Divinci API

Integrate powerful AI capabilities into your applications with our comprehensive API. Build custom AI solutions that leverage RAG systems, vector embeddings, and document processing.

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API Overview

The Divinci AI API provides REST endpoints for integrating our AI capabilities into your applications. Here's a preview of what's available.

API Architecture

The Divinci AI API follows RESTful principles and uses JSON for request and response payloads. All API requests require authentication using API keys, which you can generate from your Divinci AI dashboard.

Our API is organized around resources such as AI models, documents, embeddings, and projects. We use standard HTTP methods (GET, POST, PUT, DELETE) and return appropriate HTTP status codes to indicate success or failure.

Rate limits apply to all API endpoints, with higher limits available on paid plans. Detailed error messages are provided for troubleshooting, and comprehensive logs are available in your dashboard.

Core Endpoints

Authentication

  • POST /v1/auth/token
    Generate an authentication token using your API key
  • DELETE /v1/auth/token
    Revoke an authentication token

RAG Systems

  • POST /v1/rag/systems
    Create a new RAG system
  • GET /v1/rag/systems
    List all RAG systems
  • GET /v1/rag/systems/{system_id}
    Get details of a specific RAG system
  • PUT /v1/rag/systems/{system_id}
    Update a RAG system
  • POST /v1/rag/systems/{system_id}/query
    Query a RAG system with user input

Documents

  • POST /v1/documents
    Upload a document for processing
  • GET /v1/documents
    List all documents
  • GET /v1/documents/{document_id}
    Get document details and processing status
  • DELETE /v1/documents/{document_id}
    Delete a document
cURL
Python
JavaScript
Go
# Query a RAG system
curl -X POST \
  https://api.divinci.ai/v1/rag/systems/rag_123456/query \
  -H "Authorization: Bearer YOUR_API_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "query": "What are the key features of Divinci AI?",
    "max_results": 5,
    "include_sources": true
}'
import requests

# Set your API token
api_token = "YOUR_API_TOKEN"

# Query a RAG system
response = requests.post(
    "https://api.divinci.ai/v1/rag/systems/rag_123456/query",
    headers={
        "Authorization": f"Bearer {api_token}",
        "Content-Type": "application/json"
    },
    json={
        "query": "What are the key features of Divinci AI?",
        "max_results": 5,
        "include_sources": True
    }
)

print(response.json())
// Set your API token
const apiToken = 'YOUR_API_TOKEN';

// Query a RAG system
async function queryRagSystem() {
  const response = await fetch('https://api.divinci.ai/v1/rag/systems/rag_123456/query', {
    method: 'POST',
    headers: {
      'Authorization': `Bearer ${apiToken}`,
      'Content-Type': 'application/json'
    },
    body: JSON.stringify({
      query: 'What are the key features of Divinci AI?',
      max_results: 5,
      include_sources: true
    })
  });

  const data = await response.json();
  console.log(data);
}

queryRagSystem();
package main

import (
	"bytes"
	"encoding/json"
	"fmt"
	"net/http"
)

func main() {
	// Set your API token
	apiToken := "YOUR_API_TOKEN"

	// Query payload
	queryPayload := map[string]interface{}{
		"query":          "What are the key features of Divinci AI?",
		"max_results":    5,
		"include_sources": true,
	}

	jsonPayload, _ := json.Marshal(queryPayload)

	// Create request
	req, _ := http.NewRequest("POST", "https://api.divinci.ai/v1/rag/systems/rag_123456/query", bytes.NewBuffer(jsonPayload))
	req.Header.Set("Authorization", "Bearer " + apiToken)
	req.Header.Set("Content-Type", "application/json")

	// Send request
	client := &http.Client{}
	resp, err := client.Do(req)
	if err != nil {
		fmt.Println(err)
		return
	}
	defer resp.Body.Close()

	// Process response
	var result map[string]interface{}
	json.NewDecoder(resp.Body).Decode(&result)
	fmt.Println(result)
}

Client Libraries

We provide client libraries in multiple programming languages to make integrating with our API easier.

Python SDK

Our Python SDK provides a convenient interface to the Divinci AI API, making it easy to integrate AI capabilities into your Python applications.

Version 0.9.5
15K+ downloads
Documentation GitHub

JavaScript SDK

Use our JavaScript SDK to integrate Divinci AI into web applications and Node.js projects with a simple, Promise-based API.

Version 0.9.2
12K+ downloads
Documentation GitHub

Java SDK

Our Java SDK provides enterprise-grade integration with the Divinci AI platform, designed for performance and reliability.

Version 0.8.5
8K+ downloads
Documentation GitHub