This guide will help you navigate CyborgDB’s documentation effectively and find the information you need quickly.

Documentation Structure

Our documentation is organized into four main sections:

Quick Access

  • Search (Ctrl/Cmd + K) - Find topics instantly
  • Table of Contents - Navigate within articles
  • Breadcrumbs - Track your location

Page Types

  • Quickstart Guides - Step-by-step tutorials
  • Conceptual Guides - How things work
  • Reference Docs - Complete API documentation
  • How-to Guides - Practical solutions

Code Examples

  • Multiple Languages - Python, JavaScript, TypeScript, C++
  • Copy Button - One-click code copying
  • Runnable Examples - Complete working code

Conventions Used

Code and Commands

# Terminal commands are shown in code blocks like this
pip install cyborgdb-core
# Python code examples use syntax highlighting
from cyborgdb_core import Client
client = Client(api_key="your-key")

Information Callouts

Notes provide helpful context and additional information that’s good to know.
Tips share best practices and insider knowledge to help you succeed.
Warnings highlight important considerations that could affect your implementation.

Finding What You Need

First time learning about confidential vector search→ Start with About CyborgDB then try the Quickstart GuideGet oriented with core concepts and architecture before diving into implementation.

Search Tips

  • Exact phrases: Use quotes for exact matches: "create index"
  • Multiple terms: Search for multiple concepts: python authentication
  • Method names: Search for specific functions: similarity_search

Code Examples

Language Tabs

Many code examples include multiple language options:
from cyborgdb_core import Client
client = Client(api_key="your-key")

Runnable Examples

Look for complete, runnable examples that you can copy and execute directly:
# Complete working example
from cyborgdb_core import Client, DBConfig

client = Client(
    api_key="your-api-key",
    index_location=DBConfig("memory"),
    config_location=DBConfig("memory")
)

# Create index and add vectors
index = client.create_index("my-index", dimension=384)
index.add_vectors([1, 2, 3, ...], metadata={"doc": "example"})

# Search for similar vectors  
results = index.search([1, 2, 3, ...], k=5)
print(results)

Getting Help