Get vector store's information

{% openapi src="/assets/images/f-7T1Jx6BU3fZ2U5LsImW1.webp" path="/vector-stores/me" method="get" %} rememberizer_openapi.yml

Example Requests

{% tabs %}

curl -X GET \
  https://api.rememberizer.ai/api/v1/vector-stores/me \
  -H "x-api-key: YOUR_API_KEY"

Info

Replace YOUR_API_KEY with your actual Vector Store API key.

{% endtab %}

{% tab title="JavaScript" %}

const getVectorStoreInfo = async () => {
  const response = await fetch('https://api.rememberizer.ai/api/v1/vector-stores/me', {
    method: 'GET',
    headers: {
      'x-api-key': 'YOUR_API_KEY'
    }
  });

  const data = await response.json();
  console.log(data);
};

getVectorStoreInfo();

Info

Replace YOUR_API_KEY with your actual Vector Store API key.

{% endtab %}

{% tab title="Python" %}

import requests

def get_vector_store_info():
    headers = {
        "x-api-key": "YOUR_API_KEY"
    }

    response = requests.get(
        "https://api.rememberizer.ai/api/v1/vector-stores/me",
        headers=headers
    )

    data = response.json()
    print(data)

get_vector_store_info()

Info

Replace YOUR_API_KEY with your actual Vector Store API key.

{% endtab %}

Response Format

{
  "id": "vs_abc123",
  "name": "My Vector Store",
  "description": "A vector store for product documentation",
  "embedding_model": "sentence-transformers/all-mpnet-base-v2",
  "indexing_algorithm": "ivfflat",
  "vector_dimension": 128,
  "search_metric": "cosine_distance",
  "created": "2023-06-01T10:30:00Z",
  "modified": "2023-06-15T14:45:00Z"
}

Authentication

This endpoint requires authentication using an API key in the x-api-key header.

Error Responses

Status Code	Description
401	Unauthorized - Invalid or missing API key
404	Not Found - Vector Store not found
500	Internal Server Error

This endpoint retrieves information about the vector store associated with the provided API key. It's useful for checking configuration details, including the embedding model, dimensionality, and search metric being used. This information can be valuable for optimizing search queries and understanding the vector store's capabilities.