Add new text document to a Vector Store
Example Requests
curl -X POST \
https://api.rememberizer.ai/api/v1/vector-stores/vs_abc123/documents/create \
-H "x-api-key: YOUR_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"name": "Product Overview",
"text": "Our product is an innovative solution for managing vector embeddings. It provides seamless integration with your existing systems and offers powerful semantic search capabilities."
}'const addTextDocument = async (vectorStoreId, name, text) => {
const response = await fetch(`https://api.rememberizer.ai/api/v1/vector-stores/${vectorStoreId}/documents/create`, {
method: 'POST',
headers: {
'x-api-key': 'YOUR_API_KEY',
'Content-Type': 'application/json'
},
body: JSON.stringify({
name: name,
text: text
})
});
const data = await response.json();
console.log(data);
};
addTextDocument(
'vs_abc123',
'Product Overview',
'Our product is an innovative solution for managing vector embeddings. It provides seamless integration with your existing systems and offers powerful semantic search capabilities.'
);import requests
import json
def add_text_document(vector_store_id, name, text):
headers = {
"x-api-key": "YOUR_API_KEY",
"Content-Type": "application/json"
}
payload = {
"name": name,
"text": text
}
response = requests.post(
f"https://api.rememberizer.ai/api/v1/vector-stores/{vector_store_id}/documents/create",
headers=headers,
data=json.dumps(payload)
)
data = response.json()
print(data)
add_text_document(
'vs_abc123',
'Product Overview',
'Our product is an innovative solution for managing vector embeddings. It provides seamless integration with your existing systems and offers powerful semantic search capabilities.'
)Path Parameters
vector-store-id
string
Required. The ID of the vector store to add the document to.
Request Body
{
"name": "Product Overview",
"text": "Our product is an innovative solution for managing vector embeddings. It provides seamless integration with your existing systems and offers powerful semantic search capabilities."
}name
string
Required. The name of the document.
text
string
Required. The text content of the document.
Response Format
{
"id": 1234,
"name": "Product Overview",
"type": "text/plain",
"vector_store": "vs_abc123",
"size": 173,
"status": "processing",
"processing_status": "queued",
"indexed_on": null,
"status_error_message": null,
"created": "2023-06-15T10:15:00Z",
"modified": "2023-06-15T10:15:00Z"
}Authentication
This endpoint requires authentication using an API key in the x-api-key header.
Error Responses
400
Bad Request - Missing required fields or invalid format
401
Unauthorized - Invalid or missing API key
404
Not Found - Vector Store not found
500
Internal Server Error
This endpoint allows you to add text content directly to your vector store. It's particularly useful for storing information that might not exist in file format, such as product descriptions, knowledge base articles, or custom content. The text will be automatically processed into vector embeddings, making it searchable using semantic similarity.
Last updated