Vector databases are widely used in many application areas, especially those that require the storage, retrieval, and analysis of vector data. Here are some common vector database usage scenarios:
1. Similarity search :
- Recommendation system: used to search for similar users or items based on the user's historical behavior or interests to provide personalized recommendations.
- Image retrieval: allows users to query similar images by image, used for image search engines and content recognition.
- Natural Language Processing: Used to search for similar texts, sentences or phrases, for information retrieval and text analysis.
2. Embed vector storage :
- Deep learning model: Stores and retrieves embedding vectors in neural network models for tasks such as word embedding and image features.
- Feature Engineering: Used to store and retrieve vectors generated by feature engineering, such as TF-IDF vectors, Word2Vec vectors, etc.
3. Real-time data analysis :
- Real-time big data analytics: used to perform similarity searches or clustering on large-scale data sets to support real-time decision making.
- Internet of Things (IoT): Processing sensor data to detect similarities or anomalies between devices.
4. Bioinformatics :
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