Semantic Search

Searching using semantic similarity with vector embeddings is easy with NexusDB. Below is a simple example to get you started.

PYTHON

from nexus_python.nexusdb import NexusDB
from openai import OpenAI

# Initialize NexusDB
nexus_db = NexusDB(api_key="your_nexusdb_api_key")

# Initialize OpenAI
client = OpenAI(api_key="your_openai_api_key")

# Search for similar vectors
search_query = "What is the best database?"
response = client.embeddings.create(input=search_query, model="text-embedding-ada-002")
query_vector = response.data[0].embedding

# Perform the search using the vector
# This assumes the vector_search in NexusDB can handle raw query vectors
search_response = nexus_db.vector_search(
    query_vector=query_vector,  # Adjust parameters as needed
    number_of_results=5,  # Example parameter
)
print("Search Response:", search_response)

Additional parameters like search radius, filter statements, and access keys will be coverd in the “advanced” section, coming soon!