vector databases like Milvus are used as the tool to inject customized data. In other words, vector databases make things like customized chat bots, personalized product recommendations, and more possible.

We are hiring for Developer Advocates, Senior+ Level Engineers and Product people, and Talent Acquisition. Check out all the roles here: https://zilliz.com/careers

Good on them, I know the crustaceans are out here happy about this raise for a Rust based Vector DB!

(now I'm gonna plug what I work on)

If you're interested in a more scalable vector database written in Go, check out Milvus (https://github.com/milvus-io/milvus)

But before we do, I do want to say that 🤩 all these lovely Open-Source projects would love a little 🎉💕 love by getting a GitHub star ⭐ for their efforts. Including Open Source Milvus 🥰

Hi folks, Quickwit cofounder here.

We started Quickwit 3 years ago with a POC, "Searching the web for under $1000/month" (see HN discussions [0]), with the goal of making a robust OSS alternative to Elasticsearch / Splunk / Datadog.

We have reached a significant milestone with our latest release (0.7) [1], as we have witnessed users of the nightly version of Quickwit deploy clusters with hundreds of nodes, ingest hundreds of terabytes of data daily, and enjoy considerable cost savings.

To give you a concrete example, one company is ingesting hundreds of terabytes of logs daily and migrating from Elasticsearch to Quickwit. They divided their compute costs by 5x and storage costs by 2x while increasing retention from 3 to 30 days. They also increased their durability, accuracy with exactly-once semantics thanks to the native Kafka support, and elasticity.

The 0.7 release also brings better integrations with the Observability ecosystem: improvements of the Elasticsearch-compatible API and better support of OpenTelemetry standards, Grafana, and Jaeger.

Of course, we still have a lot of work to be a fully-fledged observability engine, and we would love to get some feedback or suggestions.

To give you a glance at our 2024 roadmap, we planned to focus on Kibana/OpenDashboard integration, metrics support, and pipe-based query language.

[0] Searching the web for under $1000/month: https://news.ycombinator.com/item?id=27074481

[1] Release blog post: https://quickwit.io/blog/quickwit-0.7

[2] Open Source Repo: https://github.com/quickwit-oss/quickwit

[3] Home Page: https://quickwit.io

We tested S3 Express for our search engine quickwit[0] a couple of weeks ago.

While this was really satisfying on the performance side, we were a bit disappointed by the price, and I mostly agree with the article on this matter.

I can see some very specific use cases where the pricing should be OK but currently, I would say most of our users should just stay on the classic S3 and add some local SSD caching if they have a lot of requests.

[0] https://github.com/quickwit-oss/quickwit/

Hi HN, I’m one of the builders of Quickwit, a cloud-native OSS search engine for observability. As of 2023, we support logs and traces, metrics will come in 2024.

You know the pitch: while software like Datadog or Splunk are great, they often comes with hefty price tags. Our mission is to offer an affordable alternative. So we’ve built Quickwit, we’ve made it compatible with the observabilty ecosystem (OpenTelemetry, Jaeger, Grafana) and above all, we’ve made it cost-efficient / “easy” to scale (well it’s never easy to scale to petabytes..).

To give you a glance at the engine performance I made a benchmark on the GitHub Archive dataset, 23 TB of events, here are the main observations:

Indexing: costs $2 per ingested TB. With 4CPU, throughput is at 20MBs However, you'll observe > 30MB throughput on simpler datasets, like logs and traces.

Search: a typical query costs $0.0002 per TB (considering both CPU time and GET request costs). Using 8CPU, a simple query on 23TB is achieved in under a second.

Storage: on S3, it costs $8 per ingested TB per month on the GitHub Archive dataset. With logs and traces, you might see costs around $5/ingested TB due to a 2x better compression ratio.

I'm eager to get your thoughts on this!

Benchmark: https://quickwit.io/blog/benchmarking-quickwit-engine-on-an-...

Github repo: https://github.com/quickwit-oss/quickwit/

Website: https://quickwit.io/

- On S3, it costs $8 per ingested TB per month on the GitHub Archive dataset. With logs and traces, you might see costs around $4/ingested TB due to a 2x better compression ratio.

I'm eager to get your thoughts on this!

[0] Benchmark: https://quickwit.io/blog/benchmarking-quickwit-engine-on-an-...

Quickwit (https://quickwit.io/) | Paris, France | Onsite and remote (based in Europe) | Full-time

The company is fully remote but we also have a small office in Paris. We prefer candidates based in Europe but can make exceptions for the right profiles.

- Senior Software Engineer 80-110k€ + 0.25-1% equity based on experience.

    We’re looking for a senior software engineer to contribute to [Quickwit](https://github.com/quickwit-oss/quickwit), our open-source search and analytics engine. We have an ambitious roadmap for the next 18 months (performance optimization, distributed storage, support for SQL, query optimizer, revamp of our execution engine, etc.), and this is a great opportunity to shape the future of Quickwit while tackling fun and challenging problems in the field of distributed databases.

In our latest blog post, we walk you through the steps of instrumenting your Rust application and monitoring the performance on Grafana using Quickwit + Jaeger.

Link: https://github.com/quickwit-oss/quickwit