# Vector Store

Vector databases make it possible to draw comparisons via semantic search, identify relationships, and understand the context within the vector Embeddings. One additional advantage of vector stores is that we can use them as a retriever, where, based on a user query, it returns the relevant information. 

A few of the popular Vector Store or Vector Databases are: Chroma, Weaviate, Qdrant, Milvus, FAISS, ElasticSearch and Pinecone. GenAI Stack currently supports  Reddis, and Weaviate.

These Vector Stores can further be connected to retrievers.

<figure><img src="/files/ZJahmfRnyZMe15ehN9bc" alt=""><figcaption></figcaption></figure>

### Weaviate

Weaviate is an open-source vector search engine that stores both objects and vectors. It also supports cloud clients to store vector embeddings.&#x20;

**Parameters**

* Documents: The chunked or loaded document component.
* Embeddings: Embedding component to convert documents into vectors.
* Index Name:  A unique identifier to save the index
* Weaviate API Key: Weaviate API key for the cluster created on Weaviate cloud
* Weaviate URL: Cluster URL on Weaviate cloud

Example usage:

<figure><img src="/files/SWyG5UuAboysaoKwkSVx" alt=""><figcaption></figcaption></figure>

### **Redis**

Redis is an open-source, in-memory vector store. It offers low-latency reads and writes, making it suitable for use cases that require a cache.&#x20;

Parameters

* Documents: The chunked or loaded document component.
* Embeddings: Embedding component to convert documents into vectors.
* Redis Index: A unique identifier for the index created in Redis
* Redis server connection string: The server connection string is the address and credentials for connecting to the Redis server

Example usage:

<figure><img src="/files/rd9uTF4kC58EML7iVIkw" alt=""><figcaption></figcaption></figure>

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