API Reference

Complete API documentation for the Enclava platform, covering both public and internal APIs with authentication, request/response formats, and integration examples.

Chat Completion API (`/api/v1/`)

External client access with OpenAI compatibility for seamless integration.

LLM Services

Chat Completions

Create a chat completion response for the given conversation.

POST /api/v1/chat/completions

Request Body:

{
  "model": "privatemode-llama-3-70b",
  "messages": [
    {
      "role": "system",
      "content": "You are a helpful assistant."
    },
    {
      "role": "user", 
      "content": "What is confidential computing?"
    }
  ],
  "temperature": 0.7,
  "max_tokens": 1000,
  "stream": false
}

Response:

{
  "id": "chatcmpl-123",
  "object": "chat.completion",
  "created": 1677652288,
  "model": "privatemode-llama-3-70b",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "Confidential computing is a security model..."
      },
      "finish_reason": "stop"
    }
  ],
  "usage": {
    "prompt_tokens": 25,
    "completion_tokens": 150,
    "total_tokens": 175
  }
}

Parameters:

model (required): Model identifier
messages (required): Array of conversation messages
temperature (optional): Sampling temperature (0-2)
max_tokens (optional): Maximum tokens to generate
stream (optional): Stream partial message deltas
stop (optional): Stop sequences
presence_penalty (optional): Presence penalty (-2 to 2)
frequency_penalty (optional): Frequency penalty (-2 to 2)

List Models

Get available models for LLM inference.

GET /api/v1/models

Response:

{
  "object": "list",
  "data": [
    {
      "id": "privatemode-llama-3-70b",
      "object": "model",
      "created": 1686935002,
      "owned_by": "privatemode",
      "permission": [],
      "root": "privatemode-llama-3-70b",
      "parent": null
    },
    {
      "id": "privatemode-llama-3-8b",
      "object": "model", 
      "created": 1686935002,
      "owned_by": "privatemode",
      "permission": [],
      "root": "privatemode-llama-3-8b",
      "parent": null
    }
  ]
}

Create Embeddings

Generate text embeddings for semantic search and RAG applications.

POST /api/v1/embeddings

Request Body:

{
  "input": ["Text to embed", "Another text"],
  "model": "text-embedding-ada-002"
}

Response:

{
  "object": "list",
  "data": [
    {
      "object": "embedding",
      "index": 0,
      "embedding": [0.0023, -0.0012, ...]
    }
  ],
  "model": "text-embedding-ada-002",
  "usage": {
    "prompt_tokens": 8,
    "total_tokens": 8
  }
}

Chatbot

Simple Chat Interface

Basic message/response interaction.

POST /api/v1/chatbot/external/{chatbot_id}/chat

Request Body:

{
  "message": "Hello, I need help with my account",
  "conversation_id": "optional-session-123"
}

Response:

{
  "conversation_id": "session_123",
  "response": "Hello! I'd be happy to help with your account. What specific issue are you experiencing?",
  "timestamp": "2024-01-15T10:30:00Z"
}

OpenAI-Compatible Chat

Uses standard OpenAI format for compatibility.

POST /api/v1/chatbot/external/{chatbot_id}/chat/completions

Request/Response: Same as standard OpenAI format above.

List Chatbots

Get available chatbot instances.

GET /api/v1/chatbot/list

Response:

{
  "chatbots": [
    {
      "id": "bot_789",
      "name": "Support Assistant",
      "type": "customer_support", 
      "model": "privatemode-llama-3-70b",
      "description": "Customer support chatbot",
      "created_at": "2024-01-15T10:00:00Z"
    }
  ]
}

Integration Examples

Standard OpenAI API

Python

from openai import OpenAI

client = OpenAI(
    api_key="enclv_your_api_key_here",
    base_url="https://your-enclava-instance.com/api/v1"
)

response = client.chat.completions.create(
    model="privatemode-llama-3-70b",
    messages=[{"role": "user", "content": "What is confidential computing?"}],
    temperature=0.7
)

print(response.choices[0].message.content)

JavaScript

import OpenAI from 'openai';

const openai = new OpenAI({
    apiKey: 'enclv_your_api_key_here',
    baseURL: 'https://your-enclava-instance.com/api/v1'
});

const response = await openai.chat.completions.create({
    model: 'privatemode-llama-3-70b',
    messages: [{ role: 'user', content: 'What is a TEE?' }]
});

console.log(response.choices[0].message.content);

cURL

curl -X POST https://your-enclava-instance.com/api/v1/chat/completions \
  -H "Authorization: Bearer enclv_your_api_key_here" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "privatemode-llama-3-70b",
    "messages": [{"role": "user", "content": "Hello!"}],
    "temperature": 0.7
  }'

Chatbot Simple API

Python

import requests

response = requests.post(
    "https://your-enclava-instance.com/api/v1/chatbot/external/your-chatbot-id/chat",
    headers={"Authorization": "Bearer enclv_your_api_key_here"},
    json={"message": "I need help with billing"}
)

data = response.json()
print(data["response"])

JavaScript

const response = await fetch(
    'https://your-enclava-instance.com/api/v1/chatbot/external/your-chatbot-id/chat',
    {
        method: 'POST',
        headers: {
            'Authorization': 'Bearer enclv_your_api_key_here',
            'Content-Type': 'application/json'
        },
        body: JSON.stringify({ message: 'I need help with billing' })
    }
);

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

cURL

curl -X POST https://your-enclava-instance.com/api/v1/chatbot/external/your-chatbot-id/chat \
  -H "Authorization: Bearer enclv_your_api_key_here" \
  -H "Content-Type: application/json" \
  -d '{"message": "I need help with billing"}'

Chatbot OpenAI API

Python

from openai import OpenAI

client = OpenAI(
    api_key="enclv_your_api_key_here",
    base_url="https://your-enclava-instance.com/api/v1/chatbot/external/your-chatbot-id"
)

response = client.chat.completions.create(
    model="any",  # Model determined by chatbot config
    messages=[{"role": "user", "content": "Help me with customer support"}]
)

print(response.choices[0].message.content)

JavaScript

import OpenAI from 'openai';

const openai = new OpenAI({
    apiKey: 'enclv_your_api_key_here',
    baseURL: 'https://your-enclava-instance.com/api/v1/chatbot/external/your-chatbot-id'
});

const response = await openai.chat.completions.create({
    model: 'any',
    messages: [{ role: 'user', content: 'I need technical support' }]
});

console.log(response.choices[0].message.content);

cURL

curl -X POST https://your-enclava-instance.com/api/v1/chatbot/external/your-chatbot-id/chat/completions \
  -H "Authorization: Bearer enclv_your_api_key_here" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "any",
    "messages": [{"role": "user", "content": "Hi there!"}]
  }'

Complete API documentation with interactive examples is available at /api/v1/docs when running your Enclava instance.

Chat Completion API (/api/v1/)​

LLM Services​

Chat Completions​

List Models​

Create Embeddings​

Chatbot ​

Simple Chat Interface​

OpenAI-Compatible Chat​

List Chatbots​

Integration Examples​

Standard OpenAI API​

Python​

JavaScript​

cURL​

Chatbot Simple API​

Python​

JavaScript​

cURL​

Chatbot OpenAI API​

Python​

JavaScript​

cURL​

Chat Completion API (`/api/v1/`)

LLM Services

Chat Completions

List Models

Create Embeddings

Chatbot

Simple Chat Interface

OpenAI-Compatible Chat

List Chatbots

Integration Examples

Standard OpenAI API

Python

JavaScript

cURL

Chatbot Simple API

Python

JavaScript

cURL

Chatbot OpenAI API

Python

JavaScript

cURL