Update vector search tutorials

Author: ViktarStarastsenka

src/vss/intro.md

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+## Vector search
+
+Data is often unstructured, which means that it isn't described by a well-defined schema.
+
+Examples of unstructured data include:
+- Text passages
+- Images
+- Videos
+- Music titles.
+
+One approach to dealing with unstructured data is to vectorize it. Vectorizing means mapping unstructured data to a flat sequence of numbers.
+
+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.
+
+You can use Redis Stack as a vector database, which allows you to:
+
+- Store vectors and the associated metadata within hashes or JSON documents
+- Retrieve vectors
+- Perform vector searches
+
+
+### Prerequisites
+
+[Redis Stack](https://redis.io/download?utm_source=redisinsight&utm_medium=main&utm_campaign=tutorials) >=7.2.0-v7 \
+OR \
+[RediSearch](https://github.com/RediSearch/RediSearch/) >=2.8.11 \
+OR \
+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").