A new and improved Milvus bootcamp offers updated guides and easier to follow code examples. Discover how to create your own similarity search solutions with the world's most advanced vector database.

With the release of Milvus 2.0, the team has revamped the Milvus [bootcamp](https://github.com/milvus-io/bootcamp). The new and improved bootcamp offers updated guides and easier to follow code examples for a variety of use cases and deployments. Additionally, this new version is updated for [Milvus 2.0](https://zilliz.com/blog/milvus2.0-redefining-vector-database), a reimagined version of the world's most advanced vector databse.
 

Jump to:
- [Stress test your system against 1M and 100M dataset benchmarks](#stress-test-your-system-against-1m-and-100m-dataset-benchmarks)
- [Explore and build popular vector similarity search solutions](#explore-and-build-popular-vector-similarity-search-solutions)
- [Quickly deploy a fully built application on any system](#quickly-deploy-a-fully-built-application-on-any-system)
- [Use scenario specific notebooks to easily deploy pre-configured applications](#use-scenario-specific-notebooks-to-easily-deploy-pre-configured-applications)
- [Learn how to deploy Milvus in different environments](#learn-how-to-deploy-milvus-in-different-environments)

 

### Stress test your system against 1M and 100M dataset benchmarks 
The [benchmark directory](https://github.com/milvus-io/bootcamp/tree/master/benchmark_test) contains 1 million and 100 million vector benchmark tests that indicate how your system will react to differently sized datasets.

 

### Explore and build popular vector similarity search solutions

The [solution directory](https://github.com/milvus-io/bootcamp/tree/master/solutions) includes the most popular vector similarity search use cases. Each use case contains a notebook solution and a docker deployable solution. Use cases include:

- [Image similarity search](https://github.com/milvus-io/bootcamp/tree/master/solutions/reverse_image_search)
- [Video similarity search](https://github.com/milvus-io/bootcamp/tree/master/solutions/video_similarity_search)
- [Audio similarity search](https://github.com/milvus-io/bootcamp/tree/master/solutions/audio_similarity_search)
- [Recommendation system](https://github.com/milvus-io/bootcamp/tree/master/solutions/recommendation_system)
- [Molecular search](https://github.com/milvus-io/bootcamp/tree/master/solutions/molecular_similarity_search)
- [Question answering system](https://github.com/milvus-io/bootcamp/tree/master/solutions/question_answering_system)

 

### Quickly deploy a fully built application on any system

The quick deploy solutions are dockerized solutions that allow users to deploy fully built applications on any system. These solutions are ideal for brief demos, but require additional work to customize and understand compared to notebooks.

 

### Use scenario specific notebooks to easily deploy pre-configured applications

The notebooks contain a simple example of deploying Milvus to solve the problem in a given use case. Each of the examples are able to be run from start to finish without the need to manage files or configurations. Each notebook is also easy to follow and modifiable, making them ideal base files for other projects.

 

### Image similarity search notebook example
Image similarity search is one of the core ideas behind many different technologies, including autonomous cars recognizing objects. This example explains how to easily build computer vision programs with Milvus.


This [notebook](https://github.com/milvus-io/bootcamp/blob/master/solutions/reverse_image_search/reverse_image_search.ipynb) revolves around three things:
- Milvus server 
- Redis server (for metadata storage)
- Pretrained Resnet-18 model. 


#### Step 1: Download required packages
Begin by downloading all the required packages for this project. This notebook includes a table listing the packages to use.

```
pip install -r requirements.txt
```

#### Step 2: Server startup
After the packages are installed, start the servers and ensure both are running properly. Be sure to follow the correct instructions for starting the [Milvus](https://milvus.io/docs/v2.0.0/install_standalone-docker.md) and [Redis](https://hub.docker.com/_/redis) servers.

#### Step 3: Download project data
By default, this notebook pulls a snippet of the VOCImage data for use as an example, but any directory with images should work as long as it follows the file structure that can be seen at the top of the notebook.

```
! gdown "https://drive.google.com/u/1/uc?id=1jdudBiUu41kL-U5lhH3ari_WBRXyedWo&export=download"
! tar -xf 'VOCdevkit.zip'
! rm 'VOCdevkit.zip'
```

#### Step 4: Connect to the servers
In this example, the servers are running on the default ports on the localhost.

```
connections.connect(host="127.0.0.1", port=19537)
red = redis.Redis(host = '127.0.0.1', port=6379, db=0)
```

#### Step 5: Create a collection
After starting the servers, create a collection in Milvus for storing all the vectors. In this example, the dimension size is set to 512, the size of the resnet-18 output, and the similarity metric is set to the Euclidean distance (L2). Milvus supports a variety of different [similarity metrics](https://milvus.io/docs/metric.md#floating).

```
collection_name = "image_similarity_search"
dim = 512
default_fields = [
 schema.FieldSchema(name="id", dtype=DataType.INT64, is_primary=True, auto_id=True),
 schema.FieldSchema(name="vector", dtype=DataType.FLOAT_VECTOR, dim=dim)
]
default_schema = schema.CollectionSchema(fields=default_fields, description="Image test collection")
collection = Collection(name=collection_name, schema=default_schema)
```

#### Step 6: Build an index for the collection
Once the collection is made, build an index for it. In this case, the IVF_SQ8 index is used. This index requires the 'nlist' parameter, which tells Milvus how many clusters to make within each datafile (segment). Different [indices](https://milvus.io/docs/index.md#CPU) require different parameters.

```
default_index = {"index_type": "IVF_SQ8", "params": {"nlist": 2048}, "metric_type": "L2"}
collection.create_index(field_name="vector", index_params=default_index)
collection.load()
```
#### Step 7: Set up model and data loader
After the IVF_SQ8 index is built, set up the neural network and data loader. The pretrained pytorch resnet-18 used in this example is sans its last layer, which compresses vectors for classification and may lose valuable information.

```
model = torch.hub.load('pytorch/vision:v0.9.0', 'resnet18', pretrained=True)
encoder = torch.nn.Sequential(*(list(model.children())[:-1]))
```

The dataset and data loader needs to be modified so that they are able to preprocess and batch the images while also providing the file paths of the images. This can be done with a slightly modified torchvision dataloader. For preprocessing, the images need to be cropped and normalized due to the resnet-18 model being trained on a specific size and value range. 

```
dataset = ImageFolderWithPaths(data_dir, transform=transforms.Compose([
 transforms.Resize(256),
 transforms.CenterCrop(224),
 transforms.ToTensor(),
 transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])]))

dataloader = torch.utils.data.DataLoader(dataset, num_workers=0, batch_si
```

#### Step 8: Insert vectors into the collection
With the collection setup, the images can be processed and loaded into the created collection. First the images are pulled by the dataloader and run through the resnet-18 model. The resulting vector embeddings are then inserted into Milvus, which returns a unique ID for each vector. The vector IDs and image file paths are then inserted as key-value pairs into the Redis server.

```
steps = len(dataloader)
step = 0
for inputs, labels, paths in dataloader:
 with torch.no_grad():
 output = encoder(inputs).squeeze()
 output = output.numpy()

 mr = collection.insert([output.tolist()])
 ids = mr.primary_keys
 for x in range(len(ids)):
 red.set(str(ids[x]), paths[x])
 if step%5 == 0:
 print("Insert Step: " + str(step) + "/" + str(steps))
 step += 1
```

#### Step 9: Conduct a vector similarity search
Once all of the data is inserted into Milvus and Redis, the actual vector similarity search can be performed. For this example, three randomly selected images are pulled out of the Redis server for a vector similarity search. 

```
random_ids = [int(red.randomkey()) for x in range(3)]
search_images = [x.decode("utf-8") for x in red.mget(random_ids)]
```
These images first go through the same preprocessing that is found in Step 7 and are then pushed through the resnet-18 model. 

```
transform_ops = transforms.Compose([
 transforms.Resize(256),
 transforms.CenterCrop(224),
 transforms.ToTensor(),
 transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])])
 
embeddings = [transform_ops(Image.open(x)) for x in search_images]
embeddings = torch.stack(embeddings, dim=0)
 
with torch.no_grad():
 embeddings = encoder(embeddings).squeeze().numpy()
```
Then the resulting vector embeddings are used to perform a search. First, set the search parameters, including the name of the collection to search, nprobe (the number of the clusters to search), and top_k (the number of returned vectors). In this example, the search should be very quick. 

```
search_params = {"metric_type": "L2", "params": {"nprobe": 32}}
start = time.time()
results = collection.search(embeddings, "vector", param=search_params, limit=3, expr=None)
end = time.time() - start
```

#### Step 10: Image search results 
The vector IDs returned from the queries are used to find the corresponding images. Matplotlib is then used to display the image search results.
 

![pic1.png](https://assets.zilliz.com/pic1_c8652c7fae.png)
![pic2.png](https://assets.zilliz.com/pic2_355b054161.png)
![pic3.png](https://assets.zilliz.com/pic3_01780c6aac.png)

 

### Learn how to deploy Milvus in different enviroments
The [deployments section](https://github.com/milvus-io/bootcamp/tree/master/deployments) of the new bootcamp contains all the information for using Milvus in different environments and setups. It includes deploying Mishards, using Kubernetes with Milvus, load balancing, and more. Each environment has a detailed step by step guide explaining how to get Milvus working in it.

 

### Don't be a stranger
- Read our our [blog](https://zilliz.com/blog).
- Interact with our open-source community on [Slack](https://join.slack.com/t/milvusio/shared_invite/zt-e0u4qu3k-bI2GDNys3ZqX1YCJ9OM~GQ).
- Use or contribute to Milvus, the world’s most popular vector database, on [Github](http://page/).

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Build, test, and customize vector similarity search solutions with Milvus, an open-source vector database.

Quickly Test and Deploy Vector Search Solutions with the Milvus 2.0 Bootcamp

Artificial Intelligence Offers a New Way to Find Your View in the CMA Collection

![blog_artlens.png](https://assets.zilliz.com/blog_artlens_9ddbfc2de5.png)


 

Art is one way people reflect and shape the world they see, and the Cleveland Museum of Art has a new way to match your view to views created by some of history’s greatest artists.

The CMA’s Digital Innovation team has launched [ArtLens AI: Share Your View](https://www.clevelandart.org/art/collection/share-your-view). This interactive tool on the museum website and on [Twitter](https://twitter.com/ArtlensAI) matches what you see in your world to art from the CMA’s remarkable collection.

This easy point of entry into the CMA answers the fatigue of people learning new technologies to stay connected to friends, family, and co-workers. We also wanted to give the community a resource in which— without previous knowledge of history or art theory — someone can make visual connections between their world and a work of art at the click of a button.

The reverse image search tool uses [Artificial Intelligence (AI)](https://en.wikipedia.org/wiki/Artificial_intelligence) to recognize the shapes, patterns, and objects in your own photos to find surprising and delightful matches from the CMA’s encyclopedic collection. The tool can even recognize famous architecture in your photo.

![Blog_ArtLens AI_ 2.jpeg](https://assets.zilliz.com/Blog_Art_Lens_AI_2_66517b5302.jpeg)

###### *ArtLens AI matches your photo with artworks from the 30,000 in the CMA’s Open Access collection. Right: [Santa Maria Della Salute #1](https://www.clevelandart.org/art/1996.221), 1910. Mortimer Menpes (British, 1860–1938). Etching; sheet: 44.7 x 28 cm. The Cleveland Museum of Art, Gift of Carole W. and Charles B. Rosenblatt, 1996.221. Image courtesy of the Cleveland Museum of Art.*

 

When making a match on the website, you can shuffle through other close matches, download a composite image to share with your friends, or add the image to our visitor gallery. Links to the CMA’s [Collection Online](https://www.clevelandart.org/art/collection/search) offer you more than 36 fields of metadata about a work of art, such as an artist biography, citations, and curator videos. On [Twitter](https://twitter.com/ArtlensAI), attach a photo or an image to your tweet and mention @ArtLensAI to share your view. The Twitter bot will automatically reply with a matching artwork.

![Blog_ArtLens AI_ 3.png](https://assets.zilliz.com/Blog_Art_Lens_AI_3_b77e68aef9.png)
###### *The interface on the CMA website looks like this. Upload an image to the left box, then watch ArtLens AI find the closest match in the right box. You can shuffle through many matches. Image courtesy of the Cleveland Museum of Art.*

 

![Blog_ArtLens AI_ 4.png](https://assets.zilliz.com/Blog_Art_Lens_AI_4_f5cfe6af29.png)

###### *On Twitter, attach a photo or image to your tweet and mention @ArtLensAI to share your view. The Twitter bot will automatically reply with a matching artwork from the CMA’s collection. [Egham Lock](https://www.clevelandart.org/art/1961.300). Francis Seymour Haden (British, 1818–1910). Etching; The Cleveland Museum of Art, Gift of Mrs. T. Wingate Todd from the Collection of Dr. T. Wingate Todd, 1961.300. Image courtesy of the Cleveland Museum of Art.*

 

Using Machine Learning and the open source vector similarity engine [Milvus](https://milvus.io/), the CMA’s Digital Innovation development team created the AI tool with [Design IO](https://www.design-io.com/), who had previously collaborated on the museum’s [ArtLens Studio](https://www.clevelandart.org/artlens-gallery/artlens-studio) in 2016, and Jeff Schuler, a partner who leads the CMA team on website development. The new initiative builds on AI used in the museum’s on-site ArtLens Exhibition to read emotion and match a visitor’s face to portraits in the CMA’s collection.

We hope this will be another source of inspiration and fun in these uncertain times. We know that people need new, creative, and easy ways to engage with art. The CMA has grown as a digital museum during the COVID-19 pandemic, with many new gifts to the community. But CMA staffers feel the “Zoom fatigue” of remote work and video conferencing as much as anyone else after six months of self-isolation. Share Your View is one answer to the cautious steps we all are taking into a hybrid experience of life online and life in safe physical spaces.

ArtLens AI: Share Your View can help students, teachers, and parents feeling drained by the continuation of virtual learning. Schools in hybrid mode still rely on video calls and online platforms for classroom engagement. College and high school students are learning from their homes and dorms, without access to on-campus resources. The CMA’s education team has already begun to share this tool as a way to spark conversations about composition, palette, and pattern. Placing an artwork side-by-side with a contemporary view creates new comparisons and opportunities for critical thinking.

![Blog_ArtLens AI_ 5.jpeg](https://assets.zilliz.com/Blog_Art_Lens_AI_5_6fc48ea113.jpeg)
###### *Right: Homage to Goya: [In My Dream I Saw in the Sky a Face of Mystery](https://www.clevelandart.org/art/1927.344.1), 1885. Odilon Redon (French, 1840–1916), printed by Lemercier & Cie.. Lithograph on China paper laid on wove paper; image: 29.1 x 23.9 cm. The Cleveland Museum of Art, Gift of The Print Club of Cleveland, 1927.344.1. Image courtesy of the Cleveland Museum of Art.*

 

Since the launch of [ARTLENS Gallery](https://www.clevelandart.org/artlens-gallery/about) in 2012, the CMA has been a leader in creating digital connections to art. This year we have added many [new curator videos](https://www.clevelandart.org/home-where-art-video-series) for online viewing and created the [ArtLens for Slack app](https://www.clevelandart.org/artlens-for-slack) so workplaces can share a daily moment of discussion and interaction centered around artwork in the museum’s [Open Access](https://www.clevelandart.org/open-access) collection. We hope the new AI feature will help you find yourself in some surprising and inspiring places inside some of the most beautiful art in the world.

![Blog_ArtLens AI_ 6.png](https://assets.zilliz.com/Blog_Art_Lens_AI_6_36d8922e8c.png)
###### *Right: [The Acropolis of Athens: Western Portico of the Parthenon](https://www.clevelandart.org/art/1993.34), 1882. William James Stillman (American, 1828–1901). Albumen print from gelatin dry plate negative; image: 38.2 x 36.7 cm. The Cleveland Museum of Art, John L. Severance Fund, 1993.34. Image courtesy of the Cleveland Museum of Art.*

 

*This blog article is reposted from https://medium.com/cma-thinker/artlens-ai-share-your-view-5a326c15943f*

Read other [user stories](https://zilliz.com/user-stories) to learn more about making things with Milvus.

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ArtLens AI: Share Your View

Building a news content recommendation system with semantic vectors is easy with Milvus, an open-source vector database. 

With [71% of Americans](https://www.socialmediatoday.com/news/new-research-shows-that-71-of-americans-now-get-news-content-via-social-pl/593255/) getting their news recommendations from social platforms, personalized content has quickly become how new media is discovered. Whether people are searching for specific topics, or interacting with recommended content, everything users see is optimized by algorithms to improve click-through rates (CTR), engagement, and relevance. Sohu is a NASDAQ-listed Chinese online media, video, search and gaming group. It leveraged [Milvus](https://milvus.io/), an open-source vector database built by [Zilliz](https://zilliz.com/), to build a semantic vector search engine inside its news app. This article explains how the company used user profiles to fine-tune personalized content recommendations over time, improving user experience and engagement.

## Recommending content using semantic vector search

Sohu News user profiles are built from browsing history and adjusted as users search for, and interact with, news content. Sohu’s recommender system uses semantic vector search to find relevant news articles. The system works by identifying a set of tags that are expected to be of interest to each user based on browsing history. It then quickly searches for relevant articles and sorts the results by popularity (measured by average CTR), before serving them to users. 

The New York Times alone publishes [230 pieces of content](https://www.theatlantic.com/technology/archive/2016/05/how-many-stories-do-newspapers-publish-per-day/483845/) a day, which offers a glimpse at the magnitude of new content an effective recommendation system must be capable of processing. Ingesting large volumes of news demands millisecond similarity search and hourly matching of tags to new content. Sohu chose Milvus because it processes massive datasets efficiently and accurately, reduces memory usage during search, and supports high-performance deployments.

## Understanding a news recommendation system workflow

Sohu’s semantic vector search-based content recommendation relies on the Deep Structured Semantic Model (DSSM), which uses two neural networks to represent user queries and news articles as vectors. The model calculates the cosine similarity of the two semantic vectors, then the most similar batch of news is sent to the recommendation candidates pool. Next, news articles are ranked based on their estimated CTR, and those with the highest predicted click-through rate are displayed to users.

### Encoding news articles into semantic vectors with BERT-as-service

To encode news articles into semantic vectors, the system uses the [BERT-as-service](https://github.com/hanxiao/bert-as-service.git) tool. If the word count of any piece of content exceeds 512 while using this model, information loss occurs during the embedding process. To help overcome this, the system first extracts a summary and encodes it into a 768-dimensional semantic vector. Then the two most relevant topics from each news article are extracted, and the corresponding pre-trained topic vectors (200-dimensions) are identified based on topic ID. Next the topic vectors are spliced into the 768-dimensional semantic vector extracted from the article summary, forming a 968-dimensional semantic vector. 

New content continuously comes in through Kafta, and is converted into semantic vectors before being inserted into the Milvus database.

### Extracting semantically similar tags from user profiles with BERT-as-service

The other neural network of the model is user semantic vector. Semantically similar tags (e.g., coronavirus, covid, COVID-19, pandemic, novel strain, pneumonia) are extracted from user profiles based on interests, search queries, and browsing history. The list of acquired tags is sorted by weight, and the top 200 are divided into different semantic groups. Permutations of the tags within each semantic group are used to generate new tag phrases, which are then encoded into semantic vectors through BERT-as-service

For each user profile, sets of tag phrases have a [corresponding set of topics](https://github.com/baidu/Familia) that are marked by a weight indicating a user's interest level. The top two topics out of all relevant topics are selected and encoded by the machine learning (ML) model to be spliced into the corresponding tag semantic vector, forming a 968-dimensional user semantic vector. Even if the system generates the same tags for different users, different weights for tags and their corresponding topics, as well as explicit variance between each user's topic vectors, ensures recommendations are unique

The system is able to make personalized news recommendations by calculating the cosine similarity of the semantic vectors extracted from both user profiles and news articles.

![Sohu01.jpg](https://assets.zilliz.com/Sohu01_1e466fe0c3.jpg)
###### *Sohu News DSSM structure.*

### Computing new semantic user profile vectors and inserting them to Milvus

Semantic user profile vectors are computed daily, with data from the previous 24-hour period processed the following evening. Vectors are inserted into Milvus individually and run through the query process to serve relevant news results to users. News content is inherently topical, requiring computation to be run hourly to generate a current newsfeed that contains content that has a high predicted click-through rate and is relevant to users. News content is also sorted into partitions by date, and old news is purged daily.

### Decreasing semantic vector extraction time from days to hours

Retrieving content using semantic vectors requires converting tens of millions of tag phrases into semantic vectors every day. This is a time-consuming process that would require days to complete even when running on graphics processing units (GPU), which accelerate this type of computation. To overcome this technical issue, semantic vectors from the previous embedding must be optimized so that when similar tag phrases surface corresponding semantic vectors are directly retrieved.

The semantic vector of the existing set of tag phrases is stored, and a new set of tag phrases that is generated daily gets encoded into MinHash vectors. [Jaccard distance](https://milvus.io/docs/metric.md) is used to compute similarity between the MinHash vector of the new tag phrase and the saved tag phrase vector. If the Jaccard distance exceeds a pre-defined threshold, the two sets are considered similar. If the similarity threshold is met, new phrases can leverage the semantic information from previous embeddings. Tests suggest a distance above 0.8 should guarantee enough accuracy for most situations.

Through this process, the daily conversion of the tens of millions of vectors mentioned above is reduced from days to around two hours. Although other methods of storing semantic vectors might be more appropriate depending on specific project requirements, computing similarity between two tag phrases using Jaccard distance in a Milvus database remains an efficient and accurate method in a wide variety of scenarios.

![Sohu02.jpg](https://assets.zilliz.com/Sohu02_d50fccc538.jpg)
###### *Using Jaccard distance to compute similarity between two sets.*

## Overcoming “bad cases” of short text classification

When classifying news text, short news articles have fewer features for extraction than longer ones. Because of this, classification algorithms fail when content of varying lengths is run through the same classifier. Milvus helps solve this issue by searching for multiple pieces of long text classification information with similar semantics and reliable scores, then using a voting mechanism to modify short text classification. 

### Identifying and resolving misclassified short text 

Precise classification of each news article is crucial to providing useful content recommendations. Since short news articles have fewer features, applying the same classifier for news with different lengths results in a higher error rate for short text classification. Human labeling is too slow and inaccurate for this task, so BERT-as-service and Milvus are used to quickly identify misclassified short text in batches, correctly reclassify them, then use batches of data as a corpus for training against this problem.

BERT-as-service is used to encode a total number of five million long news articles with a classifier score greater than 0.9 into semantic vectors. After inserting the long text articles into Milvus, short text news is encoded into semantic vectors. Each short news semantic vector is used to query the Milvus database and obtain the top 20 long news articles with the highest cosine similarity to the target short news. If 18 of the top 20 semantically similar long news appear to be in the same classification and it differs from that of the query short news, then the short news classification is considered incorrect and must be adjusted to align with the 18 long news articles.

This process quickly identifies and corrects inaccurate short text classifications. Random sampling statistics show that after short text classifications are corrected, the overall accuracy of text classification exceeds 95%. By leveraging the classification of high-confidence long text to correct the classification of short text, the majority of bad classification cases are corrected in a short amount of time. This also offers a good corpus for training a short text classifier.

![Sohu03.jpg](https://assets.zilliz.com/Sohu03_a43074cf5f.jpg)
###### *Flowchart of discovery of "bad cases" of short text classification.*

## Milvus can power real-time news content recommendation and more

Milvus greatly improved the real-time performance of Sohu’s news recommendation system, and also bolstered the efficiency of identifying misclassified short text. If you’re interested in learning more about Milvus and its various applications:

- Read our [blog](https://zilliz.com/blog).
- Interact with our open-source community on [Slack](https://join.slack.com/t/milvusio/shared_invite/zt-e0u4qu3k-bI2GDNys3ZqX1YCJ9OM~GQ).
- Use or contribute to the world’s most popular vector database on [GitHub](https://github.com/milvus-io/milvus/).
- Quickly test and deploy AI applications with our new [bootcamp](https://github.com/milvus-io/bootcamp).

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Learn how Milvus was used to build an intelligent news recommendation system inside an app.

Building an Intelligent News Recommendation System Inside Sohu News App

Recommending engaging content at lightening speed inside Xiaomi’s mobile news aggregator. 

Xiaomi is a global electronics company that manufactures and invests in smartphones, mobile applications, laptops, home appliances, laptops, and many other products. The company ships its smartphones with a home brewed web browser that has a news aggregator embedded within it. To consistently make content recommendations that appeal to individual users Xiaomi leveraged artificial intelligence (AI).

Milvus, an open-source vector database built by Zilliz, is used to power the core recommendation engine inside Xiaomi's smart news aggregator.


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Xiaomi’s Smart News Aggregator

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Extracting highlights from massive video data for individual marathon runners or participants in other events.

iYUNDONG builds artificial intelligence (AI) tools that analyze media captured during sporting events to automatically extract key highlights. For example, by uploading a single photo, images of individual marathon runners can be automatically retrieved from a massive dataset that includes photos and videos from the event. 

iYUNDONG leverages Milvus, an open-source vector database built by Zilliz, to power its core image retrieval system.

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iYUNDONG‘s AI Automatically Extracts Sporting Event Highlights

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Helping people create relevant, high-quality documents at the click of a button inside WPS Office's Writer.

WPS Office is a productivity tool developed by Kingsoft with over 300M monthly active users worldwide. The company’s artificial intelligence (AI) department built a smart writing assistant from scratch using semantic matching algorithms such as intent recognition and text clustering. The tool helps users quickly create outlines, individual paragraphs, and entire documents by simply inputting a title and selecting up to five keywords.

Kingsoft’s writing assistant uses Milvus, an open-source vector database built by Zilliz, to power its core vector processing module.



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Kingsoft’s AI-Powered Writing Assistant

Reducing vector retrieval time from days to hours and minimizing false positive results in short text classification. 



Sohu is a Nasdaq-listed company that offers advertising, search engine, online multiplayer gaming, and many other services. The company built the Sohu News App to provide its users with personalized content. To improve the click-through rate (CTR) and relevance of the app’s recommendations, Sohu leveraged semantic vector similarity search.

Sohu used Milvus, an open-source vector database built by Zilliz, to power the core semantic similarity search engine inside its intelligent news aggregator.



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Sohu’s AI News Aggregator

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Recommending outfits and enabling image search in Mozat's Stylepedia lifestyle app.

[Mozat](http://www.mozat.com/home) is a Singaporean start-up focused on building innovative social media, communication, and lifestyle applications. [Stylepedia](https://stylepedia.com/) is a digital closet and outfit planning app that helps people passionate about fashion discover and discuss couture. Stylepedia users can create their own digital closets, receive personalized styling suggestions, and interact with other users to share fashion tips and trends.

Milvus, an open-source vector database, is used to power the image similarity search system that Stylepedia relies on to realize its image search and outfit recommender functionalities.

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Mozat's wardrobe app powered by an image search system built with Milvus

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Matching what people see in everyday life with artworks from the Cleveland Museum of Art's extensive collection.

Determined to adapt amid the limitations placed on in-person experiences by the coronavirus pandemic, the Cleveland Museum of art sought new, creative, and easy ways for people to engage with art. In this vein, CMA developed a reverse image search tool called "Artlens AI: Share Your View" that is capable of recognizing shapes, patterns, objects, and even architecture in user uploaded photos. After a user submits a photo, ArtLens AI returns the closest match from the CMA's vast art collection. The museum's hope is that by placing artwork next to contemporary views people will be given new perspectives and opportunities for critical thinking.

Milvus, an open-source vector database built by Zilliz, was used to power Artlens AI's core vector management module.

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The Cleveland Museum of Art Uses AI to Connect Daily Life with Art History

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Enriching video content recommendations for Opera.

Opera is a Norwegian software company that creates some of the world's most popular web browsers, Opera News, and other services. The company is a pioneer in leveraging artificial intelligence to make digital content recommendations. To ensure users are served a wide variety of videos, and to avoid recommending the same content multiple times, Opera built a smart video reduplication system inside its video content recommendation platform.

Milvus, an open-source vector database built by Zilliz, is used to power Opera’s smart video deduplication system.

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Opera’s Intelligent Video Deduplication System

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Mitigating threats to critical data and strengthening cybersecurity with real-time virus detection.

Trend Micro is a global leader in hybrid cloud security, network defense, small business security, and endpoint security. To protect Android devices from viruses, the company built Trend Micro Mobile Security—a mobile app that compares APKs from the Google Play Store to a database of known malware. If an APK with nefarious code is found, the app provides users with detailed information about the virus and its threat level.


Milvus, an open-source vector database built by Zilliz, is used to power Trend Micro Mobile Security’s virus vector comparison module. 



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Trend Micro’s Mobile Security App

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Energizing a personalized recommender service with unstructured data search on Vipshop’s e-commerce platform.

Vipshop is a leading online discount retailer for brands in China. The company offers high quality and popular branded products to consumers throughout China at a significant discount from retail prices. To optimize the shopping experience for their customers, the company built a personalized search recommendation system based on user query keywords and user portraits.
Milvus, an open-source vector database built by Zilliz, is used to power the personalized recommender system on Vipshop’s mobile apps.

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Vipshop's Personalized Recommender System

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Making search results more relevant and boosting online product sales on Tokopedia's e-commerce platform.

Tokopedia is a technology company specializing in e-commerce. Its marketplace is the most popular online shopping platform in Indonesia, with 100+ million monthly active users and 9.7+ million merchants. To make search results more relevant and improve user engagement, Tokopedia built a smart search engine capable of pairing low fill rate keywords with high fill rate keywords.

Milvus, an open-source vector database built by Zilliz, is used to index the underlying product database and optimize Tokopedia's AI-enabled shopping experience.

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Tokopedia’s AI-enabled E-commerce Site Search

Powering semantic search inside LucidWorks Fusion data discovery platform.

Lucidworks is an enterprise search technology company that offers an application development platform, commercial support, and value-add software for open-source Apache Lucene and Apache Solr. The company built Lucidworks Fusion in a modern, containerized, cloud-native architecture that simplifies the process of deploying AI-powered data discovery and search applications. To ensure users find relevant data fast, Lucidworks implemented semantic search using the vector similarity approach.

Milvus, an open-source vector database built by Zilliz, is used to power the core semantic search module inside Lucidworks Fusion.

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Lucidworks Fusion Data Discovery Platform

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Powering a unique shopping experience with image search on VOVA’s e-commerce platform.

VOVA is an emerging e-commerce platform that focuses on affordability and offering a positive shopping experience to its users, with listings covering millions of products and support for 20 languages and 35 major currencies. To enhance the shopping experience for its users, the company built image search functionality into its e-commerce platform.

Milvus, an open-source vector database built by Zilliz, is used to power the Search by Image tool on VOVA’s mobile apps.

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VOVA’s Search by Image Shopping Experience

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Enabling users to build a reliable, production-ready semantic search pipeline.

Haystack is an end-to-end framework that enables users to build powerful and production-ready pipelines for different search use cases. Using state-of-the-art natural language processing (NLP) models, Haystack users can create neural question answering (QA) or semantic document search applications. This helps create unique search experiences and improve the relevance of QA systems.

Haystack integrates Milvus, an open-source vector database built by Zilliz, to power its core vector processing module.

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Mitigating threats to critical data and strengthening cybersecurity with real-time virus detection.

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Quickly Test and Deploy Vector Search Solutions with the Milvus 2.0 Bootcamp

ArtLens AI: Share Your View

Building an Intelligent News Recommendation System Inside Sohu News App