QuickEmbed AI Python SDK 🐍

Official Python client for QuickEmbed AI (quickembedai.com) - The ultimate RAG Infrastructure for developers.

💡 Why use QuickEmbed AI?

Struggling with vector databases or complex chunking? QuickEmbed AI provides a unified API to handle massive data ingestion (PDFs, URLs, CSVs) and high-performance AI retrieval at scale.

Perfect for:

• Multi-tenant SaaS products
• Automated document analysis (The Nexus Pipeline)
• Real-time AI chatbots with zero infra management

With Python 3.8+ support and full type hints, it's never been easier to integrate enterprise-grade AI features into your analytics or backend systems.

🔗 Official Portal

Visit quickembedai.com to get your API key, manage your tenants, and monitor your AI consumption. Full docs available at quickembedai.com/help-center.

Installation

pip install quickembedai

Quick Start

from quickembedai import QuickEmbedAIClient

# Initialize the client (defaults to quickembedai.com API)
client = QuickEmbedAIClient(api_key="your_secret_api_key")

# Perform RAG search
response = client.query("What is our refund policy?", tenant_id="acme-corp")
print(f"Answer: {response['answer']}")

Features

• Scalable RAG: Ingest documents of any size into vector-isolated tenants.
• Advanced Control: Fine-tune temperature, model selection, and prompt context.
• Custom Error Handling: QuickEmbedAIError with granular API status codes.
• Type Hinting: Clean, robust client for modern Python services.

API Documentation

QuickEmbedAIClient(api_key, base_url=None, timeout=30)

• api_key: Your personal API secret.
• base_url: Defaults to https://quickembedai.com/api/v1.

client.query(message, tenant_id, model=None, temperature=0.7, **kwargs)

Performs a RAG search and AI generation.

client.ingest_url(url, tenant_id)

Scrapes and indexes a public URL.

client.ingest_file(file_path, tenant_id)

Uploads a local file for vectorization.

client.get_insights(tenant_id, **filters)

Retrieves AI analysis and Nexus metadata.

License

MIT © QuickEmbed AI Team