Completion Function - completion()

The Input params are exactly the same as the OpenAI Create chat completion, and let you call Azure OpenAI, Anthropic, Cohere, Replicate models in the same format.

In addition, liteLLM allows you to pass in the following Optional liteLLM args:
forceTimeout, azure, logger_fn, verbose

Input - Request Body

model string Required
ID of the model to use. See the model endpoint compatibility table for details on which models work with the Chat API.

messages array Required
A list of messages comprising the conversation so far. Example Python Code

from litellm import completion

messages=
    [
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Knock knock."},
        {"role": "assistant", "content": "Who's there?"},
        {"role": "user", "content": "Orange."},
    ]   

# openai call
response = completion(model="gpt-3.5-turbo", messages=messages, temperature=0)

# cohere call
response = completion(model="command-nightly", messages=messages, temperature=0)

role string Required
The role of the messages author. One of system, user, assistant, or function.

content string Required
The contents of the message. content is required for all messages, and may be null for assistant messages with function calls.

name string Optional
The name of the author of this message. name is required if role is function, and it should be the name of the function whose response is in the content. May contain a-z, A-Z, 0-9, and underscores, with a maximum length of 64 characters.

function_call object Optional
The name and arguments of a function that should be called, as generated by the model.

functions array Optional
A list of functions the model may generate JSON inputs for.

name string Required
The name of the function to be called. Must be a-z, A-Z, 0-9, or contain underscores and dashes, with a maximum length of 64.

description string Optional
A description of what the function does, used by the model to choose when and how to call the function.

parameters object Required
The parameters the functions accept, described as a JSON Schema object. To describe a function that accepts no parameters, provide the value {"type": "object", "properties": {}}.

function_call string or object Optional
Controls how the model responds to function calls. "none" means the model does not call a function, and responds to the end-user. "auto" means the model can pick between an end-user or calling a function. Specifying a particular function via {"name": "my_function"} forces the model to call that function. "none" is the default when no functions are present. "auto" is the default if functions are present.

temperature number Optional, Defaults to 1
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.

top_p number Optional, Defaults to 1
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 1temperature` but not both.

n integer Optional, Defaults to 1
How many chat completion choices to generate for each input message.

stream boolean Optional, Defaults to false
If set, partial message deltas will be sent, like in ChatGPT. Tokens will be sent as data-only server-sent events as they become available, with the stream terminated by a data: [DONE] message.

stop string or array Optional, Defaults to null
Up to 4 sequences where the API will stop generating further tokens.

max_tokens integer Optional, Defaults to inf
The maximum number of tokens to generate in the chat completion. The total length of input tokens and generated tokens is limited by the model's context length

presence_penalty number Optional, Defaults to 0
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.

frequency_penalty number Optional, Defaults to 0
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.

logit_bias map Optional, Defaults to null
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 the likelihood of selection; values like -100 or 100 should result in a ban or exclusive selection of the relevant token.

user string Optional
A unique identifier representing your end-user, which can help liteLLM to monitor and detect abuse.

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Completion Function - completion()

Input - Request Body

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