bootcamp
nessie
bootcamp | nessie | |
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24 | 13 | |
1,634 | 843 | |
2.8% | 4.6% | |
9.1 | 9.9 | |
1 day ago | 3 days ago | |
HTML | Java | |
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.
bootcamp
- FLaNK AI - 01 April 2024
- FLaNK Stack Weekly 22 January 2024
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Milvus Adventures Jan 5, 2023
Metadata Filtering with Zilliz Cloud Pipelines This tutorial discuss scalar or metadata filtering and how you can perform metadata filtering in Zilliz Cloud. This blog continues on the previous blog on Getting started with RAG in just 5 minutes. You can find its code in this notebook and scroll down to Cell #27.
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Build a search engine, not a vector DB
Partially agree.
Vector DBs are critical components in retrieval systems. What most applications need are retrieval systems, rather than building blocks of retrieval systems. That doesn't mean the building blocks are not important.
As someone working on vector DB, I find many users struggling in building their own retrieval systems with building blocks such as embedding service (openai,cohere), logic orchestration framework (langchain/llamaindex) and vector databases, some even with reranker models. Putting them together is not as easy as it looks. A fairly changeling system work. Letting alone quality tuning and devops.
The struggle is no surprise to me, as tech companies who are experts on this (google,meta) all have dedicated teams working on retrieval system alone, making tons of optimizations and develop a whole feedback loop of evaluating and improving the quality. Most developers don't get access to such resource.
No one size fits all. I think there shall exist a service that democratize AI-powered retrieval, in simple words the know-how of using embedding+vectordb and a bunch of tricks to achieve SOTA retrieval quality.
With this idea I built a Retrieval-as-a-service solution, and here is its demo:
https://github.com/milvus-io/bootcamp/blob/master/bootcamp/R...
Curious to learn your thoughts.
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Vector Database in a Jupyter Notebook
Although it's common to use vector databases in conjunction with LLMs, I like to talk about vector databases in the context of unstructured data, i.e. any data that you can vectorize with (or without) an ML model. Yes, this includes text, but it also includes things like visual data, molecular structures, and geospatial data.
For folks who want to learn a bit more, there are examples of vector database use cases beyond semantic text search in our bootcamp: https://github.com/milvus-io/bootcamp
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Beginner-ish resources for choosing a vector database?
Easy to get started: Here are some tutorials for Milvus in a Jupyter Notebook that I wrote - reverse image search, semantic text search
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Semantic Similarity Search
I think you can just store your vector embeddings in the vector store somewhere and then query with your second document. I created a short tutorial on this that shows how to get the top 2 vector embeddings from a text query
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[D] Looking for open source projects to contribute
For more beginner tasks associated with the Milvus vector database, you can contribute to the Bootcamp project( https://github.com/milvus-io/bootcamp), where we build a lot of data-driven solutions using ML and Milvus vector database, including reverse image search, recommender systems, etc.
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I built an image similarity search system... Suggestions needed: what are some fun image datasets or scenarios I can use with this? :)
Source code here: https://github.com/milvus-io/bootcamp/tree/master/solutions/reverse_image_search
- Faiss: Facebook's open source vector search library
nessie
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A deep dive into the concept and world of Apache Iceberg Catalogs
Nessie is an innovative open-source catalog that extends beyond the traditional catalog capabilities in the Apache Iceberg ecosystem, introducing git-like features to data management. This catalog not only tracks table metadata but also allows users to capture commits at a holistic level, enabling advanced operations such as multi-table transactions, rollbacks, branching, and tagging. These features provide a new layer of flexibility and control over data changes, resembling version control systems in software development.
- FLaNK Stack Weekly 22 January 2024
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Why is Hive Metastore everywhere? (Especially Iceberg)
Try Nessie https://github.com/projectnessie/nessie - it recently got trino support as well ..
- What are the main things I need to know to be hired as a Java developer?
- Is learning and mastering Spring & Spring boot worth it in 2023 ?
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Which lakehouse table format do you expect your organization will be using by the end of 2023?
Project Nessie (https://projectnessie.org/) will be the catalog that eventually decouples Iceberg from Hive. At that point, I think it will be a no brainer to go Iceberg over Delta.
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5 Reasons Your Data Lakehouse should Embrace Dremio Cloud
The Dremio Sonar query engine can query your data where it exists whether it's AWS Glue, S3, Nessie Catalogs, MySQL, Postgres, RedShift and an ever growing list of sources.
- Project Nessie: Transactional Catalog for Data Lakes with Git-Like Semantics
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Introduction to The World of Data - (OLTP, OLAP, Data Warehouses, Data Lakes and more)
We will also need a catalog to track all of these tables, with the open source Project Nessie we can do just that, and also get great versioning features similar to using Git when developing applications allowing data engineers to practice "data as code" and "write-audit-publish" patterns on their data.
- DoltLab v0.2.0
What are some alternatives?
Milvus - A cloud-native vector database, storage for next generation AI applications
git-bug - Distributed, offline-first bug tracker embedded in git, with bridges
google-research - Google Research
dvc - 🦉 ML Experiments and Data Management with Git
docarray - Represent, send, store and search multimodal data
hiveberg - Demonstration of a Hive Input Format for Iceberg
es-clip-image-search - Sample implementation of natural language image search with OpenAI's CLIP and Elasticsearch or Opensearch.
dremio-oss - Dremio - the missing link in modern data
habitat-sim - A flexible, high-performance 3D simulator for Embodied AI research.
noms - The versioned, forkable, syncable database
annoy - Approximate Nearest Neighbors in C++/Python optimized for memory usage and loading/saving to disk
dolt - Dolt – Git for Data