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Community : Add OpenAI prompt caching and reasoning tokens tracking (#27135)

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Added Token tracking for OpenAI's prompt caching and reasoning tokens
Costs updated from https://openai.com/api/pricing/

usage example
```python
from langchain_community.callbacks import get_openai_callback
from langchain_openai import ChatOpenAI
llm = ChatOpenAI(model_name="o1-mini",temperature=1)

with get_openai_callback() as cb:
    response = llm.invoke("hi "*1500)
    print(cb)
```
Output
```
Tokens Used: 1720
	Prompt Tokens: 1508
		Prompt Tokens Cached: 1408
	Completion Tokens: 212
		Reasoning Tokens: 192
Successful Requests: 1
Total Cost (USD): $0.0049559999999999995
```

---------

Co-authored-by: Chester Curme <chester.curme@gmail.com>
This commit is contained in:
Vignesh A 2024-12-19 20:01:13 +05:30 committed by GitHub
parent 97f1e1d39f
commit 4c9acdfbf1
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GPG Key ID: B5690EEEBB952194
2 changed files with 123 additions and 10 deletions
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93
libs/community/langchain_community/callbacks/openai_info.py
View File

@ -1,8 +1,10 @@
"""Callback Handler that prints to std out."""
import threading
from enum import Enum, auto
from typing import Any, Dict, List
from langchain_core._api import warn_deprecated
from langchain_core.callbacks import BaseCallbackHandler
from langchain_core.messages import AIMessage
from langchain_core.outputs import ChatGeneration, LLMResult
@ -10,26 +12,34 @@ from langchain_core.outputs import ChatGeneration, LLMResult
MODEL_COST_PER_1K_TOKENS = {
# OpenAI o1-preview input
"o1-preview": 0.015,
"o1-preview-cached": 0.0075,
"o1-preview-2024-09-12": 0.015,
"o1-preview-2024-09-12-cached": 0.0075,
# OpenAI o1-preview output
"o1-preview-completion": 0.06,
"o1-preview-2024-09-12-completion": 0.06,
# OpenAI o1-mini input
"o1-mini": 0.003,
"o1-mini-cached": 0.0015,
"o1-mini-2024-09-12": 0.003,
"o1-mini-2024-09-12-cached": 0.0015,
# OpenAI o1-mini output
"o1-mini-completion": 0.012,
"o1-mini-2024-09-12-completion": 0.012,
# GPT-4o-mini input
"gpt-4o-mini": 0.00015,
"gpt-4o-mini-cached": 0.000075,
"gpt-4o-mini-2024-07-18": 0.00015,
"gpt-4o-mini-2024-07-18-cached": 0.000075,
# GPT-4o-mini output
"gpt-4o-mini-completion": 0.0006,
"gpt-4o-mini-2024-07-18-completion": 0.0006,
# GPT-4o input
"gpt-4o": 0.0025,
"gpt-4o-cached": 0.00125,
"gpt-4o-2024-05-13": 0.005,
"gpt-4o-2024-08-06": 0.0025,
"gpt-4o-2024-08-06-cached": 0.00125,
"gpt-4o-2024-11-20": 0.0025,
# GPT-4o output
"gpt-4o-completion": 0.01,
@ -140,9 +150,19 @@ MODEL_COST_PER_1K_TOKENS = {
}
class TokenType(Enum):
"""Token type enum."""
PROMPT = auto()
PROMPT_CACHED = auto()
COMPLETION = auto()
def standardize_model_name(
model_name: str,
is_completion: bool = False,
*,
token_type: TokenType = TokenType.PROMPT,
) -> str:
"""
Standardize the model name to a format that can be used in the OpenAI API.
@ -150,12 +170,24 @@ def standardize_model_name(
Args:
model_name: Model name to standardize.
is_completion: Whether the model is used for completion or not.
Defaults to False.
Defaults to False. Deprecated in favor of ``token_type``.
token_type: Token type. Defaults to ``TokenType.PROMPT``.
Returns:
Standardized model name.
"""
if is_completion:
warn_deprecated(
since="0.3.13",
message=(
"is_completion is deprecated. Use token_type instead. Example:\n\n"
"from langchain_community.callbacks.openai_info import TokenType\n\n"
"standardize_model_name('gpt-4o', token_type=TokenType.COMPLETION)\n"
),
removal="1.0",
)
token_type = TokenType.COMPLETION
model_name = model_name.lower()
if ".ft-" in model_name:
model_name = model_name.split(".ft-")[0] + "-azure-finetuned"
@ -163,7 +195,7 @@ def standardize_model_name(
model_name = model_name.split(":")[0] + "-finetuned-legacy"
if "ft:" in model_name:
model_name = model_name.split(":")[1] + "-finetuned"
if is_completion and (
if token_type == TokenType.COMPLETION and (
model_name.startswith("gpt-4")
or model_name.startswith("gpt-3.5")
or model_name.startswith("gpt-35")
@ -171,12 +203,20 @@ def standardize_model_name(
or ("finetuned" in model_name and "legacy" not in model_name)
):
return model_name + "-completion"
if token_type == TokenType.PROMPT_CACHED and (
model_name.startswith("gpt-4o") or model_name.startswith("o1")
):
return model_name + "-cached"
else:
return model_name
def get_openai_token_cost_for_model(
model_name: str, num_tokens: int, is_completion: bool = False
model_name: str,
num_tokens: int,
is_completion: bool = False,
*,
token_type: TokenType = TokenType.PROMPT,
) -> float:
"""
Get the cost in USD for a given model and number of tokens.
@ -185,12 +225,24 @@ def get_openai_token_cost_for_model(
model_name: Name of the model
num_tokens: Number of tokens.
is_completion: Whether the model is used for completion or not.
Defaults to False.
Defaults to False. Deprecated in favor of ``token_type``.
token_type: Token type. Defaults to ``TokenType.PROMPT``.
Returns:
Cost in USD.
"""
model_name = standardize_model_name(model_name, is_completion=is_completion)
if is_completion:
warn_deprecated(
since="0.3.13",
message=(
"is_completion is deprecated. Use token_type instead. Example:\n\n"
"from langchain_community.callbacks.openai_info import TokenType\n\n"
"get_openai_token_cost_for_model('gpt-4o', 10, token_type=TokenType.COMPLETION)\n" # noqa: E501
),
removal="1.0",
)
token_type = TokenType.COMPLETION
model_name = standardize_model_name(model_name, token_type=token_type)
if model_name not in MODEL_COST_PER_1K_TOKENS:
raise ValueError(
f"Unknown model: {model_name}. Please provide a valid OpenAI model name."
@ -204,7 +256,9 @@ class OpenAICallbackHandler(BaseCallbackHandler):
total_tokens: int = 0
prompt_tokens: int = 0
prompt_tokens_cached: int = 0
completion_tokens: int = 0
reasoning_tokens: int = 0
successful_requests: int = 0
total_cost: float = 0.0
@ -216,7 +270,9 @@ class OpenAICallbackHandler(BaseCallbackHandler):
return (
f"Tokens Used: {self.total_tokens}\n"
f"\tPrompt Tokens: {self.prompt_tokens}\n"
f"\t\tPrompt Tokens Cached: {self.prompt_tokens_cached}\n"
f"\tCompletion Tokens: {self.completion_tokens}\n"
f"\t\tReasoning Tokens: {self.reasoning_tokens}\n"
f"Successful Requests: {self.successful_requests}\n"
f"Total Cost (USD): ${self.total_cost}"
)
@ -258,6 +314,10 @@ class OpenAICallbackHandler(BaseCallbackHandler):
else:
usage_metadata = None
response_metadata = None
prompt_tokens_cached = 0
reasoning_tokens = 0
if usage_metadata:
token_usage = {"total_tokens": usage_metadata["total_tokens"]}
completion_tokens = usage_metadata["output_tokens"]
@ -270,7 +330,12 @@ class OpenAICallbackHandler(BaseCallbackHandler):
model_name = standardize_model_name(
response.llm_output.get("model_name", "")
)
if "cache_read" in usage_metadata.get("input_token_details", {}):
prompt_tokens_cached = usage_metadata["input_token_details"][
"cache_read"
]
if "reasoning" in usage_metadata.get("output_token_details", {}):
reasoning_tokens = usage_metadata["output_token_details"]["reasoning"]
else:
if response.llm_output is None:
return None
@ -287,11 +352,19 @@ class OpenAICallbackHandler(BaseCallbackHandler):
model_name = standardize_model_name(
response.llm_output.get("model_name", "")
)
if model_name in MODEL_COST_PER_1K_TOKENS:
completion_cost = get_openai_token_cost_for_model(
model_name, completion_tokens, is_completion=True
uncached_prompt_tokens = prompt_tokens - prompt_tokens_cached
uncached_prompt_cost = get_openai_token_cost_for_model(
model_name, uncached_prompt_tokens, token_type=TokenType.PROMPT
)
cached_prompt_cost = get_openai_token_cost_for_model(
model_name, prompt_tokens_cached, token_type=TokenType.PROMPT_CACHED
)
prompt_cost = uncached_prompt_cost + cached_prompt_cost
completion_cost = get_openai_token_cost_for_model(
model_name, completion_tokens, token_type=TokenType.COMPLETION
)
prompt_cost = get_openai_token_cost_for_model(model_name, prompt_tokens)
else:
completion_cost = 0
prompt_cost = 0
@ -301,7 +374,9 @@ class OpenAICallbackHandler(BaseCallbackHandler):
self.total_cost += prompt_cost + completion_cost
self.total_tokens += token_usage.get("total_tokens", 0)
self.prompt_tokens += prompt_tokens
self.prompt_tokens_cached += prompt_tokens_cached
self.completion_tokens += completion_tokens
self.reasoning_tokens += reasoning_tokens
self.successful_requests += 1
def __copy__(self) -> "OpenAICallbackHandler":

40
libs/community/tests/unit_tests/callbacks/test_openai_info.py
View File

@ -3,7 +3,8 @@ from uuid import uuid4
import numpy as np
import pytest
from langchain_core.outputs import LLMResult
from langchain_core.messages import AIMessage
from langchain_core.outputs import ChatGeneration, LLMResult
from langchain_core.utils.pydantic import get_fields
from langchain_community.callbacks import OpenAICallbackHandler
@ -35,6 +36,43 @@ def test_on_llm_end(handler: OpenAICallbackHandler) -> None:
assert handler.total_cost > 0
def test_on_llm_end_with_chat_generation(handler: OpenAICallbackHandler) -> None:
response = LLMResult(
generations=[
[
ChatGeneration(
text="Hello, world!",
message=AIMessage(
content="Hello, world!",
usage_metadata={
"input_tokens": 2,
"output_tokens": 2,
"total_tokens": 4,
"input_token_details": {
"cache_read": 1,
},
"output_token_details": {
"reasoning": 1,
},
},
),
)
]
],
llm_output={
"model_name": get_fields(BaseOpenAI)["model_name"].default,
},
)
handler.on_llm_end(response)
assert handler.successful_requests == 1
assert handler.total_tokens == 4
assert handler.prompt_tokens == 2
assert handler.prompt_tokens_cached == 1
assert handler.completion_tokens == 2
assert handler.reasoning_tokens == 1
assert handler.total_cost > 0
def test_on_llm_end_custom_model(handler: OpenAICallbackHandler) -> None:
response = LLMResult(
generations=[],