forked from phoenix/litellm-mirror
(code cleanup) remove unused and undocumented logging integrations - litedebugger, berrispend (#6406)
* code cleanup remove unused and undocumented code files * fix unused logging integrations cleanup
This commit is contained in:
Ishaan Jaff
GitHub
parent
17e81d861c
commit
c731ba4044
4 changed files with 1 additions and 336 deletions
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@ -1,167 +0,0 @@
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#### What this does ####
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# On success + failure, log events to aispend.io
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import datetime
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import os
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import traceback
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import dotenv
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model_cost = {
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"gpt-3.5-turbo": {
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"max_tokens": 4000,
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"input_cost_per_token": 0.0000015,
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"output_cost_per_token": 0.000002,
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},
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"gpt-35-turbo": {
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"max_tokens": 4000,
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"input_cost_per_token": 0.0000015,
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"output_cost_per_token": 0.000002,
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}, # azure model name
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"gpt-3.5-turbo-0613": {
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"max_tokens": 4000,
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"input_cost_per_token": 0.0000015,
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"output_cost_per_token": 0.000002,
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},
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"gpt-3.5-turbo-0301": {
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"max_tokens": 4000,
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"input_cost_per_token": 0.0000015,
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"output_cost_per_token": 0.000002,
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},
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"gpt-3.5-turbo-16k": {
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"max_tokens": 16000,
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"input_cost_per_token": 0.000003,
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"output_cost_per_token": 0.000004,
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},
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"gpt-35-turbo-16k": {
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"max_tokens": 16000,
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"input_cost_per_token": 0.000003,
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"output_cost_per_token": 0.000004,
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}, # azure model name
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"gpt-3.5-turbo-16k-0613": {
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"max_tokens": 16000,
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"input_cost_per_token": 0.000003,
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"output_cost_per_token": 0.000004,
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},
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"gpt-4": {
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"max_tokens": 8000,
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"input_cost_per_token": 0.000003,
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"output_cost_per_token": 0.00006,
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},
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"gpt-4-0613": {
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"max_tokens": 8000,
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"input_cost_per_token": 0.000003,
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"output_cost_per_token": 0.00006,
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},
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"gpt-4-32k": {
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"max_tokens": 8000,
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"input_cost_per_token": 0.00006,
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"output_cost_per_token": 0.00012,
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},
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"claude-instant-1": {
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"max_tokens": 100000,
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"input_cost_per_token": 0.00000163,
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"output_cost_per_token": 0.00000551,
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},
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"claude-2": {
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"max_tokens": 100000,
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"input_cost_per_token": 0.00001102,
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"output_cost_per_token": 0.00003268,
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},
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"text-bison-001": {
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"max_tokens": 8192,
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"input_cost_per_token": 0.000004,
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"output_cost_per_token": 0.000004,
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},
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"chat-bison-001": {
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"max_tokens": 4096,
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"input_cost_per_token": 0.000002,
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"output_cost_per_token": 0.000002,
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},
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"command-nightly": {
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"max_tokens": 4096,
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"input_cost_per_token": 0.000015,
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"output_cost_per_token": 0.000015,
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},
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}
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class AISpendLogger:
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# Class variables or attributes
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def __init__(self):
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# Instance variables
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self.account_id = os.getenv("AISPEND_ACCOUNT_ID")
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self.api_key = os.getenv("AISPEND_API_KEY")
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def price_calculator(self, model, response_obj, start_time, end_time):
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# try and find if the model is in the model_cost map
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# else default to the average of the costs
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prompt_tokens_cost_usd_dollar = 0
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completion_tokens_cost_usd_dollar = 0
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if model in model_cost:
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prompt_tokens_cost_usd_dollar = (
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model_cost[model]["input_cost_per_token"]
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* response_obj["usage"]["prompt_tokens"]
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)
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completion_tokens_cost_usd_dollar = (
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model_cost[model]["output_cost_per_token"]
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* response_obj["usage"]["completion_tokens"]
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)
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elif "replicate" in model:
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# replicate models are charged based on time
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# llama 2 runs on an nvidia a100 which costs $0.0032 per second - https://replicate.com/replicate/llama-2-70b-chat
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model_run_time = end_time - start_time # assuming time in seconds
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cost_usd_dollar = model_run_time * 0.0032
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prompt_tokens_cost_usd_dollar = cost_usd_dollar / 2
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completion_tokens_cost_usd_dollar = cost_usd_dollar / 2
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else:
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# calculate average input cost
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input_cost_sum = 0
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output_cost_sum = 0
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for model in model_cost:
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input_cost_sum += model_cost[model]["input_cost_per_token"]
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output_cost_sum += model_cost[model]["output_cost_per_token"]
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prompt_tokens_cost_usd_dollar = (
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model_cost[model]["input_cost_per_token"]
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* response_obj["usage"]["prompt_tokens"]
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)
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completion_tokens_cost_usd_dollar = (
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model_cost[model]["output_cost_per_token"]
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* response_obj["usage"]["completion_tokens"]
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)
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return prompt_tokens_cost_usd_dollar, completion_tokens_cost_usd_dollar
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def log_event(self, model, response_obj, start_time, end_time, print_verbose):
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# Method definition
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try:
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print_verbose(
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f"AISpend Logging - Enters logging function for model {model}"
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)
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response_timestamp = datetime.datetime.fromtimestamp(
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int(response_obj["created"])
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).strftime("%Y-%m-%d")
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(
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prompt_tokens_cost_usd_dollar,
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completion_tokens_cost_usd_dollar,
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) = self.price_calculator(model, response_obj, start_time, end_time)
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prompt_tokens_cost_usd_cent = prompt_tokens_cost_usd_dollar * 100
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completion_tokens_cost_usd_cent = completion_tokens_cost_usd_dollar * 100
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data = [
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{
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"requests": 1,
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"requests_context": 1,
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"context_tokens": response_obj["usage"]["prompt_tokens"],
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"requests_generated": 1,
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"generated_tokens": response_obj["usage"]["completion_tokens"],
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"recorded_date": response_timestamp,
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"model_id": response_obj["model"],
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"generated_tokens_cost_usd_cent": prompt_tokens_cost_usd_cent,
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"context_tokens_cost_usd_cent": completion_tokens_cost_usd_cent,
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}
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]
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print_verbose(f"AISpend Logging - final data object: {data}")
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except Exception:
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print_verbose(f"AISpend Logging Error - {traceback.format_exc()}")
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pass
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@ -1,86 +0,0 @@
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#### What this does ####
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# On success + failure, log events to aispend.io
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import datetime
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import os
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import traceback
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import dotenv
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import requests # type: ignore
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model_cost = {
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"gpt-3.5-turbo": {
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"max_tokens": 4000,
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"input_cost_per_token": 0.0000015,
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"output_cost_per_token": 0.000002,
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},
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"gpt-35-turbo": {
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"max_tokens": 4000,
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"input_cost_per_token": 0.0000015,
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"output_cost_per_token": 0.000002,
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}, # azure model name
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"gpt-3.5-turbo-0613": {
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"max_tokens": 4000,
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"input_cost_per_token": 0.0000015,
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"output_cost_per_token": 0.000002,
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},
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"gpt-3.5-turbo-0301": {
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"max_tokens": 4000,
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"input_cost_per_token": 0.0000015,
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"output_cost_per_token": 0.000002,
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},
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"gpt-3.5-turbo-16k": {
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"max_tokens": 16000,
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"input_cost_per_token": 0.000003,
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"output_cost_per_token": 0.000004,
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},
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"gpt-35-turbo-16k": {
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"max_tokens": 16000,
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"input_cost_per_token": 0.000003,
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"output_cost_per_token": 0.000004,
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}, # azure model name
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"gpt-3.5-turbo-16k-0613": {
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"max_tokens": 16000,
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"input_cost_per_token": 0.000003,
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"output_cost_per_token": 0.000004,
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},
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"gpt-4": {
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"max_tokens": 8000,
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"input_cost_per_token": 0.000003,
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"output_cost_per_token": 0.00006,
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},
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"gpt-4-0613": {
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"max_tokens": 8000,
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"input_cost_per_token": 0.000003,
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"output_cost_per_token": 0.00006,
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},
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"gpt-4-32k": {
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"max_tokens": 8000,
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"input_cost_per_token": 0.00006,
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"output_cost_per_token": 0.00012,
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},
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"claude-instant-1": {
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"max_tokens": 100000,
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"input_cost_per_token": 0.00000163,
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"output_cost_per_token": 0.00000551,
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},
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"claude-2": {
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"max_tokens": 100000,
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"input_cost_per_token": 0.00001102,
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"output_cost_per_token": 0.00003268,
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},
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"text-bison-001": {
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"max_tokens": 8192,
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"input_cost_per_token": 0.000004,
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"output_cost_per_token": 0.000004,
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},
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"chat-bison-001": {
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"max_tokens": 4096,
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"input_cost_per_token": 0.000002,
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"output_cost_per_token": 0.000002,
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},
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"command-nightly": {
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"max_tokens": 4096,
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"input_cost_per_token": 0.000015,
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"output_cost_per_token": 0.000015,
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},
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}
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@ -1,77 +0,0 @@
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import json
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import os
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import traceback
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import types
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import requests
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class LiteDebugger:
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user_email = None
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dashboard_url = None
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def __init__(self, email=None):
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self.api_url = "https://api.litellm.ai/debugger"
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self.validate_environment(email)
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pass
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def validate_environment(self, email):
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try:
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self.user_email = (
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email or os.getenv("LITELLM_TOKEN") or os.getenv("LITELLM_EMAIL")
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)
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if (
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self.user_email is None
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): # if users are trying to use_client=True but token not set
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raise ValueError(
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"litellm.use_client = True but no token or email passed. Please set it in litellm.token"
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)
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self.dashboard_url = "https://admin.litellm.ai/" + self.user_email
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if self.user_email is None:
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raise ValueError(
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"[Non-Blocking Error] LiteLLMDebugger: Missing LITELLM_TOKEN. Set it in your environment. Eg.: os.environ['LITELLM_TOKEN']= <your_email>"
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)
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except Exception:
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raise ValueError(
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"[Non-Blocking Error] LiteLLMDebugger: Missing LITELLM_TOKEN. Set it in your environment. Eg.: os.environ['LITELLM_TOKEN']= <your_email>"
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)
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def input_log_event(
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self,
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model,
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messages,
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end_user,
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litellm_call_id,
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call_type,
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print_verbose,
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litellm_params,
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optional_params,
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):
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"""
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This integration is not implemented yet.
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"""
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return
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def post_call_log_event(
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self, original_response, litellm_call_id, print_verbose, call_type, stream
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):
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"""
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This integration is not implemented yet.
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"""
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return
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def log_event(
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self,
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end_user,
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response_obj,
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start_time,
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end_time,
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litellm_call_id,
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print_verbose,
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call_type,
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stream=False,
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):
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"""
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This integration is not implemented yet.
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"""
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return
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@ -58,7 +58,6 @@ from litellm.utils import (
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prompt_token_calculator,
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)
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from ..integrations.aispend import AISpendLogger
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from ..integrations.argilla import ArgillaLogger
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from ..integrations.arize_ai import ArizeLogger
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from ..integrations.athina import AthinaLogger
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@ -73,7 +72,6 @@ from ..integrations.lago import LagoLogger
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from ..integrations.langfuse.langfuse import LangFuseLogger
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from ..integrations.langfuse.langfuse_handler import LangFuseHandler
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from ..integrations.langsmith import LangsmithLogger
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from ..integrations.litedebugger import LiteDebugger
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from ..integrations.literal_ai import LiteralAILogger
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from ..integrations.logfire_logger import LogfireLevel, LogfireLogger
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from ..integrations.lunary import LunaryLogger
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@ -124,7 +122,6 @@ s3Logger = None
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genericAPILogger = None
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greenscaleLogger = None
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lunaryLogger = None
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aispendLogger = None
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supabaseClient = None
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callback_list: Optional[List[str]] = []
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user_logger_fn = None
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@ -2044,7 +2041,7 @@ def set_callbacks(callback_list, function_id=None): # noqa: PLR0915
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"""
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Globally sets the callback client
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"""
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global sentry_sdk_instance, capture_exception, add_breadcrumb, posthog, slack_app, alerts_channel, traceloopLogger, athinaLogger, heliconeLogger, aispendLogger, supabaseClient, lunaryLogger, promptLayerLogger, langFuseLogger, customLogger, weightsBiasesLogger, logfireLogger, dynamoLogger, s3Logger, dataDogLogger, prometheusLogger, greenscaleLogger, openMeterLogger
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global sentry_sdk_instance, capture_exception, add_breadcrumb, posthog, slack_app, alerts_channel, traceloopLogger, athinaLogger, heliconeLogger, supabaseClient, lunaryLogger, promptLayerLogger, langFuseLogger, customLogger, weightsBiasesLogger, logfireLogger, dynamoLogger, s3Logger, dataDogLogger, prometheusLogger, greenscaleLogger, openMeterLogger
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try:
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for callback in callback_list:
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@ -2124,8 +2121,6 @@ def set_callbacks(callback_list, function_id=None): # noqa: PLR0915
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weightsBiasesLogger = WeightsBiasesLogger()
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elif callback == "logfire":
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logfireLogger = LogfireLogger()
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elif callback == "aispend":
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aispendLogger = AISpendLogger()
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elif callback == "supabase":
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print_verbose("instantiating supabase")
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supabaseClient = Supabase()
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