Create
Creates a model response for the given chat conversation.
Creates a model response for the given chat conversation.
Parameters
Model ID used to generate the response.
Number between -2.0 and 2.0. Positive values penalize new tokens based on their existing frequency in the text so far, decreasing the model's likelihood to repeat the same line verbatim.
Modify the likelihood of specified tokens appearing in the completion.
Accepts a JSON object that maps tokens (specified by their token ID in the tokenizer) to an associated bias value from -100 to 100. Mathematically, the bias is added to the logits generated by the model prior to sampling. The exact effect will vary per model, but values between -1 and 1 should decrease or increase likelihood of selection; values like -100 or 100 should result in a ban or exclusive selection of the relevant token.
Whether to return log probabilities of the output tokens or not. If true,
returns the log probabilities of each output token returned in the
content of message.
The maximum number of completion tokens that may be used over the course of the run. The run will make a best effort to use only the number of completion tokens specified, across multiple turns of the run.
The maximum number of tokens that can be generated in the completion.
The token count of your prompt plus max_tokens cannot exceed the model's context length.
Set of 16 key-value pairs that can be attached to an object. This can be useful for storing additional information about the object in a structured format, and querying for objects via API or the dashboard.
Keys are strings with a maximum length of 64 characters. Values are strings with a maximum length of 512 characters.
How many chat completion choices to generate for each input message. Note that you will be charged based on the number of generated tokens across all of the choices. Keep n as 1 to minimize costs.
Number between -2.0 and 2.0. Positive values penalize new tokens based on whether they appear in the text so far, increasing the model's likelihood to talk about new topics.
Up to 4 sequences where the API will stop generating further tokens. The returned text will not contain the stop sequence.
If set to true, the model response data will be streamed to the client as it is generated using server-sent events.
Options for streaming response. Only set this when you set stream: true.
What sampling temperature to use, between 0 and 2. Higher values like 0.8 will make the output more random, while lower values like 0.2 will make it more focused and deterministic.
We generally recommend altering this or top_p but not both.
Controls which (if any) tool is called by the model.
none means the model will not call any tool and instead generates a message.
auto means the model can pick between generating a message or calling one or more tools.
required means the model must call one or more tools.
Specifying a particular tool via {"type": "function", "function": {"name": "my_function"}} forces the model to call that tool.
none is the default when no tools are present. auto is the default if tools are present.
A list of tools the model may call. Currently, only functions are supported as a tool.
An integer between 0 and 20 specifying the number of most likely tokens to
return at each token position, each with an associated log probability.
logprobs must be set to true if this parameter is used.
An alternative to sampling with temperature, called nucleus sampling, where the model considers the results of the tokens with top_p probability mass. So 0.1 means only the tokens comprising the top 10% probability mass are considered.
We generally recommend altering this or temperature but not both.
A unique identifier representing your end-user, which can help DigitalOcean to monitor and detect abuse.
Returns
Represents a chat completion response returned by model, based on the provided input.
from do_gradientai import GradientAI
client = GradientAI(
api_key="My API Key",
)
completion = client.chat.completions.create(
messages=[{
"content": "string",
"role": "system",
}],
model="llama3-8b-instruct",
)
print(completion.id){
"id": "id",
"choices": [
{
"finish_reason": "stop",
"index": 0,
"logprobs": {
"content": [
{
"token": "token",
"bytes": [
0
],
"logprob": 0,
"top_logprobs": [
{
"token": "token",
"bytes": [
0
],
"logprob": 0
}
]
}
],
"refusal": [
{
"token": "token",
"bytes": [
0
],
"logprob": 0,
"top_logprobs": [
{
"token": "token",
"bytes": [
0
],
"logprob": 0
}
]
}
]
},
"message": {
"content": "content",
"refusal": "refusal",
"role": "assistant",
"tool_calls": [
{
"id": "id",
"function": {
"arguments": "arguments",
"name": "name"
},
"type": "function"
}
]
}
}
],
"created": 0,
"model": "model",
"object": "chat.completion",
"usage": {
"completion_tokens": 0,
"prompt_tokens": 0,
"total_tokens": 0
}
}