# Supported Vector Data Store

This page provides instructions for setting up a vector data store to be used in your retrieval pipeline.

## Chroma

1. **ChromaDB Memory**

   1. Store type: `chroma`
   2. Client type: `memory`
   3. Example: 

   ```python
   store = build_data_store(
       store_type="chroma",
       index_name="your_index_name",
       embedding=your_embedding,
       config: {"client_type": "memory"}
   )
   ```
2. **ChromaDB Persistent**

   1. Store type: `chroma`
   2. Client type: `persistent`
   3. Example: 

   ```python
   store = build_data_store(
       store_type="chroma",
       index_name="your_index_name",
       embedding=your_embedding,
       config={"client_type": "persistent", "persist_directory": "/path/to/dir"}
   )
   ```
3. **ChromaDB HTTP**

   1. Store type: `chroma`
   2. Client type: `http`
   3. Example:

   ```python
   store = build_data_store(
       store_type="chroma",
       index_name="your_index_name",
       embedding=your_embedding,
       host="localhost",
       port=8000,
       config={"client_type": "http"}
   )
   ```

## Elasticsearch

1. Store type: `elasticsearch`
2. Set up a managed Elasticsearch instance with vector search via [Elastic Cloud](https://cloud.elastic.co/login) or [local development setup](https://www.elastic.co/docs/deploy-manage/deploy/self-managed/local-development-installation-quickstart)
3. Example:

```python
store = build_data_store(
    store_type="elasticsearch",
    index_name="your_index_name",
    embedding=your_embedding,
    config={"url": "https://your-elasticsearch-endpoint", "api_key": os.getenv("ELASTIC_API_KEY")}
)
```