Chat Completions

Generate chat completions using an OpenAI-style chat completions API or the eighty80 sdk.

API

The 8080 chat completions endpoint is designed to match the OpenAI API for generating chat completions. To learn more, refer to the OpenAI docs.

cURL

curl https://api.8080.io/v1/chat/completions \
    -H "Content-Type: application/json" \
    -H "Authorization: Bearer $_8080_API_KEY" \
    -d '{
        "model": "8080/taalas/llama3.1-8b-instruct",
        "messages": [
            {
                "role": "user",
                "content": "Tell me a joke"
            }
        ]
    }'

python

import requests
import json
import os

response = requests.post(
    "https://api.8080.io/v1/chat/completions",
    headers={
        "Authorization": f"Bearer ${os.environ.get("_8080_API_KEY")}",
        "Content-Type": "application/json"
    },
    json={
        "model": "8080/taalas/llama3.1-8b-instruct",
        "messages": [
            {
                "role": "developer",
                "content": "why is the sky blue?"
            }
        ]
    }
)

if not response.ok:
    raise Exception(f"API Error {response.status_code}: {response.json()}")

print(response.json())

Request body

Required Parameters

Parameter	Type	Description
messages required	array	A list of messages comprising the conversation so far. Supports different message types like text, images, and audio depending on the model.
model required	string	Model ID used to generate the response, like 8080/llama-8b.
frequency_penalty	number	0
logit_bias	map	null
max_completion_tokens	integer	null
n	integer	1
presence_penalty	number	0
reasoning_effort	string	”medium”
seed	integer	null
stop	string/array	null
stream	boolean	false
temperature	number	1
top_p	number	1
tools	array	null

Message Types

The messages array can contain different types of messages:

• Developer message: Instructions for the model to follow, replacing system messages in newer models
• System message: Legacy instructions for the model (prefer developer messages for newer models)
• User message: Messages from end users containing prompts or context
• Assistant message: Model-generated responses
• Tool message: Messages related to tool/function calling

Tool calling

Pass a tools array in the request to let the model call your functions. When the model returns tool_calls, add tool messages with the results and call the API again until the model sends a final text response. See the Tool Calling guide for the request format and a full Python example.