Artificial Intelligence Chat API

API ID 13186

OpenAI-compatible multi-model AI API for chat, reasoning, coding, and intelligent model routing.

2,154 ms avg response

API Documentation

Endpoints

Request

Generates chat completion responses from input messages and optional generation settings.

Endpoint ID: 26807
POST https://creators.zylalabs.com/api/13186/artificial+intelligence+chat+api/26807/chat+completions
INPUT PARAMETERS

Chat Completions — Endpoint Features

Object Description
Request Body Required Json

Free test requests remaining: 3 of 3.


INPUT PARAMETERS

API EXAMPLE RESPONSE
JSON
{"id":"chatcmpl_codxer_mrjvzhjh_acmdnhoxj4","object":"chat.completion","created":1783987316,"model":"CodXER-Low-2-X1.0","choices":[{"index":0,"message":{"role":"assistant","content":"Hello! How can I help you today?"},"finish_reason":"stop"}],"usage":{"prompt_tokens":1901,"completion_tokens":141,"total_tokens":2042},"_codxer":{"provider":"agixer","codxer_model":"CodXER Low-2 X1.0","upstream_model":"CodXER-Low-2-X1.0"}}
Chat Completions — CODE SNIPPETS

curl --location --request POST 'https://zylalabs.com/api/13186/artificial+intelligence+chat+api/26807/chat+completions' --header 'Authorization: Bearer YOUR_API_KEY' 

--data-raw '{
  "model": "CodXER Low-2 X1.0",
  "messages": [
    {
      "role": "user",
      "content": "Hello"
    }
  ],
  "max_tokens": 256
}'

    

API Access Key & Authentication

After signing up, every developer is assigned a personal API access key, a unique combination of letters and digits provided to access to our API endpoint. To authenticate with the Artificial Intelligence Chat API simply include your bearer token in the Authorization header.

Headers
Header Description
Authorization Required Should be Bearer access_key. See "Your API Access Key" above when you are subscribed.

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Overview

This API generates text responses for chat, reasoning, coding and content generation. It accepts structured text prompts and returns generated text in JSON responses. It supports streaming responses and model routing. It can be integrated into websites, mobile applications, automation workflows and software agents through HTTPS requests.

Artificial Intelligence Chat API FAQs

The Chat Completions endpoint returns a JSON object containing generated text responses based on input messages. It includes details such as the assistant's reply, usage statistics, and model information.

Key fields in the response include "id" (unique identifier), "object" (response type), "created" (timestamp), "model" (used model), "choices" (generated messages), and "usage" (token counts).

Users can customize requests using parameters like "messages" (input messages), "temperature" (response randomness), and "max_tokens" (response length). These parameters help tailor the output.

The response data is structured as a JSON object. It contains an array of "choices," each with a "message" field that includes the assistant's content and a "finish_reason" indicating how the response ended.

Typical use cases include customer support chatbots, coding assistance, content generation, and interactive applications where users seek conversational AI responses.

Users can extract the "content" field from the "message" object to display the assistant's response. Additionally, they can analyze "usage" data to monitor token consumption for optimization.

Data accuracy is maintained through continuous model training and updates based on user interactions and feedback. This ensures that the AI generates relevant and contextually appropriate responses.

If the response contains partial or empty results, users should check the "finish_reason" field for context. They can also adjust input parameters to refine the request for more complete outputs.

The Chat Completions endpoint provides generated text responses based on user input messages. It includes the assistant's reply, token usage statistics, and metadata about the model used for generation, allowing for diverse applications in chat, coding, and content creation.

Users can customize requests by adjusting parameters such as "messages" (input text), "temperature" (to control randomness), and "max_tokens" (to limit response length). This flexibility allows for tailored interactions based on specific needs.

The JSON response is structured with fields like "id," "object," "created," "model," and an array of "choices." Each choice contains a "message" with the assistant's content and a "finish_reason" that indicates how the response concluded.

The "finish_reason" field provides context on how the response was generated. It can indicate whether the response completed normally, was truncated, or stopped due to reaching a token limit, helping users understand the output's completeness.

The "usage" data includes "prompt_tokens," "completion_tokens," and "total_tokens," which help users monitor their token consumption. This information is valuable for optimizing requests and managing resource usage effectively.

Common scenarios include developing chatbots for customer support, creating coding assistants for developers, generating content for blogs or articles, and building interactive applications that require conversational AI capabilities.

The API maintains response quality through continuous model training and updates based on user interactions. Feedback loops help refine the models, ensuring they produce relevant and contextually appropriate outputs.

If responses are unexpected or incomplete, review the "finish_reason" for insights. Adjusting input parameters like "temperature" or "max_tokens" can help refine the output. Additionally, providing clearer prompts may yield better results.

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