Milvus
chroma
Milvus | chroma | |
---|---|---|
105 | 32 | |
27,068 | 12,432 | |
2.5% | 6.3% | |
10.0 | 9.8 | |
about 3 hours ago | 3 days ago | |
Go | Python | |
Apache License 2.0 | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Milvus
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Computer Vision Meetup: Develop a Legal Search Application from Scratch using Milvus and DSPy!
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.
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Ask HN: Who is hiring? (April 2024)
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.
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Unlock Advanced Search Capabilities with Milvus and Read about RAG
Get started with Milvus on GitHub.
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Milvus VS pgvecto.rs - a user suggested alternative
2 projects | 13 Mar 2024
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How to choose the right type of database
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.
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Simplifying the Milvus Selection Process
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.
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7 Vector Databases Every Developer Should Know!
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.
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Ask HN: Who is hiring? (February 2024)
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
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Qdrant, the Vector Search Database, raised $28M in a Series A round
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)
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Open Source Advent Fun Wraps Up!
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 🥰
chroma
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Let’s build AI-tools with the help of AI and Typescript!
Package installer for Python (pip), we use this for installing the Python-based packages, such as Jupyter Lab, and we're going to use this for installing other Python-based tools like the Chroma DB vector database
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Mixtral 8x22B
Optional: You can use SillyTavern[1] for a more "rich" chat experience
The above lets me chat, at least superficially, with my friend. It's nice for simple interactions and banter; I've found it to be a positive and reflective experience.
0. https://www.trychroma.com/
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7 Vector Databases Every Developer Should Know!
Chroma DB is a newer entrant in the vector database arena, designed specifically for handling high-dimensional color vectors. It's particularly useful for applications in digital media, e-commerce, and content discovery, where color similarity plays a crucial role in search and recommendation algorithms.
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AI Grant Traction in OSS Startups
View on GitHub
- Qdrant, the Vector Search Database, raised $28M in a Series A round
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Vector Databases: A Technical Primer [pdf]
For Python I believe Chroma [1] can be used embedded.
For Go I recently started building chromem-go, inspired by the Chroma interface: https://github.com/philippgille/chromem-go
It's neither advanced nor for scale yet, but the RAG demo works.
[1] https://github.com/chroma-core/chroma
- Chroma – the open-source embedding database
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Show HN: Embeddings Solution for Personal Journal
The formatting is a bit off.
The web app is here: https://jumblejournal.org
The DB used is here: https://www.trychroma.com/
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SQLite vs. Chroma: A Comparative Analysis for Managing Vector Embeddings
Whether you’re navigating through well-known options like SQLite, enriched with the sqlite-vss extension, or exploring other avenues like Chroma, an open-source vector database, selecting the right tool is paramount. This article compares these two choices, guiding you through the pros and cons of each, helping you choose the right tool for storing and querying vector embeddings for your project.
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How to use Chroma to store and query vector embeddings
Create a new project directory for our example project. Next, we need to clone the Chroma repository to get started. At the root of your project directory let's clone Chroma into it:
What are some alternatives?
pgvector - Open-source vector similarity search for Postgres
SillyTavern - LLM Frontend for Power Users.
faiss - A library for efficient similarity search and clustering of dense vectors.
qdrant - Qdrant - High-performance, massive-scale Vector Database for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/
golang-ical - A ICS / ICal parser and serialiser for Golang.
Weaviate - Weaviate is an open-source vector database that stores both objects and vectors, allowing for the combination of vector search with structured filtering with the fault tolerance and scalability of a cloud-native database.
AutoGPT - AutoGPT is the vision of accessible AI for everyone, to use and to build on. Our mission is to provide the tools, so that you can focus on what matters.
Elasticsearch - Free and Open, Distributed, RESTful Search Engine
Face Recognition - The world's simplest facial recognition api for Python and the command line
SillyTavern - LLM Frontend for Power Users. [Moved to: https://github.com/SillyTavern/SillyTavern]