Vector Search

// Store document with embedding vector
const doc = await db.create('documents', {
  title: 'Introduction to AI',
  content: 'Artificial intelligence is...',
  embedding: [0.123, -0.456, 0.789, ...],  // 1536-dim vector
  metadata: {
    author: 'John Doe',
    date: '2024-01-15'
  }
});

// Find similar documents
const queryEmbedding = [0.111, -0.432, 0.765, ...];

const results = await db.vectorSearch('documents', {
  vector: queryEmbedding,
  limit: 10,
  threshold: 0.8  // Minimum similarity score
});

results.forEach(doc => {
  console.log(`${doc.title}: ${doc.similarity}`);
});

from nexadb_client import NexaClient

# Connect to NexaDB
client = NexaClient(
    host='localhost',
    port=6970,
    username='root',
    password='nexadb123'
)
client.connect()

# Insert movies with semantic vectors
movies = [
    {
        'title': 'The Dark Knight',
        'year': 2008,
        'genre': 'Action/Thriller',
        'vector': [0.95, 0.1, 0.3, 0.7]  # High action, low romance
    },
    {
        'title': 'The Notebook',
        'year': 2004,
        'genre': 'Romance/Drama',
        'vector': [0.1, 0.98, 0.05, 0.85]  # Very high romance
    },
    {
        'title': 'The Matrix',
        'year': 1999,
        'genre': 'Sci-Fi/Action',
        'vector': [0.9, 0.15, 0.97, 0.5]  # High action + sci-fi
    },
    # ... 7 more movies
]

client.batch_write('movies', movies)

# Search for action movies
action_results = client.vector_search(
    collection='movies',
    vector=[0.95, 0.05, 0.3, 0.4],  # Action-heavy query
    limit=3,
    dimensions=4
)

# Results show semantic similarity!
# 1. Mad Max: Fury Road    - 99.15% similar
# 2. The Dark Knight       - 97.30% similar
# 3. The Matrix            - 88.54% similar

# Clone the repo and run the demo
git clone https://github.com/krishcdbry/nexadb.git
cd nexadb
python3 demo_vector_search.py

# Output shows beautiful results with similarity scores!

import OpenAI from 'openai';
import NexaClient from 'nexaclient';

const openai = new OpenAI();
const db = new NexaClient({
  host: 'localhost',
  port: 6970,
  username: 'root',
  password: 'nexadb123'
});
await db.connect();

async function answerQuestion(question) {
  // 1. Generate embedding for question
  const embedding = await openai.embeddings.create({
    model: 'text-embedding-3-small',
    input: question
  });

  // 2. Find relevant documents
  const context = await db.vectorSearch('knowledge', {
    vector: embedding.data[0].embedding,
    limit: 5
  });

  // 3. Generate answer with context
  const response = await openai.chat.completions.create({
    model: 'gpt-4',
    messages: [
      { role: 'system', content: 'Answer using the context' },
      { role: 'user', content: question },
      { role: 'assistant', content: JSON.stringify(context) }
    ]
  });

  return response.choices[0].message.content;
}

const answer = await answerQuestion('What is NexaDB?');
console.log(answer);