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Developer Documentation

Integrate the AI Semantic Engine into your applications using our official SDKs or raw HTTP APIs. All endpoints require authentication via the X-API-KEY header.

View Full API Reference →

Python SDK

pip install ai-semantic-engine-pythonsdk

from ai_semantic_engine.client import AiSemanticEngineClient

# Initialize the client
client = AiSemanticEngineClient(base_url="https://ai.quizcore.org", api_key="YOUR_API_KEY")

# 1. Embed Text
embeddings = client.embed("Hello world", model="fast")

# 2. Compute Similarity
score = client.similarity("Apple", "Orange")
print(f"Similarity: {score}%")

# 3. Store Knowledge
client.store_item(text="Machine learning is fascinating.", metadata={"topic": "AI", "source": "wiki"})

# 4. Search Knowledge
results = client.search(query="Tell me about AI", top_k=3, filters={"topic": "AI"})
for r in results:
    print(r["score"], r["text"])

PHP SDK

composer require obrainwave/ai-semantic-engine-phpsdk

<?php
require 'vendor/autoload.php';
use AiSemanticEngine\Client;

$client = new Client('https://ai.quizcore.org', 'YOUR_API_KEY');

// 1. Embed Text
$embeddings = $client->embed("Hello world");

// 2. Compute Similarity
$score = $client->similarity("Apple", "Orange");

// 3. Store Knowledge
$client->store("Machine learning is fascinating.", ["topic" => "AI"]);

// 4. Search Knowledge
$results = $client->search("Tell me about AI", 3);
print_r($results);

HTTP API Endpoints

1. Embeddings (POST /embed & POST /embed-batch)

curl -X POST "${origin}/embed" \
  -H "X-API-KEY: YOUR_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "text": "Generate embeddings for this text",
    "model": "fast",
    "store": false
  }'

Response:

{
  "embedding": [0.012, -0.045, 0.103, ...],
  "model": "fast",
  "tokens": 6
}

2. Semantic Similarity (POST /similarity)

curl -X POST "${origin}/similarity" \
  -H "X-API-KEY: YOUR_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "text1": "Fast apple",
    "text2": "Quick fruit",
    "model": "fast"
  }'

Response:

{
  "similarity_score": 0.842,
  "model": "fast"
}

3. Store Knowledge (POST /items)

Store a text snippet in your dedicated vector database collection.

curl -X POST "${origin}/items" \
  -H "X-API-KEY: YOUR_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "text": "Artificial intelligence is key.",
    "model": "fast",
    "metadata": {"category": "tech"}
  }'

Response:

{
  "id": "550e8400-e29b-41d4-a716-446655440000",
  "text": "Artificial intelligence is key.",
  "metadata": {"category": "tech"}
}

4. Semantic Search (POST /search)

Search your stored knowledge using semantic meaning instead of keywords.

curl -X POST "${origin}/search" \
  -H "X-API-KEY: YOUR_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "query": "machine learning",
    "top_k": 3,
    "model": "fast",
    "filters": {"category": "tech"}
  }'

Response:

{
  "results": [
    {
      "id": "550e8400-...",
      "text": "Artificial intelligence is key.",
      "score": 0.912,
      "metadata": {"category": "tech"}
    }
  ]
}

5. Batch Embeddings (POST /embed-batch)

Generate embeddings for an array of texts in a single request.

curl -X POST "${origin}/embed-batch" \
  -H "X-API-KEY: YOUR_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "texts": ["First text", "Second text"],
    "model": "fast"
  }'

Response:

{
  "embeddings": [
    [0.012, -0.045, ...],
    [-0.033, 0.088, ...]
  ],
  "model": "fast",
  "total_tokens": 12
}

6. Detect Duplicates (POST /detect-duplicates)

Find nearest neighbors for a single text within your storage.

curl -X POST "${origin}/detect-duplicates" \
  -H "X-API-KEY: YOUR_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "text": "Text to look up",
    "top_k": 5
  }'

Response:

{
  "duplicates": [
    {
      "id": "...",
      "text": "Text to look up",
      "similarity": 0.999
    }
  ]
}

7. Bulk Job Management

Submit massive datasets to be embedded asynchronously in the background.

# Submit Job
curl -X POST "${origin}/jobs/bulk-embed" \
  -H "X-API-KEY: YOUR_KEY" \
  -H "Content-Type: application/json" \
  -d '{"texts": ["item 1", "item 2", "..."], "store": true}'

Submit Response:

{
  "job_id": "job-123",
  "status": "pending",
  "total_items": 1000
}

# Check Status
curl "${origin}/jobs/<JOB_ID>" -H "X-API-KEY: YOUR_KEY"

Status Response:

{
  "id": "job-123",
  "status": "completed",
  "completed": 1000,
  "failed": 0
}

8. Webhooks (PUT /webhook-settings)

Receive async HTTP callbacks when long running background jobs finish.

curl -X PUT "${origin}/webhook-settings" \
  -H "Cookie: tenant_session=..." \
  -H "Content-Type: application/json" \
  -d '{
    "webhook_url": "https://your-server.com/hooks",
    "webhook_secret": "my-hmac-secret"
  }'

Webhook Payload Example (Sent to your server):

{
  "event": "job.completed",
  "data": {
    "job_id": "job-123",
    "status": "completed",
    "completed": 1000,
    "failed": 0,
    "total": 1000
  }
}