# 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": "vFAq1kIu8wffP8oojNaVhnaq4SQyWJBPK4on745wjCo", "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": "vFAq1kIu8wffP8oojNaVhnaq4SQyWJBPK4on745wjCo", "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": "vFAq1kIu8wffP8oojNaVhnaq4SQyWJBPK4on745wjCo",
    "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": "vFAq1kIu8wffP8oojNaVhnaq4SQyWJBPK4on745wjCo",
    "level": 2
}
)
print(response.status_code)
print(response.json())
        

Python

The clip about Large Language Models taken from a podcast of Lex Fridman with Jeff Bezos. 

Jeff Bezos on large language models  Lex Fridman Podcast Clips

 And, you know, your average 16 year old figures it out with many fewer miles.

 So there are still some tricks, I think, that we have yet to learn.

 I don't think we've learned the last trick.

 I don't think it's just a question of scaling things up.