Generate Text

Generate Text.

Return the generated text based on the given inputs.

HTTP Request

POST {API_URL}/chat/completions

where the API_URL = https://inference.nebulablock.com/v1. The body has the following parameters:

• messages array: An array of message objects. Each object should have:

  • role string: The role of the message sender (e.g., "user").

  • content string: The content of the message.

• model string: The model to use for generating the response.

• max_tokens integer or null: The maximum number of tokens to generate. If null, the model's default will be used.

• temperature float: Sampling temperature. Higher values make the output more random, while lower values make it more focused and deterministic.

• top_p float: Nucleus sampling probability. The model will consider the results of the tokens with top_p probability mass. In other words, a higher value will result in more diverse outputs, while a lower value will result in more repetitive outputs.

• stream boolean: Whether to stream the response in chunks or not.

Response Attributes

id string

A unique identifier for the completion request.

created integer

A Unix timestamp representing when the response was generated.

model string

The specific AI model used to generate the response.

object string

The type of response object (e.g., "chat.completion.chunk" for a streamed chunk or chat.completion for a non-chunked completion).

system_fingerprint string

A unique identifier for the system that generated the response, if available.

choices array

An array containing completion objects. Each object has the following fields:

• finish_reason string: The reason the completion finished.

• index integer: An index demarking this completion object.

• message dict: Contains data on the generated output.

  • content string: The generated text for this completion object.

  • role string: Specifies the role of the AI (e.g., "assistant").

  • tool_calls array: Contains information about the tools used in generating the completion, if available.

  • function_calls array: Contains information about the functions used in generating the completion, if available.

usage dict

A dictionary containing information about the inference request, in key-value pairs:

• completion_tokens integer: The number of tokens generated in the completion for a completion action.

• prompt_tokens integer: The number of tokens in the prompt.

• total_tokens integer: The total number of tokens (prompt and completion combined).

• completion_tokens_details null: Additional details about the completion tokens, if available.

• prompt_tokens_details null: Additional details about the prompt tokens, if available.

service_tier string

The service tier used for the completion request.

prompt_logprobs array

An array containing the log probabilities of the tokens in the prompt, if available.

Example

Request

curl -X GET '{API_URL}/api/v1/chat/completions' \
-H 'Authorization: Bearer {TOKEN/KEY}' \
-H 'Content-Type: application/json' \
-d '{
    "messages": [
        {
            "role": "user",
            "content": "insert your prompt here"
        }
    ],
    "model": "meta-llama/Llama-3.1-8B-Instruct",
    "max_tokens": null,
    "temperature": 1,
    "top_p": 0.9,
    "stream": true
}'

Response

Here's an example of a successful response in the non-streaming option. This response contains a chat.completion object and represents the entire generated text:

{
    "id": "chatcmpl-ec0014bc38e2cad1e45d47f7f01f6569",
    "created": 1740432179,
    "model": "meta-llama/Llama-3.1-8B-Instruct",
    "object": "chat.completion",
    "system_fingerprint": null,
    "choices": [
        {
            "finish_reason": "stop",
            "index": 0,
            "message": {
                "content": "Yes! Montreal is the home of cutting edge ... research.",
                "role": "assistant",
                "tool_calls": null,
                "function_call": null
            }
        }
    ],
    "usage": {
        "completion_tokens": 695,
        "prompt_tokens": 42,
        "total_tokens": 737,
        "completion_tokens_details": null,
        "prompt_tokens_details": null
    },
    "service_tier": null,
    "prompt_logprobs": null
}

Alternatively, if you set stream to True you'll get a stream of chat.completion.chunk objects. The entire collection of chunks represents the complete generated response.

{
    "id": "chatcmpl-3061dfd6d9170825ba0fb54086c4dad3",
    "created": 1740081592,
    "model": "meta-llama/Llama-3.1-8B-Instruct",
    "object": "chat.completion.chunk",
    "choices": [
        {
            "index": 0,
            "delta": {
                "content": "It",
                "role": "assistant"
            }
        }
    ]
}
{
    "id": "chatcmpl-3061dfd6d9170825ba0fb54086c4dad3",
    "created": 1740081592,
    "model": "meta-llama/Llama-3.1-8B-Instruct",
    "object": "chat.completion.chunk",
    "choices": [
        {
            "index": 0,
            "delta": {
                "content": " looks"
            }
        }
    ]
}
...
[DONE]

For more examples, see the Inference_Models section.