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API usage examples
Here’s a simple example using our OpenAI-compatible chat completions endpoint:
import requests import json BASE_URL = "https://nano-gpt.com/api/v1" API_KEY = "YOUR_API_KEY" # Replace with your API key headers = { "Authorization" : f "Bearer { API_KEY } " , "Content-Type" : "application/json" , "Accept" : "text/event-stream" # Required for SSE streaming } def stream_chat_completion ( messages , model = "chatgpt-4o-latest" ): """ Send a streaming chat completion request using the OpenAI-compatible endpoint. """ data = { "model" : model, "messages" : messages, "stream" : True # Enable streaming } response = requests.post( f " { BASE_URL } /chat/completions" , headers = headers, json = data, stream = True ) if response.status_code != 200 : raise Exception ( f "Error: { response.status_code } " ) for line in response.iter_lines(): if line: line = line.decode( 'utf-8' ) if line.startswith( 'data: ' ): line = line[ 6 :] if line == '[DONE]' : break try : chunk = json.loads(line) if chunk[ 'choices' ][ 0 ][ 'delta' ].get( 'content' ): yield chunk[ 'choices' ][ 0 ][ 'delta' ][ 'content' ] except json.JSONDecodeError: continue # Example usage messages = [ { "role" : "system" , "content" : "You are a helpful assistant." }, { "role" : "user" , "content" : "Please explain the concept of artificial intelligence." } ] try : print ( "Assistant's Response:" ) for content_chunk in stream_chat_completion(messages): print (content_chunk, end = '' , flush = True ) print ( "" ) except Exception as e: print ( f "Error: { str (e) } " ) For more detailed examples and other text generation endpoints, check out our Text Generation Guide .
OpenAI-Compatible Endpoint (v1/images/generations) You can also generate images using our OpenAI-compatible endpoint:
curl https://nano-gpt.com/v1/images/generations \ -H "Authorization: Bearer YOUR_API_KEY" \ -H "Content-Type: application/json" \ -d '{ "model": "hidream", "prompt": "A serene landscape at sunset", "n": 1, "size": "1024x1024" }' Here’s an example using our image generation endpoint with the Recraft model:
import requests import json import base64 from PIL import Image import io BASE_URL = "https://nano-gpt.com/api" API_KEY = "YOUR_API_KEY" headers = { "x-api-key" : API_KEY , "Content-Type" : "application/json" } def generate_image ( prompt , model = "recraft-v3" , width = 1024 , height = 1024 ): """ Generate an image using the Recraft model. """ data = { "prompt" : prompt, "model" : model, "width" : width, "height" : height, "negative_prompt" : "blurry, bad quality, distorted, deformed" , "nImages" : 1 , "num_steps" : 30 , "resolution" : "1024x1024" , "sampler_name" : "DPM++ 2M Karras" , "scale" : 7.5 } response = requests.post( f " { BASE_URL } /generate-image" , headers = headers, json = data ) if response.status_code != 200 : raise Exception ( f "Error: { response.status_code } " ) result = response.json() # Decode and save the image image_data = base64.b64decode(result[ 'image' ]) image = Image.open(io.BytesIO(image_data)) image.save( "generated_image.png" ) return result # Example usage prompt = "A serene landscape with mountains and a lake at sunset, digital art style" try : result = generate_image(prompt) print ( "Image generated successfully!" ) print ( "Cost:" , result.get( 'cost' , 'N/A' )) print ( "Image saved as 'generated_image.png'" ) except Exception as e: print ( f "Error: { str (e) } " ) For more detailed examples and other image generation options, check out our Image Generation Guide .
Enable real-time web search for any model by adding suffixes to the model name:
import requests import json BASE_URL = "https://nano-gpt.com/api/v1" API_KEY = "YOUR_API_KEY" headers = { "Authorization" : f "Bearer { API_KEY } " , "Content-Type" : "application/json" } # Standard web search ($0.006 per request) data = { "model" : "chatgpt-4o-latest:online" , "messages" : [ { "role" : "user" , "content" : "What are the latest AI announcements this week?" } ], "stream" : False } response = requests.post( f " { BASE_URL } /chat/completions" , headers = headers, json = data ) print ( "Response:" , response.json()[ 'choices' ][ 0 ][ 'message' ][ 'content' ]) # Deep web search for comprehensive research ($0.06 per request) deep_data = { "model" : "claude-3-5-sonnet-20241022:online/linkup-deep" , "messages" : [ { "role" : "user" , "content" : "Provide a detailed analysis of recent breakthroughs in quantum computing" } ] } deep_response = requests.post( f " { BASE_URL } /chat/completions" , headers = headers, json = deep_data ) Web search works with all models and provides:
Access to real-time information (updated less than a minute ago)
10x improvement in factuality
Standard search: 10 results quickly
Deep search: Iterative searching for comprehensive information
For more advanced web search capabilities including structured output, domain filtering, and date filtering, see the Web Search API .
Check out our Chat Completion Guide for more examples.
NanoGPT supports TEE-backed models for verifiable privacy. You can fetch attestation reports and signatures for chat completions made with these models.
Here’s how to fetch an attestation report:
curl "https://nano-gpt.com/api/v1/tee/attestation?model=TEE/hermes-3-llama-3.1-70b" \ -H "Authorization: Bearer YOUR_API_KEY" After making a chat request with a TEE model, you can get its signature:
# First, make a chat request (see Text Generation accordion or TEE Verification guide) # Then, use the request_id from the chat response: curl "https://nano-gpt.com/api/v1/tee/signature/YOUR_CHAT_REQUEST_ID?model=TEE/hermes-3-llama-3.1-70b&signing_algo=ecdsa" \ -H "Authorization: Bearer YOUR_API_KEY" For a complete Python example and more details, see the TEE Verification Guide .
