# 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": "MsOKWCM1iDTDj3lqG_lE-pvd93GilqT5H-Me_EGsvOE", "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": "MsOKWCM1iDTDj3lqG_lE-pvd93GilqT5H-Me_EGsvOE", "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": "MsOKWCM1iDTDj3lqG_lE-pvd93GilqT5H-Me_EGsvOE",
    "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": "MsOKWCM1iDTDj3lqG_lE-pvd93GilqT5H-Me_EGsvOE",
    "level": 2
}
)
print(response.status_code)
print(response.json())
        

Python

This dataset is derived from the GermanDPR dataset and enhances it by providing fully formulated answers instead of answer spans. It can be used to finetune for retrieval augmented generation tasks (RAG) in German. It was taken from https://huggingface.co/datasets/DiscoResearch/germanrag