Supported Vector Data Store

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

1. ChromaDB Memory

   1. Store type: chroma

   2. Client type: memory

   3. Example:

   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:

   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:

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

4. Elasticsearch

   1. Store type: elasticsearch

   2. Set up a managed Elasticsearch instance with vector search via Elastic Cloud or local development setup

   3. Example:

   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")}
   )