mirror of
https://github.com/BerriAI/litellm.git
synced 2025-04-24 18:24:20 +00:00
version 0.1.2
This commit is contained in:
Krrish Dholakia
parent
740208d643
commit
fa65f960e3
18 changed files with 888 additions and 234 deletions
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@ -1,5 +1,4 @@
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OPENAI_API_KEY = ""
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COHERE_API_KEY = ""
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OPENROUTER_API_KEY = ""
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OR_SITE_URL = ""
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OR_APP_NAME = "LiteLLM Example app"
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@ -1,7 +1,17 @@
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import os, openai, cohere, dotenv
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import os, openai, cohere, replicate, sys
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from typing import Any
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from func_timeout import func_set_timeout, FunctionTimedOut
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from anthropic import Anthropic, HUMAN_PROMPT, AI_PROMPT
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import json
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import traceback
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import threading
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import dotenv
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import traceback
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import subprocess
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####### ENVIRONMENT VARIABLES ###################
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# Loading env variables using dotenv
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dotenv.load_dotenv()
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set_verbose = False
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####### COMPLETION MODELS ###################
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open_ai_chat_completion_models = [
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@ -16,16 +26,9 @@ cohere_models = [
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'command-nightly',
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]
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openrouter_models = [
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'google/palm-2-codechat-bison',
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'google/palm-2-chat-bison',
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'openai/gpt-3.5-turbo',
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'openai/gpt-3.5-turbo-16k',
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'openai/gpt-4-32k',
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'anthropic/claude-2',
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'anthropic/claude-instant-v1',
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'meta-llama/llama-2-13b-chat',
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'meta-llama/llama-2-70b-chat'
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anthropic_models = [
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"claude-2",
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"claude-instant-1"
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]
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####### EMBEDDING MODELS ###################
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@ -38,122 +41,389 @@ open_ai_embedding_models = [
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####### COMPLETION ENDPOINTS ################
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#############################################
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def completion(model, messages, azure=False):
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if azure == True:
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# azure configs
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openai.api_type = "azure"
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openai.api_base = os.environ.get("AZURE_API_BASE")
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openai.api_version = os.environ.get("AZURE_API_VERSION")
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openai.api_key = os.environ.get("AZURE_API_KEY")
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response = openai.ChatCompletion.create(
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engine=model,
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messages = messages
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)
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elif "replicate" in model:
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prompt = " ".join([message["content"] for message in messages])
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output = replicate.run(
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model,
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input={
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"prompt": prompt,
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})
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print(f"output: {output}")
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response = ""
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for item in output:
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print(f"item: {item}")
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response += item
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new_response = {
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"choices": [
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{
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"finish_reason": "stop",
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"index": 0,
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"message": {
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"content": response,
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"role": "assistant"
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}
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}
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]
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}
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print(f"new response: {new_response}")
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response = new_response
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elif model in cohere_models:
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cohere_key = os.environ.get("COHERE_API_KEY")
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co = cohere.Client(cohere_key)
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prompt = " ".join([message["content"] for message in messages])
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response = co.generate(
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model=model,
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prompt = prompt
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)
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new_response = {
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"choices": [
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{
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"finish_reason": "stop",
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"index": 0,
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"message": {
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"content": response[0],
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"role": "assistant"
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}
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}
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],
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}
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response = new_response
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elif model in open_ai_chat_completion_models:
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openai.api_type = "openai"
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openai.api_base = "https://api.openai.com/v1"
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openai.api_version = None
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openai.api_key = os.environ.get("OPENAI_API_KEY")
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response = openai.ChatCompletion.create(
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model=model,
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@func_set_timeout(10, allowOverride=True) ## https://pypi.org/project/func-timeout/ - timeouts, in case calls hang (e.g. Azure)
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def completion(model, messages, max_tokens=None, forceTimeout=10, azure=False, logger_fn=None):
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try:
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if azure == True:
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# azure configs
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openai.api_type = "azure"
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openai.api_base = os.environ.get("AZURE_API_BASE")
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openai.api_version = os.environ.get("AZURE_API_VERSION")
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openai.api_key = os.environ.get("AZURE_API_KEY")
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## LOGGING
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logging(model=model, input=input, azure=azure, logger_fn=logger_fn)
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## COMPLETION CALL
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response = openai.ChatCompletion.create(
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engine=model,
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messages = messages
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)
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elif model in open_ai_text_completion_models:
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openai.api_type = "openai"
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openai.api_base = "https://api.openai.com/v1"
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openai.api_version = None
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openai.api_key = os.environ.get("OPENAI_API_KEY")
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prompt = " ".join([message["content"] for message in messages])
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response = openai.Completion.create(
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)
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elif "replicate" in model:
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# replicate defaults to os.environ.get("REPLICATE_API_TOKEN")
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# checking in case user set it to REPLICATE_API_KEY instead
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if not os.environ.get("REPLICATE_API_TOKEN") and os.environ.get("REPLICATE_API_KEY"):
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replicate_api_token = os.environ.get("REPLICATE_API_KEY")
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os.environ["REPLICATE_API_TOKEN"] = replicate_api_token
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prompt = " ".join([message["content"] for message in messages])
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input = [{"prompt": prompt}]
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if max_tokens:
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input["max_length"] = max_tokens # for t5 models
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input["max_new_tokens"] = max_tokens # for llama2 models
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## LOGGING
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logging(model=model, input=input, azure=azure, additional_args={"max_tokens": max_tokens}, logger_fn=logger_fn)
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## COMPLETION CALL
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output = replicate.run(
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model,
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input=input)
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response = ""
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for item in output:
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response += item
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new_response = {
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"choices": [
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{
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"finish_reason": "stop",
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"index": 0,
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"message": {
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"content": response,
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"role": "assistant"
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}
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}
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]
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}
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response = new_response
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elif model in anthropic_models:
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#anthropic defaults to os.environ.get("ANTHROPIC_API_KEY")
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prompt = f"{HUMAN_PROMPT}"
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for message in messages:
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if "role" in message:
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if message["role"] == "user":
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prompt += f"{HUMAN_PROMPT}{message['content']}"
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else:
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prompt += f"{AI_PROMPT}{message['content']}"
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else:
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prompt += f"{HUMAN_PROMPT}{message['content']}"
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prompt += f"{AI_PROMPT}"
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anthropic = Anthropic()
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if max_tokens:
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max_tokens_to_sample = max_tokens
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else:
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max_tokens_to_sample = 300 # default in Anthropic docs https://docs.anthropic.com/claude/reference/client-libraries
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## LOGGING
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logging(model=model, input=prompt, azure=azure, additional_args={"max_tokens": max_tokens}, logger_fn=logger_fn)
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## COMPLETION CALL
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completion = anthropic.completions.create(
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model=model,
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prompt=prompt,
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max_tokens_to_sample=max_tokens_to_sample
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)
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new_response = {
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"choices": [
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{
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"finish_reason": "stop",
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"index": 0,
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"message": {
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"content": completion.completion,
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"role": "assistant"
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}
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}
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]
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}
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print(f"new response: {new_response}")
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response = new_response
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elif model in cohere_models:
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cohere_key = os.environ.get("COHERE_API_KEY")
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co = cohere.Client(cohere_key)
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prompt = " ".join([message["content"] for message in messages])
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## LOGGING
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logging(model=model, input=prompt, azure=azure, logger_fn=logger_fn)
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## COMPLETION CALL
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response = co.generate(
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model=model,
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prompt = prompt
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)
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elif model in openrouter_models:
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openai.api_base = "https://openrouter.ai/api/v1"
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openai.api_key = os.environ.get("OPENROUTER_API_KEY")
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prompt = " ".join([message["content"] for message in messages])
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response = openai.ChatCompletion.create(
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model=model,
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messages=messages,
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headers={
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"HTTP-Referer": os.environ.get("OR_SITE_URL"), # To identify your app
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"X-Title": os.environ.get("OR_APP_NAME")
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},
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)
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reply = response.choices[0].message
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return response
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)
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new_response = {
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"choices": [
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{
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"finish_reason": "stop",
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"index": 0,
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||||
"message": {
|
||||
"content": response[0],
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||||
"role": "assistant"
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||||
}
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||||
}
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||||
],
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||||
}
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response = new_response
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elif model in open_ai_chat_completion_models:
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||||
openai.api_type = "openai"
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||||
openai.api_base = "https://api.openai.com/v1"
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||||
openai.api_version = None
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||||
openai.api_key = os.environ.get("OPENAI_API_KEY")
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## LOGGING
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logging(model=model, input=messages, azure=azure, logger_fn=logger_fn)
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## COMPLETION CALL
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response = openai.ChatCompletion.create(
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model=model,
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messages = messages
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)
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elif model in open_ai_text_completion_models:
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openai.api_type = "openai"
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||||
openai.api_base = "https://api.openai.com/v1"
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||||
openai.api_version = None
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||||
openai.api_key = os.environ.get("OPENAI_API_KEY")
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prompt = " ".join([message["content"] for message in messages])
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## LOGGING
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||||
logging(model=model, input=prompt, azure=azure, logger_fn=logger_fn)
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## COMPLETION CALL
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||||
response = openai.Completion.create(
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||||
model=model,
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prompt = prompt
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)
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||||
else:
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logging(model=model, input=messages, azure=azure, logger_fn=logger_fn)
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return response
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except Exception as e:
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logging(model=model, input=messages, azure=azure, additional_args={"max_tokens": max_tokens}, logger_fn=logger_fn)
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raise e
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### EMBEDDING ENDPOINTS ####################
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def embedding(model, input=[], azure=False):
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@func_set_timeout(60, allowOverride=True) ## https://pypi.org/project/func-timeout/
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def embedding(model, input=[], azure=False, forceTimeout=60, logger_fn=None):
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response = None
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if azure == True:
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# azure configs
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openai.api_type = "azure"
|
||||
openai.api_base = os.environ.get("AZURE_API_BASE")
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openai.api_version = os.environ.get("AZURE_API_VERSION")
|
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openai.api_key = os.environ.get("AZURE_API_KEY")
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openai.api_key = os.environ.get("AZURE_API_KEY")
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## LOGGING
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logging(model=model, input=input, azure=azure, logger_fn=logger_fn)
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## EMBEDDING CALL
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response = openai.Embedding.create(input=input, engine=model)
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print_verbose(f"response_value: {str(response)[:50]}")
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elif model in open_ai_embedding_models:
|
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openai.api_type = "openai"
|
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openai.api_base = "https://api.openai.com/v1"
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openai.api_version = None
|
||||
openai.api_key = os.environ.get("OPENAI_API_KEY")
|
||||
## LOGGING
|
||||
logging(model=model, input=input, azure=azure, logger_fn=logger_fn)
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## EMBEDDING CALL
|
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response = openai.Embedding.create(input=input, model=model)
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print_verbose(f"response_value: {str(response)[:50]}")
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else:
|
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logging(model=model, input=input, azure=azure, logger_fn=logger_fn)
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return response
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#############################################
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#############################################
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### CLIENT CLASS #################### make it easy to push completion/embedding runs to different sources -> sentry/posthog/slack, etc.
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class litellm_client:
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def __init__(self, success_callback=[], failure_callback=[], verbose=False): # Constructor
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set_verbose = verbose
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self.success_callback = success_callback
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self.failure_callback = failure_callback
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self.logger_fn = None # if user passes in their own logging function
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self.callback_list = list(set(self.success_callback + self.failure_callback))
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self.set_callbacks()
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## COMPLETION CALL
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def completion(self, model, messages, max_tokens=None, forceTimeout=10, azure=False, logger_fn=None, additional_details={}) -> Any:
|
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try:
|
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self.logger_fn = logger_fn
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response = completion(model=model, messages=messages, max_tokens=max_tokens, forceTimeout=forceTimeout, azure=azure, logger_fn=self.handle_input)
|
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my_thread = threading.Thread(target=self.handle_success, args=(model, messages, additional_details)) # don't interrupt execution of main thread
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my_thread.start()
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return response
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except Exception as e:
|
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args = locals() # get all the param values
|
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self.handle_failure(e, args)
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raise e
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|
||||
## EMBEDDING CALL
|
||||
def embedding(self, model, input=[], azure=False, logger_fn=None, forceTimeout=60, additional_details={}) -> Any:
|
||||
try:
|
||||
self.logger_fn = logger_fn
|
||||
response = embedding(model, input, azure=azure, logger_fn=self.handle_input)
|
||||
my_thread = threading.Thread(target=self.handle_success, args=(model, input, additional_details)) # don't interrupt execution of main thread
|
||||
my_thread.start()
|
||||
return response
|
||||
except Exception as e:
|
||||
args = locals() # get all the param values
|
||||
self.handle_failure(e, args)
|
||||
raise e
|
||||
|
||||
|
||||
def set_callbacks(self): #instantiate any external packages
|
||||
for callback in self.callback_list: # only install what's required
|
||||
if callback == "sentry":
|
||||
try:
|
||||
import sentry_sdk
|
||||
except ImportError:
|
||||
print_verbose("Package 'sentry_sdk' is missing. Installing it...")
|
||||
subprocess.check_call([sys.executable, '-m', 'pip', 'install', 'sentry_sdk'])
|
||||
import sentry_sdk
|
||||
self.sentry_sdk = sentry_sdk
|
||||
self.sentry_sdk.init(dsn=os.environ.get("SENTRY_API_URL"), traces_sample_rate=float(os.environ.get("SENTRY_API_TRACE_RATE")))
|
||||
self.capture_exception = self.sentry_sdk.capture_exception
|
||||
self.add_breadcrumb = self.sentry_sdk.add_breadcrumb
|
||||
elif callback == "posthog":
|
||||
try:
|
||||
from posthog import Posthog
|
||||
except:
|
||||
print_verbose("Package 'posthog' is missing. Installing it...")
|
||||
subprocess.check_call([sys.executable, '-m', 'pip', 'install', 'posthog'])
|
||||
from posthog import Posthog
|
||||
self.posthog = Posthog(
|
||||
project_api_key=os.environ.get("POSTHOG_API_KEY"),
|
||||
host=os.environ.get("POSTHOG_API_URL"))
|
||||
elif callback == "slack":
|
||||
try:
|
||||
from slack_bolt import App
|
||||
except ImportError:
|
||||
print_verbose("Package 'slack_bolt' is missing. Installing it...")
|
||||
subprocess.check_call([sys.executable, '-m', 'pip', 'install', 'slack_bolt'])
|
||||
from slack_bolt import App
|
||||
self.slack_app = App(
|
||||
token=os.environ.get("SLACK_API_TOKEN"),
|
||||
signing_secret=os.environ.get("SLACK_API_SECRET")
|
||||
)
|
||||
self.alerts_channel = os.environ["SLACK_API_CHANNEL"]
|
||||
|
||||
def handle_input(self, model_call_details={}):
|
||||
if len(model_call_details.keys()) > 0:
|
||||
model = model_call_details["model"] if "model" in model_call_details else None
|
||||
if model:
|
||||
for callback in self.callback_list:
|
||||
if callback == "sentry": # add a sentry breadcrumb if user passed in sentry integration
|
||||
self.add_breadcrumb(
|
||||
category=f'{model}',
|
||||
message='Trying request model {} input {}'.format(model, json.dumps(model_call_details)),
|
||||
level='info',
|
||||
)
|
||||
if self.logger_fn and callable(self.logger_fn):
|
||||
self.logger_fn(model_call_details)
|
||||
pass
|
||||
|
||||
def handle_success(self, model, messages, additional_details):
|
||||
success_handler = additional_details.pop("success_handler", None)
|
||||
failure_handler = additional_details.pop("failure_handler", None)
|
||||
additional_details["litellm_model"] = str(model)
|
||||
additional_details["litellm_messages"] = str(messages)
|
||||
for callback in self.success_callback:
|
||||
try:
|
||||
if callback == "posthog":
|
||||
ph_obj = {}
|
||||
for detail in additional_details:
|
||||
ph_obj[detail] = additional_details[detail]
|
||||
event_name = additional_details["successful_event"] if "successful_event" in additional_details else "litellm.succes_query"
|
||||
if "user_id" in additional_details:
|
||||
self.posthog.capture(additional_details["user_id"], event_name, ph_obj)
|
||||
else:
|
||||
self.posthog.capture(event_name, ph_obj)
|
||||
pass
|
||||
elif callback == "slack":
|
||||
slack_msg = ""
|
||||
if len(additional_details.keys()) > 0:
|
||||
for detail in additional_details:
|
||||
slack_msg += f"{detail}: {additional_details[detail]}\n"
|
||||
slack_msg += f"Successful call"
|
||||
self.slack_app.client.chat_postMessage(channel=self.alerts_channel, text=slack_msg)
|
||||
except:
|
||||
pass
|
||||
|
||||
if success_handler and callable(success_handler):
|
||||
call_details = {
|
||||
"model": model,
|
||||
"messages": messages,
|
||||
"additional_details": additional_details
|
||||
}
|
||||
success_handler(call_details)
|
||||
pass
|
||||
|
||||
def handle_failure(self, exception, args):
|
||||
args.pop("self")
|
||||
additional_details = args.pop("additional_details", {})
|
||||
|
||||
success_handler = additional_details.pop("success_handler", None)
|
||||
failure_handler = additional_details.pop("failure_handler", None)
|
||||
|
||||
for callback in self.failure_callback:
|
||||
try:
|
||||
if callback == "slack":
|
||||
slack_msg = ""
|
||||
for param in args:
|
||||
slack_msg += f"{param}: {args[param]}\n"
|
||||
if len(additional_details.keys()) > 0:
|
||||
for detail in additional_details:
|
||||
slack_msg += f"{detail}: {additional_details[detail]}\n"
|
||||
slack_msg += f"Traceback: {traceback.format_exc()}"
|
||||
self.slack_app.client.chat_postMessage(channel=self.alerts_channel, text=slack_msg)
|
||||
elif callback == "sentry":
|
||||
self.capture_exception(exception)
|
||||
elif callback == "posthog":
|
||||
if len(additional_details.keys()) > 0:
|
||||
ph_obj = {}
|
||||
for param in args:
|
||||
ph_obj[param] += args[param]
|
||||
for detail in additional_details:
|
||||
ph_obj[detail] = additional_details[detail]
|
||||
event_name = additional_details["failed_event"] if "failed_event" in additional_details else "litellm.failed_query"
|
||||
if "user_id" in additional_details:
|
||||
self.posthog.capture(additional_details["user_id"], event_name, ph_obj)
|
||||
else:
|
||||
self.posthog.capture(event_name, ph_obj)
|
||||
else:
|
||||
pass
|
||||
except:
|
||||
print(f"got an error calling {callback} - {traceback.format_exc()}")
|
||||
|
||||
if failure_handler and callable(failure_handler):
|
||||
call_details = {
|
||||
"exception": exception,
|
||||
"additional_details": additional_details
|
||||
}
|
||||
failure_handler(call_details)
|
||||
pass
|
||||
####### HELPER FUNCTIONS ################
|
||||
|
||||
#Logging function -> log the exact model details + what's being sent | Non-Blocking
|
||||
def logging(model, input, azure=False, additional_args={}, logger_fn=None):
|
||||
try:
|
||||
model_call_details = {}
|
||||
model_call_details["model"] = model
|
||||
model_call_details["input"] = input
|
||||
model_call_details["azure"] = azure
|
||||
model_call_details["additional_args"] = additional_args
|
||||
if logger_fn and callable(logger_fn):
|
||||
try:
|
||||
# log additional call details -> api key, etc.
|
||||
if azure == True or model in open_ai_chat_completion_models or model in open_ai_chat_completion_models or model in open_ai_embedding_models:
|
||||
model_call_details["api_type"] = openai.api_type
|
||||
model_call_details["api_base"] = openai.api_base
|
||||
model_call_details["api_version"] = openai.api_version
|
||||
model_call_details["api_key"] = openai.api_key
|
||||
elif "replicate" in model:
|
||||
model_call_details["api_key"] = os.environ.get("REPLICATE_API_TOKEN")
|
||||
elif model in anthropic_models:
|
||||
model_call_details["api_key"] = os.environ.get("ANTHROPIC_API_KEY")
|
||||
elif model in cohere_models:
|
||||
model_call_details["api_key"] = os.environ.get("COHERE_API_KEY")
|
||||
|
||||
logger_fn(model_call_details) # Expectation: any logger function passed in by the user should accept a dict object
|
||||
except:
|
||||
print_verbose(f"Basic model call details: {model_call_details}")
|
||||
print_verbose(f"[Non-Blocking] Exception occurred while logging {traceback.format_exc()}")
|
||||
pass
|
||||
else:
|
||||
print_verbose(f"Basic model call details: {model_call_details}")
|
||||
pass
|
||||
except:
|
||||
pass
|
||||
|
||||
## Set verbose to true -> ```litellm.verbose = True```
|
||||
def print_verbose(print_statement):
|
||||
if set_verbose:
|
||||
print(f"LiteLLM: {print_statement}")
|
||||
print("Get help - https://discord.com/invite/wuPM9dRgDw")
|
||||
|
|
@ -26,9 +26,4 @@ print(response)
|
|||
# cohere call
|
||||
response = completion("command-nightly", messages)
|
||||
print("\nCohere call")
|
||||
print(response)
|
||||
|
||||
# openrouter call
|
||||
response = completion("google/palm-2-codechat-bison", messages)
|
||||
print("\OpenRouter call")
|
||||
print(response)
|
||||
BIN
dist/litellm-0.1.0-py3-none-any.whl
vendored
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dist/litellm-0.1.0-py3-none-any.whl
vendored
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dist/litellm-0.1.0.tar.gz
vendored
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dist/litellm-0.1.0.tar.gz
vendored
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dist/litellm-0.1.1-py3-none-any.whl
vendored
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dist/litellm-0.1.1-py3-none-any.whl
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dist/litellm-0.1.1.tar.gz
vendored
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dist/litellm-0.1.1.tar.gz
vendored
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BIN
dist/litellm-0.1.2-py3-none-any.whl
vendored
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dist/litellm-0.1.2-py3-none-any.whl
vendored
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dist/litellm-0.1.2.tar.gz
vendored
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BIN
dist/litellm-0.1.2.tar.gz
vendored
Normal file
Binary file not shown.
|
|
@ -1,12 +1,6 @@
|
|||
Metadata-Version: 2.1
|
||||
Name: litellm
|
||||
Version: 0.1.1
|
||||
Version: 0.1.2
|
||||
Summary: Library to easily interface with LLM API providers
|
||||
Home-page: UNKNOWN
|
||||
Author: Ishaan Jaffer
|
||||
License: UNKNOWN
|
||||
Platform: UNKNOWN
|
||||
Author: BerriAI
|
||||
License-File: LICENSE
|
||||
|
||||
UNKNOWN
|
||||
|
||||
|
|
|
|||
BIN
litellm/.DS_Store
vendored
Normal file
BIN
litellm/.DS_Store
vendored
Normal file
Binary file not shown.
Binary file not shown.
487
litellm/main.py
487
litellm/main.py
|
|
@ -1,7 +1,17 @@
|
|||
import os, openai, cohere, dotenv
|
||||
|
||||
import os, openai, cohere, replicate, sys
|
||||
from typing import Any
|
||||
from func_timeout import func_set_timeout, FunctionTimedOut
|
||||
from anthropic import Anthropic, HUMAN_PROMPT, AI_PROMPT
|
||||
import json
|
||||
import traceback
|
||||
import threading
|
||||
import dotenv
|
||||
import traceback
|
||||
import subprocess
|
||||
####### ENVIRONMENT VARIABLES ###################
|
||||
# Loading env variables using dotenv
|
||||
dotenv.load_dotenv()
|
||||
set_verbose = False
|
||||
|
||||
####### COMPLETION MODELS ###################
|
||||
open_ai_chat_completion_models = [
|
||||
|
|
@ -16,16 +26,9 @@ cohere_models = [
|
|||
'command-nightly',
|
||||
]
|
||||
|
||||
openrouter_models = [
|
||||
'google/palm-2-codechat-bison',
|
||||
'google/palm-2-chat-bison',
|
||||
'openai/gpt-3.5-turbo',
|
||||
'openai/gpt-3.5-turbo-16k',
|
||||
'openai/gpt-4-32k',
|
||||
'anthropic/claude-2',
|
||||
'anthropic/claude-instant-v1',
|
||||
'meta-llama/llama-2-13b-chat',
|
||||
'meta-llama/llama-2-70b-chat'
|
||||
anthropic_models = [
|
||||
"claude-2",
|
||||
"claude-instant-1"
|
||||
]
|
||||
|
||||
####### EMBEDDING MODELS ###################
|
||||
|
|
@ -38,123 +41,389 @@ open_ai_embedding_models = [
|
|||
|
||||
####### COMPLETION ENDPOINTS ################
|
||||
#############################################
|
||||
def completion(model, messages, azure=False):
|
||||
if azure == True:
|
||||
# azure configs
|
||||
openai.api_type = "azure"
|
||||
openai.api_base = os.environ.get("AZURE_API_BASE")
|
||||
openai.api_version = os.environ.get("AZURE_API_VERSION")
|
||||
openai.api_key = os.environ.get("AZURE_API_KEY")
|
||||
response = openai.ChatCompletion.create(
|
||||
engine=model,
|
||||
messages = messages
|
||||
)
|
||||
elif "replicate" in model:
|
||||
prompt = " ".join([message["content"] for message in messages])
|
||||
output = replicate.run(
|
||||
model,
|
||||
input={
|
||||
"prompt": prompt,
|
||||
})
|
||||
print(f"output: {output}")
|
||||
response = ""
|
||||
for item in output:
|
||||
print(f"item: {item}")
|
||||
response += item
|
||||
new_response = {
|
||||
"choices": [
|
||||
{
|
||||
"finish_reason": "stop",
|
||||
"index": 0,
|
||||
"message": {
|
||||
"content": response,
|
||||
"role": "assistant"
|
||||
}
|
||||
}
|
||||
]
|
||||
}
|
||||
print(f"new response: {new_response}")
|
||||
response = new_response
|
||||
elif model in cohere_models:
|
||||
cohere_key = os.environ.get("COHERE_API_KEY")
|
||||
co = cohere.Client(cohere_key)
|
||||
prompt = " ".join([message["content"] for message in messages])
|
||||
response = co.generate(
|
||||
model=model,
|
||||
prompt = prompt
|
||||
)
|
||||
new_response = {
|
||||
"choices": [
|
||||
{
|
||||
"finish_reason": "stop",
|
||||
"index": 0,
|
||||
"message": {
|
||||
"content": response[0],
|
||||
"role": "assistant"
|
||||
}
|
||||
}
|
||||
],
|
||||
}
|
||||
|
||||
response = new_response
|
||||
|
||||
elif model in open_ai_chat_completion_models:
|
||||
openai.api_type = "openai"
|
||||
openai.api_base = "https://api.openai.com/v1"
|
||||
openai.api_version = None
|
||||
openai.api_key = os.environ.get("OPENAI_API_KEY")
|
||||
response = openai.ChatCompletion.create(
|
||||
model=model,
|
||||
@func_set_timeout(10, allowOverride=True) ## https://pypi.org/project/func-timeout/ - timeouts, in case calls hang (e.g. Azure)
|
||||
def completion(model, messages, max_tokens=None, forceTimeout=10, azure=False, logger_fn=None):
|
||||
try:
|
||||
if azure == True:
|
||||
# azure configs
|
||||
openai.api_type = "azure"
|
||||
openai.api_base = os.environ.get("AZURE_API_BASE")
|
||||
openai.api_version = os.environ.get("AZURE_API_VERSION")
|
||||
openai.api_key = os.environ.get("AZURE_API_KEY")
|
||||
## LOGGING
|
||||
logging(model=model, input=input, azure=azure, logger_fn=logger_fn)
|
||||
## COMPLETION CALL
|
||||
response = openai.ChatCompletion.create(
|
||||
engine=model,
|
||||
messages = messages
|
||||
)
|
||||
elif model in open_ai_text_completion_models:
|
||||
openai.api_type = "openai"
|
||||
openai.api_base = "https://api.openai.com/v1"
|
||||
openai.api_version = None
|
||||
openai.api_key = os.environ.get("OPENAI_API_KEY")
|
||||
prompt = " ".join([message["content"] for message in messages])
|
||||
response = openai.Completion.create(
|
||||
)
|
||||
elif "replicate" in model:
|
||||
# replicate defaults to os.environ.get("REPLICATE_API_TOKEN")
|
||||
# checking in case user set it to REPLICATE_API_KEY instead
|
||||
if not os.environ.get("REPLICATE_API_TOKEN") and os.environ.get("REPLICATE_API_KEY"):
|
||||
replicate_api_token = os.environ.get("REPLICATE_API_KEY")
|
||||
os.environ["REPLICATE_API_TOKEN"] = replicate_api_token
|
||||
prompt = " ".join([message["content"] for message in messages])
|
||||
input = [{"prompt": prompt}]
|
||||
if max_tokens:
|
||||
input["max_length"] = max_tokens # for t5 models
|
||||
input["max_new_tokens"] = max_tokens # for llama2 models
|
||||
## LOGGING
|
||||
logging(model=model, input=input, azure=azure, additional_args={"max_tokens": max_tokens}, logger_fn=logger_fn)
|
||||
## COMPLETION CALL
|
||||
output = replicate.run(
|
||||
model,
|
||||
input=input)
|
||||
response = ""
|
||||
for item in output:
|
||||
response += item
|
||||
new_response = {
|
||||
"choices": [
|
||||
{
|
||||
"finish_reason": "stop",
|
||||
"index": 0,
|
||||
"message": {
|
||||
"content": response,
|
||||
"role": "assistant"
|
||||
}
|
||||
}
|
||||
]
|
||||
}
|
||||
response = new_response
|
||||
elif model in anthropic_models:
|
||||
#anthropic defaults to os.environ.get("ANTHROPIC_API_KEY")
|
||||
prompt = f"{HUMAN_PROMPT}"
|
||||
for message in messages:
|
||||
if "role" in message:
|
||||
if message["role"] == "user":
|
||||
prompt += f"{HUMAN_PROMPT}{message['content']}"
|
||||
else:
|
||||
prompt += f"{AI_PROMPT}{message['content']}"
|
||||
else:
|
||||
prompt += f"{HUMAN_PROMPT}{message['content']}"
|
||||
prompt += f"{AI_PROMPT}"
|
||||
anthropic = Anthropic()
|
||||
if max_tokens:
|
||||
max_tokens_to_sample = max_tokens
|
||||
else:
|
||||
max_tokens_to_sample = 300 # default in Anthropic docs https://docs.anthropic.com/claude/reference/client-libraries
|
||||
## LOGGING
|
||||
logging(model=model, input=prompt, azure=azure, additional_args={"max_tokens": max_tokens}, logger_fn=logger_fn)
|
||||
## COMPLETION CALL
|
||||
completion = anthropic.completions.create(
|
||||
model=model,
|
||||
prompt=prompt,
|
||||
max_tokens_to_sample=max_tokens_to_sample
|
||||
)
|
||||
new_response = {
|
||||
"choices": [
|
||||
{
|
||||
"finish_reason": "stop",
|
||||
"index": 0,
|
||||
"message": {
|
||||
"content": completion.completion,
|
||||
"role": "assistant"
|
||||
}
|
||||
}
|
||||
]
|
||||
}
|
||||
print(f"new response: {new_response}")
|
||||
response = new_response
|
||||
elif model in cohere_models:
|
||||
cohere_key = os.environ.get("COHERE_API_KEY")
|
||||
co = cohere.Client(cohere_key)
|
||||
prompt = " ".join([message["content"] for message in messages])
|
||||
## LOGGING
|
||||
logging(model=model, input=prompt, azure=azure, logger_fn=logger_fn)
|
||||
## COMPLETION CALL
|
||||
response = co.generate(
|
||||
model=model,
|
||||
prompt = prompt
|
||||
)
|
||||
|
||||
elif model in openrouter_models:
|
||||
openai.api_base = "https://openrouter.ai/api/v1"
|
||||
openai.api_key = os.environ.get("OPENROUTER_API_KEY")
|
||||
|
||||
prompt = " ".join([message["content"] for message in messages])
|
||||
|
||||
response = openai.ChatCompletion.create(
|
||||
model=model,
|
||||
messages=messages,
|
||||
headers={
|
||||
"HTTP-Referer": os.environ.get("OR_SITE_URL"), # To identify your app
|
||||
"X-Title": os.environ.get("OR_APP_NAME")
|
||||
},
|
||||
)
|
||||
reply = response.choices[0].message
|
||||
return response
|
||||
)
|
||||
new_response = {
|
||||
"choices": [
|
||||
{
|
||||
"finish_reason": "stop",
|
||||
"index": 0,
|
||||
"message": {
|
||||
"content": response[0],
|
||||
"role": "assistant"
|
||||
}
|
||||
}
|
||||
],
|
||||
}
|
||||
response = new_response
|
||||
|
||||
elif model in open_ai_chat_completion_models:
|
||||
openai.api_type = "openai"
|
||||
openai.api_base = "https://api.openai.com/v1"
|
||||
openai.api_version = None
|
||||
openai.api_key = os.environ.get("OPENAI_API_KEY")
|
||||
## LOGGING
|
||||
logging(model=model, input=messages, azure=azure, logger_fn=logger_fn)
|
||||
## COMPLETION CALL
|
||||
response = openai.ChatCompletion.create(
|
||||
model=model,
|
||||
messages = messages
|
||||
)
|
||||
elif model in open_ai_text_completion_models:
|
||||
openai.api_type = "openai"
|
||||
openai.api_base = "https://api.openai.com/v1"
|
||||
openai.api_version = None
|
||||
openai.api_key = os.environ.get("OPENAI_API_KEY")
|
||||
prompt = " ".join([message["content"] for message in messages])
|
||||
## LOGGING
|
||||
logging(model=model, input=prompt, azure=azure, logger_fn=logger_fn)
|
||||
## COMPLETION CALL
|
||||
response = openai.Completion.create(
|
||||
model=model,
|
||||
prompt = prompt
|
||||
)
|
||||
else:
|
||||
logging(model=model, input=messages, azure=azure, logger_fn=logger_fn)
|
||||
return response
|
||||
except Exception as e:
|
||||
logging(model=model, input=messages, azure=azure, additional_args={"max_tokens": max_tokens}, logger_fn=logger_fn)
|
||||
raise e
|
||||
|
||||
|
||||
### EMBEDDING ENDPOINTS ####################
|
||||
def embedding(model, input=[], azure=False):
|
||||
@func_set_timeout(60, allowOverride=True) ## https://pypi.org/project/func-timeout/
|
||||
def embedding(model, input=[], azure=False, forceTimeout=60, logger_fn=None):
|
||||
response = None
|
||||
if azure == True:
|
||||
# azure configs
|
||||
openai.api_type = "azure"
|
||||
openai.api_base = os.environ.get("AZURE_API_BASE")
|
||||
openai.api_version = os.environ.get("AZURE_API_VERSION")
|
||||
openai.api_key = os.environ.get("AZURE_API_KEY")
|
||||
openai.api_key = os.environ.get("AZURE_API_KEY")
|
||||
## LOGGING
|
||||
logging(model=model, input=input, azure=azure, logger_fn=logger_fn)
|
||||
## EMBEDDING CALL
|
||||
response = openai.Embedding.create(input=input, engine=model)
|
||||
print_verbose(f"response_value: {str(response)[:50]}")
|
||||
elif model in open_ai_embedding_models:
|
||||
openai.api_type = "openai"
|
||||
openai.api_base = "https://api.openai.com/v1"
|
||||
openai.api_version = None
|
||||
openai.api_key = os.environ.get("OPENAI_API_KEY")
|
||||
## LOGGING
|
||||
logging(model=model, input=input, azure=azure, logger_fn=logger_fn)
|
||||
## EMBEDDING CALL
|
||||
response = openai.Embedding.create(input=input, model=model)
|
||||
print_verbose(f"response_value: {str(response)[:50]}")
|
||||
else:
|
||||
logging(model=model, input=input, azure=azure, logger_fn=logger_fn)
|
||||
|
||||
return response
|
||||
|
||||
|
||||
#############################################
|
||||
#############################################
|
||||
### CLIENT CLASS #################### make it easy to push completion/embedding runs to different sources -> sentry/posthog/slack, etc.
|
||||
class litellm_client:
|
||||
def __init__(self, success_callback=[], failure_callback=[], verbose=False): # Constructor
|
||||
set_verbose = verbose
|
||||
self.success_callback = success_callback
|
||||
self.failure_callback = failure_callback
|
||||
self.logger_fn = None # if user passes in their own logging function
|
||||
self.callback_list = list(set(self.success_callback + self.failure_callback))
|
||||
self.set_callbacks()
|
||||
|
||||
## COMPLETION CALL
|
||||
def completion(self, model, messages, max_tokens=None, forceTimeout=10, azure=False, logger_fn=None, additional_details={}) -> Any:
|
||||
try:
|
||||
self.logger_fn = logger_fn
|
||||
response = completion(model=model, messages=messages, max_tokens=max_tokens, forceTimeout=forceTimeout, azure=azure, logger_fn=self.handle_input)
|
||||
my_thread = threading.Thread(target=self.handle_success, args=(model, messages, additional_details)) # don't interrupt execution of main thread
|
||||
my_thread.start()
|
||||
return response
|
||||
except Exception as e:
|
||||
args = locals() # get all the param values
|
||||
self.handle_failure(e, args)
|
||||
raise e
|
||||
|
||||
## EMBEDDING CALL
|
||||
def embedding(self, model, input=[], azure=False, logger_fn=None, forceTimeout=60, additional_details={}) -> Any:
|
||||
try:
|
||||
self.logger_fn = logger_fn
|
||||
response = embedding(model, input, azure=azure, logger_fn=self.handle_input)
|
||||
my_thread = threading.Thread(target=self.handle_success, args=(model, input, additional_details)) # don't interrupt execution of main thread
|
||||
my_thread.start()
|
||||
return response
|
||||
except Exception as e:
|
||||
args = locals() # get all the param values
|
||||
self.handle_failure(e, args)
|
||||
raise e
|
||||
|
||||
|
||||
def set_callbacks(self): #instantiate any external packages
|
||||
for callback in self.callback_list: # only install what's required
|
||||
if callback == "sentry":
|
||||
try:
|
||||
import sentry_sdk
|
||||
except ImportError:
|
||||
print_verbose("Package 'sentry_sdk' is missing. Installing it...")
|
||||
subprocess.check_call([sys.executable, '-m', 'pip', 'install', 'sentry_sdk'])
|
||||
import sentry_sdk
|
||||
self.sentry_sdk = sentry_sdk
|
||||
self.sentry_sdk.init(dsn=os.environ.get("SENTRY_API_URL"), traces_sample_rate=float(os.environ.get("SENTRY_API_TRACE_RATE")))
|
||||
self.capture_exception = self.sentry_sdk.capture_exception
|
||||
self.add_breadcrumb = self.sentry_sdk.add_breadcrumb
|
||||
elif callback == "posthog":
|
||||
try:
|
||||
from posthog import Posthog
|
||||
except:
|
||||
print_verbose("Package 'posthog' is missing. Installing it...")
|
||||
subprocess.check_call([sys.executable, '-m', 'pip', 'install', 'posthog'])
|
||||
from posthog import Posthog
|
||||
self.posthog = Posthog(
|
||||
project_api_key=os.environ.get("POSTHOG_API_KEY"),
|
||||
host=os.environ.get("POSTHOG_API_URL"))
|
||||
elif callback == "slack":
|
||||
try:
|
||||
from slack_bolt import App
|
||||
except ImportError:
|
||||
print_verbose("Package 'slack_bolt' is missing. Installing it...")
|
||||
subprocess.check_call([sys.executable, '-m', 'pip', 'install', 'slack_bolt'])
|
||||
from slack_bolt import App
|
||||
self.slack_app = App(
|
||||
token=os.environ.get("SLACK_API_TOKEN"),
|
||||
signing_secret=os.environ.get("SLACK_API_SECRET")
|
||||
)
|
||||
self.alerts_channel = os.environ["SLACK_API_CHANNEL"]
|
||||
|
||||
def handle_input(self, model_call_details={}):
|
||||
if len(model_call_details.keys()) > 0:
|
||||
model = model_call_details["model"] if "model" in model_call_details else None
|
||||
if model:
|
||||
for callback in self.callback_list:
|
||||
if callback == "sentry": # add a sentry breadcrumb if user passed in sentry integration
|
||||
self.add_breadcrumb(
|
||||
category=f'{model}',
|
||||
message='Trying request model {} input {}'.format(model, json.dumps(model_call_details)),
|
||||
level='info',
|
||||
)
|
||||
if self.logger_fn and callable(self.logger_fn):
|
||||
self.logger_fn(model_call_details)
|
||||
pass
|
||||
|
||||
def handle_success(self, model, messages, additional_details):
|
||||
success_handler = additional_details.pop("success_handler", None)
|
||||
failure_handler = additional_details.pop("failure_handler", None)
|
||||
additional_details["litellm_model"] = str(model)
|
||||
additional_details["litellm_messages"] = str(messages)
|
||||
for callback in self.success_callback:
|
||||
try:
|
||||
if callback == "posthog":
|
||||
ph_obj = {}
|
||||
for detail in additional_details:
|
||||
ph_obj[detail] = additional_details[detail]
|
||||
event_name = additional_details["successful_event"] if "successful_event" in additional_details else "litellm.succes_query"
|
||||
if "user_id" in additional_details:
|
||||
self.posthog.capture(additional_details["user_id"], event_name, ph_obj)
|
||||
else:
|
||||
self.posthog.capture(event_name, ph_obj)
|
||||
pass
|
||||
elif callback == "slack":
|
||||
slack_msg = ""
|
||||
if len(additional_details.keys()) > 0:
|
||||
for detail in additional_details:
|
||||
slack_msg += f"{detail}: {additional_details[detail]}\n"
|
||||
slack_msg += f"Successful call"
|
||||
self.slack_app.client.chat_postMessage(channel=self.alerts_channel, text=slack_msg)
|
||||
except:
|
||||
pass
|
||||
|
||||
if success_handler and callable(success_handler):
|
||||
call_details = {
|
||||
"model": model,
|
||||
"messages": messages,
|
||||
"additional_details": additional_details
|
||||
}
|
||||
success_handler(call_details)
|
||||
pass
|
||||
|
||||
def handle_failure(self, exception, args):
|
||||
args.pop("self")
|
||||
additional_details = args.pop("additional_details", {})
|
||||
|
||||
success_handler = additional_details.pop("success_handler", None)
|
||||
failure_handler = additional_details.pop("failure_handler", None)
|
||||
|
||||
for callback in self.failure_callback:
|
||||
try:
|
||||
if callback == "slack":
|
||||
slack_msg = ""
|
||||
for param in args:
|
||||
slack_msg += f"{param}: {args[param]}\n"
|
||||
if len(additional_details.keys()) > 0:
|
||||
for detail in additional_details:
|
||||
slack_msg += f"{detail}: {additional_details[detail]}\n"
|
||||
slack_msg += f"Traceback: {traceback.format_exc()}"
|
||||
self.slack_app.client.chat_postMessage(channel=self.alerts_channel, text=slack_msg)
|
||||
elif callback == "sentry":
|
||||
self.capture_exception(exception)
|
||||
elif callback == "posthog":
|
||||
if len(additional_details.keys()) > 0:
|
||||
ph_obj = {}
|
||||
for param in args:
|
||||
ph_obj[param] += args[param]
|
||||
for detail in additional_details:
|
||||
ph_obj[detail] = additional_details[detail]
|
||||
event_name = additional_details["failed_event"] if "failed_event" in additional_details else "litellm.failed_query"
|
||||
if "user_id" in additional_details:
|
||||
self.posthog.capture(additional_details["user_id"], event_name, ph_obj)
|
||||
else:
|
||||
self.posthog.capture(event_name, ph_obj)
|
||||
else:
|
||||
pass
|
||||
except:
|
||||
print(f"got an error calling {callback} - {traceback.format_exc()}")
|
||||
|
||||
if failure_handler and callable(failure_handler):
|
||||
call_details = {
|
||||
"exception": exception,
|
||||
"additional_details": additional_details
|
||||
}
|
||||
failure_handler(call_details)
|
||||
pass
|
||||
####### HELPER FUNCTIONS ################
|
||||
|
||||
#Logging function -> log the exact model details + what's being sent | Non-Blocking
|
||||
def logging(model, input, azure=False, additional_args={}, logger_fn=None):
|
||||
try:
|
||||
model_call_details = {}
|
||||
model_call_details["model"] = model
|
||||
model_call_details["input"] = input
|
||||
model_call_details["azure"] = azure
|
||||
model_call_details["additional_args"] = additional_args
|
||||
if logger_fn and callable(logger_fn):
|
||||
try:
|
||||
# log additional call details -> api key, etc.
|
||||
if azure == True or model in open_ai_chat_completion_models or model in open_ai_chat_completion_models or model in open_ai_embedding_models:
|
||||
model_call_details["api_type"] = openai.api_type
|
||||
model_call_details["api_base"] = openai.api_base
|
||||
model_call_details["api_version"] = openai.api_version
|
||||
model_call_details["api_key"] = openai.api_key
|
||||
elif "replicate" in model:
|
||||
model_call_details["api_key"] = os.environ.get("REPLICATE_API_TOKEN")
|
||||
elif model in anthropic_models:
|
||||
model_call_details["api_key"] = os.environ.get("ANTHROPIC_API_KEY")
|
||||
elif model in cohere_models:
|
||||
model_call_details["api_key"] = os.environ.get("COHERE_API_KEY")
|
||||
|
||||
logger_fn(model_call_details) # Expectation: any logger function passed in by the user should accept a dict object
|
||||
except:
|
||||
print_verbose(f"Basic model call details: {model_call_details}")
|
||||
print_verbose(f"[Non-Blocking] Exception occurred while logging {traceback.format_exc()}")
|
||||
pass
|
||||
else:
|
||||
print_verbose(f"Basic model call details: {model_call_details}")
|
||||
pass
|
||||
except:
|
||||
pass
|
||||
|
||||
## Set verbose to true -> ```litellm.verbose = True```
|
||||
def print_verbose(print_statement):
|
||||
if set_verbose:
|
||||
print(f"LiteLLM: {print_statement}")
|
||||
print("Get help - https://discord.com/invite/wuPM9dRgDw")
|
||||
20
litellm/tests/test_bad_params.py
Normal file
20
litellm/tests/test_bad_params.py
Normal file
|
|
@ -0,0 +1,20 @@
|
|||
import sys, os
|
||||
import traceback
|
||||
sys.path.append('..') # Adds the parent directory to the system path
|
||||
import main
|
||||
from main import litellm_client
|
||||
client = litellm_client(success_callback=["posthog"], failure_callback=["slack", "sentry", "posthog"], verbose=True)
|
||||
completion = client.completion
|
||||
embedding = client.embedding
|
||||
|
||||
main.set_verbose = True
|
||||
|
||||
user_message = "Hello, how are you?"
|
||||
messages = [{ "content": user_message,"role": "user"}]
|
||||
model_val = None
|
||||
# test on empty
|
||||
try:
|
||||
response = completion(model=model_val, messages=messages)
|
||||
except:
|
||||
print(f"error occurred: {traceback.format_exc()}")
|
||||
pass
|
||||
59
litellm/tests/test_client.py
Normal file
59
litellm/tests/test_client.py
Normal file
|
|
@ -0,0 +1,59 @@
|
|||
import sys, os
|
||||
import traceback
|
||||
sys.path.append('..') # Adds the parent directory to the system path
|
||||
import main
|
||||
from main import litellm_client
|
||||
client = litellm_client(success_callback=["posthog"], failure_callback=["slack", "sentry", "posthog"], verbose=True)
|
||||
completion = client.completion
|
||||
embedding = client.embedding
|
||||
|
||||
main.set_verbose = True
|
||||
|
||||
def logger_fn(model_call_object: dict):
|
||||
print(f"model call details: {model_call_object}")
|
||||
|
||||
user_message = "Hello, how are you?"
|
||||
messages = [{ "content": user_message,"role": "user"}]
|
||||
|
||||
# test on openai completion call
|
||||
try:
|
||||
response = completion(model="gpt-3.5-turbo", messages=messages, logger_fn=logger_fn)
|
||||
except:
|
||||
print(f"error occurred: {traceback.format_exc()}")
|
||||
pass
|
||||
|
||||
|
||||
# test on openai completion call
|
||||
try:
|
||||
response = completion(model="gpt-3.5-turbo", messages=messages, logger_fn=logger_fn)
|
||||
except:
|
||||
print(f"error occurred: {traceback.format_exc()}")
|
||||
pass
|
||||
|
||||
# test on non-openai completion call
|
||||
try:
|
||||
response = completion(model="claude-instant-1", messages=messages, logger_fn=logger_fn)
|
||||
except:
|
||||
print(f"error occurred: {traceback.format_exc()}")
|
||||
pass
|
||||
|
||||
# test on openai embedding call
|
||||
try:
|
||||
response = embedding(model='text-embedding-ada-002', input=[user_message], logger_fn=logger_fn)
|
||||
print(f"response: {str(response)[:50]}")
|
||||
except:
|
||||
traceback.print_exc()
|
||||
|
||||
# test on bad azure openai embedding call -> missing azure flag and this isn't an embedding model
|
||||
try:
|
||||
response = embedding(model='chatgpt-test', input=[user_message], logger_fn=logger_fn)
|
||||
print(f"response: {str(response)[:50]}")
|
||||
except:
|
||||
traceback.print_exc()
|
||||
|
||||
# test on good azure openai embedding call
|
||||
try:
|
||||
response = embedding(model='azure-embedding-model', input=[user_message], azure=True, logger_fn=logger_fn)
|
||||
print(f"response: {str(response)[:50]}")
|
||||
except:
|
||||
traceback.print_exc()
|
||||
48
litellm/tests/test_logging.py
Normal file
48
litellm/tests/test_logging.py
Normal file
|
|
@ -0,0 +1,48 @@
|
|||
import sys, os
|
||||
import traceback
|
||||
sys.path.append('..') # Adds the parent directory to the system path
|
||||
import main
|
||||
from main import completion, embedding
|
||||
|
||||
main.verbose = True ## Replace to: ```litellm.verbose = True``` when using pypi package
|
||||
|
||||
def logger_fn(model_call_object: dict):
|
||||
print(f"model call details: {model_call_object}")
|
||||
|
||||
user_message = "Hello, how are you?"
|
||||
messages = [{ "content": user_message,"role": "user"}]
|
||||
|
||||
# test on openai completion call
|
||||
try:
|
||||
response = completion(model="gpt-3.5-turbo", messages=messages)
|
||||
except:
|
||||
print(f"error occurred: {traceback.format_exc()}")
|
||||
pass
|
||||
|
||||
# test on non-openai completion call
|
||||
try:
|
||||
response = completion(model="claude-instant-1", messages=messages, logger_fn=logger_fn)
|
||||
except:
|
||||
print(f"error occurred: {traceback.format_exc()}")
|
||||
pass
|
||||
|
||||
# test on openai embedding call
|
||||
try:
|
||||
response = embedding(model='text-embedding-ada-002', input=[user_message], logger_fn=logger_fn)
|
||||
print(f"response: {str(response)[:50]}")
|
||||
except:
|
||||
traceback.print_exc()
|
||||
|
||||
# test on bad azure openai embedding call -> missing azure flag and this isn't an embedding model
|
||||
try:
|
||||
response = embedding(model='chatgpt-test', input=[user_message], logger_fn=logger_fn)
|
||||
print(f"response: {str(response)[:50]}")
|
||||
except:
|
||||
traceback.print_exc()
|
||||
|
||||
# test on good azure openai embedding call
|
||||
try:
|
||||
response = embedding(model='azure-embedding-model', input=[user_message], azure=True, logger_fn=logger_fn)
|
||||
print(f"response: {str(response)[:50]}")
|
||||
except:
|
||||
traceback.print_exc()
|
||||
4
setup.py
4
setup.py
|
|
@ -2,9 +2,9 @@ from setuptools import setup, find_packages
|
|||
|
||||
setup(
|
||||
name='litellm',
|
||||
version='0.1.202',
|
||||
version='0.1.2',
|
||||
description='Library to easily interface with LLM API providers',
|
||||
author='Ishaan Jaffer',
|
||||
author='BerriAI',
|
||||
packages=[
|
||||
'litellm'
|
||||
],
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue