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src/vss/intro.md

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## Vector search
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Data is often unstructured, which means that it isn't described by a well-defined schema.
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Examples of unstructured data include:
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- Text passages
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- Images
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- Videos
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- Music titles.
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One approach to dealing with unstructured data is to vectorize it. Vectorizing means mapping unstructured data to a flat sequence of numbers.
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Vectors represent the data embedded in an N-dimensional space. Machine learning models have facilitated the rise of embeddings as a widely embraced method for generating dense, low-dimensional vector representations. Given a suitable machine learning model, the generated embeddings can encapsulate complex patterns and semantic meanings inherent in data.
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You can use Redis Stack as a vector database, which allows you to:
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- Store vectors and the associated metadata within hashes or JSON documents
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- Retrieve vectors
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- Perform vector searches
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### Prerequisites
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[Redis Stack](https://redis.io/download?utm_source=redisinsight&utm_medium=main&utm_campaign=tutorials) >=7.2.0-v7 \
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OR \
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[RediSearch](https://github.com/RediSearch/RediSearch/) >=2.8.11 \
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OR \
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A free Redis Stack instance on [Redis Cloud](https://redis.com/try-free/?utm_source=redis\&utm_medium=app\&utm_campaign=redisinsight_vecsim_guide "Redis Cloud").

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