# Search

curl -X POST "https://search.dria.co/hnsw/search" \
-H "x-api-key: <YOUR_API_KEY>" \
-H "Content-Type: application/json" \
-d '{"rerank": true, "top_n": 10, "contract_id": "8bj7kna4ojvVvH2zDS34icC9R5S6vRjK1lCpSfZ1AU4", "query": "What is alexanDRIA library?"}'
        
# Query

curl -X POST "https://search.dria.co/hnsw/query" \
-H "x-api-key: <YOUR_API_KEY>" \
-H "Content-Type: application/json" \
-d '{"vector": [0.123, 0.5236], "top_n": 10, "contract_id": "8bj7kna4ojvVvH2zDS34icC9R5S6vRjK1lCpSfZ1AU4", "level": 2}'
        

Bash

# Search

response = requests.post(
    "https://search.dria.co/hnsw/search",
    headers={'x-api-key': '<YOUR_API_KEY>', 'Content-Type': 'application/json'},
    json={
    "rerank": True,
    "top_n": 10,
    "contract_id": "8bj7kna4ojvVvH2zDS34icC9R5S6vRjK1lCpSfZ1AU4",
    "query": "What is alexanDRIA library?"
}
)
print(response.status_code)
print(response.json())
        
# Query

response = requests.post(
    "https://search.dria.co/hnsw/query",
    headers={'x-api-key': '<YOUR_API_KEY>', 'Content-Type': 'application/json'},
    json={
    "vector": [
        0.123,
        0.5236
    ],
    "top_n": 10,
    "contract_id": "8bj7kna4ojvVvH2zDS34icC9R5S6vRjK1lCpSfZ1AU4",
    "level": 2
}
)
print(response.status_code)
print(response.json())
        

Python

Wikipedia[simple] is a RAG built using the '20220301.simple' subset of HuggingFace dataset wikipedia. 
    It contains ~250,000 articles which 50.000 more than original dataset. 
    The RAG is built using the jina-embeddings-v2-base-en model.