Dealing with all unstructured data, such as reverse image search, audio search, molecular search, video analysis, question and answer systems, NLP, etc.
Report Bug or Request Feature
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Reverse Image search | Chatbots | Chemical structure search |
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Table of Contents
📣 About Milvus Bootcamp
Embed everything, thanks to AI, we can use neural networks to extract feature vectors from unstructured data, such as image, audio and vide etc. Then analyse the unstructured data by calculating the feature vectors, for example calculating the Euclidean or Cosine distance of the vectors to get the similarity.
Milvus Bootcamp is designed to expose users to both the simplicity and depth of the Milvus vector database. Discover how to run benchmark tests as well as build similarity search applications like chatbots, recommender systems, reverse image search, molecular search, video search, audio search, and more.
📝 Solutions
🍦 Run locally
Here are several solutions for a wide range of scenarios. Each solution contains a Jupyter Notebook and a Docker deployable solution, meaning anyone can run it on their local machine. In addition to this there are also some related technical articles and live streams.
You can also refer to the Bootcamp FAQ for troubleshooting, and if you have any ideas or suggestions, you are more than welcome to submit an issue.
Solutions | Have fun with it | Article | Video |
Reverse Image Search
Build a reverse image search system using Milvus paired with Towhee for feature extraction. |
- Building a Search by Image Shopping Experience with VOVA and Milvus |
- 中文 |
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Search for matched or related images given an input text by Milvus and Towhee. |
- Jupyter notebook | ||
System Build an intelligent chatbot using Milvus and Towhee for natural language processing (NLP). |
-中文 |
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Build a text search engine using Milvus and BERT model. |
- Milvus 实战 | Milvus 与 BERT 搭建文本搜索 | - 中文 | |
Build an AI-powered movie recommender system using Milvus paired with PaddlePaddle’s deep learning framework. |
- 强强联手!Milvus 与 PaddlePaddle 深度整合,赋能工业级 AI 应用 | ||
Build a video similarity search engine using Milvus and Towhee. |
- Building a Video Analysis System with Milvus Vector Database |
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Build a video deduplication system to detect copied video sharing duplicate segments. |
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Search for matched or related videos given an input text by Milvus and Towhee. |
- Jupyter notebook | - 5分钟实现「视频检索」:基于内容理解,无需任何标签 | |
Build an audio search engine using Milvus paired with PANNs for audio pattern recognition. |
- 基于 Milvus 的音频检索系统 | ||
Build an audio classification engine using Milvus & Towhee to classify audio. |
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Build engines based on audio fingerprints using Milvus & Towhee, such as music detection system. |
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Build a molecular similarity search system using Milvus paired with RDKit for cheminformatics. |
- Milvus 赋能 AI 药物研发 | - 中文 | |
Build a DNA sequence classification system using Milvus with k-mers & CountVectorizer. |
- 用 AI 识别基因,从向量化 DNA 序列开始 | ||
Build a Face Recognition Pipeline using Milvus Vector Database to store face embeddings & perform face similaity search. |
🎬 Live Demo
We have built online demos for reverse image search, chatbot and molecular search that everyone can have fun with.
🔍 Benchmark Tests
The Benchmark Test contains 1 million and 100 million vector tests that indicate how your system will react to differently sized datasets.
1 million benchmark testing
🎯We extracted one million vectors from the SIFT1B Dataset for accuracy tests and performance tests. Through this test, you can learn the basic operations of Milvus, including creating collections, inserting data, building indexes, searching, etc.
100 million benchmark testing
🎨We extracted 100 million vectors from the SIFT1B Dataset for accuracy tests and performance tests. Through this test, you can learn the basic operations of Milvus, including creating collections, inserting data, building indexes, searching, etc.
👭 Collaborations
Milvus_and_ONNX
👏Build a reverse image search system with Milvus using various AI models in collaboration with the Open Neural Network Exchange (ONNX).
📝 Contributing
Contributions to Milvus Bootcamp are welcome from everyone. See Guidelines for Contributing for details.
🔥 Supports
Join the Milvus community on Slack to give feedback, ask for advice, and direct questions to our engineering team. We also have a WeChat group.