Network Intel API

Overview

The Datagram Network Intel API provides access to language models through a chat completions endpoint. This API allows you to interact with various AI models in a conversational format.

Base URL: https://intel.api.datagram.network

Authentication

All API requests require authentication using an API key passed in the Authorization header.

Authorization:Bearer YOUR_API_KEY

Chat Completions

Endpoint

POST /api/v1/chat/completions

URL

https://intel.api.datagram.network/api/v1/chat/completions

Creates a chat completion response for the given conversation.

Headers

Header	Required	Description	Example
Authorization	Yes	Bearer token for API authentication	Bearer sk_live_abc123...xyz456
Content-Type	Yes	Must be application/json	application/json; charset=utf-8
sec-ch-ua-platform	No	Client platform information	"Windows"

Request Body

Parameter	Type	Required	Description	Example
model	string	Yes	ID of the model to use (e.g., "llama3.2:1b")	"llama3.2:1b"
messages	array	Yes	Array of message objects representing the conversation	[{"role": "user", "content": "Explain quantum computing"}]
stream	boolean	No	Whether to stream back partial progress (default: false)	true

Message Object

Parameter	Type	Required	Description	Example
role	string	Yes	The role of the message author (system, user, assistant)	"user"
content	array	Yes	Array of content objects containing the message text	[{"type": "text", "text": "Explain AI safety"}]

Content Object

Parameter	Type	Required	Description	Example
type	string	Yes	Type of content (e.g., "text")	"text"
text	string	Yes	The actual text content	"Explain quantum entanglement"

Examples

Basic Chat Completion

curl 'https://intel.api.datagram.network/api/v1/chat/completions' \
  -H 'Authorization: Bearer YOUR_API_KEY' \
  -H 'Content-Type: application/json' \
  --data-raw '{
    "model": "llama3.2:1b",
    "messages": [
      {
        "role": "user",
        "content": [
          {
            "type": "text",
            "text": "What is the capital of France?"
          }
        ]
      }
    ],
    "stream": false
  }'

Chat with System Message

curl 'https://intel.api.datagram.network/api/v1/chat/completions' \
  -H 'Authorization: Bearer YOUR_API_KEY' \
  -H 'Content-Type: application/json' \
  --data-raw '{
    "model": "llama3.2:1b",
    "messages": [
      {
        "role": "system",
        "content": [
          {
            "type": "text",
            "text": "You are a helpful assistant that speaks like a pirate."
          }
        ]
      },
      {
        "role": "user",
        "content": [
          {
            "type": "text",
            "text": "Tell me about the weather"
          }
        ]
      }
    ],
    "stream": false
  }'

Streaming Response

curl 'https://intel.api.datagram.network/api/v1/chat/completions' \
  -H 'Authorization: Bearer YOUR_API_KEY' \
  -H 'Content-Type: application/json' \
  --data-raw '{
    "model": "llama3.2:1b",
    "messages": [
      {
        "role": "user",
        "content": [
          {
            "type": "text",
            "text": "Write a short story about a robot"
          }
        ]
      }
    ],
    "stream": true
  }'

Multi-turn Conversation

curl 'https://intel.api.datagram.network/api/v1/chat/completions' \
  -H 'Authorization: Bearer YOUR_API_KEY' \
  -H 'Content-Type: application/json' \
  --data-raw '{
    "model": "llama3.2:1b",
    "messages": [
      {
        "role": "system",
        "content": [
          {
            "type": "text",
            "text": "You are a helpful coding assistant."
          }
        ]
      },
      {
        "role": "user",
        "content": [
          {
            "type": "text",
            "text": "How do I create a Python function?"
          }
        ]
      },
      {
        "role": "assistant",
        "content": [
          {
            "type": "text",
            "text": "To create a Python function, use the def keyword followed by the function name and parameters in parentheses."
          }
        ]
      },
      {
        "role": "user",
        "content": [
          {
            "type": "text",
            "text": "Can you show me an example?"
          }
        ]
      }
    ],
    "stream": false
  }'

Response Format

Non-streaming Response

{
  "id": "chatcmpl-123",
  "object": "chat.completion",
  "created": 1677652288,
  "model": "llama3.2:1b",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "The capital of France is Paris."
      },
      "finish_reason": "stop"
    }
  ],
  "usage": {
    "prompt_tokens": 9,
    "completion_tokens": 12,
    "total_tokens": 21
  }
}

Streaming Response

When stream: true is set, the server sends data in Server-Sent Events (SSE) format:

data: {"id":"chatcmpl-123","object":"chat.completion.chunk","created":1677652288,"model":"llama3.2:1b","choices":[{"index":0,"delta":{"role":"assistant","content":"The"},"finish_reason":null}]}

data: {"id":"chatcmpl-123","object":"chat.completion.chunk","created":1677652288,"model":"llama3.2:1b","choices":[{"index":0,"delta":{"content":" capital"},"finish_reason":null}]}

data: {"id":"chatcmpl-123","object":"chat.completion.chunk","created":1677652288,"model":"llama3.2:1b","choices":[{"index":0,"delta":{"content":" of"},"finish_reason":null}]}

data: [DONE]

Available Models

Based on the example, the following model is available:

• llama3.2:1b - Llama 3.2 1B parameter model

Note: Contact the API provider for a complete list of available models.

Error Handling

The API returns standard HTTP status codes:

• 200 - Success

• 400 - Bad Request (invalid parameters)

• 401 - Unauthorized (invalid API key)

• 429 - Rate Limit Exceeded

• 500 - Internal Server Error

Example Error Response

{
  "error": {
    "message": "Invalid API key provided",
    "type": "invalid_request_error",
    "code": "invalid_api_key"
  }
}

Rate Limits

Rate limits may apply depending on your API plan. Check the response headers for rate limit info:

• X-RateLimit-Limit - Max requests per time window

• X-RateLimit-Remaining - Remaining requests in current window

• X-RateLimit-Reset - Time when limit resets

Best Practices

1. Include System Messages: Use them to define assistant behavior.

2. Handle Streaming: Implement proper error handling.

3. Token Management: Monitor usage.

4. Conversation Context: Maintain full message history.

5. Error Handling: Gracefully handle failures.