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Batching Completion()

LiteLLM allows you to:

  • Send many completion calls to 1 model
  • Send 1 completion call to many models: Return Fastest Response
  • Send 1 completion call to many models: Return All Responses
info

Send multiple completion calls to 1 model

In the batch_completion method, you provide a list of messages where each sub-list of messages is passed to litellm.completion(), allowing you to process multiple prompts efficiently in a single API call.

Open In Colab

Example Code

import litellm
import os
from litellm import batch_completion

os.environ['ANTHROPIC_API_KEY'] = ""


responses = batch_completion(
model="claude-2",
messages = [
[
{
"role": "user",
"content": "good morning? "
}
],
[
{
"role": "user",
"content": "what's the time? "
}
]
]
)

Send 1 completion call to many models: Return Fastest Response

This makes parallel calls to the specified models and returns the first response

Use this to reduce latency

Example Code

import litellm
import os
from litellm import batch_completion_models

os.environ['ANTHROPIC_API_KEY'] = ""
os.environ['OPENAI_API_KEY'] = ""
os.environ['COHERE_API_KEY'] = ""

response = batch_completion_models(
models=["gpt-3.5-turbo", "claude-instant-1.2", "command-nightly"],
messages=[{"role": "user", "content": "Hey, how's it going"}]
)
print(result)

Output

Returns the first response

{
"object": "chat.completion",
"choices": [
{
"finish_reason": "stop",
"index": 0,
"message": {
"content": " I'm doing well, thanks for asking! I'm an AI assistant created by Anthropic to be helpful, harmless, and honest.",
"role": "assistant",
"logprobs": null
}
}
],
"id": "chatcmpl-23273eed-e351-41be-a492-bafcf5cf3274",
"created": 1695154628.2076092,
"model": "command-nightly",
"usage": {
"prompt_tokens": 6,
"completion_tokens": 14,
"total_tokens": 20
}
}

Send 1 completion call to many models: Return All Responses

This makes parallel calls to the specified models and returns all responses

Use this to process requests concurrently and get responses from multiple models.

Example Code

import litellm
import os
from litellm import batch_completion_models_all_responses

os.environ['ANTHROPIC_API_KEY'] = ""
os.environ['OPENAI_API_KEY'] = ""
os.environ['COHERE_API_KEY'] = ""

responses = batch_completion_models_all_responses(
models=["gpt-3.5-turbo", "claude-instant-1.2", "command-nightly"],
messages=[{"role": "user", "content": "Hey, how's it going"}]
)
print(responses)

Output

[<ModelResponse chat.completion id=chatcmpl-e673ec8e-4e8f-4c9e-bf26-bf9fa7ee52b9 at 0x103a62160> JSON: {
"object": "chat.completion",
"choices": [
{
"finish_reason": "stop_sequence",
"index": 0,
"message": {
"content": " It's going well, thank you for asking! How about you?",
"role": "assistant",
"logprobs": null
}
}
],
"id": "chatcmpl-e673ec8e-4e8f-4c9e-bf26-bf9fa7ee52b9",
"created": 1695222060.917964,
"model": "claude-instant-1.2",
"usage": {
"prompt_tokens": 14,
"completion_tokens": 9,
"total_tokens": 23
}
}, <ModelResponse chat.completion id=chatcmpl-ab6c5bd3-b5d9-4711-9697-e28d9fb8a53c at 0x103a62b60> JSON: {
"object": "chat.completion",
"choices": [
{
"finish_reason": "stop",
"index": 0,
"message": {
"content": " It's going well, thank you for asking! How about you?",
"role": "assistant",
"logprobs": null
}
}
],
"id": "chatcmpl-ab6c5bd3-b5d9-4711-9697-e28d9fb8a53c",
"created": 1695222061.0445492,
"model": "command-nightly",
"usage": {
"prompt_tokens": 6,
"completion_tokens": 14,
"total_tokens": 20
}
}, <OpenAIObject chat.completion id=chatcmpl-80szFnKHzCxObW0RqCMw1hWW1Icrq at 0x102dd6430> JSON: {
"id": "chatcmpl-80szFnKHzCxObW0RqCMw1hWW1Icrq",
"object": "chat.completion",
"created": 1695222061,
"model": "gpt-3.5-turbo-0613",
"choices": [
{
"index": 0,
"message": {
"role": "assistant",
"content": "Hello! I'm an AI language model, so I don't have feelings, but I'm here to assist you with any questions or tasks you might have. How can I help you today?"
},
"finish_reason": "stop"
}
],
"usage": {
"prompt_tokens": 13,
"completion_tokens": 39,
"total_tokens": 52
}
}]